Open Targets: a platform for therapeutic target identification and validation

PubMed Central

Koscielny, Gautier; An, Peter; Carvalho-Silva, Denise; Cham, Jennifer A.; Fumis, Luca; Gasparyan, Rippa; Hasan, Samiul; Karamanis, Nikiforos; Maguire, Michael; Papa, Eliseo; Pierleoni, Andrea; Pignatelli, Miguel; Platt, Theo; Rowland, Francis; Wankar, Priyanka; Bento, A. Patrícia; Burdett, Tony; Fabregat, Antonio; Forbes, Simon; Gaulton, Anna; Gonzalez, Cristina Yenyxe; Hermjakob, Henning; Hersey, Anne; Jupe, Steven; Kafkas, Şenay; Keays, Maria; Leroy, Catherine; Lopez, Francisco-Javier; Magarinos, Maria Paula; Malone, James; McEntyre, Johanna; Munoz-Pomer Fuentes, Alfonso; O'Donovan, Claire; Papatheodorou, Irene; Parkinson, Helen; Palka, Barbara; Paschall, Justin; Petryszak, Robert; Pratanwanich, Naruemon; Sarntivijal, Sirarat; Saunders, Gary; Sidiropoulos, Konstantinos; Smith, Thomas; Sondka, Zbyslaw; Stegle, Oliver; Tang, Y. Amy; Turner, Edward; Vaughan, Brendan; Vrousgou, Olga; Watkins, Xavier; Martin, Maria-Jesus; Sanseau, Philippe; Vamathevan, Jessica; Birney, Ewan; Barrett, Jeffrey; Dunham, Ian

2017-01-01

We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org. PMID:27899665

How many genomics targets can a portfolio afford?

PubMed

Betz, Ulrich A K

2005-08-01

The pharmaceutical industry can look back at a history of successful innovations. Although genomics technologies have provided drug discovery pipelines with a plethora of new potential drug targets, solid target validation is crucial to avoiding high attrition rates. Biomarkers for patient stratification and approaches for personalized medicine will further help to reduce the risk associated with new targets. To achieve an overall risk balance, portfolios have to be supplemented with precedented targets, me-too approaches and line extensions of existing drugs. However, capitalizing on genomics investments and working on unprecedented targets is essential for a continuous stream of innovative drugs.

Target-similarity search using Plasmodium falciparum proteome identifies approved drugs with anti-malarial activity and their possible targets

PubMed Central

Akala, Hoseah M.; Macharia, Rosaline W.; Juma, Dennis W.; Cheruiyot, Agnes C.; Andagalu, Ben; Brown, Mathew L.; El-Shemy, Hany A.; Nyanjom, Steven G.

2017-01-01

Malaria causes about half a million deaths annually, with Plasmodium falciparum being responsible for 90% of all the cases. Recent reports on artemisinin resistance in Southeast Asia warrant urgent discovery of novel drugs for the treatment of malaria. However, most bioactive compounds fail to progress to treatments due to safety concerns. Drug repositioning offers an alternative strategy where drugs that have already been approved as safe for other diseases could be used to treat malaria. This study screened approved drugs for antimalarial activity using an in silico chemogenomics approach prior to in vitro verification. All the P. falciparum proteins sequences available in NCBI RefSeq were mined and used to perform a similarity search against DrugBank, TTD and STITCH databases to identify similar putative drug targets. Druggability indices of the potential P. falciparum drug targets were obtained from TDR targets database. Functional amino acid residues of the drug targets were determined using ConSurf server which was used to fine tune the similarity search. This study predicted 133 approved drugs that could target 34 P. falciparum proteins. A literature search done at PubMed and Google Scholar showed 105 out of the 133 drugs to have been previously tested against malaria, with most showing activity. For further validation, drug susceptibility assays using SYBR Green I method were done on a representative group of 10 predicted drugs, eight of which did show activity against P. falciparum 3D7 clone. Seven had IC50 values ranging from 1 μM to 50 μM. This study also suggests drug-target association and hence possible mechanisms of action of drugs that did show antiplasmodial activity. The study results validate the use of proteome-wide target similarity approach in identifying approved drugs with activity against P. falciparum and could be adapted for other pathogens. PMID:29088219

CIDR

Science.gov Websites

Consortium Developed Arrays Infinium Human Drug Core Array The Illumina nfinium DrugDev Consortium array drug target discovery, validation and treatment response. Detailed Information on Array Infinium Human

Using genetic methods to define the targets of compounds with antimalarial activity

PubMed Central

Flannery, Erika L.; Fidock, David A.; Winzeler, Elizabeth A.

2013-01-01

Although phenotypic cellular screening has been used to drive antimalarial drug discovery in recent years, in some cases target-based drug discovery remains more attractive. This is especially true when appropriate high-throughput cellular assays are lacking, as is the case for drug discovery efforts that aim to provide a replacement for primaquine (4-N-(6-methoxyquinolin-8-yl)pentane-1,4-diamine), the only drug that can block Plasmodium transmission to Anopheles mosquitoes and eliminate liver-stage hypnozoites. At present, however, there are no known chemically validated parasite protein targets that are important in all Plasmodium parasite developmental stages and that can be used in traditional biochemical compound screens. We propose that a plethora of novel, chemically validated, cross-stage antimalarial targets still remain to be discovered from the ~5,500 proteins encoded by the Plasmodium genomes. Here we discuss how in vitro evolution of drug-resistant strains of Plasmodium falciparum and subsequent whole-genome analysis can be used to find the targets of some of the many compounds discovered in whole-cell phenotypic screens. PMID:23927658

How reliable are ligand-centric methods for Target Fishing?

NASA Astrophysics Data System (ADS)

Peon, Antonio; Dang, Cuong; Ballester, Pedro

2016-04-01

Computational methods for Target Fishing (TF), also known as Target Prediction or Polypharmacology Prediction, can be used to discover new targets for small-molecule drugs. This may result in repositioning the drug in a new indication or improving our current understanding of its efficacy and side effects. While there is a substantial body of research on TF methods, there is still a need to improve their validation, which is often limited to a small part of the available targets and not easily interpretable by the user. Here we discuss how target-centric TF methods are inherently limited by the number of targets that can possibly predict (this number is by construction much larger in ligand-centric techniques). We also propose a new benchmark to validate TF methods, which is particularly suited to analyse how predictive performance varies with the query molecule. On average over approved drugs, we estimate that only five predicted targets will have to be tested to find two true targets with submicromolar potency (a strong variability in performance is however observed). In addition, we find that an approved drug has currently an average of eight known targets, which reinforces the notion that polypharmacology is a common and strong event. Furthermore, with the assistance of a control group of randomly-selected molecules, we show that the targets of approved drugs are generally harder to predict.

Drug Target Protein-Protein Interaction Networks: A Systematic Perspective

PubMed Central

2017-01-01

The identification and validation of drug targets are crucial in biomedical research and many studies have been conducted on analyzing drug target features for getting a better understanding on principles of their mechanisms. But most of them are based on either strong biological hypotheses or the chemical and physical properties of those targets separately. In this paper, we investigated three main ways to understand the functional biomolecules based on the topological features of drug targets. There are no significant differences between targets and common proteins in the protein-protein interactions network, indicating the drug targets are neither hub proteins which are dominant nor the bridge proteins. According to some special topological structures of the drug targets, there are significant differences between known targets and other proteins. Furthermore, the drug targets mainly belong to three typical communities based on their modularity. These topological features are helpful to understand how the drug targets work in the PPI network. Particularly, it is an alternative way to predict potential targets or extract nontargets to test a new drug target efficiently and economically. By this way, a drug target's homologue set containing 102 potential target proteins is predicted in the paper. PMID:28691014

Genetically Validated Drug Targets in Leishmania: Current Knowledge and Future Prospects.

PubMed

Jones, Nathaniel G; Catta-Preta, Carolina M C; Lima, Ana Paula C A; Mottram, Jeremy C

2018-04-13

There has been a very limited number of high-throughput screening campaigns carried out with Leishmania drug targets. In part, this is due to the small number of suitable target genes that have been shown by genetic or chemical methods to be essential for the parasite. In this perspective, we discuss the state of genetic target validation in the field of Leishmania research and review the 200 Leishmania genes and 36 Trypanosoma cruzi genes for which gene deletion attempts have been made since the first published case in 1990. We define a quality score for the different genetic deletion techniques that can be used to identify potential drug targets. We also discuss how the advances in genome-scale gene disruption techniques have been used to assist target-based and phenotypic-based drug development in other parasitic protozoa and why Leishmania has lacked a similar approach so far. The prospects for this scale of work are considered in the context of the application of CRISPR/Cas9 gene editing as a useful tool in Leishmania.

Advancing cancer drug discovery towards more agile development of targeted combination therapies.

PubMed

Carragher, Neil O; Unciti-Broceta, Asier; Cameron, David A

2012-01-01

Current drug-discovery strategies are typically 'target-centric' and are based upon high-throughput screening of large chemical libraries against nominated targets and a selection of lead compounds with optimized 'on-target' potency and selectivity profiles. However, high attrition of targeted agents in clinical development suggest that combinations of targeted agents will be most effective in treating solid tumors if the biological networks that permit cancer cells to subvert monotherapies are identified and retargeted. Conventional drug-discovery and development strategies are suboptimal for the rational design and development of novel drug combinations. In this article, we highlight a series of emerging technologies supporting a less reductionist, more agile, drug-discovery and development approach for the rational design, validation, prioritization and clinical development of novel drug combinations.

Drug Repositioning by Kernel-Based Integration of Molecular Structure, Molecular Activity, and Phenotype Data

PubMed Central

Wang, Yongcui; Chen, Shilong; Deng, Naiyang; Wang, Yong

2013-01-01

Computational inference of novel therapeutic values for existing drugs, i.e., drug repositioning, offers the great prospect for faster and low-risk drug development. Previous researches have indicated that chemical structures, target proteins, and side-effects could provide rich information in drug similarity assessment and further disease similarity. However, each single data source is important in its own way and data integration holds the great promise to reposition drug more accurately. Here, we propose a new method for drug repositioning, PreDR (Predict Drug Repositioning), to integrate molecular structure, molecular activity, and phenotype data. Specifically, we characterize drug by profiling in chemical structure, target protein, and side-effects space, and define a kernel function to correlate drugs with diseases. Then we train a support vector machine (SVM) to computationally predict novel drug-disease interactions. PreDR is validated on a well-established drug-disease network with 1,933 interactions among 593 drugs and 313 diseases. By cross-validation, we find that chemical structure, drug target, and side-effects information are all predictive for drug-disease relationships. More experimentally observed drug-disease interactions can be revealed by integrating these three data sources. Comparison with existing methods demonstrates that PreDR is competitive both in accuracy and coverage. Follow-up database search and pathway analysis indicate that our new predictions are worthy of further experimental validation. Particularly several novel predictions are supported by clinical trials databases and this shows the significant prospects of PreDR in future drug treatment. In conclusion, our new method, PreDR, can serve as a useful tool in drug discovery to efficiently identify novel drug-disease interactions. In addition, our heterogeneous data integration framework can be applied to other problems. PMID:24244318

In silico Analysis of Toxins of Staphylococcus aureus for Validating Putative Drug Targets.

PubMed

Mohana, Ramadevi; Venugopal, Subhashree

2017-01-01

Toxins are one among the numerous virulence factors produced by the bacteria. These are powerful poisonous substances enabling the bacteria to encounter the defense mechanism of human body. The pathogenic system of Staphylococcus aureus is evolved with various exotoxins that cause detrimental effects on human immune system. Four toxins namely enterotoxin A, exfoliative toxin A, TSST-1 and γ-hemolysin were downloaded from Uniprot database and were analyzed to understand the nature of the toxins and for drug target validation. The results inferred that the toxins were found to interact with many protein partners and no homologous sequences for human proteome were found, and based on similarity search in Drugbank, the targets were identified as novel drug targets. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile.

PubMed

van Laarhoven, Twan; Marchiori, Elena

2013-01-01

In silico discovery of interactions between drug compounds and target proteins is of core importance for improving the efficiency of the laborious and costly experimental determination of drug-target interaction. Drug-target interaction data are available for many classes of pharmaceutically useful target proteins including enzymes, ion channels, GPCRs and nuclear receptors. However, current drug-target interaction databases contain a small number of drug-target pairs which are experimentally validated interactions. In particular, for some drug compounds (or targets) there is no available interaction. This motivates the need for developing methods that predict interacting pairs with high accuracy also for these 'new' drug compounds (or targets). We show that a simple weighted nearest neighbor procedure is highly effective for this task. We integrate this procedure into a recent machine learning method for drug-target interaction we developed in previous work. Results of experiments indicate that the resulting method predicts true interactions with high accuracy also for new drug compounds and achieves results comparable or better than those of recent state-of-the-art algorithms. Software is publicly available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2013/.

Flight evaluation of advanced third-generation midwave infrared sensor

NASA Astrophysics Data System (ADS)

Shen, Chyau N.; Donn, Matthew

1998-08-01

In FY-97 the Counter Drug Optical Upgrade (CDOU) demonstration program was initiated by the Program Executive Office for Counter Drug to increase the detection and classification ranges of P-3 counter drug aircraft by using advanced staring infrared sensors. The demonstration hardware is a `pin-for-pin' replacement of the AAS-36 Infrared Detection Set (IRDS) located under the nose radome of a P-3 aircraft. The hardware consists of a 3rd generation mid-wave infrared (MWIR) sensor integrated into a three axis-stabilized turret. The sensor, when installed on the P- 3, has a hemispheric field of regard and analysis has shown it will be capable of detecting and classifying Suspected Drug Trafficking Aircraft and Vessels at ranges several factors over the current IRDS. This paper will discuss the CDOU system and it's lab, ground, and flight evaluation results. Test targets included target templates, range targets, dedicated target boats, and targets of opportunity at the Naval Air Warfare Center Aircraft Division and at operational test sites. The objectives of these tests were to: (1) Validate the integration concept of the CDOU package into the P-3 aircraft. (2) Validate the end-to-end functionality of the system, including sensor/turret controls and recording of imagery during flight. (3) Evaluate the system sensitivity and resolution on a set of verified resolution targets templates. (4) Validate the ability of the 3rd generation MWIR sensor to detect and classify targets at a significantly increased range.

Characterizing and Targeting Replication Stress Response Defects in Breast Cancer

DTIC Science & Technology

2013-08-01

This project is to use cutting-edge technologies to characterize novel RSR genes and their functions in tumor suppression; identify gene signature...and membrane proteins associated with defective RSR; identify drugs that target these defects; and develop RSR-defect-targeting nanoparticles for...screening and validation of drugs that target RSR-defect cells. The progress of our third year research is described below. BODY The tasks

OCEAN: Optimized Cross rEActivity estimatioN.

PubMed

Czodrowski, Paul; Bolick, Wolf-Guido

2016-10-24

The prediction of molecular targets is highly beneficial during the drug discovery process, be it for off-target elucidation or deconvolution of phenotypic screens. Here, we present OCEAN, a target prediction tool exclusively utilizing publically available ChEMBL data. OCEAN uses a heuristics approach based on a validation set containing almost 1000 drug ← → target relationships. New ChEMBL data (ChEMBL20 as well as ChEMBL21) released after the validation was used for a prospective OCEAN performance check. The success rates of OCEAN to predict correctly the targets within the TOP10 ranks are 77% for recently marketed drugs and 62% for all new ChEMBL20 compounds and 51% for all new ChEMBL21 compounds. OCEAN is also capable of identifying polypharmacological compounds; the success rate for molecules simultaneously hitting at least two targets is 64% to be correctly predicted within the TOP10 ranks. The source code of OCEAN can be found at http://www.github.com/rdkit/OCEAN.

Tissue-Specific Analysis of Pharmacological Pathways.

PubMed

Hao, Yun; Quinnies, Kayla; Realubit, Ronald; Karan, Charles; Tatonetti, Nicholas P

2018-06-19

Understanding the downstream consequences of pharmacologically targeted proteins is essential to drug design. Current approaches investigate molecular effects under tissue-naïve assumptions. Many target proteins, however, have tissue-specific expression. A systematic study connecting drugs to target pathways in in vivo human tissues is needed. We introduced a data-driven method that integrates drug-target relationships with gene expression, protein-protein interaction, and pathway annotation data. We applied our method to four independent genomewide expression datasets and built 467,396 connections between 1,034 drugs and 954 pathways in 259 human tissues or cell lines. We validated our results using data from L1000 and Pharmacogenomics Knowledgebase (PharmGKB), and observed high precision and recall. We predicted and tested anticoagulant effects of 22 compounds experimentally that were previously unknown, and used clinical data to validate these effects retrospectively. Our systematic study provides a better understanding of the cellular response to drugs and can be applied to many research topics in systems pharmacology. © 2018 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Open access chemical probes for epigenetic targets

PubMed Central

Brown, Peter J; Müller, Susanne

2015-01-01

Background High attrition rates in drug discovery call for new approaches to improve target validation. Academia is filling gaps, but often lacks the experience and resources of the pharmaceutical industry resulting in poorly characterized tool compounds. Discussion The SGC has established an open access chemical probe consortium, currently encompassing ten pharmaceutical companies. One of its mandates is to create well-characterized inhibitors (chemical probes) for epigenetic targets to enable new biology and target validation for drug development. Conclusion Epigenetic probe compounds have proven to be very valuable and have not only spurred a plethora of novel biological findings, but also provided starting points for clinical trials. These probes have proven to be critical complementation to traditional genetic targeting strategies and provided sometimes surprising results. PMID:26397018

Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information.

PubMed

Zhang, Wen; Chen, Yanlin; Li, Dingfang

2017-11-25

Interactions between drugs and target proteins provide important information for the drug discovery. Currently, experiments identified only a small number of drug-target interactions. Therefore, the development of computational methods for drug-target interaction prediction is an urgent task of theoretical interest and practical significance. In this paper, we propose a label propagation method with linear neighborhood information (LPLNI) for predicting unobserved drug-target interactions. Firstly, we calculate drug-drug linear neighborhood similarity in the feature spaces, by considering how to reconstruct data points from neighbors. Then, we take similarities as the manifold of drugs, and assume the manifold unchanged in the interaction space. At last, we predict unobserved interactions between known drugs and targets by using drug-drug linear neighborhood similarity and known drug-target interactions. The experiments show that LPLNI can utilize only known drug-target interactions to make high-accuracy predictions on four benchmark datasets. Furthermore, we consider incorporating chemical structures into LPLNI models. Experimental results demonstrate that the model with integrated information (LPLNI-II) can produce improved performances, better than other state-of-the-art methods. The known drug-target interactions are an important information source for computational predictions. The usefulness of the proposed method is demonstrated by cross validation and the case study.

A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases

PubMed Central

Chernomoretz, Ariel; Agüero, Fernán

2016-01-01

Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature. PMID:26735851

A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases.

PubMed

Berenstein, Ariel José; Magariños, María Paula; Chernomoretz, Ariel; Agüero, Fernán

2016-01-01

Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature.

A precision oncology approach to the pharmacological targeting of mechanistic dependencies in neuroendocrine tumors. | Office of Cancer Genomics

Cancer.gov

We introduce and validate a new precision oncology framework for the systematic prioritization of drugs targeting mechanistic tumor dependencies in individual patients. Compounds are prioritized on the basis of their ability to invert the concerted activity of master regulator proteins that mechanistically regulate tumor cell state, as assessed from systematic drug perturbation assays. We validated the approach on a cohort of 212 gastroenteropancreatic neuroendocrine tumors (GEP-NETs), a rare malignancy originating in the pancreas and gastrointestinal tract.

Predicting Pharmacodynamic Drug-Drug Interactions through Signaling Propagation Interference on Protein-Protein Interaction Networks.

PubMed

Park, Kyunghyun; Kim, Docyong; Ha, Suhyun; Lee, Doheon

2015-01-01

As pharmacodynamic drug-drug interactions (PD DDIs) could lead to severe adverse effects in patients, it is important to identify potential PD DDIs in drug development. The signaling starting from drug targets is propagated through protein-protein interaction (PPI) networks. PD DDIs could occur by close interference on the same targets or within the same pathways as well as distant interference through cross-talking pathways. However, most of the previous approaches have considered only close interference by measuring distances between drug targets or comparing target neighbors. We have applied a random walk with restart algorithm to simulate signaling propagation from drug targets in order to capture the possibility of their distant interference. Cross validation with DrugBank and Kyoto Encyclopedia of Genes and Genomes DRUG shows that the proposed method outperforms the previous methods significantly. We also provide a web service with which PD DDIs for drug pairs can be analyzed at http://biosoft.kaist.ac.kr/targetrw.

Validation of the proteasome as a therapeutic target in Plasmodium using an epoxyketone inhibitor with parasite-specific toxicity

PubMed Central

Li, Hao; Ponder, Elizabeth L.; Verdoes, Martijn; Asbjornsdottir, Kristijana H.; Deu, Edgar; Edgington, Laura E.; Lee, Jeong Tae; Kirk, Christopher J.; Demo, Susan D.; Williamson, Kim C.; Bogyo, Matthew

2012-01-01

Summary The Plasmodium proteasome has been suggested to be a potential anti-malarial drug target, however toxicity of inhibitors has prevented validation of this enzyme in vivo. We report here a screen of a library of 670 analogs of the recently FDA approved inhibitor, carfilzomib, to identify compounds that selectively kill parasites. We identified one compound, PR3, that has significant parasite killing activity in vitro but dramatically reduced toxicity in host cells. We found that this parasite-specific toxicity is not due to selective targeting of the Plasmodium proteasome over the host proteasome, but instead is due to a lack of activity against one of the human proteasome subunits. Subsequently, we used PR3 to significantly reduce parasite load in P. berghei infected mice without host toxicity, thus validating the proteasome as a viable anti-malarial drug target. PMID:23142757

75 FR 36423 - Government-Owned Inventions; Availability for Licensing

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-25

... tested for antimicrobial activity against drug resistant bacteria, methicillin- resistant Staphylococcus.... Advantages: Structurally distinct antimicrobial compounds. Attack newly validated antibacterial targeted... GTPase activity. Inhibit drug-susceptible and drug-resistant bacteria. Development Status: [[Page 36424...

Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway

NASA Astrophysics Data System (ADS)

Huang, Lu; Jiang, Yuyang; Chen, Yuzong

2017-01-01

Synergistic drug combinations enable enhanced therapeutics. Their discovery typically involves the measurement and assessment of drug combination index (CI), which can be facilitated by the development and applications of in-silico CI predictive tools. In this work, we developed and tested the ability of a mathematical model of drug-targeted EGFR-ERK pathway in predicting CIs and in analyzing multiple synergistic drug combinations against observations. Our mathematical model was validated against the literature reported signaling, drug response dynamics, and EGFR-MEK drug combination effect. The predicted CIs and combination therapeutic effects of the EGFR-BRaf, BRaf-MEK, FTI-MEK, and FTI-BRaf inhibitor combinations showed consistent synergism. Our results suggest that existing pathway models may be potentially extended for developing drug-targeted pathway models to predict drug combination CI values, isobolograms, and drug-response surfaces as well as to analyze the dynamics of individual and combinations of drugs. With our model, the efficacy of potential drug combinations can be predicted. Our method complements the developed in-silico methods (e.g. the chemogenomic profile and the statistically-inferenced network models) by predicting drug combination effects from the perspectives of pathway dynamics using experimental or validated molecular kinetic constants, thereby facilitating the collective prediction of drug combination effects in diverse ranges of disease systems.

Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique.

PubMed

Hao, Ming; Wang, Yanli; Bryant, Stephen H

2016-02-25

Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision-recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. Published by Elsevier B.V.

An integrated structure- and system-based framework to identify new targets of metabolites and known drugs

PubMed Central

Naveed, Hammad; Hameed, Umar S.; Harrus, Deborah; Bourguet, William; Arold, Stefan T.; Gao, Xin

2015-01-01

Motivation: The inherent promiscuity of small molecules towards protein targets impedes our understanding of healthy versus diseased metabolism. This promiscuity also poses a challenge for the pharmaceutical industry as identifying all protein targets is important to assess (side) effects and repositioning opportunities for a drug. Results: Here, we present a novel integrated structure- and system-based approach of drug-target prediction (iDTP) to enable the large-scale discovery of new targets for small molecules, such as pharmaceutical drugs, co-factors and metabolites (collectively called ‘drugs’). For a given drug, our method uses sequence order–independent structure alignment, hierarchical clustering and probabilistic sequence similarity to construct a probabilistic pocket ensemble (PPE) that captures promiscuous structural features of different binding sites on known targets. A drug’s PPE is combined with an approximation of its delivery profile to reduce false positives. In our cross-validation study, we use iDTP to predict the known targets of 11 drugs, with 63% sensitivity and 81% specificity. We then predicted novel targets for these drugs—two that are of high pharmacological interest, the peroxisome proliferator-activated receptor gamma and the oncogene B-cell lymphoma 2, were successfully validated through in vitro binding experiments. Our method is broadly applicable for the prediction of protein-small molecule interactions with several novel applications to biological research and drug development. Availability and implementation: The program, datasets and results are freely available to academic users at http://sfb.kaust.edu.sa/Pages/Software.aspx. Contact: xin.gao@kaust.edu.sa and stefan.arold@kaust.edu.sa Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26286808

Insulin-Like Growth Factor 2 Silencing Restores Taxol Sensitivity in Drug Resistant Ovarian Cancer

PubMed Central

Brouwer-Visser, Jurriaan; Lee, Jiyeon; McCullagh, KellyAnne; Cossio, Maria J.; Wang, Yanhua; Huang, Gloria S.

2014-01-01

Drug resistance is an obstacle to the effective treatment of ovarian cancer. We and others have shown that the insulin-like growth factor (IGF) signaling pathway is a novel potential target to overcome drug resistance. The purpose of this study was to validate IGF2 as a potential therapeutic target in drug resistant ovarian cancer and to determine the efficacy of targeting IGF2 in vivo. An analysis of The Cancer Genome Atlas (TCGA) data in the serous ovarian cancer cohort showed that high IGF2 mRNA expression is significantly associated with shortened interval to disease progression and death, clinical indicators of drug resistance. In a genetically diverse panel of ovarian cancer cell lines, the IGF2 mRNA levels measured in cell lines resistant to various microtubule-stabilizing agents including Taxol were found to be significantly elevated compared to the drug sensitive cell lines. The effect of IGF2 knockdown on Taxol resistance was investigated in vitro and in vivo. Transient IGF2 knockdown significantly sensitized drug resistant cells to Taxol treatment. A Taxol-resistant ovarian cancer xenograft model, developed from HEY-T30 cells, exhibited extreme drug resistance, wherein the maximal tolerated dose of Taxol did not delay tumor growth in mice. Blocking the IGF1R (a transmembrane receptor that transmits signals from IGF1 and IGF2) using a monoclonal antibody did not alter the response to Taxol. However, stable IGF2 knockdown using short-hairpin RNA in HEY-T30 effectively restored Taxol sensitivity. These findings validate IGF2 as a potential therapeutic target in drug resistant ovarian cancer and show that directly targeting IGF2 may be a preferable strategy compared with targeting IGF1R alone. PMID:24932685

Drug Target Mining and Analysis of the Chinese Tree Shrew for Pharmacological Testing

PubMed Central

Liu, Jie; Lee, Wen-hui; Zhang, Yun

2014-01-01

The discovery of new drugs requires the development of improved animal models for drug testing. The Chinese tree shrew is considered to be a realistic candidate model. To assess the potential of the Chinese tree shrew for pharmacological testing, we performed drug target prediction and analysis on genomic and transcriptomic scales. Using our pipeline, 3,482 proteins were predicted to be drug targets. Of these predicted targets, 446 and 1,049 proteins with the highest rank and total scores, respectively, included homologs of targets for cancer chemotherapy, depression, age-related decline and cardiovascular disease. Based on comparative analyses, more than half of drug target proteins identified from the tree shrew genome were shown to be higher similarity to human targets than in the mouse. Target validation also demonstrated that the constitutive expression of the proteinase-activated receptors of tree shrew platelets is similar to that of human platelets but differs from that of mouse platelets. We developed an effective pipeline and search strategy for drug target prediction and the evaluation of model-based target identification for drug testing. This work provides useful information for future studies of the Chinese tree shrew as a source of novel targets for drug discovery research. PMID:25105297

Exoproteome and Secretome Derived Broad Spectrum Novel Drug and Vaccine Candidates in Vibrio cholerae Targeted by Piper betel Derived Compounds

PubMed Central

Barh, Debmalya; Barve, Neha; Gupta, Krishnakant; Chandra, Sudha; Jain, Neha; Tiwari, Sandeep; Leon-Sicairos, Nidia; Canizalez-Roman, Adrian; Rodrigues dos Santos, Anderson; Hassan, Syed Shah; Almeida, Síntia; Thiago Jucá Ramos, Rommel; Augusto Carvalho de Abreu, Vinicius; Ribeiro Carneiro, Adriana; de Castro Soares, Siomar; Luiz de Paula Castro, Thiago; Miyoshi, Anderson; Silva, Artur; Kumar, Anil; Narayan Misra, Amarendra; Blum, Kenneth; Braverman, Eric R.; Azevedo, Vasco

2013-01-01

Vibrio cholerae is the causal organism of the cholera epidemic, which is mostly prevalent in developing and underdeveloped countries. However, incidences of cholera in developed countries are also alarming. Because of the emergence of new drug-resistant strains, even though several generic drugs and vaccines have been developed over time, Vibrio infections remain a global health problem that appeals for the development of novel drugs and vaccines against the pathogen. Here, applying comparative proteomic and reverse vaccinology approaches to the exoproteome and secretome of the pathogen, we have identified three candidate targets (ompU, uppP and yajC) for most of the pathogenic Vibrio strains. Two targets (uppP and yajC) are novel to Vibrio, and two targets (uppP and ompU) can be used to develop both drugs and vaccines (dual targets) against broad spectrum Vibrio serotypes. Using our novel computational approach, we have identified three peptide vaccine candidates that have high potential to induce both B- and T-cell-mediated immune responses from our identified two dual targets. These two targets were modeled and subjected to virtual screening against natural compounds derived from Piper betel. Seven compounds were identified first time from Piper betel to be highly effective to render the function of these targets to identify them as emerging potential drugs against Vibrio. Our preliminary validation suggests that these identified peptide vaccines and betel compounds are highly effective against Vibrio cholerae. Currently we are exhaustively validating these targets, candidate peptide vaccines, and betel derived lead compounds against a number of Vibrio species. PMID:23382822

Systems genetics for drug target discovery

PubMed Central

Penrod, Nadia M.; Cowper-Sal_lari, Richard; Moore, Jason H.

2011-01-01

The collection and analysis of genomic data has the potential to reveal novel druggable targets by providing insight into the genetic basis of disease. However, the number of drugs, targeting new molecular entities, approved by the US Food and Drug Administration (FDA) has not increased in the years since the collection of genomic data has become commonplace. The paucity of translatable results can be partly attributed to conventional analysis methods that test one gene at a time in an effort to identify disease-associated factors as candidate drug targets. By disengaging genetic factors from their position within the genetic regulatory system, much of the information stored within the genomic data set is lost. Here we discuss how genomic data is used to identify disease-associated genes or genomic regions, how disease-associated regions are validated as functional targets, and the role network analysis can play in bridging the gap between data generation and effective drug target identification. PMID:21862141

CYP51 is an essential drug target for the treatment of primary amoebic meningoencephalitis (PAM)

PubMed Central

Debnath, Anjan; Calvet, Claudia M.; Aksenov, Alexander; Abagyan, Ruben; Nes, W. David; McKerrow, James H.

2017-01-01

Primary Amoebic Meningoencephalitis (PAM) is caused by Naegleria fowleri, a free-living amoeba that occasionally infects humans. While considered “rare” (but likely underreported) the high mortality rate and lack of established success in treatment makes PAM a particularly devastating infection. In the absence of economic inducements to invest in development of anti-PAM drugs by the pharmaceutical industry, anti-PAM drug discovery largely relies on drug ‘repurposing’—a cost effective strategy to apply known drugs for treatment of rare or neglected diseases. Similar to fungi, N. fowleri has an essential requirement for ergosterol, a building block of plasma and cell membranes. Disruption of sterol biosynthesis by small-molecule inhibitors is a validated interventional strategy against fungal pathogens of medical and agricultural importance. The N. fowleri genome encodes the sterol 14-demethylase (CYP51) target sharing ~35% sequence identity to fungal orthologues. The similarity of targets raises the possibility of repurposing anti-mycotic drugs and optimization of their usage for the treatment of PAM. In this work, we (i) systematically assessed the impact of anti-fungal azole drugs, known as conazoles, on sterol biosynthesis and viability of cultured N. fowleri trophozotes, (ii) identified the endogenous CYP51 substrate by mass spectrometry analysis of N. fowleri lipids, and (iii) analyzed the interactions between the recombinant CYP51 target and conazoles by UV-vis spectroscopy and x-ray crystallography. Collectively, the target-based and parasite-based data obtained in these studies validated CYP51 as a potentially ‘druggable’ target in N. fowleri, and conazole drugs as the candidates for assessment in the animal model of PAM. PMID:29284029

Distinctive Behaviors of Druggable Proteins in Cellular Networks

PubMed Central

Workman, Paul; Al-Lazikani, Bissan

2015-01-01

The interaction environment of a protein in a cellular network is important in defining the role that the protein plays in the system as a whole, and thus its potential suitability as a drug target. Despite the importance of the network environment, it is neglected during target selection for drug discovery. Here, we present the first systematic, comprehensive computational analysis of topological, community and graphical network parameters of the human interactome and identify discriminatory network patterns that strongly distinguish drug targets from the interactome as a whole. Importantly, we identify striking differences in the network behavior of targets of cancer drugs versus targets from other therapeutic areas and explore how they may relate to successful drug combinations to overcome acquired resistance to cancer drugs. We develop, computationally validate and provide the first public domain predictive algorithm for identifying druggable neighborhoods based on network parameters. We also make available full predictions for 13,345 proteins to aid target selection for drug discovery. All target predictions are available through canSAR.icr.ac.uk. Underlying data and tools are available at https://cansar.icr.ac.uk/cansar/publications/druggable_network_neighbourhoods/. PMID:26699810

A Computational Approach to Finding Novel Targets for Existing Drugs

PubMed Central

Li, Yvonne Y.; An, Jianghong; Jones, Steven J. M.

2011-01-01

Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM), suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects. PMID:21909252

Non-clinical studies required for new drug development - Part I: early in silico and in vitro studies, new target discovery and validation, proof of principles and robustness of animal studies.

PubMed

Andrade, E L; Bento, A F; Cavalli, J; Oliveira, S K; Freitas, C S; Marcon, R; Schwanke, R C; Siqueira, J M; Calixto, J B

2016-10-24

This review presents a historical overview of drug discovery and the non-clinical stages of the drug development process, from initial target identification and validation, through in silico assays and high throughput screening (HTS), identification of leader molecules and their optimization, the selection of a candidate substance for clinical development, and the use of animal models during the early studies of proof-of-concept (or principle). This report also discusses the relevance of validated and predictive animal models selection, as well as the correct use of animal tests concerning the experimental design, execution and interpretation, which affect the reproducibility, quality and reliability of non-clinical studies necessary to translate to and support clinical studies. Collectively, improving these aspects will certainly contribute to the robustness of both scientific publications and the translation of new substances to clinical development.

Mechanism-Based Tumor-Targeting Drug Delivery System. Validation of Efficient Vitamin Receptor-Mediated Endocytosis and Drug Release

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, S.; Wong, S.; Zhao, X.

An efficient mechanism-based tumor-targeting drug delivery system, based on tumor-specific vitamin-receptor mediated endocytosis, has been developed. The tumor-targeting drug delivery system is a conjugate of a tumor-targeting molecule (biotin: vitamin H or vitamin B-7), a mechanism-based self-immolative linker and a second-generation taxoid (SB-T-1214) as the cytotoxic agent. This conjugate (1) is designed to be (i) specific to the vitamin receptors overexpressed on tumor cell surface and (ii) internalized efficiently through receptor-mediated endocytosis, followed by smooth drug release via glutathione-triggered self-immolation of the linker. In order to monitor and validate the sequence of events hypothesized, i.e., receptor-mediated endocytosis of the conjugate,more » drug release, and drug-binding to the target protein (microtubules), three fluorescent/fluorogenic molecular probes (2, 3, and 4) were designed and synthesized. The actual occurrence of these processes was unambiguously confirmed by means of confocal fluorescence microscopy (CFM) and flow cytometry using L1210FR leukemia cells, overexpressing biotin receptors. The molecular probe 4, bearing the taxoid linked to fluorescein, was also used to examine the cell specificity (i.e., efficacy of receptor-based cell targeting) for three cell lines, L1210FR (biotin receptors overexpressed), L1210 (biotin receptors not overexpressed), and WI38 (normal human lung fibroblast, biotin receptor negative). As anticipated, the molecular probe 4 exhibited high specificity only to L1210FR. To confirm the direct correlation between the cell-specific drug delivery and anticancer activity of the probe 4, its cytotoxicity against these three cell lines was also examined. The results clearly showed a good correlation between the two methods. In the same manner, excellent cell-specific cytotoxicity of the conjugate 1 (without fluorescein attachment to the taxoid) against the same three cell lines was confirmed. This mechanism-based tumor-targeting drug delivery system will find a range of applications.« less

The identification of new protein kinase inhibitors as targets in modern drug discovery.

PubMed

Akritopoulou-Zanze, Irini

2006-07-01

In recent years there has been great interest in developing protein kinase inhibitors as therapeutic agents for a variety of diseases. This article provides an overview on the history, development and validity of kinases as drug targets, as well as a description of kinase research, including its limitations, challenges and successes.

Targeting ALK: Precision Medicine Takes On Drug Resistance

PubMed Central

Lin, Jessica J.; Riely, Gregory J.; Shaw, Alice T.

2017-01-01

Anaplastic lymphoma kinase (ALK) is a validated molecular target in several ALK-rearranged malignancies, including non-small-cell lung cancer (NSCLC). However, the clinical benefit of targeting ALK using tyrosine kinase inhibitors (TKIs) is almost universally limited by the emergence of drug resistance. Diverse mechanisms of resistance to ALK TKIs have now been discovered, and these basic mechanisms are informing the development of novel therapeutic strategies to overcome resistance in the clinic. In this Review, we summarize the current successes and challenges of targeting ALK. PMID:28122866

Advances towards the design and development of personalized non-small-cell lung cancer drug therapy.

PubMed

Vari, Sabrina; Pilotto, Sara; Maugeri-Saccà, Marcello; Ciuffreda, Ludovica; Cesta Incani, Ursula; Falcone, Italia; Del Curatolo, Anais; Ceribelli, Anna; Gelibter, Alain; De Maria, Ruggero; Tortora, Giampaolo; Cognetti, Francesco; Bria, Emilio; Milella, Michele

2013-11-01

Non-small-cell lung cancer (NSCLC) subtypes are driven by specific genetic aberrations. For reasons such as this, there is a call for treatment personalization. The ability to instigate NSCLC fragmentation poses new methodological problems, and new 'driver' molecular aberrations are being discovered at an unprecedented pace. This article describes the clinical development of epidermal growth factor-tyrosine kinase inhibitors (EGFR-TKIs) and crizotinib for EGFR-mutant and anaplastic lymphoma kinase (ALK)-rearranged NSCLC. Further, the authors briefly describe the emerging molecular targets in NSCLC, in terms of both rationale for therapeutic targeting and strategies, for clinical development. Target identification and validation in NSCLC still requires considerable effort, as not all of the molecular alterations are clear 'drivers' nor can they be efficiently targeted with available drugs. However, 50% of the NSCLC cases are without clear-defined molecular aberrations. Clinical trial methodology will need to develop novel paradigms for targeted drug development, aiming at the validation of an ideal 'biology-to-trial' approach. Despite significant challenges, a truly 'personalized' approach to NSCLC therapy appears to be within our reach.

Detecting drug-target binding in cells using fluorescence-activated cell sorting coupled with mass spectrometry analysis.

PubMed

Wilson, Kris; Webster, Scott P; Iredale, John P; Zheng, Xiaozhong; Homer, Natalie Z; Pham, Nhan T; Auer, Manfred; Mole, Damian J

2017-12-15

The assessment of drug-target engagement for determining the efficacy of a compound inside cells remains challenging, particularly for difficult target proteins. Existing techniques are more suited to soluble protein targets. Difficult target proteins include those with challenging in vitro solubility, stability or purification properties that preclude target isolation. Here, we report a novel technique that measures intracellular compound-target complex formation, as well as cellular permeability, specificity and cytotoxicity-the toxicity-affinity-permeability-selectivity (TAPS) technique. The TAPS assay is exemplified here using human kynurenine 3-monooxygenase (KMO), a challenging intracellular membrane protein target of significant current interest. TAPS confirmed target binding of known KMO inhibitors inside cells. We conclude that the TAPS assay can be used to facilitate intracellular hit validation on most, if not all intracellular drug targets.

Detecting drug-target binding in cells using fluorescence-activated cell sorting coupled with mass spectrometry analysis

NASA Astrophysics Data System (ADS)

Wilson, Kris; Webster, Scott P.; Iredale, John P.; Zheng, Xiaozhong; Homer, Natalie Z.; Pham, Nhan T.; Auer, Manfred; Mole, Damian J.

2018-01-01

The assessment of drug-target engagement for determining the efficacy of a compound inside cells remains challenging, particularly for difficult target proteins. Existing techniques are more suited to soluble protein targets. Difficult target proteins include those with challenging in vitro solubility, stability or purification properties that preclude target isolation. Here, we report a novel technique that measures intracellular compound-target complex formation, as well as cellular permeability, specificity and cytotoxicity-the toxicity-affinity-permeability-selectivity (TAPS) technique. The TAPS assay is exemplified here using human kynurenine 3-monooxygenase (KMO), a challenging intracellular membrane protein target of significant current interest. TAPS confirmed target binding of known KMO inhibitors inside cells. We conclude that the TAPS assay can be used to facilitate intracellular hit validation on most, if not all intracellular drug targets.

Open Access High Throughput Drug Discovery in the Public Domain: A Mount Everest in the Making

PubMed Central

Roy, Anuradha; McDonald, Peter R.; Sittampalam, Sitta; Chaguturu, Rathnam

2013-01-01

High throughput screening (HTS) facilitates screening large numbers of compounds against a biochemical target of interest using validated biological or biophysical assays. In recent years, a significant number of drugs in clinical trails originated from HTS campaigns, validating HTS as a bona fide mechanism for hit finding. In the current drug discovery landscape, the pharmaceutical industry is embracing open innovation strategies with academia to maximize their research capabilities and to feed their drug discovery pipeline. The goals of academic research have therefore expanded from target identification and validation to probe discovery, chemical genomics, and compound library screening. This trend is reflected in the emergence of HTS centers in the public domain over the past decade, ranging in size from modestly equipped academic screening centers to well endowed Molecular Libraries Probe Centers Network (MLPCN) centers funded by the NIH Roadmap initiative. These centers facilitate a comprehensive approach to probe discovery in academia and utilize both classical and cutting-edge assay technologies for executing primary and secondary screening campaigns. The various facets of academic HTS centers as well as their implications on technology transfer and drug discovery are discussed, and a roadmap for successful drug discovery in the public domain is presented. New lead discovery against therapeutic targets, especially those involving the rare and neglected diseases, is indeed a Mount Everestonian size task, and requires diligent implementation of pharmaceutical industry’s best practices for a successful outcome. PMID:20809896

Photoaffinity labeling in target- and binding-site identification

PubMed Central

Smith, Ewan; Collins, Ian

2015-01-01

Photoaffinity labeling (PAL) using a chemical probe to covalently bind its target in response to activation by light has become a frequently used tool in drug discovery for identifying new drug targets and molecular interactions, and for probing the location and structure of binding sites. Methods to identify the specific target proteins of hit molecules from phenotypic screens are highly valuable in early drug discovery. In this review, we summarize the principles of PAL including probe design and experimental techniques for in vitro and live cell investigations. We emphasize the need to optimize and validate probes and highlight examples of the successful application of PAL across multiple disease areas. PMID:25686004

Application of chemical biology in target identification and drug discovery.

PubMed

Zhu, Yue; Xiao, Ting; Lei, Saifei; Zhou, Fulai; Wang, Ming-Wei

2015-09-01

Drug discovery and development is vital to the well-being of mankind and sustainability of the pharmaceutical industry. Using chemical biology approaches to discover drug leads has become a widely accepted path partially because of the completion of the Human Genome Project. Chemical biology mainly solves biological problems through searching previously unknown targets for pharmacologically active small molecules or finding ligands for well-defined drug targets. It is a powerful tool to study how these small molecules interact with their respective targets, as well as their roles in signal transduction, molecular recognition and cell functions. There have been an increasing number of new therapeutic targets being identified and subsequently validated as a result of advances in functional genomics, which in turn led to the discovery of numerous active small molecules via a variety of high-throughput screening initiatives. In this review, we highlight some applications of chemical biology in the context of drug discovery.

Using computer-aided drug design and medicinal chemistry strategies in the fight against diabetes.

PubMed

Semighini, Evandro P; Resende, Jonathan A; de Andrade, Peterson; Morais, Pedro A B; Carvalho, Ivone; Taft, Carlton A; Silva, Carlos H T P

2011-04-01

The aim of this work is to present a simple, practical and efficient protocol for drug design, in particular Diabetes, which includes selection of the illness, good choice of a target as well as a bioactive ligand and then usage of various computer aided drug design and medicinal chemistry tools to design novel potential drug candidates in different diseases. We have selected the validated target dipeptidyl peptidase IV (DPP-IV), whose inhibition contributes to reduce glucose levels in type 2 diabetes patients. The most active inhibitor with complex X-ray structure reported was initially extracted from the BindingDB database. By using molecular modification strategies widely used in medicinal chemistry, besides current state-of-the-art tools in drug design (including flexible docking, virtual screening, molecular interaction fields, molecular dynamics, ADME and toxicity predictions), we have proposed 4 novel potential DPP-IV inhibitors with drug properties for Diabetes control, which have been supported and validated by all the computational tools used herewith.

A comparison of machine learning techniques for detection of drug target articles.

PubMed

Danger, Roxana; Segura-Bedmar, Isabel; Martínez, Paloma; Rosso, Paolo

2010-12-01

Important progress in treating diseases has been possible thanks to the identification of drug targets. Drug targets are the molecular structures whose abnormal activity, associated to a disease, can be modified by drugs, improving the health of patients. Pharmaceutical industry needs to give priority to their identification and validation in order to reduce the long and costly drug development times. In the last two decades, our knowledge about drugs, their mechanisms of action and drug targets has rapidly increased. Nevertheless, most of this knowledge is hidden in millions of medical articles and textbooks. Extracting knowledge from this large amount of unstructured information is a laborious job, even for human experts. Drug target articles identification, a crucial first step toward the automatic extraction of information from texts, constitutes the aim of this paper. A comparison of several machine learning techniques has been performed in order to obtain a satisfactory classifier for detecting drug target articles using semantic information from biomedical resources such as the Unified Medical Language System. The best result has been achieved by a Fuzzy Lattice Reasoning classifier, which reaches 98% of ROC area measure. Copyright © 2010 Elsevier Inc. All rights reserved.

Quantitative self-assembly prediction yields targeted nanomedicines

NASA Astrophysics Data System (ADS)

Shamay, Yosi; Shah, Janki; Işık, Mehtap; Mizrachi, Aviram; Leibold, Josef; Tschaharganeh, Darjus F.; Roxbury, Daniel; Budhathoki-Uprety, Januka; Nawaly, Karla; Sugarman, James L.; Baut, Emily; Neiman, Michelle R.; Dacek, Megan; Ganesh, Kripa S.; Johnson, Darren C.; Sridharan, Ramya; Chu, Karen L.; Rajasekhar, Vinagolu K.; Lowe, Scott W.; Chodera, John D.; Heller, Daniel A.

2018-02-01

Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. These processes are generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves. The drugs assemble with the aid of sulfated indocyanines into particles with ultrahigh drug loadings of up to 90%. We devised quantitative structure-nanoparticle assembly prediction (QSNAP) models to identify and validate electrotopological molecular descriptors as highly predictive indicators of nano-assembly and nanoparticle size. The resulting nanoparticles selectively targeted kinase inhibitors to caveolin-1-expressing human colon cancer and autochthonous liver cancer models to yield striking therapeutic effects while avoiding pERK inhibition in healthy skin. This finding enables the computational design of nanomedicines based on quantitative models for drug payload selection.

Prediction of polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space.

PubMed

Cheng, Feixiong; Li, Weihua; Wu, Zengrui; Wang, Xichuan; Zhang, Chen; Li, Jie; Liu, Guixia; Tang, Yun

2013-04-22

Prediction of polypharmacological profiles of drugs enables us to investigate drug side effects and further find their new indications, i.e. drug repositioning, which could reduce the costs while increase the productivity of drug discovery. Here we describe a new computational framework to predict polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. On the basis of our previous developed drug side effects database, named MetaADEDB, a drug side effect similarity inference (DSESI) method was developed for drug-target interaction (DTI) prediction on a known DTI network connecting 621 approved drugs and 893 target proteins. The area under the receiver operating characteristic curve was 0.882 ± 0.011 averaged from 100 simulated tests of 10-fold cross-validation for the DSESI method, which is comparative with drug structural similarity inference and drug therapeutic similarity inference methods. Seven new predicted candidate target proteins for seven approved drugs were confirmed by published experiments, with the successful hit rate more than 15.9%. Moreover, network visualization of drug-target interactions and off-target side effect associations provide new mechanism-of-action of three approved antipsychotic drugs in a case study. The results indicated that the proposed methods could be helpful for prediction of polypharmacological profiles of drugs.

One target-multiple indications: a call for an integrated common mechanisms strategy.

PubMed

Nielsch, Ulrich; Schäfer, Stefan; Wild, Hanno; Busch, Andreas

2007-12-01

Ever-increasing research and development costs are putting constant pressure on the pharmaceutical industry to improve their efficiency. Efforts to increase the output of the research pipeline have yielded limited success. Traditionally, maximization of the value of a drug is attempted through life-cycle management, which is initiated late in development, or when the drug is already on the market. Validated targets can be exploited further through development of a follow-up drug, which may offer advantages regarding safety or convenience. In this article, we propose to systematically evaluate the full therapeutic potential of a drug target, proprietary chemical lead structure, or drug candidate as broad and as early as possible and we call this the 'common mechanism' approach.

Targeting bacterial central metabolism for drug development.

PubMed

Murima, Paul; McKinney, John D; Pethe, Kevin

2014-11-20

Current antibiotics, derived mainly from natural sources, inhibit a narrow spectrum of cellular processes, namely DNA replication, protein synthesis, and cell wall biosynthesis. With the worldwide explosion of drug resistance, there is renewed interest in the investigation of alternate essential cellular processes, including bacterial central metabolic pathways, as a drug target space for the next generation of antibiotics. However, the validation of targets in central metabolism is more complex, as essentiality of such targets can be conditional and/or contextual. Bearing in mind our enhanced understanding of prokaryotic central metabolism, a key question arises: can central metabolism be bacteria's Achilles' heel and a therapeutic target for the development of new classes of antibiotics? In this review, we draw lessons from oncology and attempt to address some of the open questions related to feasibility of targeting bacterial central metabolism as a strategy for developing new antibacterial drugs. Copyright © 2014 Elsevier Ltd. All rights reserved.

Screening a fragment cocktail library using ultrafiltration

PubMed Central

Shibata, Sayaka; Zhang, Zhongsheng; Korotkov, Konstantin V.; Delarosa, Jaclyn; Napuli, Alberto; Kelley, Angela M.; Mueller, Natasha; Ross, Jennifer; Zucker, Frank H.; Buckner, Frederick S.; Merritt, Ethan A.; Verlinde, Christophe L. M. J.; Van Voorhis, Wesley C.; Hol, Wim G. J.; Fan, Erkang

2011-01-01

Ultrafiltration provides a generic method to discover ligands for protein drug targets with millimolar to micromolar Kd, the typical range of fragment-based drug discovery. This method was tailored to a 96-well format, and cocktails of fragment-sized molecules, with molecular masses between 150 and 300 Da, were screened against medical structural genomics target proteins. The validity of the method was confirmed through competitive binding assays in the presence of ligands known to bind the target proteins. PMID:21750879

Chemical proteomics for target discovery of head-to-tail cyclized mini-proteins

NASA Astrophysics Data System (ADS)

Hellinger, Roland; Thell, Kathrin; Vasileva, Mina; Muhammad, Taj; Gunasekera, Sunithi; Kümmel, Daniel; Göransson, Ulf; Becker, Christian W.; Gruber, Christian W.

2017-10-01

Target deconvolution is one of the most challenging tasks in drug discovery, but a key step in drug development. In contrast to small molecules, there is a lack of validated and robust methodologies for target elucidation of peptides. In particular, it is difficult to apply these methods to cyclic and cysteine-stabilized peptides since they exhibit reduced amenability to chemical modification and affinity capture; however, such ribosomal synthesized and post-translationally modified peptide natural products are rich sources of promising drug candidates. For example, plant-derived circular peptides called cyclotides have recently attracted much attention due to their immunosuppressive effects and oral activity in the treatment of multiple sclerosis in mice, but their molecular target has hitherto not been reported. In this study a chemical proteomics approach using photo-affinity crosslinking was developed to determine a target of the circular peptide [T20K]kalata B1. Using this prototypic nature-derived peptide enabled the identification of a possible modulation of 14-3-3 proteins. This biochemical interaction was validated via competition pull down assays as well as a cellular reporter assay indicating an effect on 14-3-3-dependent transcriptional activity. As proof of concept, the presented approach may be applicable for target elucidation of various cyclic peptides and mini-proteins, in particular cyclotides, which represent a promising class of molecules in drug discovery and development.

Proteomic and Bioinformatic Studies for the Characterization of Response to Pemetrexed in Platinum Drug Resistant Ovarian Cancer.

PubMed

Severi, Leda; Losi, Lorena; Fonda, Sergio; Taddia, Laura; Gozzi, Gaia; Marverti, Gaetano; Magni, Fulvio; Chinello, Clizia; Stella, Martina; Sheouli, Jalid; Braicu, Elena I; Genovese, Filippo; Lauriola, Angela; Marraccini, Chiara; Gualandi, Alessandra; D'Arca, Domenico; Ferrari, Stefania; Costi, Maria P

2018-01-01

Proteomics and bioinformatics are a useful combined technology for the characterization of protein expression level and modulation associated with the response to a drug and with its mechanism of action. The folate pathway represents an important target in the anticancer drugs therapy. In the present study, a discovery proteomics approach was applied to tissue samples collected from ovarian cancer patients who relapsed after the first-line carboplatin-based chemotherapy and were treated with pemetrexed (PMX), a known folate pathway targeting drug. The aim of the work is to identify the proteomic profile that can be associated to the response to the PMX treatment in pre-treatement tissue. Statistical metrics of the experimental Mass Spectrometry (MS) data were combined with a knowledge-based approach that included bioinformatics and a literature review through ProteinQuest™ tool, to design a protein set of reference (PSR). The PSR provides feedback for the consistency of MS proteomic data because it includes known validated proteins. A panel of 24 proteins with levels that were significantly different in pre-treatment samples of patients who responded to the therapy vs. the non-responder ones, was identified. The differences of the identified proteins were explained for the patients with different outcomes and the known PMX targets were further validated. The protein panel herein identified is ready for further validation in retrospective clinical trials using a targeted proteomic approach. This study may have a general relevant impact on biomarker application for cancer patients therapy selection.

Rapid inverse planning for pressure-driven drug infusions in the brain.

PubMed

Rosenbluth, Kathryn H; Martin, Alastair J; Mittermeyer, Stephan; Eschermann, Jan; Dickinson, Peter J; Bankiewicz, Krystof S

2013-01-01

Infusing drugs directly into the brain is advantageous to oral or intravenous delivery for large molecules or drugs requiring high local concentrations with low off-target exposure. However, surgeons manually planning the cannula position for drug delivery in the brain face a challenging three-dimensional visualization task. This study presents an intuitive inverse-planning technique to identify the optimal placement that maximizes coverage of the target structure while minimizing the potential for leakage outside the target. The technique was retrospectively validated using intraoperative magnetic resonance imaging of infusions into the striatum of non-human primates and into a tumor in a canine model and applied prospectively to upcoming human clinical trials.

Discovery of novel drugs for promising targets.

PubMed

Martell, Robert E; Brooks, David G; Wang, Yan; Wilcoxen, Keith

2013-09-01

Once a promising drug target is identified, the steps to actually discover and optimize a drug are diverse and challenging. The goal of this study was to provide a road map to navigate drug discovery. Review general steps for drug discovery and provide illustrating references. A number of approaches are available to enhance and accelerate target identification and validation. Consideration of a variety of potential mechanisms of action of potential drugs can guide discovery efforts. The hit to lead stage may involve techniques such as high-throughput screening, fragment-based screening, and structure-based design, with informatics playing an ever-increasing role. Biologically relevant screening models are discussed, including cell lines, 3-dimensional culture, and in vivo screening. The process of enabling human studies for an investigational drug is also discussed. Drug discovery is a complex process that has significantly evolved in recent years. © 2013 Elsevier HS Journals, Inc. All rights reserved.

Proteomic validation of protease drug targets: pharmacoproteomics of matrix metalloproteinase inhibitor drugs using isotope-coded affinity tag labelling and tandem mass spectrometry.

PubMed

Butler, G S; Overall, C M

2007-01-01

We illustrate the use of quantitative proteomics, namely isotope-coded affinity tag labelling and tandem mass spectrometry, to assess the targets and effects of the blockade of matrix metalloproteinases by an inhibitor drug in a breast cancer cell culture system. Treatment of MT1-MMP-transfected MDA-MB-231 cells with AG3340 (Prinomastat) directly affected the processing a multitude of matrix metalloproteinase substrates, and indirectly altered the expression of an array of other proteins with diverse functions. Therefore, broad spectrum blockade of MMPs has wide-ranging biological consequences. In this human breast cancer cell line, secreted substrates accumulated uncleaved in the conditioned medium and plasma membrane protein substrates were retained on the cell surface, due to reduced processing and shedding of these proteins (cell surface receptors, growth factors and bioactive molecules) to the medium in the presence of the matrix metalloproteinase inhibitor. Hence, proteomic investigation of drug-perturbed cellular proteomes can identify new protease substrates and at the same time provides valuable information for target validation, drug efficacy and potential side effects prior to commitment to clinical trials.

Sulfonylureas and Glinides as New PPARγ Agonists:. Virtual Screening and Biological Assays

NASA Astrophysics Data System (ADS)

Scarsi, Marco; Podvinec, Michael; Roth, Adrian; Hug, Hubert; Kersten, Sander; Albrecht, Hugo; Schwede, Torsten; Meyer, Urs A.; Rücker, Christoph

2007-12-01

This work combines the predictive power of computational drug discovery with experimental validation by means of biological assays. In this way, a new mode of action for type 2 diabetes drugs has been unvealed. Most drugs currently employed in the treatment of type 2 diabetes either target the sulfonylurea receptor stimulating insulin release (sulfonylureas, glinides), or target PPARγ improving insulin resistance (thiazolidinediones). Our work shows that sulfonylureas and glinides bind to PPARγ and exhibit PPARγ agonistic activity. This result was predicted in silico by virtual screening and confirmed in vitro by three biological assays. This dual mode of action of sulfonylureas and glinides may open new perspectives for the molecular pharmacology of antidiabetic drugs, since it provides evidence that drugs can be designed which target both the sulfonylurea receptor and PPARγ. Targeting both receptors could in principle allow to increase pancreatic insulin secretion, as well as to improve insulin resistance.

Genetic Validation of Aminoacyl-tRNA Synthetases as Drug Targets in Trypanosoma brucei

PubMed Central

Kalidas, Savitha; Cestari, Igor; Monnerat, Severine; Li, Qiong; Regmi, Sandesh; Hasle, Nicholas; Labaied, Mehdi; Parsons, Marilyn; Stuart, Kenneth

2014-01-01

Human African trypanosomiasis (HAT) is an important public health threat in sub-Saharan Africa. Current drugs are unsatisfactory, and new drugs are being sought. Few validated enzyme targets are available to support drug discovery efforts, so our goal was to obtain essentiality data on genes with proven utility as drug targets. Aminoacyl-tRNA synthetases (aaRSs) are known drug targets for bacterial and fungal pathogens and are required for protein synthesis. Here we survey the essentiality of eight Trypanosoma brucei aaRSs by RNA interference (RNAi) gene expression knockdown, covering an enzyme from each major aaRS class: valyl-tRNA synthetase (ValRS) (class Ia), tryptophanyl-tRNA synthetase (TrpRS-1) (class Ib), arginyl-tRNA synthetase (ArgRS) (class Ic), glutamyl-tRNA synthetase (GluRS) (class 1c), threonyl-tRNA synthetase (ThrRS) (class IIa), asparaginyl-tRNA synthetase (AsnRS) (class IIb), and phenylalanyl-tRNA synthetase (α and β) (PheRS) (class IIc). Knockdown of mRNA encoding these enzymes in T. brucei mammalian stage parasites showed that all were essential for parasite growth and survival in vitro. The reduced expression resulted in growth, morphological, cell cycle, and DNA content abnormalities. ThrRS was characterized in greater detail, showing that the purified recombinant enzyme displayed ThrRS activity and that the protein localized to both the cytosol and mitochondrion. Borrelidin, a known inhibitor of ThrRS, was an inhibitor of T. brucei ThrRS and showed antitrypanosomal activity. The data show that aaRSs are essential for T. brucei survival and are likely to be excellent targets for drug discovery efforts. PMID:24562907

Plasmodial sugar transporters as anti-malarial drug targets and comparisons with other protozoa

PubMed Central

2011-01-01

Glucose is the primary source of energy and a key substrate for most cells. Inhibition of cellular glucose uptake (the first step in its utilization) has, therefore, received attention as a potential therapeutic strategy to treat various unrelated diseases including malaria and cancers. For malaria, blood forms of parasites rely almost entirely on glycolysis for energy production and, without energy stores, they are dependent on the constant uptake of glucose. Plasmodium falciparum is the most dangerous human malarial parasite and its hexose transporter has been identified as being the major glucose transporter. In this review, recent progress regarding the validation and development of the P. falciparum hexose transporter as a drug target is described, highlighting the importance of robust target validation through both chemical and genetic methods. Therapeutic targeting potential of hexose transporters of other protozoan pathogens is also reviewed and discussed. PMID:21676209

Plasmodial sugar transporters as anti-malarial drug targets and comparisons with other protozoa.

PubMed

Slavic, Ksenija; Krishna, Sanjeev; Derbyshire, Elvira T; Staines, Henry M

2011-06-15

Glucose is the primary source of energy and a key substrate for most cells. Inhibition of cellular glucose uptake (the first step in its utilization) has, therefore, received attention as a potential therapeutic strategy to treat various unrelated diseases including malaria and cancers. For malaria, blood forms of parasites rely almost entirely on glycolysis for energy production and, without energy stores, they are dependent on the constant uptake of glucose. Plasmodium falciparum is the most dangerous human malarial parasite and its hexose transporter has been identified as being the major glucose transporter. In this review, recent progress regarding the validation and development of the P. falciparum hexose transporter as a drug target is described, highlighting the importance of robust target validation through both chemical and genetic methods. Therapeutic targeting potential of hexose transporters of other protozoan pathogens is also reviewed and discussed.

Biomarkers and personalised medicine in rheumatoid arthritis: a proposal for interactions between academia, industry and regulatory bodies.

PubMed

Miossec, P; Verweij, C L; Klareskog, L; Pitzalis, C; Barton, A; Lekkerkerker, F; Reiter, S; Laslop, A; Breedveld, F; Abadie, E; Flamion, B; Dere, W; Mpofu, S; Goel, N; Ethgen, D; Mitlak, B; Ormarsdóttir, S; Rao, R; Tsouderos, Y; Reginster, J-Y

2011-10-01

Rheumatoid arthritis (RA) is one of the most appropriate conditions for the application of personalised medicine as a high degree of heterogeneity has been recognised, which remains to be explained. Such heterogeneity is also reflected in the large number of treatment targets and options. A growing number of biologics as well as small molecules are already in use and there are promising new drugs in development. In order to make the best use of treatment options, both targeted and non-targeted biomarkers have to be identified and validated. To this aim, new rules are needed for the interaction between academia and industry under regulatory control. Setting up multi-centre biosample collections with clear definition of access, organising early, possibly non-committing discussions with regulatory authorities, and defining a clear route for the validation, qualification and registration of the biomarker-drug combination are some of the more critical areas where effective collaboration between the drug industry, academia and regulators is needed.

Pharmacology and Clinical Drug Candidates in Redox Medicine

PubMed Central

Casas, Ana I.; Maghzal, Ghassan J.; Seredenina, Tamara; Kaludercic, Nina; Robledinos-Anton, Natalia; Di Lisa, Fabio; Stocker, Roland; Ghezzi, Pietro; Jaquet, Vincent; Cuadrado, Antonio

2015-01-01

Abstract Significance: Oxidative stress is suggested to be a disease mechanism common to a wide range of disorders affecting human health. However, so far, the pharmacotherapeutic exploitation of this, for example, based on chemical scavenging of pro-oxidant molecules, has been unsuccessful. Recent Advances: An alternative emerging approach is to target the enzymatic sources of disease-relevant oxidative stress. Several such enzymes and isoforms have been identified and linked to different pathologies. For some targets, the respective pharmacology is quite advanced, that is, up to late-stage clinical development or even on the market; for others, drugs are already in clinical use, although not for indications based on oxidative stress, and repurposing seems to be a viable option. Critical Issues: For all other targets, reliable preclinical validation and drug ability are key factors for any translation into the clinic. In this study, specific pharmacological agents with optimal pharmacokinetic profiles are still lacking. Moreover, these enzymes also serve largely unknown physiological functions and their inhibition may lead to unwanted side effects. Future Directions: The current promising data based on new targets, drugs, and drug repurposing are mainly a result of academic efforts. With the availability of optimized compounds and coordinated efforts from academia and industry scientists, unambiguous validation and translation into proof-of-principle studies seem achievable in the very near future, possibly leading towards a new era of redox medicine. Antioxid. Redox Signal. 23, 1113–1129. PMID:26415051

gene2drug: a computational tool for pathway-based rational drug repositioning.

PubMed

Napolitano, Francesco; Carrella, Diego; Mandriani, Barbara; Pisonero-Vaquero, Sandra; Sirci, Francesco; Medina, Diego L; Brunetti-Pierri, Nicola; di Bernardo, Diego

2018-05-01

Drug repositioning has been proposed as an effective shortcut to drug discovery. The availability of large collections of transcriptional responses to drugs enables computational approaches to drug repositioning directly based on measured molecular effects. We introduce a novel computational methodology for rational drug repositioning, which exploits the transcriptional responses following treatment with small molecule. Specifically, given a therapeutic target gene, a prioritization of potential effective drugs is obtained by assessing their impact on the transcription of genes in the pathway(s) including the target. We performed in silico validation and comparison with a state-of-art technique based on similar principles. We next performed experimental validation in two different real-case drug repositioning scenarios: (i) upregulation of the glutamate-pyruvate transaminase (GPT), which has been shown to induce reduction of oxalate levels in a mouse model of primary hyperoxaluria, and (ii) activation of the transcription factor TFEB, a master regulator of lysosomal biogenesis and autophagy, whose modulation may be beneficial in neurodegenerative disorders. A web tool for Gene2drug is freely available at http://gene2drug.tigem.it. An R package is under development and can be obtained from https://github.com/franapoli/gep2pep. dibernardo@tigem.it. Supplementary data are available at Bioinformatics online.

Predicting Drug-Target Interactions With Multi-Information Fusion.

PubMed

Peng, Lihong; Liao, Bo; Zhu, Wen; Li, Zejun; Li, Keqin

2017-03-01

Identifying potential associations between drugs and targets is a critical prerequisite for modern drug discovery and repurposing. However, predicting these associations is difficult because of the limitations of existing computational methods. Most models only consider chemical structures and protein sequences, and other models are oversimplified. Moreover, datasets used for analysis contain only true-positive interactions, and experimentally validated negative samples are unavailable. To overcome these limitations, we developed a semi-supervised based learning framework called NormMulInf through collaborative filtering theory by using labeled and unlabeled interaction information. The proposed method initially determines similarity measures, such as similarities among samples and local correlations among the labels of the samples, by integrating biological information. The similarity information is then integrated into a robust principal component analysis model, which is solved using augmented Lagrange multipliers. Experimental results on four classes of drug-target interaction networks suggest that the proposed approach can accurately classify and predict drug-target interactions. Part of the predicted interactions are reported in public databases. The proposed method can also predict possible targets for new drugs and can be used to determine whether atropine may interact with alpha1B- and beta1- adrenergic receptors. Furthermore, the developed technique identifies potential drugs for new targets and can be used to assess whether olanzapine and propiomazine may target 5HT2B. Finally, the proposed method can potentially address limitations on studies of multitarget drugs and multidrug targets.

Early Probe and Drug Discovery in Academia: A Minireview.

PubMed

Roy, Anuradha

2018-02-09

Drug discovery encompasses processes ranging from target selection and validation to the selection of a development candidate. While comprehensive drug discovery work flows are implemented predominantly in the big pharma domain, early discovery focus in academia serves to identify probe molecules that can serve as tools to study targets or pathways. Despite differences in the ultimate goals of the private and academic sectors, the same basic principles define the best practices in early discovery research. A successful early discovery program is built on strong target definition and validation using a diverse set of biochemical and cell-based assays with functional relevance to the biological system being studied. The chemicals identified as hits undergo extensive scaffold optimization and are characterized for their target specificity and off-target effects in in vitro and in animal models. While the active compounds from screening campaigns pass through highly stringent chemical and Absorption, Distribution, Metabolism, and Excretion (ADME) filters for lead identification, the probe discovery involves limited medicinal chemistry optimization. The goal of probe discovery is identification of a compound with sub-µM activity and reasonable selectivity in the context of the target being studied. The compounds identified from probe discovery can also serve as starting scaffolds for lead optimization studies.

76 FR 18564 - Government-Owned Inventions; Availability for Licensing

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-04

... attractive antimicrobial target. The chrysophaetin exhibits antimicrobial activity against drug resistant... analogues will show similar antimicrobial activity to the natural products and will utilize the same... distinct antimicrobial compounds. Attack newly validated antibacterial targeted protein FtsZ. These...

The topoisomerase II-Hsp90 complex: a new chemotherapeutic target?

PubMed

Barker, Catherine R; Hamlett, Jane; Pennington, Stephen R; Burrows, Francis; Lundgren, Karen; Lough, Rachel; Watson, Alastair J M; Jenkins, John R

2006-06-01

The modulation of DNA topology by topoisomerase II plays a crucial role during chromosome condensation and segregation in mitosis and has thus become a highly attractive target for chemotherapeutic drugs. However, these drugs are highly toxic, and so new approaches are required. One such strategy is to target topoisomerase II-interacting proteins. Here we report the identification of potential topoisomerase II-associated proteins using immunoprecipitation, followed by 1-D and 2-D gel electrophoresis and MALDI-TOF mass spectrometry. A total of 23 proteins were identified and, of these, 17 were further validated as topoisomerase IIalpha-associated proteins by coimmunoprecipitation and Western blot. Six of the interacting proteins were cellular chaperones, including 3 members of the heat shock protein-90 (Hsp90) family, and so the effect of Hsp90 modulation on the antitumor activity of topoisomerase II drugs was tested using the sulforhodamine B assay, clonogenic assays and a xenograft model. The Hsp90 inhibitors geldanamycin, 17-AAG (17-allylamino-17-demethoxygeldanamycin) and radicicol significantly enhanced the activity of the topoisomerase II poisons etoposide and mitoxantrone in vitro and in vivo. Thus, our method of identifying topoisomerase II-interacting proteins appears to be effective, and at least 1 novel topoisomerase IIalpha-associated protein, Hsp90, may represent a valid drug target in the context of topoisomerase II-directed chemotherapy.

Validation of N-myristoyltransferase as an antimalarial drug target using an integrated chemical biology approach

NASA Astrophysics Data System (ADS)

Wright, Megan H.; Clough, Barbara; Rackham, Mark D.; Rangachari, Kaveri; Brannigan, James A.; Grainger, Munira; Moss, David K.; Bottrill, Andrew R.; Heal, William P.; Broncel, Malgorzata; Serwa, Remigiusz A.; Brady, Declan; Mann, David J.; Leatherbarrow, Robin J.; Tewari, Rita; Wilkinson, Anthony J.; Holder, Anthony A.; Tate, Edward W.

2014-02-01

Malaria is an infectious disease caused by parasites of the genus Plasmodium, which leads to approximately one million deaths per annum worldwide. Chemical validation of new antimalarial targets is urgently required in view of rising resistance to current drugs. One such putative target is the enzyme N-myristoyltransferase, which catalyses the attachment of the fatty acid myristate to protein substrates (N-myristoylation). Here, we report an integrated chemical biology approach to explore protein myristoylation in the major human parasite P. falciparum, combining chemical proteomic tools for identification of the myristoylated and glycosylphosphatidylinositol-anchored proteome with selective small-molecule N-myristoyltransferase inhibitors. We demonstrate that N-myristoyltransferase is an essential and chemically tractable target in malaria parasites both in vitro and in vivo, and show that selective inhibition of N-myristoylation leads to catastrophic and irreversible failure to assemble the inner membrane complex, a critical subcellular organelle in the parasite life cycle. Our studies provide the basis for the development of new antimalarials targeting N-myristoyltransferase.

Targeting BRCAness in Gastric Cancer

DTIC Science & Technology

2017-10-01

inhibitors. We also generated a modified CRISPR system using dCas9-KRAB expressing variants of these cells, and validated them for CRISPRi screening...Figure 2. Validation of CRISPR activity following transduction with sgRNAs targeting CD55 and FACS staining with the anti-CD55 antibody. Data shown...interpretation of CRISPR experiments Morgan Diolaiti Specialist UCSF PH.D. Experimental planning and reporting Jefferson Woods SRA UCSF B.S. Perform drug

Survey of phosphorylation near drug binding sites in the Protein Data Bank (PDB) and their effects.

PubMed

Smith, Kyle P; Gifford, Kathleen M; Waitzman, Joshua S; Rice, Sarah E

2015-01-01

While it is currently estimated that 40 to 50% of eukaryotic proteins are phosphorylated, little is known about the frequency and local effects of phosphorylation near pharmaceutical inhibitor binding sites. In this study, we investigated how frequently phosphorylation may affect the binding of drug inhibitors to target proteins. We examined the 453 non-redundant structures of soluble mammalian drug target proteins bound to inhibitors currently available in the Protein Data Bank (PDB). We cross-referenced these structures with phosphorylation data available from the PhosphoSitePlus database. Three hundred twenty-two of 453 (71%) of drug targets have evidence of phosphorylation that has been validated by multiple methods or labs. For 132 of 453 (29%) of those, the phosphorylation site is within 12 Å of the small molecule-binding site, where it would likely alter small molecule binding affinity. We propose a framework for distinguishing between drug-phosphorylation site interactions that are likely to alter the efficacy of drugs versus those that are not. In addition we highlight examples of well-established drug targets, such as estrogen receptor alpha, for which phosphorylation may affect drug affinity and clinical efficacy. Our data suggest that phosphorylation may affect drug binding and efficacy for a significant fraction of drug target proteins. © 2014 Wiley Periodicals, Inc.

Genomes2Drugs: Identifies Target Proteins and Lead Drugs from Proteome Data

PubMed Central

Toomey, David; Hoppe, Heinrich C.; Brennan, Marian P.; Nolan, Kevin B.; Chubb, Anthony J.

2009-01-01

Background Genome sequencing and bioinformatics have provided the full hypothetical proteome of many pathogenic organisms. Advances in microarray and mass spectrometry have also yielded large output datasets of possible target proteins/genes. However, the challenge remains to identify new targets for drug discovery from this wealth of information. Further analysis includes bioinformatics and/or molecular biology tools to validate the findings. This is time consuming and expensive, and could fail to yield novel drugs if protein purification and crystallography is impossible. To pre-empt this, a researcher may want to rapidly filter the output datasets for proteins that show good homology to proteins that have already been structurally characterised or proteins that are already targets for known drugs. Critically, those researchers developing novel antibiotics need to select out the proteins that show close homology to any human proteins, as future inhibitors are likely to cross-react with the host protein, causing off-target toxicity effects later in clinical trials. Methodology/Principal Findings To solve many of these issues, we have developed a free online resource called Genomes2Drugs which ranks sequences to identify proteins that are (i) homologous to previously crystallized proteins or (ii) targets of known drugs, but are (iii) not homologous to human proteins. When tested using the Plasmodium falciparum malarial genome the program correctly enriched the ranked list of proteins with known drug target proteins. Conclusions/Significance Genomes2Drugs rapidly identifies proteins that are likely to succeed in drug discovery pipelines. This free online resource helps in the identification of potential drug targets. Importantly, the program further highlights proteins that are likely to be inhibited by FDA-approved drugs. These drugs can then be rapidly moved into Phase IV clinical studies under ‘change-of-application’ patents. PMID:19593435

Predicting drug-target interactions by dual-network integrated logistic matrix factorization

NASA Astrophysics Data System (ADS)

Hao, Ming; Bryant, Stephen H.; Wang, Yanli

2017-01-01

In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.

Target validation: linking target and chemical properties to desired product profile.

PubMed

Wyatt, Paul G; Gilbert, Ian H; Read, Kevin D; Fairlamb, Alan H

2011-01-01

The discovery of drugs is a lengthy, high-risk and expensive business taking at least 12 years and is estimated to cost upwards of US$800 million for each drug to be successfully approved for clinical use. Much of this cost is driven by the late phase clinical trials and therefore the ability to terminate early those projects destined to fail is paramount to prevent unwanted costs and wasted effort. Although neglected diseases drug discovery is driven more by unmet medical need rather than financial considerations, the need to minimise wasted money and resources is even more vital in this under-funded area. To ensure any drug discovery project is addressing the requirements of the patients and health care providers and delivering a benefit over existing therapies, the ideal attributes of a novel drug needs to be pre-defined by a set of criteria called a target product profile. Using a target product profile the drug discovery process, clinical study design, and compound characteristics can be defined all the way back through to the suitability or druggability of the intended biochemical target. Assessment and prioritisation of the most promising targets for entry into screening programmes is crucial for maximising chances of success.

Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases.

PubMed

Williams, Kevin; Bilsland, Elizabeth; Sparkes, Andrew; Aubrey, Wayne; Young, Michael; Soldatova, Larisa N; De Grave, Kurt; Ramon, Jan; de Clare, Michaela; Sirawaraporn, Worachart; Oliver, Stephen G; King, Ross D

2015-03-06

There is an urgent need to make drug discovery cheaper and faster. This will enable the development of treatments for diseases currently neglected for economic reasons, such as tropical and orphan diseases, and generally increase the supply of new drugs. Here, we report the Robot Scientist 'Eve' designed to make drug discovery more economical. A Robot Scientist is a laboratory automation system that uses artificial intelligence (AI) techniques to discover scientific knowledge through cycles of experimentation. Eve integrates and automates library-screening, hit-confirmation, and lead generation through cycles of quantitative structure activity relationship learning and testing. Using econometric modelling we demonstrate that the use of AI to select compounds economically outperforms standard drug screening. For further efficiency Eve uses a standardized form of assay to compute Boolean functions of compound properties. These assays can be quickly and cheaply engineered using synthetic biology, enabling more targets to be assayed for a given budget. Eve has repositioned several drugs against specific targets in parasites that cause tropical diseases. One validated discovery is that the anti-cancer compound TNP-470 is a potent inhibitor of dihydrofolate reductase from the malaria-causing parasite Plasmodium vivax.

Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases

PubMed Central

Williams, Kevin; Bilsland, Elizabeth; Sparkes, Andrew; Aubrey, Wayne; Young, Michael; Soldatova, Larisa N.; De Grave, Kurt; Ramon, Jan; de Clare, Michaela; Sirawaraporn, Worachart; Oliver, Stephen G.; King, Ross D.

2015-01-01

There is an urgent need to make drug discovery cheaper and faster. This will enable the development of treatments for diseases currently neglected for economic reasons, such as tropical and orphan diseases, and generally increase the supply of new drugs. Here, we report the Robot Scientist ‘Eve’ designed to make drug discovery more economical. A Robot Scientist is a laboratory automation system that uses artificial intelligence (AI) techniques to discover scientific knowledge through cycles of experimentation. Eve integrates and automates library-screening, hit-confirmation, and lead generation through cycles of quantitative structure activity relationship learning and testing. Using econometric modelling we demonstrate that the use of AI to select compounds economically outperforms standard drug screening. For further efficiency Eve uses a standardized form of assay to compute Boolean functions of compound properties. These assays can be quickly and cheaply engineered using synthetic biology, enabling more targets to be assayed for a given budget. Eve has repositioned several drugs against specific targets in parasites that cause tropical diseases. One validated discovery is that the anti-cancer compound TNP-470 is a potent inhibitor of dihydrofolate reductase from the malaria-causing parasite Plasmodium vivax. PMID:25652463

Computational approaches for drug discovery.

PubMed

Hung, Che-Lun; Chen, Chi-Chun

2014-09-01

Cellular proteins are the mediators of multiple organism functions being involved in physiological mechanisms and disease. By discovering lead compounds that affect the function of target proteins, the target diseases or physiological mechanisms can be modulated. Based on knowledge of the ligand-receptor interaction, the chemical structures of leads can be modified to improve efficacy, selectivity and reduce side effects. One rational drug design technology, which enables drug discovery based on knowledge of target structures, functional properties and mechanisms, is computer-aided drug design (CADD). The application of CADD can be cost-effective using experiments to compare predicted and actual drug activity, the results from which can used iteratively to improve compound properties. The two major CADD-based approaches are structure-based drug design, where protein structures are required, and ligand-based drug design, where ligand and ligand activities can be used to design compounds interacting with the protein structure. Approaches in structure-based drug design include docking, de novo design, fragment-based drug discovery and structure-based pharmacophore modeling. Approaches in ligand-based drug design include quantitative structure-affinity relationship and pharmacophore modeling based on ligand properties. Based on whether the structure of the receptor and its interaction with the ligand are known, different design strategies can be seed. After lead compounds are generated, the rule of five can be used to assess whether these have drug-like properties. Several quality validation methods, such as cost function analysis, Fisher's cross-validation analysis and goodness of hit test, can be used to estimate the metrics of different drug design strategies. To further improve CADD performance, multi-computers and graphics processing units may be applied to reduce costs. © 2014 Wiley Periodicals, Inc.

A method for predicting target drug efficiency in cancer based on the analysis of signaling pathway activation.

PubMed

Artemov, Artem; Aliper, Alexander; Korzinkin, Michael; Lezhnina, Ksenia; Jellen, Leslie; Zhukov, Nikolay; Roumiantsev, Sergey; Gaifullin, Nurshat; Zhavoronkov, Alex; Borisov, Nicolas; Buzdin, Anton

2015-10-06

A new generation of anticancer therapeutics called target drugs has quickly developed in the 21st century. These drugs are tailored to inhibit cancer cell growth, proliferation, and viability by specific interactions with one or a few target proteins. However, despite formally known molecular targets for every "target" drug, patient response to treatment remains largely individual and unpredictable. Choosing the most effective personalized treatment remains a major challenge in oncology and is still largely trial and error. Here we present a novel approach for predicting target drug efficacy based on the gene expression signature of the individual tumor sample(s). The enclosed bioinformatic algorithm detects activation of intracellular regulatory pathways in the tumor in comparison to the corresponding normal tissues. According to the nature of the molecular targets of a drug, it predicts whether the drug can prevent cancer growth and survival in each individual case by blocking the abnormally activated tumor-promoting pathways or by reinforcing internal tumor suppressor cascades. To validate the method, we compared the distribution of predicted drug efficacy scores for five drugs (Sorafenib, Bevacizumab, Cetuximab, Sorafenib, Imatinib, Sunitinib) and seven cancer types (Clear Cell Renal Cell Carcinoma, Colon cancer, Lung adenocarcinoma, non-Hodgkin Lymphoma, Thyroid cancer and Sarcoma) with the available clinical trials data for the respective cancer types and drugs. The percent of responders to a drug treatment correlated significantly (Pearson's correlation 0.77 p = 0.023) with the percent of tumors showing high drug scores calculated with the current algorithm.

DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank.

PubMed

Yuan, Qingjun; Gao, Junning; Wu, Dongliang; Zhang, Shihua; Mamitsuka, Hiroshi; Zhu, Shanfeng

2016-06-15

Identifying drug-target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug-target interactions of new candidate drugs or targets. Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning. The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs. http://datamining-iip.fudan.edu.cn/service/DrugE-Rank zhusf@fudan.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

ATOM - Accelerating therapeutics through opportunities in medicine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mcmahon, Benjamin Hamilton; Dotson, Paul Jeffrey

Create a new paradigm of drug discovery that would reduce the time from an identified drug target to clinical candidate from the current ~6 years to just 12 months. ATOM will develop, test, and validate a multidisciplinary approach to drug discovery in which modern science, technology and engineering, supercomputing, simulations, data science, and artificial intelligence are highly integrated into a single drug-discovery platform that can ultimately be shared with the drug development community at-large.

Genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease

PubMed Central

Scott, Robert A.; Freitag, Daniel F.; Li, Li; Chu, Audrey Y.; Surendran, Praveen; Young, Robin; Grarup, Niels; Stancáková, Alena; Chen, Yuning; V.Varga, Tibor; Yaghootkar, Hanieh; Luan, Jian'an; Zhao, Jing Hua; Willems, Sara M.; Wessel, Jennifer; Wang, Shuai; Maruthur, Nisa; Michailidou, Kyriaki; Pirie, Ailith; van der Lee, Sven J.; Gillson, Christopher; Olama, Ali Amin Al; Amouyel, Philippe; Arriola, Larraitz; Arveiler, Dominique; Aviles-Olmos, Iciar; Balkau, Beverley; Barricarte, Aurelio; Barroso, Inês; Garcia, Sara Benlloch; Bis, Joshua C.; Blankenberg, Stefan; Boehnke, Michael; Boeing, Heiner; Boerwinkle, Eric; Borecki, Ingrid B.; Bork-Jensen, Jette; Bowden, Sarah; Caldas, Carlos; Caslake, Muriel; Cupples, L. Adrienne; Cruchaga, Carlos; Czajkowski, Jacek; den Hoed, Marcel; Dunn, Janet A.; Earl, Helena M.; Ehret, Georg B.; Ferrannini, Ele; Ferrieres, Jean; Foltynie, Thomas; Ford, Ian; Forouhi, Nita G.; Gianfagna, Francesco; Gonzalez, Carlos; Grioni, Sara; Hiller, Louise; Jansson, Jan-Håkan; Jørgensen, Marit E.; Jukema, J. Wouter; Kaaks, Rudolf; Kee, Frank; Kerrison, Nicola D.; Key, Timothy J.; Kontto, Jukka; Kote-Jarai, Zsofia; Kraja, Aldi T.; Kuulasmaa, Kari; Kuusisto, Johanna; Linneberg, Allan; Liu, Chunyu; Marenne, Gaëlle; Mohlke, Karen L.; Morris, Andrew P.; Muir, Kenneth; Müller-Nurasyid, Martina; Munroe, Patricia B.; Navarro, Carmen; Nielsen, Sune F.; Nilsson, Peter M.; Nordestgaard, Børge G.; Packard, Chris J.; Palli, Domenico; Panico, Salvatore; Peloso, Gina M.; Perola, Markus; Peters, Annette; Poole, Christopher J.; Quirós, J. Ramón; Rolandsson, Olov; Sacerdote, Carlotta; Salomaa, Veikko; Sánchez, María-José; Sattar, Naveed; Sharp, Stephen J.; Sims, Rebecca; Slimani, Nadia; Smith, Jennifer A.; Thompson, Deborah J.; Trompet, Stella; Tumino, Rosario; van der A, Daphne L.; van der Schouw, Yvonne T.; Virtamo, Jarmo; Walker, Mark; Walter, Klaudia; Abraham, Jean E.; Amundadottir, Laufey T.; Aponte, Jennifer L.; Butterworth, Adam S.; Dupuis, Josée; Easton, Douglas F.; Eeles, Rosalind A.; Erdmann, Jeanette; Franks, Paul W.; Frayling, Timothy M.; Hansen, Torben; Howson, Joanna M. M.; Jørgensen, Torben; Kooner, Jaspal; Laakso, Markku; Langenberg, Claudia; McCarthy, Mark I.; Pankow, James S.; Pedersen, Oluf; Riboli, Elio; Rotter, Jerome I.; Saleheen, Danish; Samani, Nilesh J.; Schunkert, Heribert; Vollenweider, Peter; O'Rahilly, Stephen; Deloukas, Panos; Danesh, John; Goodarzi, Mark O.; Kathiresan, Sekar; Meigs, James B.; Ehm, Margaret G.; Wareham, Nicholas J.; Waterworth, Dawn M.

2016-01-01

Regulatory authorities have indicated that new drugs to treat type 2 diabetes (T2D) should not be associated with an unacceptable increase in cardiovascular risk. Human genetics may be able to inform development of antidiabetic therapies by predicting cardiovascular and other health endpoints. We therefore investigated the association of variants in 6 genes that encode drug targets for obesity or T2D with a range of metabolic traits in up to 11,806 individuals by targeted exome sequencing, and follow-up in 39,979 individuals by targeted genotyping, with additional in silico follow up in consortia. We used these data to first compare associations of variants in genes encoding drug targets with the effects of pharmacological manipulation of those targets in clinical trials. We then tested the association those variants with disease outcomes, including coronary heart disease, to predict cardiovascular safety of these agents. A low-frequency missense variant (Ala316Thr;rs10305492) in the gene encoding glucagon-like peptide-1 receptor (GLP1R), the target of GLP1R agonists, was associated with lower fasting glucose and lower T2D risk, consistent with GLP1R agonist therapies. The minor allele was also associated with protection against heart disease, thus providing evidence that GLP1R agonists are not likely to be associated with an unacceptable increase in cardiovascular risk. Our results provide an encouraging signal that these agents may be associated with benefit, a question currently being addressed in randomised controlled trials. Genetic variants associated with metabolic traits and multiple disease outcomes can be used to validate therapeutic targets at an early stage in the drug development process. PMID:27252175

Focus on flaviviruses: current and future drug targets.

PubMed

Geiss, Brian J; Stahla, Hillary; Hannah, Amanda M; Gari, Amanda M; Keenan, Susan M

2009-05-01

Infection by mosquito-borne flaviviruses (family Flaviviridae) is increasing in prevalence worldwide. The vast global, social and economic impact due to the morbidity and mortality associated with the diseases caused by these viruses necessitates therapeutic intervention. There is currently no effective clinical treatment for any flaviviral infection. Therefore, there is a great need for the identification of novel inhibitors to target the virus life cycle. In this article, we discuss structural and nonstructural viral proteins that are the focus of current target validation and drug discovery efforts. Both inhibition of essential enzymatic activities and disruption of necessary protein–protein interactions are considered. In addition, we address promising new targets for future research. As our molecular and biochemical understanding of the flavivirus life cycle increases, the number of targets for antiviral therapeutic discovery grows and the possibility for novel drug discovery continues to strengthen.

Assessment of deoxyhypusine hydroxylase as a putative, novel drug target.

PubMed

Kerscher, B; Nzukou, E; Kaiser, A

2010-02-01

Antimalarial drug resistance has nowadays reached each drug class on the market for longer than 10 years. The focus on validated, classical targets has severe drawbacks. If resistance is arising or already present in the field, a target-based High-Throughput-Screening (HTS) with the respective target involves the risk of identifying compounds to which field populations are also resistant. Thus, it appears that a rewarding albeit demanding challenge for target-based drug discovery is to identify novel drug targets. In the search for new targets for antimalarials, we have investigated the biosynthesis of hypusine, present in eukaryotic initiation factor 5A (eIF5A). Deoxyhypusine hydroxylase (DOHH), which has recently been cloned and expressed from P. falciparum, completes the modification of eIF5A through hydroxylation. Here, we assess the present druggable data on Plasmodium DOHH and its human counterpart. Plasmodium DOHH arose from a cyanobacterial phycobilin lyase by loss of function. It has a low FASTA score of 27 to its human counterpart. The HEAT-like repeats present in the parasite DOHH differ in number and amino acid identity from its human ortholog and might be of considerable interest for inhibitor design.

Identification of inhibitors for putative malaria drug targets amongst novel antimalarial compounds

PubMed Central

Crowther, Gregory J.; Napuli, Alberto J.; Gilligan, James H.; Gagaring, Kerstin; Borboa, Rachel; Francek, Carolyn; Chen, Zhong; Dagostino, Eleanor F.; Stockmyer, Justin B.; Wang, Yu; Rodenbough, Philip P.; Castaneda, Lisa J.; Leibly, David J.; Bhandari, Janhavi; Gelb, Michael H.; Brinker, Achim; Engels, Ingo; Taylor, Jennifer; Chatterjee, Arnab K.; Fantauzzi, Pascal; Glynne, Richard J.; Van Voorhis, Wesley C.; Kuhen, Kelli L.

2011-01-01

The efficacy of most marketed antimalarial drugs has been compromised by evolution of parasite resistance, underscoring an urgent need to find new drugs with new mechanisms of action. We have taken a high-throughput approach toward identifying novel antimalarial chemical inhibitors of prioritized drug targets for P. falciparum, excluding targets which are inhibited by currently used drugs. A screen of commercially available libraries identified 5,655 low molecular weight compounds that inhibit growth of P. falciparum cultures with EC50 values below 1.25 μM. These compounds were then tested in 384- or 1536-well biochemical assays for activity against nine Plasmodium enzymes: adenylosuccinate synthetase (AdSS), choline kinase (CK), deoxyuridine triphosphate nucleotidohydrolase (dUTPase), glutamate dehydrogenase (GDH), guanylate kinase (GK), N-myristoyltransferase (NMT), orotidine 5′-monophosphate decarboxylase (OMPDC), farnesyl pyrophosphate synthase (FPPS) and S-adenosylhomocysteine hydrolase (SAHH). These enzymes were selected using TDRtargets.org, and are believed to have excellent potential as drug targets based on criteria such as their likely essentiality, druggability, and amenability to high-throughput biochemical screening. Six of these targets were inhibited by one or more of the antimalarial scaffolds and may have potential use in drug development, further target validation studies and exploration of P. falciparum biochemistry and biology. PMID:20813141

Identification of inhibitors for putative malaria drug targets among novel antimalarial compounds.

PubMed

Crowther, Gregory J; Napuli, Alberto J; Gilligan, James H; Gagaring, Kerstin; Borboa, Rachel; Francek, Carolyn; Chen, Zhong; Dagostino, Eleanor F; Stockmyer, Justin B; Wang, Yu; Rodenbough, Philip P; Castaneda, Lisa J; Leibly, David J; Bhandari, Janhavi; Gelb, Michael H; Brinker, Achim; Engels, Ingo H; Taylor, Jennifer; Chatterjee, Arnab K; Fantauzzi, Pascal; Glynne, Richard J; Van Voorhis, Wesley C; Kuhen, Kelli L

2011-01-01

The efficacy of most marketed antimalarial drugs has been compromised by evolution of parasite resistance, underscoring an urgent need to find new drugs with new mechanisms of action. We have taken a high-throughput approach toward identifying novel antimalarial chemical inhibitors of prioritized drug targets for Plasmodium falciparum, excluding targets which are inhibited by currently used drugs. A screen of commercially available libraries identified 5655 low molecular weight compounds that inhibit growth of P. falciparum cultures with EC(50) values below 1.25μM. These compounds were then tested in 384- or 1536-well biochemical assays for activity against nine Plasmodium enzymes: adenylosuccinate synthetase (AdSS), choline kinase (CK), deoxyuridine triphosphate nucleotidohydrolase (dUTPase), glutamate dehydrogenase (GDH), guanylate kinase (GK), N-myristoyltransferase (NMT), orotidine 5'-monophosphate decarboxylase (OMPDC), farnesyl pyrophosphate synthase (FPPS) and S-adenosylhomocysteine hydrolase (SAHH). These enzymes were selected using TDRtargets.org, and are believed to have excellent potential as drug targets based on criteria such as their likely essentiality, druggability, and amenability to high-throughput biochemical screening. Six of these targets were inhibited by one or more of the antimalarial scaffolds and may have potential use in drug development, further target validation studies and exploration of P. falciparum biochemistry and biology. Copyright © 2010 Elsevier B.V. All rights reserved.

Life in the fast lane: high-throughput chemistry for lead generation and optimisation.

PubMed

Hunter, D

2001-01-01

The pharmaceutical industry has come under increasing pressure due to regulatory restrictions on the marketing and pricing of drugs, competition, and the escalating costs of developing new drugs. These forces can be addressed by the identification of novel targets, reductions in the development time of new drugs, and increased productivity. Emphasis has been placed on identifying and validating new targets and on lead generation: the response from industry has been very evident in genomics and high throughput screening, where new technologies have been applied, usually coupled with a high degree of automation. The combination of numerous new potential biological targets and the ability to screen large numbers of compounds against many of these targets has generated the need for large diverse compound collections. To address this requirement, high-throughput chemistry has become an integral part of the drug discovery process. Copyright 2002 Wiley-Liss, Inc.

Proposal for a new therapy for drug-resistant malaria using Plasmodium synthetic lethality inference.

PubMed

Lee, Sang Joon; Seo, Eunseok; Cho, Yonghyun

2013-12-01

Many antimalarial drugs kill malaria parasites, but antimalarial drug resistance (ADR) and toxicity to normal cells limit their usefulness. To solve this problem, we suggest a new therapy for drug-resistant malaria. The approach consists of data integration and inference through homology analysis of yeast-human-Plasmodium. If one gene of a Plasmodium synthetic lethal (SL) gene pair has a mutation that causes ADR, a drug targeting the other gene of the SL pair might be used as an effective treatment for drug-resistant strains of malaria. A simple computational tool to analyze the inferred SL genes of Plasmodium species (malaria parasites Plasmodium falciparum and Plasmodium vivax for human malarial therapy, and rodent parasite Plasmodium berghei for in vivo studies of human malarias) was established to identify SL genes that can be used as drug targets. Information on SL gene pairs with ADR genes and their first neighbors was inferred from yeast SL genes to search for pertinent antimalarial drug targets. We not only suggest drug target gene candidates for further experimental validation, but also provide information on new usage for already-described drugs. The proposed specific antimalarial drug candidates can be inferred by searching drugs that cause a fitness defect in yeast SL genes.

The growing pipeline of natural aminoacyl-tRNA synthetase inhibitors for malaria treatment.

PubMed

Saint-Léger, Adélaïde; Sinadinos, Christopher; Ribas de Pouplana, Lluís

2016-04-02

Malaria remains a major global health problem. Parasite resistance to existing drugs makes development of new antimalarials an urgency. The protein synthesis machinery is an excellent target for the development of new anti-infectives, and aminoacyl-tRNA synthetases (aaRS) have been validated as antimalarial drug targets. However, avoiding the emergence of drug resistance and improving selectivity to target aaRS in apicomplexan parasites, such as Plasmodium falciparum, remain crucial challenges. Here we discuss such issues using examples of known inhibitors of P. falciparum aaRS, namely halofuginone, cladosporin and borrelidin (inhibitors of ProRS, LysRS and ThrRS, respectively). Encouraging recent results provide useful guidelines to facilitate the development of novel drug candidates which are more potent and selective against these essential enzymes.

The growing pipeline of natural aminoacyl-tRNA synthetase inhibitors for malaria treatment

PubMed Central

Saint-Léger, Adélaïde; Sinadinos, Christopher; Ribas de Pouplana, Lluís

2016-01-01

ABSTRACT Malaria remains a major global health problem. Parasite resistance to existing drugs makes development of new antimalarials an urgency. The protein synthesis machinery is an excellent target for the development of new anti-infectives, and aminoacyl-tRNA synthetases (aaRS) have been validated as antimalarial drug targets. However, avoiding the emergence of drug resistance and improving selectivity to target aaRS in apicomplexan parasites, such as Plasmodium falciparum, remain crucial challenges. Here we discuss such issues using examples of known inhibitors of P. falciparum aaRS, namely halofuginone, cladosporin and borrelidin (inhibitors of ProRS, LysRS and ThrRS, respectively). Encouraging recent results provide useful guidelines to facilitate the development of novel drug candidates which are more potent and selective against these essential enzymes. PMID:26963157

An ion channel library for drug discovery and safety screening on automated platforms.

PubMed

Wible, Barbara A; Kuryshev, Yuri A; Smith, Stephen S; Liu, Zhiqi; Brown, Arthur M

2008-12-01

Ion channels represent the third largest class of targets in drug discovery after G-protein coupled receptors and kinases. In spite of this ranking, ion channels continue to be under exploited as drug targets compared with the other two groups for several reasons. First, with 400 ion channel genes and an even greater number of functional channels due to mixing and matching of individual subunits, a systematic collection of ion channel-expressing cell lines for drug discovery and safety screening has not been available. Second, the lack of high-throughput functional assays for ion channels has limited their use as drug targets. Now that automated electrophysiology has come of age and provided the technology to assay ion channels at medium to high throughput, we have addressed the need for a library of ion channel cell lines by constructing the Ion Channel Panel (ChanTest Corp., Cleveland, OH). From 400 ion channel genes, a collection of 82 of the most relevant human ion channels for drug discovery, safety, and human disease has been assembled.Each channel has been stably overexpressed in human embryonic kidney 293 or Chinese hamster ovary cells. Cell lines have been selected and validated on automated electrophysiology systems to facilitate cost-effective screening for safe and selective compounds at earlier stages in the drug development process. The screening and validation processes as well as the relative advantages of different screening platforms are discussed.

Revealing the Effects of the Herbal Pair of Euphorbia kansui and Glycyrrhiza on Hepatocellular Carcinoma Ascites with Integrating Network Target Analysis and Experimental Validation

PubMed Central

Zhang, Yanqiong; Lin, Ya; Zhao, Haiyu; Guo, Qiuyan; Yan, Chen; Lin, Na

2016-01-01

Although the herbal pair of Euphorbia kansui (GS) and Glycyrrhiza (GC) is one of the so-called "eighteen antagonistic medicaments" in Chinese medicinal literature, it is prescribed in a classic Traditional Chinese Medicine (TCM) formula Gansui-Banxia-Tang for cancerous ascites, suggesting that GS and GC may exhibit synergistic or antagonistic effects in different combination designs. Here, we modeled the effects of GS/GC combination with a target interaction network and clarified the associations between the network topologies involving the drug targets and the drug combination effects. Moreover, the "edge-betweenness" values, which is defined as the frequency with which edges are placed on the shortest paths between all pairs of modules in network, were calculated, and the ADRB1-PIK3CG interaction exhibited the greatest edge-betweenness value, suggesting its crucial role in connecting the other edges in the network. Because ADRB1 and PIK3CG were putative targets of GS and GC, respectively, and both had functional interactions with AVPR2 approved as known therapeutic target for ascites, we proposed that the ADRB1-PIK3CG-AVPR2 signal axis might be involved in the effects of the GS-GC combination on ascites. This proposal was further experimentally validated in a H22 hepatocellular carcinoma (HCC) ascites model. Collectively, this systems-level investigation integrated drug target prediction and network analysis to reveal the combination principles of the herbal pair of GS and GC. Experimental validation in an in vivo system provided convincing evidence that different combination designs of GS and GC might result in synergistic or antagonistic effects on HCC ascites that might be partially related to their regulation of the ADRB1-PIK3CG-AVPR2 signal axis. PMID:27143956

DrugE-Rank: improving drug–target interaction prediction of new candidate drugs or targets by ensemble learning to rank

PubMed Central

Yuan, Qingjun; Gao, Junning; Wu, Dongliang; Zhang, Shihua; Mamitsuka, Hiroshi; Zhu, Shanfeng

2016-01-01

Motivation: Identifying drug–target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug–target interactions of new candidate drugs or targets. Methods: Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning. Results: The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs. Availability: http://datamining-iip.fudan.edu.cn/service/DrugE-Rank Contact: zhusf@fudan.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307615

Chemical and protein structural basis for biological crosstalk between PPAR α and COX enzymes

NASA Astrophysics Data System (ADS)

Cleves, Ann E.; Jain, Ajay N.

2015-02-01

We have previously validated a probabilistic framework that combined computational approaches for predicting the biological activities of small molecule drugs. Molecule comparison methods included molecular structural similarity metrics and similarity computed from lexical analysis of text in drug package inserts. Here we present an analysis of novel drug/target predictions, focusing on those that were not obvious based on known pharmacological crosstalk. Considering those cases where the predicted target was an enzyme with known 3D structure allowed incorporation of information from molecular docking and protein binding pocket similarity in addition to ligand-based comparisons. Taken together, the combination of orthogonal information sources led to investigation of a surprising predicted relationship between a transcription factor and an enzyme, specifically, PPAR α and the cyclooxygenase enzymes. These predictions were confirmed by direct biochemical experiments which validate the approach and show for the first time that PPAR α agonists are cyclooxygenase inhibitors.

New strategy for drug discovery by large-scale association analysis of molecular networks of different species.

PubMed

Zhang, Bo; Fu, Yingxue; Huang, Chao; Zheng, Chunli; Wu, Ziyin; Zhang, Wenjuan; Yang, Xiaoyan; Gong, Fukai; Li, Yuerong; Chen, Xiaoyu; Gao, Shuo; Chen, Xuetong; Li, Yan; Lu, Aiping; Wang, Yonghua

2016-02-25

The development of modern omics technology has not significantly improved the efficiency of drug development. Rather precise and targeted drug discovery remains unsolved. Here a large-scale cross-species molecular network association (CSMNA) approach for targeted drug screening from natural sources is presented. The algorithm integrates molecular network omics data from humans and 267 plants and microbes, establishing the biological relationships between them and extracting evolutionarily convergent chemicals. This technique allows the researcher to assess targeted drugs for specific human diseases based on specific plant or microbe pathways. In a perspective validation, connections between the plant Halliwell-Asada (HA) cycle and the human Nrf2-ARE pathway were verified and the manner by which the HA cycle molecules act on the human Nrf2-ARE pathway as antioxidants was determined. This shows the potential applicability of this approach in drug discovery. The current method integrates disparate evolutionary species into chemico-biologically coherent circuits, suggesting a new cross-species omics analysis strategy for rational drug development.

Cardiovascular drug discovery in the academic setting: building infrastructure, harnessing strengths, and seeking synergies.

PubMed

Gardell, Stephen J; Roth, Gregory P; Kelly, Daniel P

2010-10-01

The flow of innovative, effective, and safe new drugs from pharmaceutical laboratories for the treatment and prevention of cardiovascular disease has slowed to a trickle. While the need for breakthrough cardiovascular disease drugs is still paramount, the incentive to develop these agents has been blunted by burgeoning clinical development costs coupled with a heightened risk of failure due to the unprecedented nature of the emerging drug targets and increasingly challenging regulatory environment. A fuller understanding of the drug targets and employing novel biomarker strategies in clinical trials should serve to mitigate the risk. In any event, these current challenges have evoked changing trends in the pharmaceutical industry, which have created an opportunity for non-profit biomedical research institutions to play a pivotal partnering role in early stage drug discovery. The obvious strengths of academic research institutions is the breadth of their scientific programs and the ability and motivation to "go deep" to identify and characterize new target pathways that unlock the specific mysteries of cardiovascular diseases--leading to a bounty of novel therapeutic targets and prescient biomarkers. However, success in the drug discovery arena within the academic environment is contingent upon assembling the requisite infrastructure, annexing the talent to interrogate and validate the drug targets, and building translational bridges with pharmaceutical organizations and patient-oriented researchers.

In Situ Target Engagement Studies in Adherent Cells.

PubMed

Axelsson, Hanna; Almqvist, Helena; Otrocka, Magdalena; Vallin, Michaela; Lundqvist, Sara; Hansson, Pia; Karlsson, Ulla; Lundbäck, Thomas; Seashore-Ludlow, Brinton

2018-04-20

A prerequisite for successful drugs is effective binding of the desired target protein in the complex environment of a living system. Drug-target engagement has typically been difficult to monitor in physiologically relevant models, and with current methods, especially, while maintaining spatial information. One recent technique for quantifying drug-target engagement is the cellular thermal shift assay (CETSA), in which ligand-induced protein stabilization is measured after a heat challenge. Here, we describe a CETSA protocol in live A431 cells for p38α (MAPK14), where remaining soluble protein is detected in situ, using high-content imaging in 384-well, microtiter plates. We validate this assay concept using a number of known p38α inhibitors and further demonstrate the potential of this technology for chemical probe and drug discovery purposes by performing a small pilot screen for novel p38α binders. Importantly, this protocol creates a workflow that is amenable to adherent cells in their native state and yields spatially resolved target engagement information measurable at the single-cell level.

Screening the receptorome to discover the molecular targets for plant-derived psychoactive compounds: a novel approach for CNS drug discovery.

PubMed

Roth, Bryan L; Lopez, Estela; Beischel, Scott; Westkaemper, Richard B; Evans, Jon M

2004-05-01

Because psychoactive plants exert profound effects on human perception, emotion, and cognition, discovering the molecular mechanisms responsible for psychoactive plant actions will likely yield insights into the molecular underpinnings of human consciousness. Additionally, it is likely that elucidation of the molecular targets responsible for psychoactive drug actions will yield validated targets for CNS drug discovery. This review article focuses on an unbiased, discovery-based approach aimed at uncovering the molecular targets responsible for psychoactive drug actions wherein the main active ingredients of psychoactive plants are screened at the "receptorome" (that portion of the proteome encoding receptors). An overview of the receptorome is given and various in silico, public-domain resources are described. Newly developed tools for the in silico mining of data derived from the National Institute of Mental Health Psychoactive Drug Screening Program's (NIMH-PDSP) K(i) Database (K(i) DB) are described in detail. Additionally, three case studies aimed at discovering the molecular targets responsible for Hypericum perforatum, Salvia divinorum, and Ephedra sinica actions are presented. Finally, recommendations are made for future studies.

The enemy within: Targeting host–parasite interaction for antileishmanial drug discovery

PubMed Central

Späth, Gerald F.; Rachidi, Najma; Prina, Eric

2017-01-01

The state of antileishmanial chemotherapy is strongly compromised by the emergence of drug-resistant Leishmania. The evolution of drug-resistant phenotypes has been linked to the parasites’ intrinsic genome instability, with frequent gene and chromosome amplifications causing fitness gains that are directly selected by environmental factors, including the presence of antileishmanial drugs. Thus, even though the unique eukaryotic biology of Leishmania and its dependence on parasite-specific virulence factors provide valid opportunities for chemotherapeutical intervention, all strategies that target the parasite in a direct fashion are likely prone to select for resistance. Here, we review the current state of antileishmanial chemotherapy and discuss the limitations of ongoing drug discovery efforts. We finally propose new strategies that target Leishmania viability indirectly via mechanisms of host–parasite interaction, including parasite-released ectokinases and host epigenetic regulation, which modulate host cell signaling and transcriptional regulation, respectively, to establish permissive conditions for intracellular Leishmania survival. PMID:28594938

The enemy within: Targeting host-parasite interaction for antileishmanial drug discovery.

PubMed

Lamotte, Suzanne; Späth, Gerald F; Rachidi, Najma; Prina, Eric

2017-06-01

The state of antileishmanial chemotherapy is strongly compromised by the emergence of drug-resistant Leishmania. The evolution of drug-resistant phenotypes has been linked to the parasites' intrinsic genome instability, with frequent gene and chromosome amplifications causing fitness gains that are directly selected by environmental factors, including the presence of antileishmanial drugs. Thus, even though the unique eukaryotic biology of Leishmania and its dependence on parasite-specific virulence factors provide valid opportunities for chemotherapeutical intervention, all strategies that target the parasite in a direct fashion are likely prone to select for resistance. Here, we review the current state of antileishmanial chemotherapy and discuss the limitations of ongoing drug discovery efforts. We finally propose new strategies that target Leishmania viability indirectly via mechanisms of host-parasite interaction, including parasite-released ectokinases and host epigenetic regulation, which modulate host cell signaling and transcriptional regulation, respectively, to establish permissive conditions for intracellular Leishmania survival.

MicroRNA regulation of human protease genes essential for influenza virus replication.

PubMed

Meliopoulos, Victoria A; Andersen, Lauren E; Brooks, Paula; Yan, Xiuzhen; Bakre, Abhijeet; Coleman, J Keegan; Tompkins, S Mark; Tripp, Ralph A

2012-01-01

Influenza A virus causes seasonal epidemics and periodic pandemics threatening the health of millions of people each year. Vaccination is an effective strategy for reducing morbidity and mortality, and in the absence of drug resistance, the efficacy of chemoprophylaxis is comparable to that of vaccines. However, the rapid emergence of drug resistance has emphasized the need for new drug targets. Knowledge of the host cell components required for influenza replication has been an area targeted for disease intervention. In this study, the human protease genes required for influenza virus replication were determined and validated using RNA interference approaches. The genes validated as critical for influenza virus replication were ADAMTS7, CPE, DPP3, MST1, and PRSS12, and pathway analysis showed these genes were in global host cell pathways governing inflammation (NF-κB), cAMP/calcium signaling (CRE/CREB), and apoptosis. Analyses of host microRNAs predicted to govern expression of these genes showed that eight miRNAs regulated gene expression during virus replication. These findings identify unique host genes and microRNAs important for influenza replication providing potential new targets for disease intervention strategies.

Fruitful research: drug target discovery for neurodegenerative diseases in Drosophila.

PubMed

Konsolaki, Mary

2013-12-01

Although vertebrate model systems have obvious advantages in the study of human disease, invertebrate organisms have contributed enormously to this field as well. The conservation of genome structure and physiology among organisms poses unexpected peculiarities, and the redundancy in certain gene families or the presence of polymorphisms that can slightly alter gene expression can, in certain instances, bring invertebrate systems, such as Drosophila, closer to humans than mice and vice versa. This necessitates the analysis of disease pathways in multiple model organisms. The author highlights findings from Drosophila models of neurodegenerative diseases that have occurred in the past few years. She also highlights and discusses various molecular, genetic and genomic tools used in flies, as well as methods for generating disease models. Finally, the author describes Drosophila models of Alzheimer's, Parkinson's tri-nucleotide repeat diseases, and Fragile X syndrome and summarizes insights in disease mechanisms that have been discovered directly in fly models. Full genome genetic screens in Drosophila can lead to the rapid identification of drug target candidates that can be subsequently validated in a vertebrate system. In addition, the Drosophila models of neurodegeneration may often show disease phenotypes that are absent in equivalent mouse models. The author believes that the extensive contribution of Drosophila to both new disease drug target discovery, in addition to target validation, makes them indispensible to drug discovery and development.

In Silico Knockout Screening of Plasmodium falciparum Reactions and Prediction of Novel Essential Reactions by Analysing the Metabolic Network

PubMed Central

Isewon, Itunuoluwa; Aromolaran, Olufemi; Oladipupo, Olufunke

2018-01-01

Malaria is an infectious disease that affects close to half a million individuals every year and Plasmodium falciparum is a major cause of malaria. The treatment of this disease could be done effectively if the essential enzymes of this parasite are specifically targeted. Nevertheless, the development of the parasite in resisting existing drugs now makes discovering new drugs a core responsibility. In this study, a novel computational model that makes the prediction of new and validated antimalarial drug target cheaper, easier, and faster has been developed. We have identified new essential reactions as potential targets for drugs in the metabolic network of the parasite. Among the top seven (7) predicted essential reactions, four (4) have been previously identified in earlier studies with biological evidence and one (1) has been with computational evidence. The results from our study were compared with an extensive list of seventy-seven (77) essential reactions with biological evidence from a previous study. We present a list of thirty-one (31) potential candidates for drug targets in Plasmodium falciparum which includes twenty-four (24) new potential candidates for drug targets. PMID:29789805

Biophysics: for HTS hit validation, chemical lead optimization, and beyond.

PubMed

Genick, Christine C; Wright, S Kirk

2017-09-01

There are many challenges to the drug discovery process, including the complexity of the target, its interactions, and how these factors play a role in causing the disease. Traditionally, biophysics has been used for hit validation and chemical lead optimization. With its increased throughput and sensitivity, biophysics is now being applied earlier in this process to empower target characterization and hit finding. Areas covered: In this article, the authors provide an overview of how biophysics can be utilized to assess the quality of the reagents used in screening assays, to validate potential tool compounds, to test the integrity of screening assays, and to create follow-up strategies for compound characterization. They also briefly discuss the utilization of different biophysical methods in hit validation to help avoid the resource consuming pitfalls caused by the lack of hit overlap between biophysical methods. Expert opinion: The use of biophysics early on in the drug discovery process has proven crucial to identifying and characterizing targets of complex nature. It also has enabled the identification and classification of small molecules which interact in an allosteric or covalent manner with the target. By applying biophysics in this manner and at the early stages of this process, the chances of finding chemical leads with novel mechanisms of action are increased. In the future, focused screens with biophysics as a primary readout will become increasingly common.

Mathematical modeling for novel cancer drug discovery and development.

PubMed

Zhang, Ping; Brusic, Vladimir

2014-10-01

Mathematical modeling enables: the in silico classification of cancers, the prediction of disease outcomes, optimization of therapy, identification of promising drug targets and prediction of resistance to anticancer drugs. In silico pre-screened drug targets can be validated by a small number of carefully selected experiments. This review discusses the basics of mathematical modeling in cancer drug discovery and development. The topics include in silico discovery of novel molecular drug targets, optimization of immunotherapies, personalized medicine and guiding preclinical and clinical trials. Breast cancer has been used to demonstrate the applications of mathematical modeling in cancer diagnostics, the identification of high-risk population, cancer screening strategies, prediction of tumor growth and guiding cancer treatment. Mathematical models are the key components of the toolkit used in the fight against cancer. The combinatorial complexity of new drugs discovery is enormous, making systematic drug discovery, by experimentation, alone difficult if not impossible. The biggest challenges include seamless integration of growing data, information and knowledge, and making them available for a multiplicity of analyses. Mathematical models are essential for bringing cancer drug discovery into the era of Omics, Big Data and personalized medicine.

Target identification of small molecules based on chemical biology approaches.

PubMed

Futamura, Yushi; Muroi, Makoto; Osada, Hiroyuki

2013-05-01

Recently, a phenotypic approach-screens that assess the effects of compounds on cells, tissues, or whole organisms-has been reconsidered and reintroduced as a complementary strategy of a target-based approach for drug discovery. Although the finding of novel bioactive compounds from large chemical libraries has become routine, the identification of their molecular targets is still a time-consuming and difficult process, making this step rate-limiting in drug development. In the last decade, we and other researchers have amassed a large amount of phenotypic data through progress in omics research and advances in instrumentation. Accordingly, the profiling methodologies using these datasets expertly have emerged to identify and validate specific molecular targets of drug candidates, attaining some progress in current drug discovery (e.g., eribulin). In the case of a compound that shows an unprecedented phenotype likely by inhibiting a first-in-class target, however, such phenotypic profiling is invalid. Under the circumstances, a photo-crosslinking affinity approach should be beneficial. In this review, we describe and summarize recent progress in both affinity-based (direct) and phenotypic profiling (indirect) approaches for chemical biology target identification.

Network-based drug discovery by integrating systems biology and computational technologies

PubMed Central

Leung, Elaine L.; Cao, Zhi-Wei; Jiang, Zhi-Hong; Zhou, Hua

2013-01-01

Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network. However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple ‘-omics’ databases. The newly developed algorithm- or network-based computational models can tightly integrate ‘-omics’ databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various ‘-omics’ platforms and computational tools would accelerate development of network-based drug discovery and network medicine. PMID:22877768

Tools for in silico target fishing.

PubMed

Cereto-Massagué, Adrià; Ojeda, María José; Valls, Cristina; Mulero, Miquel; Pujadas, Gerard; Garcia-Vallve, Santiago

2015-01-01

Computational target fishing methods are designed to identify the most probable target of a query molecule. This process may allow the prediction of the bioactivity of a compound, the identification of the mode of action of known drugs, the detection of drug polypharmacology, drug repositioning or the prediction of the adverse effects of a compound. The large amount of information regarding the bioactivity of thousands of small molecules now allows the development of these types of methods. In recent years, we have witnessed the emergence of many methods for in silico target fishing. Most of these methods are based on the similarity principle, i.e., that similar molecules might bind to the same targets and have similar bioactivities. However, the difficult validation of target fishing methods hinders comparisons of the performance of each method. In this review, we describe the different methods developed for target prediction, the bioactivity databases most frequently used by these methods, and the publicly available programs and servers that enable non-specialist users to obtain these types of predictions. It is expected that target prediction will have a large impact on drug development and on the functional food industry. Copyright © 2014 Elsevier Inc. All rights reserved.

Optimization of vascular-targeting drugs in a computational model of tumor growth

NASA Astrophysics Data System (ADS)

Gevertz, Jana

2012-04-01

A biophysical tool is introduced that seeks to provide a theoretical basis for helping drug design teams assess the most promising drug targets and design optimal treatment strategies. The tool is grounded in a previously validated computational model of the feedback that occurs between a growing tumor and the evolving vasculature. In this paper, the model is particularly used to explore the therapeutic effectiveness of two drugs that target the tumor vasculature: angiogenesis inhibitors (AIs) and vascular disrupting agents (VDAs). Using sensitivity analyses, the impact of VDA dosing parameters is explored, as is the effects of administering a VDA with an AI. Further, a stochastic optimization scheme is utilized to identify an optimal dosing schedule for treatment with an AI and a chemotherapeutic. The treatment regimen identified can successfully halt simulated tumor growth, even after the cessation of therapy.

The use of functional chemical-protein associations to identify multi-pathway renoprotectants.

PubMed

Xu, Jia; Meng, Kexin; Zhang, Rui; Yang, He; Liao, Chang; Zhu, Wenliang; Jiao, Jundong

2014-01-01

Typically, most nephropathies can be categorized as complex human diseases in which the cumulative effect of multiple minor genes, combined with environmental and lifestyle factors, determines the disease phenotype. Thus, multi-target drugs would be more likely to facilitate comprehensive renoprotection than single-target agents. In this study, functional chemical-protein association analysis was performed to retrieve multi-target drugs of high pathway wideness from the STITCH 3.1 database. Pathway wideness of a drug evaluated the efficiency of regulation of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in quantity. We identified nine experimentally validated renoprotectants that exerted remarkable impact on KEGG pathways by targeting a limited number of proteins. We selected curcumin as an illustrative compound to display the advantage of multi-pathway drugs on renoprotection. We compared curcumin with hemin, an agonist of heme oxygenase-1 (HO-1), which significantly affects only one KEGG pathway, porphyrin and chlorophyll metabolism (adjusted p = 1.5×10-5). At the same concentration (10 µM), both curcumin and hemin equivalently mitigated oxidative stress in H2O2-treated glomerular mesangial cells. The benefit of using hemin was derived from its agonistic effect on HO-1, providing relief from oxidative stress. Selective inhibition of HO-1 completely blocked the action of hemin but not that of curcumin, suggesting simultaneous multi-pathway intervention by curcumin. Curcumin also increased cellular autophagy levels, enhancing its protective effect; however, hemin had no effects. Based on the fact that the dysregulation of multiple pathways is implicated in the etiology of complex diseases, we proposed a feasible method for identifying multi-pathway drugs from compounds with validated targets. Our efforts will help identify multi-pathway agents capable of providing comprehensive protection against renal injuries.

Predicting essential genes for identifying potential drug targets in Aspergillus fumigatus.

PubMed

Lu, Yao; Deng, Jingyuan; Rhodes, Judith C; Lu, Hui; Lu, Long Jason

2014-06-01

Aspergillus fumigatus (Af) is a ubiquitous and opportunistic pathogen capable of causing acute, invasive pulmonary disease in susceptible hosts. Despite current therapeutic options, mortality associated with invasive Af infections remains unacceptably high, increasing 357% since 1980. Therefore, there is an urgent need for the development of novel therapeutic strategies, including more efficacious drugs acting on new targets. Thus, as noted in a recent review, "the identification of essential genes in fungi represents a crucial step in the development of new antifungal drugs". Expanding the target space by rapidly identifying new essential genes has thus been described as "the most important task of genomics-based target validation". In previous research, we were the first to show that essential gene annotation can be reliably transferred between distantly related four Prokaryotic species. In this study, we extend our machine learning approach to the much more complex Eukaryotic fungal species. A compendium of essential genes is predicted in Af by transferring known essential gene annotations from another filamentous fungus Neurospora crassa. This approach predicts essential genes by integrating diverse types of intrinsic and context-dependent genomic features encoded in microbial genomes. The predicted essential datasets contained 1674 genes. We validated our results by comparing our predictions with known essential genes in Af, comparing our predictions with those predicted by homology mapping, and conducting conditional expressed alleles. We applied several layers of filters and selected a set of potential drug targets from the predicted essential genes. Finally, we have conducted wet lab knockout experiments to verify our predictions, which further validates the accuracy and wide applicability of the machine learning approach. The approach presented here significantly extended our ability to predict essential genes beyond orthologs and made it possible to predict an inventory of essential genes in Eukaryotic fungal species, amongst which a preferred subset of suitable drug targets may be selected. By selecting the best new targets, we believe that resultant drugs would exhibit an unparalleled clinical impact against a naive pathogen population. Additional benefits that a compendium of essential genes can provide are important information on cell function and evolutionary biology. Furthermore, mapping essential genes to pathways may also reveal critical check points in the pathogen's metabolism. Finally, this approach is highly reproducible and portable, and can be easily applied to predict essential genes in many more pathogenic microbes, especially those unculturable. Copyright © 2014 Elsevier Ltd. All rights reserved.

Adverse drug reaction prediction using scores produced by large-scale drug-protein target docking on high-performance computing machines.

PubMed

LaBute, Montiago X; Zhang, Xiaohua; Lenderman, Jason; Bennion, Brian J; Wong, Sergio E; Lightstone, Felice C

2014-01-01

Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60-0.69 and 0.61-0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number of CPUs to tens of thousands of protein targets and millions of potential drug candidates.

Assessment of Dengue virus helicase and methyltransferase as targets for fragment-based drug discovery.

PubMed

Coutard, Bruno; Decroly, Etienne; Li, Changqing; Sharff, Andrew; Lescar, Julien; Bricogne, Gérard; Barral, Karine

2014-06-01

Seasonal and pandemic flaviviruses continue to be leading global health concerns. With the view to help drug discovery against Dengue virus (DENV), a fragment-based experimental approach was applied to identify small molecule ligands targeting two main components of the flavivirus replication complex: the NS3 helicase (Hel) and the NS5 mRNA methyltransferase (MTase) domains. A library of 500 drug-like fragments was first screened by thermal-shift assay (TSA) leading to the identification of 36 and 32 fragment hits binding Hel and MTase from DENV, respectively. In a second stage, we set up a fragment-based X-ray crystallographic screening (FBS-X) in order to provide both validated fragment hits and structural binding information. No fragment hit was confirmed for DENV Hel. In contrast, a total of seven fragments were identified as DENV MTase binders and structures of MTase-fragment hit complexes were solved at resolution at least 2.0Å or better. All fragment hits identified contain either a five- or six-membered aromatic ring or both, and three novel binding sites were located on the MTase. To further characterize the fragment hits identified by TSA and FBS-X, we performed enzymatic assays to assess their inhibition effect on the N7- and 2'-O-MTase enzymatic activities: five of these fragment hits inhibit at least one of the two activities with IC50 ranging from 180μM to 9mM. This work validates the FBS-X strategy for identifying new anti-flaviviral hits targeting MTase, while Hel might not be an amenable target for fragment-based drug discovery (FBDD). This approach proved to be a fast and efficient screening method for FBDD target validation and discovery of starting hits for the development of higher affinity molecules that bind to novel allosteric sites. Copyright © 2014 Elsevier B.V. All rights reserved.

A compact targeted drug delivery mechanism for a next generation wireless capsule endoscope.

PubMed

Woods, Stephen P; Constandinou, Timothy G

2016-01-01

This paper reports a novel medication release and delivery mechanism as part of a next generation wireless capsule endoscope (WCE) for targeted drug delivery. This subsystem occupies a volume of only 17.9mm 3 for the purpose of delivering a 1 ml payload to a target site of interest in the small intestinal tract. An in-depth analysis of the method employed to release and deliver the medication is described and a series of experiments is presented which validates the drug delivery system. The results show that a variable pitch conical compression spring manufactured from stainless steel can deliver 0.59 N when it is fully compressed and that this would be sufficient force to deliver the onboard medication.

Bioinformatics in protein kinases regulatory network and drug discovery.

PubMed

Chen, Qingfeng; Luo, Haiqiong; Zhang, Chengqi; Chen, Yi-Ping Phoebe

2015-04-01

Protein kinases have been implicated in a number of diseases, where kinases participate many aspects that control cell growth, movement and death. The deregulated kinase activities and the knowledge of these disorders are of great clinical interest of drug discovery. The most critical issue is the development of safe and efficient disease diagnosis and treatment for less cost and in less time. It is critical to develop innovative approaches that aim at the root cause of a disease, not just its symptoms. Bioinformatics including genetic, genomic, mathematics and computational technologies, has become the most promising option for effective drug discovery, and has showed its potential in early stage of drug-target identification and target validation. It is essential that these aspects are understood and integrated into new methods used in drug discovery for diseases arisen from deregulated kinase activity. This article reviews bioinformatics techniques for protein kinase data management and analysis, kinase pathways and drug targets and describes their potential application in pharma ceutical industry. Copyright © 2015 Elsevier Inc. All rights reserved.

In Silico Repositioning-Chemogenomics Strategy Identifies New Drugs with Potential Activity against Multiple Life Stages of Schistosoma mansoni

PubMed Central

Neves, Bruno J.; Braga, Rodolpho C.; Bezerra, José C. B.; Cravo, Pedro V. L.; Andrade, Carolina H.

2015-01-01

Morbidity and mortality caused by schistosomiasis are serious public health problems in developing countries. Because praziquantel is the only drug in therapeutic use, the risk of drug resistance is a concern. In the search for new schistosomicidal drugs, we performed a target-based chemogenomics screen of a dataset of 2,114 proteins to identify drugs that are approved for clinical use in humans that may be active against multiple life stages of Schistosoma mansoni. Each of these proteins was treated as a potential drug target, and its amino acid sequence was used to interrogate three databases: Therapeutic Target Database (TTD), DrugBank and STITCH. Predicted drug-target interactions were refined using a combination of approaches, including pairwise alignment, conservation state of functional regions and chemical space analysis. To validate our strategy, several drugs previously shown to be active against Schistosoma species were correctly predicted, such as clonazepam, auranofin, nifedipine, and artesunate. We were also able to identify 115 drugs that have not yet been experimentally tested against schistosomes and that require further assessment. Some examples are aprindine, gentamicin, clotrimazole, tetrabenazine, griseofulvin, and cinnarizine. In conclusion, we have developed a systematic and focused computer-aided approach to propose approved drugs that may warrant testing and/or serve as lead compounds for the design of new drugs against schistosomes. PMID:25569258

Large Cancer Drug Trial Helps Move Precision Medicine Toward the Mainstream | Poster

Cancer.gov

A landmark cancer drug trial is helping set the stage for moving precision medicine into the mainstream of clinical practice, according to a new study. The study, reported in the Journal of Molecular Diagnostics, validates a procedure used in the drug trial that identifies the unique genetic mutations in a patient’s tumor, which is then used as the basis for selecting targeted

Predicting New Indications for Approved Drugs Using a Proteo-Chemometric Method

PubMed Central

Dakshanamurthy, Sivanesan; Issa, Naiem T; Assefnia, Shahin; Seshasayee, Ashwini; Peters, Oakland J; Madhavan, Subha; Uren, Aykut; Brown, Milton L; Byers, Stephen W

2012-01-01

The most effective way to move from target identification to the clinic is to identify already approved drugs with the potential for activating or inhibiting unintended targets (repurposing or repositioning). This is usually achieved by high throughput chemical screening, transcriptome matching or simple in silico ligand docking. We now describe a novel rapid computational proteo-chemometric method called “Train, Match, Fit, Streamline” (TMFS) to map new drug-target interaction space and predict new uses. The TMFS method combines shape, topology and chemical signatures, including docking score and functional contact points of the ligand, to predict potential drug-target interactions with remarkable accuracy. Using the TMFS method, we performed extensive molecular fit computations on 3,671 FDA approved drugs across 2,335 human protein crystal structures. The TMFS method predicts drug-target associations with 91% accuracy for the majority of drugs. Over 58% of the known best ligands for each target were correctly predicted as top ranked, followed by 66%, 76%, 84% and 91% for agents ranked in the top 10, 20, 30 and 40, respectively, out of all 3,671 drugs. Drugs ranked in the top 1–40, that have not been experimentally validated for a particular target now become candidates for repositioning. Furthermore, we used the TMFS method to discover that mebendazole, an anti-parasitic with recently discovered and unexpected anti-cancer properties, has the structural potential to inhibit VEGFR2. We confirmed experimentally that mebendazole inhibits VEGFR2 kinase activity as well as angiogenesis at doses comparable with its known effects on hookworm. TMFS also predicted, and was confirmed with surface plasmon resonance, that dimethyl celecoxib and the anti-inflammatory agent celecoxib can bind cadherin-11, an adhesion molecule important in rheumatoid arthritis and poor prognosis malignancies for which no targeted therapies exist. We anticipate that expanding our TMFS method to the >27,000 clinically active agents available worldwide across all targets will be most useful in the repositioning of existing drugs for new therapeutic targets. PMID:22780961

Literature evidence in open targets - a target validation platform.

PubMed

Kafkas, Şenay; Dunham, Ian; McEntyre, Johanna

2017-06-06

We present the Europe PMC literature component of Open Targets - a target validation platform that integrates various evidence to aid drug target identification and validation. The component identifies target-disease associations in documents and ranks the documents based on their confidence from the Europe PMC literature database, by using rules utilising expert-provided heuristic information. The confidence score of a given document represents how valuable the document is in the scope of target validation for a given target-disease association by taking into account the credibility of the association based on the properties of the text. The component serves the platform regularly with the up-to-date data since December, 2015. Currently, there are a total number of 1168365 distinct target-disease associations text mined from >26 million PubMed abstracts and >1.2 million Open Access full text articles. Our comparative analyses on the current available evidence data in the platform revealed that 850179 of these associations are exclusively identified by literature mining. This component helps the platform's users by providing the most relevant literature hits for a given target and disease. The text mining evidence along with the other types of evidence can be explored visually through https://www.targetvalidation.org and all the evidence data is available for download in json format from https://www.targetvalidation.org/downloads/data .

2-Aryl-5-carboxytetrazole as a New Photoaffinity Label for Drug Target Identification.

PubMed

Herner, András; Marjanovic, Jasmina; Lewandowski, Tracey M; Marin, Violeta; Patterson, Melanie; Miesbauer, Laura; Ready, Damien; Williams, Jon; Vasudevan, Anil; Lin, Qing

2016-11-09

Photoaffinity labels are powerful tools for dissecting ligand-protein interactions, and they have a broad utility in medicinal chemistry and drug discovery. Traditional photoaffinity labels work through nonspecific C-H/X-H bond insertion reactions with the protein of interest by the highly reactive photogenerated intermediate. Herein, we report a new photoaffinity label, 2-aryl-5-carboxytetrazole (ACT), that interacts with the target protein via a unique mechanism in which the photogenerated carboxynitrile imine reacts with a proximal nucleophile near the target active site. In two distinct case studies, we demonstrate that the attachment of ACT to a ligand does not significantly alter the binding affinity and specificity of the parent drug. Compared with diazirine and benzophenone, two commonly used photoaffinity labels, in two case studies ACT showed higher photo-cross-linking yields toward their protein targets in vitro based on mass spectrometry analysis. In the in situ target identification studies, ACT successfully captured the desired targets with an efficiency comparable to the diazirine. We expect that further development of this class of photoaffinity labels will lead to a broad range of applications across target identification, and validation and elucidation of the binding site in drug discovery.

2-Aryl-5-carboxytetrazole as a New Photoaffinity Label for Drug Target Identification

PubMed Central

2016-01-01

Photoaffinity labels are powerful tools for dissecting ligand–protein interactions, and they have a broad utility in medicinal chemistry and drug discovery. Traditional photoaffinity labels work through nonspecific C–H/X–H bond insertion reactions with the protein of interest by the highly reactive photogenerated intermediate. Herein, we report a new photoaffinity label, 2-aryl-5-carboxytetrazole (ACT), that interacts with the target protein via a unique mechanism in which the photogenerated carboxynitrile imine reacts with a proximal nucleophile near the target active site. In two distinct case studies, we demonstrate that the attachment of ACT to a ligand does not significantly alter the binding affinity and specificity of the parent drug. Compared with diazirine and benzophenone, two commonly used photoaffinity labels, in two case studies ACT showed higher photo-cross-linking yields toward their protein targets in vitro based on mass spectrometry analysis. In the in situ target identification studies, ACT successfully captured the desired targets with an efficiency comparable to the diazirine. We expect that further development of this class of photoaffinity labels will lead to a broad range of applications across target identification, and validation and elucidation of the binding site in drug discovery. PMID:27740749

Pharmacokinetics of Chinese medicines: strategies and perspectives.

PubMed

Yan, Ru; Yang, Ying; Chen, Yijia

2018-01-01

The modernization and internationalization of Chinese medicines (CMs) are hampered by increasing concerns on the safety and the efficacy. Pharmacokinetic (PK) study is indispensable to establish concentration-activity/toxicity relationship and facilitate target identification and new drug discovery from CMs. To cope with tremendous challenges rooted from chemical complexity of CMs, the classic PK strategies have evolved rapidly from PK study focusing on marker/main drug components to PK-PD correlation study adopting metabolomics approaches to characterize associations between disposition of global drug-related components and host metabolic network shifts. However, the majority of PK studies of CMs have adopted the approaches tailored for western medicines and focused on the systemic exposures of drug-related components, most of which were found to be too low to account for the holistic benefits of CMs. With an area under concentration-time curve- or activity-weighted approach, integral PK attempts to understand the PK-PD relevance with the integrated PK profile of multiple co-existing structural analogs (prototyes/metabolites). Cellular PK-PD complements traditional PK-PD when drug targets localize inside the cells, instead of at the surface of cell membrane or extracellular space. Considering the validated clinical benefits of CMs, reverse pharmacology-based reverse PK strategy was proposed to facilitate target identification and new drug discovery. Recently, gut microbiota have demonstrated multifaceted roles in drug efficacy/toxicity. In traditional oral intake, the presystemic interactions of CMs with gut microbiota seem inevitable, which can contribute to the holistic benefits of CMs through biotransforming CMs components, acting as the peripheral target, and regulating host drug disposition. Hence, we propose a global PK-PD approach which includes the presystemic interaction of CMs with gut microbiota and combines omics with physiologically based pharmacokinetic modeling to offer a comprehensive understanding of the PK-PD relationship of CMs. Moreover, validated clinical benefits of CMs and poor translational potential of animal PK data urge more research efforts in human PK study.

Complex network theory for the identification and assessment of candidate protein targets.

PubMed

McGarry, Ken; McDonald, Sharon

2018-06-01

In this work we use complex network theory to provide a statistical model of the connectivity patterns of human proteins and their interaction partners. Our intention is to identify important proteins that may be predisposed to be potential candidates as drug targets for therapeutic interventions. Target proteins usually have more interaction partners than non-target proteins, but there are no hard-and-fast rules for defining the actual number of interactions. We devise a statistical measure for identifying hub proteins, we score our target proteins with gene ontology annotations. The important druggable protein targets are likely to have similar biological functions that can be assessed for their potential therapeutic value. Our system provides a statistical analysis of the local and distant neighborhood protein interactions of the potential targets using complex network measures. This approach builds a more accurate model of drug-to-target activity and therefore the likely impact on treating diseases. We integrate high quality protein interaction data from the HINT database and disease associated proteins from the DrugTarget database. Other sources include biological knowledge from Gene Ontology and drug information from DrugBank. The problem is a very challenging one since the data is highly imbalanced between target proteins and the more numerous nontargets. We use undersampling on the training data and build Random Forest classifier models which are used to identify previously unclassified target proteins. We validate and corroborate these findings from the available literature. Copyright © 2018 Elsevier Ltd. All rights reserved.

Robust linear parameter-varying control of blood pressure using vasoactive drugs

NASA Astrophysics Data System (ADS)

Luspay, Tamas; Grigoriadis, Karolos

2015-10-01

Resuscitation of emergency care patients requires fast restoration of blood pressure to a target value to achieve hemodynamic stability and vital organ perfusion. A robust control design methodology is presented in this paper for regulating the blood pressure of hypotensive patients by means of the closed-loop administration of vasoactive drugs. To this end, a dynamic first-order delay model is utilised to describe the vasoactive drug response with varying parameters that represent intra-patient and inter-patient variability. The proposed framework consists of two components: first, an online model parameter estimation is carried out using a multiple-model extended Kalman-filter. Second, the estimated model parameters are used for continuously scheduling a robust linear parameter-varying (LPV) controller. The closed-loop behaviour is characterised by parameter-varying dynamic weights designed to regulate the mean arterial pressure to a target value. Experimental data of blood pressure response of anesthetised pigs to phenylephrine injection are used for validating the LPV blood pressure models. Simulation studies are provided to validate the online model estimation and the LPV blood pressure control using phenylephrine drug injection models representing patients showing sensitive, nominal and insensitive response to the drug.

Chemical approaches to targeted protein degradation through modulation of the ubiquitin-proteasome pathway.

PubMed

Collins, Ian; Wang, Hannah; Caldwell, John J; Chopra, Raj

2017-03-15

Manipulation of the ubiquitin-proteasome system to achieve targeted degradation of proteins within cells using chemical tools and drugs has the potential to transform pharmacological and therapeutic approaches in cancer and other diseases. An increased understanding of the molecular mechanism of thalidomide and its analogues following their clinical use has unlocked small-molecule modulation of the substrate specificity of the E3 ligase cereblon (CRBN), which in turn has resulted in the advancement of new immunomodulatory drugs (IMiDs) into the clinic. The degradation of multiple context-specific proteins by these pleiotropic small molecules provides a means to uncover new cell biology and to generate future drug molecules against currently undruggable targets. In parallel, the development of larger bifunctional molecules that bring together highly specific protein targets in complexes with CRBN, von Hippel-Lindau, or other E3 ligases to promote ubiquitin-dependent degradation has progressed to generate selective chemical compounds with potent effects in cells and in vivo models, providing valuable tools for biological target validation and with future potential for therapeutic use. In this review, we survey recent breakthroughs achieved in these two complementary methods and the discovery of new modes of direct and indirect engagement of target proteins with the proteasome. We discuss the experimental characterisation that validates the use of molecules that promote protein degradation as chemical tools, the preclinical and clinical examples disclosed to date, and the future prospects for this exciting area of chemical biology. © 2017 The Author(s).

A novel in silico approach to drug discovery via computational intelligence.

PubMed

Hecht, David; Fogel, Gary B

2009-04-01

A computational intelligence drug discovery platform is introduced as an innovative technology designed to accelerate high-throughput drug screening for generalized protein-targeted drug discovery. This technology results in collections of novel small molecule compounds that bind to protein targets as well as details on predicted binding modes and molecular interactions. The approach was tested on dihydrofolate reductase (DHFR) for novel antimalarial drug discovery; however, the methods developed can be applied broadly in early stage drug discovery and development. For this purpose, an initial fragment library was defined, and an automated fragment assembly algorithm was generated. These were combined with a computational intelligence screening tool for prescreening of compounds relative to DHFR inhibition. The entire method was assayed relative to spaces of known DHFR inhibitors and with chemical feasibility in mind, leading to experimental validation in future studies.

Targeting cysteine proteases in trypanosomatid disease drug discovery.

PubMed

Ferreira, Leonardo G; Andricopulo, Adriano D

2017-12-01

Chagas disease and human African trypanosomiasis are endemic conditions in Latin America and Africa, respectively, for which no effective and safe therapy is available. Efforts in drug discovery have focused on several enzymes from these protozoans, among which cysteine proteases have been validated as molecular targets for pharmacological intervention. These enzymes are expressed during the entire life cycle of trypanosomatid parasites and are essential to many biological processes, including infectivity to the human host. As a result of advances in the knowledge of the structural aspects of cysteine proteases and their role in disease physiopathology, inhibition of these enzymes by small molecules has been demonstrated to be a worthwhile approach to trypanosomatid drug research. This review provides an update on drug discovery strategies targeting the cysteine peptidases cruzain from Trypanosoma cruzi and rhodesain and cathepsin B from Trypanosoma brucei. Given that current chemotherapy for Chagas disease and human African trypanosomiasis has several drawbacks, cysteine proteases will continue to be actively pursued as valuable molecular targets in trypanosomatid disease drug discovery efforts. Copyright © 2017. Published by Elsevier Inc.

Prolonged-acting, Multi-targeting Gallium Nanoparticles Potently Inhibit Growth of Both HIV and Mycobacteria in Co-Infected Human Macrophages

PubMed Central

Narayanasamy, Prabagaran; Switzer, Barbara L.; Britigan, Bradley E.

2015-01-01

Human immunodeficiency virus (HIV) infection and Mycobacterium tuberculosis (TB) are responsible for two of the major global human infectious diseases that result in significant morbidity, mortality and socioeconomic impact. Furthermore, severity and disease prevention of both infections is enhanced by co-infection. Parallel limitations also exist in access to effective drug therapy and the emergence of resistance. Furthermore, drug-drug interactions have proven problematic during treatment of co-incident HIV and TB infections. Thus, improvements in drug access and simplified treatment regimens are needed immediately. One of the key host cells infected by both HIV and TB is the mononuclear phagocyte (MP; monocyte, macrophage and dendritic cell). Therefore, we hypothesized that one way this can be achieved is through drug-targeting by a nanoformulated drug that ideally would be active against both HIV and TB. Accordingly, we validated macrophage targeted long acting (sustained drug release) gallium (Ga) nanoformulation against HIV-mycobacterium co-infection. The multi-targeted Ga nanoparticle agent inhibited growth of both HIV and TB in the macrophage. The Ga nanoparticles reduced the growth of mycobacterium and HIV for up to 15 days following single drug loading. These results provide a potential new approach to treat HIV-TB co-infection that could eventually lead to improved clinical outcomes. PMID:25744727

Data-Driven prioritisation of antibody-drug conjugate targets in head and neck squamous cell carcinoma.

PubMed

Hanemaaijer, Saskia H; van Gijn, Stephanie E; Oosting, Sjoukje F; Plaat, Boudewijn E C; Moek, Kirsten L; Schuuring, Ed M; van der Laan, Bernard F A M; Roodenburg, Jan L N; van Vugt, Marcel A T M; van der Vegt, Bert; Fehrmann, Rudolf S N

2018-05-01

For patients with recurrent or metastatic head and neck squamous cell carcinoma (HNSCC) palliative treatment options that improve overall survival are limited. The prognosis in this group remains poor and there is an unmet need for new therapeutic options. An emerging class of therapeutics, targeting tumor-specific antigens, are antibodies bound to a cytotoxic agent, known as antibody-drug conjugates (ADCs). The aim of this study was to prioritize ADC targets in HNSCC. With a systematic search, we identified 55 different ADC targets currently targeted by registered ADCs and ADCs under clinical evaluation. For these 55 ADC targets, protein overexpression was predicted in a dataset containing 344 HNSCC mRNA expression profiles by using a method called functional genomic mRNA profiling. The ADC target with the highest predicted overexpression was validated by performing immunohistochemistry (IHC) on an independent tissue microarray containing 414 HNSCC tumors. The predicted top 5 overexpressed ADC targets in HNSCC were: glycoprotein nmb (GPNMB), SLIT and NTRK-like family member 6, epidermal growth factor receptor, CD74 and CD44. IHC validation showed combined cytoplasmic and membranous GPNMB protein expression in 92.0% of the cases. Strong expression was seen in 65.9% of the cases. In addition, 86.5% and 67.7% of cases showed ≥5% and >25% GPNMB positive tumor cells, respectively. This study provides a data-driven prioritization of ADCs targets that will facilitate clinicians and drug developers in deciding which ADC should be taken for further clinical evaluation in HNSCC. This might help to improve disease outcome of HNSCC patients. Copyright © 2018 Elsevier Ltd. All rights reserved.

Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features

PubMed Central

Shi, Xiao-He; Hu, Le-Le; Kong, Xiangyin; Cai, Yu-Dong; Chou, Kuo-Chen

2010-01-01

Background Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner. Methods/Principal Findings To realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively. Conclusion/Significance Our results indicate that the network prediction system thus established is quite promising and encouraging. PMID:20300175

Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions.

PubMed

La, Mary K; Sedykh, Alexander; Fourches, Denis; Muratov, Eugene; Tropsha, Alexander

2018-06-06

Given that adverse drug effects (ADEs) have led to post-market patient harm and subsequent drug withdrawal, failure of candidate agents in the drug development process, and other negative outcomes, it is essential to attempt to forecast ADEs and other relevant drug-target-effect relationships as early as possible. Current pharmacologic data sources, providing multiple complementary perspectives on the drug-target-effect paradigm, can be integrated to facilitate the inference of relationships between these entities. This study aims to identify both existing and unknown relationships between chemicals (C), protein targets (T), and ADEs (E) based on evidence in the literature. Cheminformatics and data mining approaches were employed to integrate and analyze publicly available clinical pharmacology data and literature assertions interrelating drugs, targets, and ADEs. Based on these assertions, a C-T-E relationship knowledge base was developed. Known pairwise relationships between chemicals, targets, and ADEs were collected from several pharmacological and biomedical data sources. These relationships were curated and integrated according to Swanson's paradigm to form C-T-E triangles. Missing C-E edges were then inferred as C-E relationships. Unreported associations between drugs, targets, and ADEs were inferred, and inferences were prioritized as testable hypotheses. Several C-E inferences, including testosterone → myocardial infarction, were identified using inferences based on the literature sources published prior to confirmatory case reports. Timestamping approaches confirmed the predictive ability of this inference strategy on a larger scale. The presented workflow, based on free-access databases and an association-based inference scheme, provided novel C-E relationships that have been validated post hoc in case reports. With refinement of prioritization schemes for the generated C-E inferences, this workflow may provide an effective computational method for the early detection of potential drug candidate ADEs that can be followed by targeted experimental investigations.

Antibody-enabled small-molecule drug discovery.

PubMed

Lawson, Alastair D G

2012-06-29

Although antibody-based therapeutics have become firmly established as medicines for serious diseases, the value of antibodies as tools in the early stages of small-molecule drug discovery is only beginning to be realized. In particular, antibodies may provide information to reduce risk in small-molecule drug discovery by enabling the validation of targets and by providing insights into the design of small-molecule screening assays. Moreover, antibodies can act as guides in the quest for small molecules that have the ability to modulate protein-protein interactions, which have traditionally only been considered to be tractable targets for biological drugs. The development of small molecules that have similar therapeutic effects to current biologics has the potential to benefit a broader range of patients at earlier stages of disease.

Human tumor xenografts in mouse as a model for evaluating therapeutic efficacy of monoclonal antibodies or antibody-drug conjugate targeting receptor tyrosine kinases.

PubMed

Feng, Liang; Wang, Wei; Yao, Hang-Ping; Zhou, Jianwei; Zhang, Ruiwen; Wang, Ming-Hai

2015-01-01

Targeting receptor tyrosine kinases by therapeutic monoclonal antibodies and antibody-drug conjugates has met with tremendous success in clinical oncology. Currently, numerous therapeutic monoclonal antibodies are under preclinical development. The potential for moving candidate antibodies into clinical trials relies heavily on therapeutic efficacy validated by human tumor xenografts in mice. Here we describe methods used to determine therapeutic efficacy of monoclonal antibodies or antibody-drug conjugates specific to human receptor tyrosine kinase using human tumor xenografts in mice as the model. The end point of the study is to determine whether treatment of tumor-bearing mice with a monoclonal antibody or antibody-drug conjugates results in significant delay of tumor growth.

Organs-on-chips at the frontiers of drug discovery

PubMed Central

Esch, Eric W.; Bahinski, Anthony; Huh, Dongeun

2016-01-01

Improving the effectiveness of preclinical predictions of human drug responses is critical to reducing costly failures in clinical trials. Recent advances in cell biology, microfabrication and microfluidics have enabled the development of microengineered models of the functional units of human organs — known as organs-on-chips — that could provide the basis for preclinical assays with greater predictive power. Here, we examine the new opportunities for the application of organ-on-chip technologies in a range of areas in preclinical drug discovery, such as target identification and validation, target-based screening, and phenotypic screening. We also discuss emerging drug discovery opportunities enabled by organs-on-chips, as well as important challenges in realizing the full potential of this technology. PMID:25792263

Fragment Based Strategies for Discovery of Novel HIV-1 Reverse Transcriptase and Integrase Inhibitors.

PubMed

Latham, Catherine F; La, Jennifer; Tinetti, Ricky N; Chalmers, David K; Tachedjian, Gilda

2016-01-01

Human immunodeficiency virus (HIV) remains a global health problem. While combined antiretroviral therapy has been successful in controlling the virus in patients, HIV can develop resistance to drugs used for treatment, rendering available drugs less effective and limiting treatment options. Initiatives to find novel drugs for HIV treatment are ongoing, although traditional drug design approaches often focus on known binding sites for inhibition of established drug targets like reverse transcriptase and integrase. These approaches tend towards generating more inhibitors in the same drug classes already used in the clinic. Lack of diversity in antiretroviral drug classes can result in limited treatment options, as cross-resistance can emerge to a whole drug class in patients treated with only one drug from that class. A fresh approach in the search for new HIV-1 drugs is fragment-based drug discovery (FBDD), a validated strategy for drug discovery based on using smaller libraries of low molecular weight molecules (<300 Da) screened using primarily biophysical assays. FBDD is aimed at not only finding novel drug scaffolds, but also probing the target protein to find new, often allosteric, inhibitory binding sites. Several fragment-based strategies have been successful in identifying novel inhibitory sites or scaffolds for two proven drug targets for HIV-1, reverse transcriptase and integrase. While any FBDD-generated HIV-1 drugs have yet to enter the clinic, recent FBDD initiatives against these two well-characterised HIV-1 targets have reinvigorated antiretroviral drug discovery and the search for novel classes of HIV-1 drugs.

MicroRNA Regulation of Human Protease Genes Essential for Influenza Virus Replication

PubMed Central

Meliopoulos, Victoria A.; Andersen, Lauren E.; Brooks, Paula; Yan, Xiuzhen; Bakre, Abhijeet; Coleman, J. Keegan; Tompkins, S. Mark; Tripp, Ralph A.

2012-01-01

Influenza A virus causes seasonal epidemics and periodic pandemics threatening the health of millions of people each year. Vaccination is an effective strategy for reducing morbidity and mortality, and in the absence of drug resistance, the efficacy of chemoprophylaxis is comparable to that of vaccines. However, the rapid emergence of drug resistance has emphasized the need for new drug targets. Knowledge of the host cell components required for influenza replication has been an area targeted for disease intervention. In this study, the human protease genes required for influenza virus replication were determined and validated using RNA interference approaches. The genes validated as critical for influenza virus replication were ADAMTS7, CPE, DPP3, MST1, and PRSS12, and pathway analysis showed these genes were in global host cell pathways governing inflammation (NF-κB), cAMP/calcium signaling (CRE/CREB), and apoptosis. Analyses of host microRNAs predicted to govern expression of these genes showed that eight miRNAs regulated gene expression during virus replication. These findings identify unique host genes and microRNAs important for influenza replication providing potential new targets for disease intervention strategies. PMID:22606348

Drug targeting of oncogenic pathways in melanoma.

PubMed

Fecher, Leslie A; Amaravadi, Ravi K; Schuchter, Lynn M; Flaherty, Keith T

2009-06-01

Melanoma continues to be one of the most aggressive and morbid malignancies once metastatic. Overall survival for advanced unresectable melanoma has not changed over the past several decades. However, the presence of some long-term survivors of metastatic melanoma highlights the heterogeneity of this disease and the potential for improved outcomes. Current research is uncovering the molecular and genetic scaffolding of normal and aberrant cell function. The known oncogenic pathways in melanoma and the attempts to develop therapy for them are discussed. The targeting of certain cellular processes, downstream of the common genetic alterations, for which the issues of target and drug validation are somewhat distinct, are also highlighted.

Identification and characterization of potential druggable targets among hypothetical proteins of extensively drug resistant Mycobacterium tuberculosis (XDR KZN 605) through subtractive genomics approach.

PubMed

Uddin, Reaz; Siddiqui, Quratulain Nehal; Azam, Syed Sikander; Saima, Bibi; Wadood, Abdul

2018-03-01

Among the resistant isolates of tuberculosis (TB), the multidrug resistance tuberculosis (MDR-TB) and extensively drug resistant tuberculosis (XDR-TB) are the areas of growing concern for which the front-line antibiotics are no more effective. As a result, the search of new therapeutic targets against TB is an imperative need of time. On the other hand, the target identification is an a priori step in drug discovery based research. Furthermore, the availability of the complete proteomic data of extensively drug resistant Mycobacterium tuberculosis (XDR-MTB) made it possible to carry out in silico analysis for the discovery of new drug targets. In the current study, we aimed to prioritize the potential drug targets among the hypothetical proteins of XDR-TB via subtractive genomics approach. In the subtractive genomics, we stepwise reduced the complete proteome of XDR-MTB to only two hypothetical proteins and evidently proposed them as new therapeutic targets. The 3D structure of one of the two target proteins was predicted via homology modeling and later on, validated by various analysis tools. Our study suggested that the domains identified and the motif hits found in the sequences of the shortlisted drug targets are crucial for the survival of the XDR-MTB. To the best of our knowledge, the current study is the first attempt in which the complete proteomic data of XDR-MTB was subjected to the computational subtractive genomics approach and therefore, would provide an opportunity to identify the unique therapeutic targets against deadly XDR-MTB. Copyright © 2017 Elsevier B.V. All rights reserved.

Huntington Disease: Linking Pathogenesis to the Development of Experimental Therapeutics.

PubMed

Mestre, Tiago A; Sampaio, Cristina

2017-02-01

Huntington disease (HD) is an autosomal dominant neurodegenerative condition caused by a CAG trinucleotide expansion in the huntingtin gene. At present, the HD field is experiencing exciting times with the assessment for the first time in human subjects of interventions aimed at core disease mechanisms. Out of a portfolio of interventions that claim a potential disease-modifying effect in HD, the target huntingtin has more robust validation. In this review, we discuss the spectrum of huntingtin-lowering therapies that are currently being considered. We provide a critical appraisal of the validation of huntingtin as a drug target, describing the advantages, challenges, and limitations of the proposed therapeutic interventions. The development of these new therapies relies strongly on the knowledge of HD pathogenesis and the ability to translate this knowledge into validated pharmacodynamic biomarkers. Altogether, the goal is to support a rational drug development that is ethical and cost-effective. Among the pharmacodynamic biomarkers under development, the quantification of mutant huntingtin in the cerebral spinal fluid and PET imaging targeting huntingtin or phosphodiesterase 10A deserve special attention. Huntingtin-lowering therapeutics are eagerly awaited as the first interventions that may be able to change the course of HD in a meaningful way.

A combined pre-clinical meta-analysis and randomized confirmatory trial approach to improve data validity for therapeutic target validation.

PubMed

Kleikers, Pamela W M; Hooijmans, Carlijn; Göb, Eva; Langhauser, Friederike; Rewell, Sarah S J; Radermacher, Kim; Ritskes-Hoitinga, Merel; Howells, David W; Kleinschnitz, Christoph; Schmidt, Harald H H W

2015-08-27

Biomedical research suffers from a dramatically poor translational success. For example, in ischemic stroke, a condition with a high medical need, over a thousand experimental drug targets were unsuccessful. Here, we adopt methods from clinical research for a late-stage pre-clinical meta-analysis (MA) and randomized confirmatory trial (pRCT) approach. A profound body of literature suggests NOX2 to be a major therapeutic target in stroke. Systematic review and MA of all available NOX2(-/y) studies revealed a positive publication bias and lack of statistical power to detect a relevant reduction in infarct size. A fully powered multi-center pRCT rejects NOX2 as a target to improve neurofunctional outcomes or achieve a translationally relevant infarct size reduction. Thus stringent statistical thresholds, reporting negative data and a MA-pRCT approach can ensure biomedical data validity and overcome risks of bias.

Rapid identification and validation of novel targeted approaches for Glioblastoma: A combined ex vivo-in vivo pharmaco-omic model.

PubMed

Daher, Ahmad; de Groot, John

2018-01-01

Tumor heterogeneity is a major factor in glioblastoma's poor response to therapy and seemingly inevitable recurrence. Only two glioblastoma drugs have received Food and Drug Administration approval since 1998, highlighting the urgent need for new therapies. Profiling "omics" analyses have helped characterize glioblastoma molecularly and have thus identified multiple molecular targets for precision medicine. These molecular targets have influenced clinical trial design; many "actionable" mutation-focused trials are underway, but because they have not yet led to therapeutic breakthroughs, new strategies for treating glioblastoma, especially those with a pharmacological functional component, remain in high demand. In that regard, high-throughput screening that allows for expedited preclinical drug testing and the use of GBM models that represent tumor heterogeneity more accurately than traditional cancer cell lines is necessary to maximize the successful translation of agents into the clinic. High-throughput screening has been successfully used in the testing, discovery, and validation of potential therapeutics in various cancer models, but it has not been extensively utilized in glioblastoma models. In this report, we describe the basic aspects of high-throughput screening and propose a modified high-throughput screening model in which ex vivo and in vivo drug testing is complemented by post-screening pharmacological, pan-omic analysis to expedite anti-glioma drugs' preclinical testing and develop predictive biomarker datasets that can aid in personalizing glioblastoma therapy and inform clinical trial design. Copyright © 2017 Elsevier Inc. All rights reserved.

Contribution of NIH funding to new drug approvals 2010–2016

PubMed Central

Beierlein, Jennifer M.; Khanuja, Navleen Surjit; McNamee, Laura M.; Ledley, Fred D.

2018-01-01

This work examines the contribution of NIH funding to published research associated with 210 new molecular entities (NMEs) approved by the Food and Drug Administration from 2010–2016. We identified >2 million publications in PubMed related to the 210 NMEs (n = 131,092) or their 151 known biological targets (n = 1,966,281). Of these, >600,000 (29%) were associated with NIH-funded projects in RePORTER. This funding included >200,000 fiscal years of NIH project support (1985–2016) and project costs >$100 billion (2000–2016), representing ∼20% of the NIH budget over this period. NIH funding contributed to every one of the NMEs approved from 2010–2016 and was focused primarily on the drug targets rather than on the NMEs themselves. There were 84 first-in-class products approved in this interval, associated with >$64 billion of NIH-funded projects. The percentage of fiscal years of project funding identified through target searches, but not drug searches, was greater for NMEs discovered through targeted screening than through phenotypic methods (95% versus 82%). For targeted NMEs, funding related to targets preceded funding related to the NMEs, consistent with the expectation that basic research provides validated targets for targeted screening. This analysis, which captures basic research on biological targets as well as applied research on NMEs, suggests that the NIH contribution to research associated with new drug approvals is greater than previously appreciated and highlights the risk of reducing federal funding for basic biomedical research. PMID:29440428

Crowd Sourcing a New Paradigm for Interactome Driven Drug Target Identification in Mycobacterium tuberculosis

PubMed Central

Rohira, Harsha; Bhat, Ashwini G.; Passi, Anurag; Mukherjee, Keya; Choudhary, Kumari Sonal; Kumar, Vikas; Arora, Anshula; Munusamy, Prabhakaran; Subramanian, Ahalyaa; Venkatachalam, Aparna; S, Gayathri; Raj, Sweety; Chitra, Vijaya; Verma, Kaveri; Zaheer, Salman; J, Balaganesh; Gurusamy, Malarvizhi; Razeeth, Mohammed; Raja, Ilamathi; Thandapani, Madhumohan; Mevada, Vishal; Soni, Raviraj; Rana, Shruti; Ramanna, Girish Muthagadhalli; Raghavan, Swetha; Subramanya, Sunil N.; Kholia, Trupti; Patel, Rajesh; Bhavnani, Varsha; Chiranjeevi, Lakavath; Sengupta, Soumi; Singh, Pankaj Kumar; Atray, Naresh; Gandhi, Swati; Avasthi, Tiruvayipati Suma; Nisthar, Shefin; Anurag, Meenakshi; Sharma, Pratibha; Hasija, Yasha; Dash, Debasis; Sharma, Arun; Scaria, Vinod; Thomas, Zakir; Chandra, Nagasuma; Brahmachari, Samir K.; Bhardwaj, Anshu

2012-01-01

A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative ‘Connect to Decode’ (C2D) to generate the first and largest manually curated interactome of Mtb termed ‘interactome pathway’ (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach. PMID:22808064

A network-based drug repositioning infrastructure for precision cancer medicine through targeting significantly mutated genes in the human cancer genomes.

PubMed

Cheng, Feixiong; Zhao, Junfei; Fooksa, Michaela; Zhao, Zhongming

2016-07-01

Development of computational approaches and tools to effectively integrate multidomain data is urgently needed for the development of newly targeted cancer therapeutics. We proposed an integrative network-based infrastructure to identify new druggable targets and anticancer indications for existing drugs through targeting significantly mutated genes (SMGs) discovered in the human cancer genomes. The underlying assumption is that a drug would have a high potential for anticancer indication if its up-/down-regulated genes from the Connectivity Map tended to be SMGs or their neighbors in the human protein interaction network. We assembled and curated 693 SMGs in 29 cancer types and found 121 proteins currently targeted by known anticancer or noncancer (repurposed) drugs. We found that the approved or experimental cancer drugs could potentially target these SMGs in 33.3% of the mutated cancer samples, and this number increased to 68.0% by drug repositioning through surveying exome-sequencing data in approximately 5000 normal-tumor pairs from The Cancer Genome Atlas. Furthermore, we identified 284 potential new indications connecting 28 cancer types and 48 existing drugs (adjusted P < .05), with a 66.7% success rate validated by literature data. Several existing drugs (e.g., niclosamide, valproic acid, captopril, and resveratrol) were predicted to have potential indications for multiple cancer types. Finally, we used integrative analysis to showcase a potential mechanism-of-action for resveratrol in breast and lung cancer treatment whereby it targets several SMGs (ARNTL, ASPM, CTTN, EIF4G1, FOXP1, and STIP1). In summary, we demonstrated that our integrative network-based infrastructure is a promising strategy to identify potential druggable targets and uncover new indications for existing drugs to speed up molecularly targeted cancer therapeutics. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Glycyrrhetinic acid-functionalized mesoporous silica nanoparticles as hepatocellular carcinoma-targeted drug carrier.

PubMed

Lv, Yongjiu; Li, Jingjing; Chen, Huali; Bai, Yan; Zhang, Liangke

2017-01-01

In this study, a glycyrrhetinic acid-functionalized mesoporous silica nanoparticle (MSN-GA) was prepared for active tumor targeting. MSN-GA exhibited satisfactory loading capacity for insoluble drugs, uniform size distribution, and specific tumor cell targeting. Glycyrrhetinic acid, a hepatocellular carcinoma-targeting group, was covalently decorated on the surface of MSN via an amido bond. The successful synthesis of MSN-GA was validated by the results of Fourier transform infrared spectroscopy, transmission electron microscopy (TEM), and zeta potential measurement. TEM images revealed the spherical morphology and uniform size distribution of the naked MSN and MSN-GA. Curcumin (CUR), an insoluble model drug, was loaded into MSN-GA (denoted as MSN-GA-CUR) with a high-loading capacity (8.78%±1.24%). The results of the in vitro cellular experiment demonstrated that MSN-GA-CUR significantly enhanced cytotoxicity and cellular uptake toward hepatocellular carcinoma (HepG2) cells via a specific GA receptor-mediated endocytosis mechanism. The results of this study provide a promising nanoplatform for the targeting of hepatocellular carcinoma.

Using exosomes, naturally-equipped nanocarriers, for drug delivery.

PubMed

Batrakova, Elena V; Kim, Myung Soo

2015-12-10

Exosomes offer distinct advantages that uniquely position them as highly effective drug carriers. Comprised of cellular membranes with multiple adhesive proteins on their surface, exosomes are known to specialize in cell-cell communications and provide an exclusive approach for the delivery of various therapeutic agents to target cells. In addition, exosomes can be amended through their parental cells to express a targeting moiety on their surface, or supplemented with desired biological activity. Development and validation of exosome-based drug delivery systems are the focus of this review. Different techniques of exosome isolation, characterization, drug loading, and applications in experimental disease models and clinic are discussed. Exosome-based drug formulations may be applied to a wide variety of disorders such as cancer, various infectious, cardiovascular, and neurodegenerative disorders. Overall, exosomes combine benefits of both synthetic nanocarriers and cell-mediated drug delivery systems while avoiding their limitations. Published by Elsevier B.V.

Target specific delivery of anticancer drug in silk fibroin based 3D distribution model of bone-breast cancer cells.

PubMed

Subia, Bano; Dey, Tuli; Sharma, Shaily; Kundu, Subhas C

2015-02-04

To avoid the indiscriminating action of anticancer drugs, the cancer cell specific targeting of drug molecule becomes a preferred choice for the treatment. The successful screening of the drug molecules in 2D culture system requires further validation. The failure of target specific drug in animal model raises the issue of creating a platform in between the in vitro (2D) and in vivo animal testing. The metastatic breast cancer cells migrate and settle at different sites such as bone tissue. This work evaluates the in vitro 3D model of the breast cancer and bone cells to understand the cellular interactions in the presence of a targeted anticancer drug delivery system. The silk fibroin based cytocompatible 3D scaffold is used as in vitro 3D distribution model. Human breast adenocarcinoma and osteoblast like cells are cocultured to evaluate the efficiency of doxorubicin loaded folic acid conjugated silk fibroin nanoparticle as drug delivery system. Decreasing population of the cancer cells, which lower the levels of vascular endothelial growth factors, glucose consumption, and lactate production are observed in the drug treated coculture constructs. The drug treated constructs do not show any major impact on bone mineralization. The diminished expression of osteogenic markers such as osteocalcein and alkaline phosphatase are recorded. The result indicates that this type of silk based 3D in vitro coculture model may be utilized as a bridge between the traditional 2D and animal model system to evaluate the new drug molecule (s) or to reassay the known drug molecules or to develop target specific drug in cancer research.

A high content, high throughput cellular thermal stability assay for measuring drug-target engagement in living cells.

PubMed

Massey, Andrew J

2018-01-01

Determining and understanding drug target engagement is critical for drug discovery. This can be challenging within living cells as selective readouts are often unavailable. Here we describe a novel method for measuring target engagement in living cells based on the principle of altered protein thermal stabilization / destabilization in response to ligand binding. This assay (HCIF-CETSA) utilizes high content, high throughput single cell immunofluorescent detection to determine target protein levels following heating of adherent cells in a 96 well plate format. We have used target engagement of Chk1 by potent small molecule inhibitors to validate the assay. Target engagement measured by this method was subsequently compared to target engagement measured by two alternative methods (autophosphorylation and CETSA). The HCIF-CETSA method appeared robust and a good correlation in target engagement measured by this method and CETSA for the selective Chk1 inhibitor V158411 was observed. However, these EC50 values were 23- and 12-fold greater than the autophosphorylation IC50. The described method is therefore a valuable advance in the CETSA method allowing the high throughput determination of target engagement in adherent cells.

Clinical Implementation of Integrated Genomic Profiling in Patients with Advanced Cancers.

PubMed

Borad, Mitesh J; Egan, Jan B; Condjella, Rachel M; Liang, Winnie S; Fonseca, Rafael; Ritacca, Nicole R; McCullough, Ann E; Barrett, Michael T; Hunt, Katherine S; Champion, Mia D; Patel, Maitray D; Young, Scott W; Silva, Alvin C; Ho, Thai H; Halfdanarson, Thorvardur R; McWilliams, Robert R; Lazaridis, Konstantinos N; Ramanathan, Ramesh K; Baker, Angela; Aldrich, Jessica; Kurdoglu, Ahmet; Izatt, Tyler; Christoforides, Alexis; Cherni, Irene; Nasser, Sara; Reiman, Rebecca; Cuyugan, Lori; McDonald, Jacquelyn; Adkins, Jonathan; Mastrian, Stephen D; Valdez, Riccardo; Jaroszewski, Dawn E; Von Hoff, Daniel D; Craig, David W; Stewart, A Keith; Carpten, John D; Bryce, Alan H

2016-12-23

DNA focused panel sequencing has been rapidly adopted to assess therapeutic targets in advanced/refractory cancer. Integrated Genomic Profiling (IGP) utilising DNA/RNA with tumour/normal comparisons in a Clinical Laboratory Improvement Amendments (CLIA) compliant setting enables a single assay to provide: therapeutic target prioritisation, novel target discovery/application and comprehensive germline assessment. A prospective study in 35 advanced/refractory cancer patients was conducted using CLIA-compliant IGP. Feasibility was assessed by estimating time to results (TTR), prioritising/assigning putative therapeutic targets, assessing drug access, ascertaining germline alterations, and assessing patient preferences/perspectives on data use/reporting. Therapeutic targets were identified using biointelligence/pathway analyses and interpreted by a Genomic Tumour Board. Seventy-five percent of cases harboured 1-3 therapeutically targetable mutations/case (median 79 mutations of potential functional significance/case). Median time to CLIA-validated results was 116 days with CLIA-validation of targets achieved in 21/22 patients. IGP directed treatment was instituted in 13 patients utilising on/off label FDA approved drugs (n = 9), clinical trials (n = 3) and single patient IND (n = 1). Preliminary clinical efficacy was noted in five patients (two partial response, three stable disease). Although barriers to broader application exist, including the need for wider availability of therapies, IGP in a CLIA-framework is feasible and valuable in selection/prioritisation of anti-cancer therapeutic targets.

Selection of phage-displayed accessible recombinant targeted antibodies (SPARTA): methodology and applications.

PubMed

D'Angelo, Sara; Staquicini, Fernanda I; Ferrara, Fortunato; Staquicini, Daniela I; Sharma, Geetanjali; Tarleton, Christy A; Nguyen, Huynh; Naranjo, Leslie A; Sidman, Richard L; Arap, Wadih; Bradbury, Andrew Rm; Pasqualini, Renata

2018-05-03

We developed a potentially novel and robust antibody discovery methodology, termed selection of phage-displayed accessible recombinant targeted antibodies (SPARTA). This combines an in vitro screening step of a naive human antibody library against known tumor targets, with in vivo selections based on tumor-homing capabilities of a preenriched antibody pool. This unique approach overcomes several rate-limiting challenges to generate human antibodies amenable to rapid translation into medical applications. As a proof of concept, we evaluated SPARTA on 2 well-established tumor cell surface targets, EphA5 and GRP78. We evaluated antibodies that showed tumor-targeting selectivity as a representative panel of antibody-drug conjugates (ADCs) and were highly efficacious. Our results validate a discovery platform to identify and validate monoclonal antibodies with favorable tumor-targeting attributes. This approach may also extend to other diseases with known cell surface targets and affected tissues easily isolated for in vivo selection.

Activity-based proteome profiling of potential cellular targets of Orlistat--an FDA-approved drug with anti-tumor activities.

PubMed

Yang, Peng-Yu; Liu, Kai; Ngai, Mun Hong; Lear, Martin J; Wenk, Markus R; Yao, Shao Q

2010-01-20

Orlistat, or tetrahydrolipstatin (THL), is an FDA-approved antiobesity drug with potential antitumor activities. Cellular off-targets and potential side effects of Orlistat in cancer therapies, however, have not been extensively explored thus far. In this study, we report the total of synthesis of THL-like protein-reactive probes, in which extremely conservative modifications (i.e., an alkyne handle) were introduced in the parental THL structure to maintain the native biological properties of Orlistat, while providing the necessary functionality for target identification via the bio-orthogonal click chemistry. With these natural productlike, cell-permeable probes, we were able to demonstrate, for the first time, this chemical proteomic approach is suitable for the identification of previously unknown cellular targets of Orlistat. In addition to the expected fatty acid synthase (FAS), we identified a total of eight new targets, some of which were further validated by experiments including Western blotting, recombinant protein expression, and site-directed mutagenesis. Our findings have important implications in the consideration of Orlistat as a potential anticancer drug at its early stages of development for cancer therapy. Our strategy should be broadly useful for off-target identification against quite a number of existing drugs and/or candidates, which are also covalent modifiers of their biological targets.

In silico prediction of novel therapeutic targets using gene-disease association data.

PubMed

Ferrero, Enrico; Dunham, Ian; Sanseau, Philippe

2017-08-29

Target identification and validation is a pressing challenge in the pharmaceutical industry, with many of the programmes that fail for efficacy reasons showing poor association between the drug target and the disease. Computational prediction of successful targets could have a considerable impact on attrition rates in the drug discovery pipeline by significantly reducing the initial search space. Here, we explore whether gene-disease association data from the Open Targets platform is sufficient to predict therapeutic targets that are actively being pursued by pharmaceutical companies or are already on the market. To test our hypothesis, we train four different classifiers (a random forest, a support vector machine, a neural network and a gradient boosting machine) on partially labelled data and evaluate their performance using nested cross-validation and testing on an independent set. We then select the best performing model and use it to make predictions on more than 15,000 genes. Finally, we validate our predictions by mining the scientific literature for proposed therapeutic targets. We observe that the data types with the best predictive power are animal models showing a disease-relevant phenotype, differential expression in diseased tissue and genetic association with the disease under investigation. On a test set, the neural network classifier achieves over 71% accuracy with an AUC of 0.76 when predicting therapeutic targets in a semi-supervised learning setting. We use this model to gain insights into current and failed programmes and to predict 1431 novel targets, of which a highly significant proportion has been independently proposed in the literature. Our in silico approach shows that data linking genes and diseases is sufficient to predict novel therapeutic targets effectively and confirms that this type of evidence is essential for formulating or strengthening hypotheses in the target discovery process. Ultimately, more rapid and automated target prioritisation holds the potential to reduce both the costs and the development times associated with bringing new medicines to patients.

Designing Inhibitors of Anthrax Toxin

PubMed Central

Nestorovich, Ekaterina M.; Bezrukov, Sergey M.

2014-01-01

Introduction Present-day rational drug design approaches are based on exploiting unique features of the target biomolecules, small- or macromolecule drug candidates, and physical forces that govern their interactions. The 2013 Nobel Prize in chemistry awarded “for the development of multiscale models for complex chemical systems” once again demonstrated the importance of the tailored drug discovery that reduces the role of the trial and error approach to a minimum. The “rational drug design” term is rather comprehensive as it includes all contemporary methods of drug discovery where serendipity and screening are substituted by the information-guided search for new and existing compounds. Successful implementation of these innovative drug discovery approaches is inevitably preceded by learning the physics, chemistry, and physiology of functioning of biological structures under normal and pathological conditions. Areas covered This article provides an overview of the recent rational drug design approaches to discover inhibitors of anthrax toxin. Some of the examples include small-molecule and peptide-based post-exposure therapeutic agents as well as several polyvalent compounds. The review also directs the reader to the vast literature on the recognized advances and future possibilities in the field. Expert opinion Existing options to combat anthrax toxin lethality are limited. With the only anthrax toxin inhibiting therapy (PA-targeting with a monoclonal antibody, raxibacumab) approved to treat inhalational anthrax, in our view, the situation is still insecure. The FDA’s animal rule for drug approval, which clears compounds without validated efficacy studies on humans, creates a high level of uncertainty, especially when a well-characterized animal model does not exist. Besides, unlike PA, which is known to be unstable, LF remains active in cells and in animal tissues for days. Therefore, the effectiveness of the post-exposure treatment of the individuals with anti-PA therapeutics can be time-dependent, requiring coordinated use of membrane permeable small-molecule inhibitors, which block the LF and EF enzymatic activity intracellularly. The desperate search for an ideal anthrax antitoxin allowed researchers to gain important knowledge of the basic principles of small-molecule interactions with their protein targets that could be easily transferred to other systems. At the same time, better identification and validation of anthrax toxin therapeutic targets at the molecular level, which include understanding of the physical forces underlying the target/drug interaction, as well as elucidation of the parameters determining the corresponding therapeutic windows, require further examination. PMID:24447197

Target Abundance-Based Fitness Screening (TAFiS) Facilitates Rapid Identification of Target-Specific and Physiologically Active Chemical Probes

PubMed Central

Butts, Arielle; DeJarnette, Christian; Peters, Tracy L.; Parker, Josie E.; Kerns, Morgan E.; Eberle, Karen E.; Kelly, Steve L.

2017-01-01

ABSTRACT Traditional approaches to drug discovery are frustratingly inefficient and have several key limitations that severely constrain our capacity to rapidly identify and develop novel experimental therapeutics. To address this, we have devised a second-generation target-based whole-cell screening assay based on the principles of competitive fitness, which can rapidly identify target-specific and physiologically active compounds. Briefly, strains expressing high, intermediate, and low levels of a preselected target protein are constructed, tagged with spectrally distinct fluorescent proteins (FPs), and pooled. The pooled strains are then grown in the presence of various small molecules, and the relative growth of each strain within the mixed culture is compared by measuring the intensity of the corresponding FP tags. Chemical-induced population shifts indicate that the bioactivity of a small molecule is dependent upon the target protein’s abundance and thus establish a specific functional interaction. Here, we describe the molecular tools required to apply this technique in the prevalent human fungal pathogen Candida albicans and validate the approach using two well-characterized drug targets—lanosterol demethylase and dihydrofolate reductase. However, our approach, which we have termed target abundance-based fitness screening (TAFiS), should be applicable to a wide array of molecular targets and in essentially any genetically tractable microbe. IMPORTANCE Conventional drug screening typically employs either target-based or cell-based approaches. The first group relies on biochemical assays to detect modulators of a purified target. However, hits frequently lack drug-like characteristics such as membrane permeability and target specificity. Cell-based screens identify compounds that induce a desired phenotype, but the target is unknown, which severely restricts further development and optimization. To address these issues, we have developed a second-generation target-based whole-cell screening approach that incorporates the principles of both chemical genetics and competitive fitness, which enables the identification of target-specific and physiologically active compounds from a single screen. We have chosen to validate this approach using the important human fungal pathogen Candida albicans with the intention of pursuing novel antifungal targets. However, this approach is broadly applicable and is expected to dramatically reduce the time and resources required to progress from screening hit to lead compound. PMID:28989971

Drug discovery in the next millennium.

PubMed

Ohlstein, E H; Ruffolo, R R; Elliott, J D

2000-01-01

Selection and validation of novel molecular targets have become of paramount importance in light of the plethora of new potential therapeutic drug targets that have emerged from human gene sequencing. In response to this revolution within the pharmaceutical industry, the development of high-throughput methods in both biology and chemistry has been necessitated. This review addresses these technological advances as well as several new areas that have been created by necessity to deal with this new paradigm, such as bioinformatics, cheminformatics, and functional genomics. With many of these key components of future drug discovery now in place, it is possible to map out a critical path for this process that will be used into the new millennium.

Identification of anti-inflammatory fractions of Geranium wilfordii using tumor necrosis factor-alpha as a drug target on Herbochip® - an array-based high throughput screening platform.

PubMed

Huang, Min; Yao, Pei-Wun; Chang, Margaret Dah-Tysr; Ng, Sim-Kun; Yu, Chien-Hui; Zhang, Yun-Feng; Wen, Meng-Liang; Yang, Xiao-Yuan; Lai, Yiu-Kay

2015-05-12

Geranium wilfordii is one of the major species used as Herba Geranii (lao-guan-cao) in China, it is commonly used solely or in polyherbal formulations for treatment of joint pain resulted from rheumatoid arthritis (RA) and gout. This herb is used to validate a target-based drug screening platform called Herbochip® and evaluate anti-inflammatory effects of Geranium wilfordii ethanolic extract (GWE) using tumor necrosis factor-alpha (TNF-α) as a drug target together with subsequent in vitro and in vivo assays. A microarray-based drug screening platform was constructed by arraying HPLC fractions of herbal extracts onto a surface-activated polystyrene slide (Herbochip®). Using TNF-α as a molecular probe, fractions of 82 selected herbal extracts, including GWE, were then screened to identify plant extracts containing TNF-α-binding agents. Cytotoxicity of GWE and modulatory effects of GWE on TNF-α expression were evaluated by cell-based assays using TNF-α sensitive murine fibrosarcoma L929 cells as an in vitro model. The in vivo anti-inflammatory effects of GWE were further assessed by animal models including carrageenan-induced hind paw edema in rats and xylene-induced ear edema in mice, in comparison with aspirin. The hybridization data obtained by Herbochip® analysis showed unambiguous signals which confirmed TNF-α binding activity in 46 herbal extracts including GWE. In L929 cells GWE showed significant inhibitory effect on TNF-α expression with negligible cytotoxicity. GWE also significantly inhibited formation of carrageenan-induced hind paw edema and xylene-induced ear edema in animal models, indicating that it indeed possessed anti-inflammatory activity. We have thus validated effectiveness of the Herbochip® drug screening platform using TNF-α as a molecular target. Subsequent experiments on GWE lead us to conclude that the anti-RA activity of GWE can be attributed to inhibitory effect of GWE on the key inflammatory factor, TNF-α. Our results contribute towards validation of the traditional use of GWE in the treatment of RA and other inflammatory joint disorders.

Application of Fragment Based Drug Discovery to Membrane Proteins: Biophysical Identification of Ligands of the Integral Membrane Enzyme DsbB

PubMed Central

Früh, Virginie; Zhou, Yunpeng; Chen, Dan; Loch, Caroline; Eiso, AB; Grinkova, Yelena N.; Verheij, Herman; Sligar, Stephen G; Bushweller, John H.; Siegal, Gregg

2014-01-01

Summary Membrane proteins are important pharmaceutical targets, but they pose significant challenges for fragment based drug discovery approaches. Here we present the first successful use of biophysical methods to screen for fragment ligands to an integral membrane protein. The E. coli inner membrane protein DsbB was solubilized in detergent micelles and lipid bilayer nanodiscs. The solubilized protein was immobilized with retention of functionality and used to screen 1,071 drug fragments for binding using Target Immobilized NMR Screening. Biochemical and biophysical validation of the 8 most potent hits revealed an IC50 range of 7 to 200 μM. The ability to insert a broad array of membrane proteins into nanodiscs, combined with the efficiency of TINS, demonstrates the feasibility of finding fragments targeting membrane proteins. PMID:20797617

Drug repositioning for orphan genetic diseases through Conserved Anticoexpressed Gene Clusters (CAGCs)

PubMed Central

2013-01-01

Background The development of new therapies for orphan genetic diseases represents an extremely important medical and social challenge. Drug repositioning, i.e. finding new indications for approved drugs, could be one of the most cost- and time-effective strategies to cope with this problem, at least in a subset of cases. Therefore, many computational approaches based on the analysis of high throughput gene expression data have so far been proposed to reposition available drugs. However, most of these methods require gene expression profiles directly relevant to the pathologic conditions under study, such as those obtained from patient cells and/or from suitable experimental models. In this work we have developed a new approach for drug repositioning, based on identifying known drug targets showing conserved anti-correlated expression profiles with human disease genes, which is completely independent from the availability of ‘ad hoc’ gene expression data-sets. Results By analyzing available data, we provide evidence that the genes displaying conserved anti-correlation with drug targets are antagonistically modulated in their expression by treatment with the relevant drugs. We then identified clusters of genes associated to similar phenotypes and showing conserved anticorrelation with drug targets. On this basis, we generated a list of potential candidate drug-disease associations. Importantly, we show that some of the proposed associations are already supported by independent experimental evidence. Conclusions Our results support the hypothesis that the identification of gene clusters showing conserved anticorrelation with drug targets can be an effective method for drug repositioning and provide a wide list of new potential drug-disease associations for experimental validation. PMID:24088245

Identification and validation nucleolin as a target of curcumol in nasopharyngeal carcinoma cells.

PubMed

Wang, Juan; Wu, Jiacai; Li, Xumei; Liu, Haowei; Qin, Jianli; Bai, Zhun; Chi, Bixia; Chen, Xu

2018-06-30

Identification of the specific protein target(s) of a drug is a critical step in unraveling its mechanisms of action (MOA) in many natural products. Curcumol, isolated from well known Chinese medicinal plant Curcuma zedoary, has been shown to possess multiple biological activities. It can inhibit nasopharyngeal carcinoma (NPC) proliferation and induce apoptosis, but its target protein(s) in NPC cells remains unclear. In this study, we employed a mass spectrometry-based chemical proteomics approach reveal the possible protein targets of curcumol in NPC cells. Cellular thermal shift assay (CETSA), molecular docking and cell-based assay was used to validate the binding interactions. Chemical proteomics capturing uncovered that NCL is a target of curcumol in NPC cells, Molecular docking showed that curcumol bound to NCL with an -7.8 kcal/mol binding free energy. Cell function analysis found that curcumol's treatment leads to a degradation of NCL in NPC cells, and it showed slight effects on NP69 cells. In conclusion, our results providing evidences that NCL is a target protein of curcumol. We revealed that the anti-cancer effects of curcumol in NPC cells are mediated, at least in part, by NCL inhibition. Many natural products showed high bioactivity, while their mechanisms of action (MOA) are very poor or completely missed. Understanding the MOA of natural drugs can thoroughly exploit their therapeutic potential and minimize their adverse side effects. Identification of the specific protein target(s) of a drug is a critical step in unraveling its MOA. Compound-centric chemical proteomics is a classic chemical proteomics approach which integrates chemical synthesis with cell biology and mass spectrometry (MS) to identify protein targets of natural products determine the drug mechanism of action, describe its toxicity, and figure out the possible cause of off-target. It is an affinity-based chemical proteomics method to identify small molecule-protein interactions through affinity chromatography approach coupled with mass spectrometry, has been conventionally used to identify target proteins and has yielded good results. Curcumol, has shown effective inhibition on Nasopharyngeal Carcinoma (NPC) Cells, interacted with NCL and then initiated the anti-tumor biological effect. This research demonstrated the effectiveness of chemical proteomics approaches in natural drugs molecular target identification, revealing and understanding of the novel mechanism of actions of curcumol is crucial for cancer prevention and treatment in nasopharynx cancer. Copyright © 2018 Elsevier B.V. All rights reserved.

A Multi-Study Examination of the Validity of a Risk and Protective Factor Model for Young Adolescent Drug and Alcohol Use

ERIC Educational Resources Information Center

Schmitt, Vicki L.; Frey, Bruce B.; Dunham, Michelle L.; Carman, Carol A.

2007-01-01

Issues associated with drug and alcohol use as well as other delinquent behaviors among adolescents are of utmost importance to those concerned with student success in middle grades settings. In order to target preventive interventions for these problems, research suggests that educators should examine the risk and protective factors associated…

Structural basis for the inhibition of the essential Plasmodium falciparum M1 neutral aminopeptidase.

PubMed

McGowan, Sheena; Porter, Corrine J; Lowther, Jonathan; Stack, Colin M; Golding, Sarah J; Skinner-Adams, Tina S; Trenholme, Katharine R; Teuscher, Franka; Donnelly, Sheila M; Grembecka, Jolanta; Mucha, Artur; Kafarski, Pawel; Degori, Ross; Buckle, Ashley M; Gardiner, Donald L; Whisstock, James C; Dalton, John P

2009-02-24

Plasmodium falciparum parasites are responsible for the major global disease malaria, which results in >2 million deaths each year. With the rise of drug-resistant malarial parasites, novel drug targets and lead compounds are urgently required for the development of new therapeutic strategies. Here, we address this important problem by targeting the malarial neutral aminopeptidases that are involved in the terminal stages of hemoglobin digestion and essential for the provision of amino acids used for parasite growth and development within the erythrocyte. We characterize the structure and substrate specificity of one such aminopeptidase, PfA-M1, a validated drug target. The X-ray crystal structure of PfA-M1 alone and in complex with the generic inhibitor, bestatin, and a phosphinate dipeptide analogue with potent in vitro and in vivo antimalarial activity, hPheP[CH(2)]Phe, reveals features within the protease active site that are critical to its function as an aminopeptidase and can be exploited for drug development. These results set the groundwork for the development of antimalarial therapeutics that target the neutral aminopeptidases of the parasite.

Structural basis for the inhibition of the essential Plasmodium falciparum M1 neutral aminopeptidase

PubMed Central

McGowan, Sheena; Porter, Corrine J.; Lowther, Jonathan; Stack, Colin M.; Golding, Sarah J.; Skinner-Adams, Tina S.; Trenholme, Katharine R.; Teuscher, Franka; Donnelly, Sheila M.; Grembecka, Jolanta; Mucha, Artur; Kafarski, Pawel; DeGori, Ross; Buckle, Ashley M.; Gardiner, Donald L.; Whisstock, James C.; Dalton, John P.

2009-01-01

Plasmodium falciparum parasites are responsible for the major global disease malaria, which results in >2 million deaths each year. With the rise of drug-resistant malarial parasites, novel drug targets and lead compounds are urgently required for the development of new therapeutic strategies. Here, we address this important problem by targeting the malarial neutral aminopeptidases that are involved in the terminal stages of hemoglobin digestion and essential for the provision of amino acids used for parasite growth and development within the erythrocyte. We characterize the structure and substrate specificity of one such aminopeptidase, PfA-M1, a validated drug target. The X-ray crystal structure of PfA-M1 alone and in complex with the generic inhibitor, bestatin, and a phosphinate dipeptide analogue with potent in vitro and in vivo antimalarial activity, hPheP[CH2]Phe, reveals features within the protease active site that are critical to its function as an aminopeptidase and can be exploited for drug development. These results set the groundwork for the development of antimalarial therapeutics that target the neutral aminopeptidases of the parasite. PMID:19196988

Microdosing and Other Phase 0 Clinical Trials: Facilitating Translation in Drug Development.

PubMed

Burt, T; Yoshida, K; Lappin, G; Vuong, L; John, C; de Wildt, S N; Sugiyama, Y; Rowland, M

2016-04-01

A number of drivers and developments suggest that microdosing and other phase 0 applications will experience increased utilization in the near-to-medium future. Increasing costs of drug development and ethical concerns about the risks of exposing humans and animals to novel chemical entities are important drivers in favor of these approaches, and can be expected only to increase in their relevance. An increasing body of research supports the validity of extrapolation from the limited drug exposure of phase 0 approaches to the full, therapeutic exposure, with modeling and simulations capable of extrapolating even non-linear scenarios. An increasing number of applications and design options demonstrate the versatility and flexibility these approaches offer to drug developers including the study of PK, bioavailability, DDI, and mechanistic PD effects. PET microdosing allows study of target localization, PK and receptor binding and occupancy, while Intra-Target Microdosing (ITM) allows study of local therapeutic-level acute PD coupled with systemic microdose-level exposure. Applications in vulnerable populations and extreme environments are attractive due to the unique risks of pharmacotherapy and increasing unmet healthcare needs. All phase 0 approaches depend on the validity of extrapolation from the limited-exposure scenario to the full exposure of therapeutic intent, but in the final analysis the potential for controlled human data to reduce uncertainty about drug properties is bound to be a valuable addition to the drug development process.

High-throughput screening using pseudotyped lentiviral particles: a strategy for the identification of HIV-1 inhibitors in a cell-based assay.

PubMed

Garcia, Jean-Michel; Gao, Anhui; He, Pei-Lan; Choi, Joyce; Tang, Wei; Bruzzone, Roberto; Schwartz, Olivier; Naya, Hugo; Nan, Fa-Jun; Li, Jia; Altmeyer, Ralf; Zuo, Jian-Ping

2009-03-01

Two decades after its discovery the human immunodeficiency virus (HIV) is still spreading worldwide and killing millions. There are 25 drugs formally approved for HIV currently on the market, but side effects as well as the emergence of HIV strains showing single or multiple resistances to current drug-therapy are causes for concern. Furthermore, these drugs target only 4 steps of the viral cycle, hence the urgent need for new drugs and also new targets. In order to tackle this problem, we have devised a cell-based assay using lentiviral particles to look for post-entry inhibitors of HIV-1. We report here the assay development, validation as well as confirmation of the hits using both wild-type and drug-resistant HIV-1 viruses. The screening was performed on an original library, rich in natural compounds and pure molecules from Traditional Chinese Medicine pharmacopoeia, which had never been screened for anti-HIV activity. The identified hits belong to four chemical sub-families that appear to be all non-nucleoside reverse transcriptase inhibitors (NNRTIs). Secondary tests with live viruses showed that there was good agreement with pseudotyped particles, confirming the validity of this approach for high-throughput drug screens. This assay will be a useful tool that can be easily adapted to screen for inhibitors of viral entry.

SELF-BLM: Prediction of drug-target interactions via self-training SVM.

PubMed

Keum, Jongsoo; Nam, Hojung

2017-01-01

Predicting drug-target interactions is important for the development of novel drugs and the repositioning of drugs. To predict such interactions, there are a number of methods based on drug and target protein similarity. Although these methods, such as the bipartite local model (BLM), show promise, they often categorize unknown interactions as negative interaction. Therefore, these methods are not ideal for finding potential drug-target interactions that have not yet been validated as positive interactions. Thus, here we propose a method that integrates machine learning techniques, such as self-training support vector machine (SVM) and BLM, to develop a self-training bipartite local model (SELF-BLM) that facilitates the identification of potential interactions. The method first categorizes unlabeled interactions and negative interactions among unknown interactions using a clustering method. Then, using the BLM method and self-training SVM, the unlabeled interactions are self-trained and final local classification models are constructed. When applied to four classes of proteins that include enzymes, G-protein coupled receptors (GPCRs), ion channels, and nuclear receptors, SELF-BLM showed the best performance for predicting not only known interactions but also potential interactions in three protein classes compare to other related studies. The implemented software and supporting data are available at https://github.com/GIST-CSBL/SELF-BLM.

Neoclassic drug discovery: the case for lead generation using phenotypic and functional approaches.

PubMed

Lee, Jonathan A; Berg, Ellen L

2013-12-01

Innovation and new molecular entity production by the pharmaceutical industry has been below expectations. Surprisingly, more first-in-class small-molecule drugs approved by the U.S. Food and Drug Administration (FDA) between 1999 and 2008 were identified by functional phenotypic lead generation strategies reminiscent of pre-genomics pharmacology than contemporary molecular targeted strategies that encompass the vast majority of lead generation efforts. This observation, in conjunction with the difficulty in validating molecular targets for drug discovery, has diminished the impact of the "genomics revolution" and has led to a growing grassroots movement and now broader trend in pharma to reconsider the use of modern physiology-based or phenotypic drug discovery (PDD) strategies. This "From the Guest Editors" column provides an introduction and overview of the two-part special issues of Journal of Biomolecular Screening on PDD. Terminology and the business case for use of PDD are defined. Key issues such as assay performance, chemical optimization, target identification, and challenges to the organization and implementation of PDD are discussed. Possible solutions for these challenges and a new neoclassic vision for PDD that combines phenotypic and functional approaches with technology innovations resulting from the genomics-driven era of target-based drug discovery (TDD) are also described. Finally, an overview of the manuscripts in this special edition is provided.

Predicting drug-target interactions using restricted Boltzmann machines.

PubMed

Wang, Yuhao; Zeng, Jianyang

2013-07-01

In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action. We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process. Software and datasets are available on request. Supplementary data are available at Bioinformatics online.

Contribution of NIH funding to new drug approvals 2010-2016.

PubMed

Galkina Cleary, Ekaterina; Beierlein, Jennifer M; Khanuja, Navleen Surjit; McNamee, Laura M; Ledley, Fred D

2018-03-06

This work examines the contribution of NIH funding to published research associated with 210 new molecular entities (NMEs) approved by the Food and Drug Administration from 2010-2016. We identified >2 million publications in PubMed related to the 210 NMEs ( n = 131,092) or their 151 known biological targets ( n = 1,966,281). Of these, >600,000 (29%) were associated with NIH-funded projects in RePORTER. This funding included >200,000 fiscal years of NIH project support (1985-2016) and project costs >$100 billion (2000-2016), representing ∼20% of the NIH budget over this period. NIH funding contributed to every one of the NMEs approved from 2010-2016 and was focused primarily on the drug targets rather than on the NMEs themselves. There were 84 first-in-class products approved in this interval, associated with >$64 billion of NIH-funded projects. The percentage of fiscal years of project funding identified through target searches, but not drug searches, was greater for NMEs discovered through targeted screening than through phenotypic methods (95% versus 82%). For targeted NMEs, funding related to targets preceded funding related to the NMEs, consistent with the expectation that basic research provides validated targets for targeted screening. This analysis, which captures basic research on biological targets as well as applied research on NMEs, suggests that the NIH contribution to research associated with new drug approvals is greater than previously appreciated and highlights the risk of reducing federal funding for basic biomedical research. Copyright © 2018 the Author(s). Published by PNAS.

Drug-target interaction prediction using ensemble learning and dimensionality reduction.

PubMed

Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

2017-10-01

Experimental prediction of drug-target interactions is expensive, time-consuming and tedious. Fortunately, computational methods help narrow down the search space for interaction candidates to be further examined via wet-lab techniques. Nowadays, the number of attributes/features for drugs and targets, as well as the amount of their interactions, are increasing, making these computational methods inefficient or occasionally prohibitive. This motivates us to derive a reduced feature set for prediction. In addition, since ensemble learning techniques are widely used to improve the classification performance, it is also worthwhile to design an ensemble learning framework to enhance the performance for drug-target interaction prediction. In this paper, we propose a framework for drug-target interaction prediction leveraging both feature dimensionality reduction and ensemble learning. First, we conducted feature subspacing to inject diversity into the classifier ensemble. Second, we applied three different dimensionality reduction methods to the subspaced features. Third, we trained homogeneous base learners with the reduced features and then aggregated their scores to derive the final predictions. For base learners, we selected two classifiers, namely Decision Tree and Kernel Ridge Regression, resulting in two variants of ensemble models, EnsemDT and EnsemKRR, respectively. In our experiments, we utilized AUC (Area under ROC Curve) as an evaluation metric. We compared our proposed methods with various state-of-the-art methods under 5-fold cross validation. Experimental results showed EnsemKRR achieving the highest AUC (94.3%) for predicting drug-target interactions. In addition, dimensionality reduction helped improve the performance of EnsemDT. In conclusion, our proposed methods produced significant improvements for drug-target interaction prediction. Copyright © 2017 Elsevier Inc. All rights reserved.

Therapeutic Potential of Foldamers: From Chemical Biology Tools To Drug Candidates?

PubMed

Gopalakrishnan, Ranganath; Frolov, Andrey I; Knerr, Laurent; Drury, William J; Valeur, Eric

2016-11-10

Over the past decade, foldamers have progressively emerged as useful architectures to mimic secondary structures of proteins. Peptidic foldamers, consisting of various amino acid based backbones, have been the most studied from a therapeutic perspective, while polyaromatic foldamers have barely evolved from their nascency and remain perplexing for medicinal chemists due to their poor drug-like nature. Despite these limitations, this compound class may still offer opportunities to study challenging targets or provide chemical biology tools. The potential of foldamer drug candidates reaching the clinic is still a stretch. Nevertheless, advances in the field have demonstrated their potential for the discovery of next generation therapeutics. In this perspective, the current knowledge of foldamers is reviewed in a drug discovery context. Recent advances in the early phases of drug discovery including hit finding, target validation, and optimization and molecular modeling are discussed. In addition, challenges and focus areas are debated and gaps highlighted.

Systems-level modeling of mycobacterial metabolism for the identification of new (multi-)drug targets.

PubMed

Rienksma, Rienk A; Suarez-Diez, Maria; Spina, Lucie; Schaap, Peter J; Martins dos Santos, Vitor A P

2014-12-01

Systems-level metabolic network reconstructions and the derived constraint-based (CB) mathematical models are efficient tools to explore bacterial metabolism. Approximately one-fourth of the Mycobacterium tuberculosis (Mtb) genome contains genes that encode proteins directly involved in its metabolism. These represent potential drug targets that can be systematically probed with CB models through the prediction of genes essential (or the combination thereof) for the pathogen to grow. However, gene essentiality depends on the growth conditions and, so far, no in vitro model precisely mimics the host at the different stages of mycobacterial infection, limiting model predictions. These limitations can be circumvented by combining expression data from in vivo samples with a validated CB model, creating an accurate description of pathogen metabolism in the host. To this end, we present here a thoroughly curated and extended genome-scale CB metabolic model of Mtb quantitatively validated using 13C measurements. We describe some of the efforts made in integrating CB models and high-throughput data to generate condition specific models, and we will discuss challenges ahead. This knowledge and the framework herein presented will enable to identify potential new drug targets, and will foster the development of optimal therapeutic strategies. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Exploring the associations between drug side-effects and therapeutic indications.

PubMed

Wang, Fei; Zhang, Ping; Cao, Nan; Hu, Jianying; Sorrentino, Robert

2014-10-01

Drug therapeutic indications and side-effects are both measurable patient phenotype changes in response to the treatment. Inferring potential drug therapeutic indications and identifying clinically interesting drug side-effects are both important and challenging tasks. Previous studies have utilized either chemical structures or protein targets to predict indications and side-effects. In this study, we compared drug therapeutic indication prediction using various information including chemical structures, protein targets and side-effects. We also compared drug side-effect prediction with various information sources including chemical structures, protein targets and therapeutic indication. Prediction performance based on 10-fold cross-validation demonstrates that drug side-effects and therapeutic indications are the most predictive information source for each other. In addition, we extracted 6706 statistically significant indication-side-effect associations from all known drug-disease and drug-side-effect relationships. We further developed a novel user interface that allows the user to interactively explore these associations in the form of a dynamic bipartitie graph. Many relationship pairs provide explicit repositioning hypotheses (e.g., drugs causing postural hypotension are potential candidates for hypertension) and clear adverse-reaction watch lists (e.g., drugs for heart failure possibly cause impotence). All data sets and highly correlated disease-side-effect relationships are available at http://astro.temple.edu/∼tua87106/druganalysis.html. Copyright © 2014 Elsevier Inc. All rights reserved.

78 FR 26054 - National Institute on Aging; Notice of Closed Meetings

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-03

... Validation of Novel Targets for Alzheimer's Disease. Date: June 25, 2013. Time: 11:30 a.m. to 5:30 p.m... Committee: National Institute on Aging Special Emphasis Panel; Drug Development for Alzheimer's Disease...

Drug target ontology to classify and integrate drug discovery data.

PubMed

Lin, Yu; Mehta, Saurabh; Küçük-McGinty, Hande; Turner, John Paul; Vidovic, Dusica; Forlin, Michele; Koleti, Amar; Nguyen, Dac-Trung; Jensen, Lars Juhl; Guha, Rajarshi; Mathias, Stephen L; Ursu, Oleg; Stathias, Vasileios; Duan, Jianbin; Nabizadeh, Nooshin; Chung, Caty; Mader, Christopher; Visser, Ubbo; Yang, Jeremy J; Bologa, Cristian G; Oprea, Tudor I; Schürer, Stephan C

2017-11-09

One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Phenotypic Screening Approaches to Develop Aurora Kinase Inhibitors: Drug Discovery Perspectives.

PubMed

Marugán, Carlos; Torres, Raquel; Lallena, María José

2015-01-01

Targeting mitotic regulators as a strategy to fight cancer implies the development of drugs against key proteins, such as Aurora-A and -B. Current drugs, which target mitosis through a general mechanism of action (stabilization/destabilization of microtubules), have several side effects (neutropenia, alopecia, and emesis). Pharmaceutical companies aim at avoiding these unwanted effects by generating improved and selective drugs that increase the quality of life of the patients. However, the development of these drugs is an ambitious task that involves testing thousands of compounds through biochemical and cell-based assays. In addition, molecules usually target complex biological processes, involving several proteins and different molecular pathways, further emphasizing the need for high-throughput screening techniques and multiplexing technologies in order to identify drugs with the desired phenotype. We will briefly describe two multiplexing technologies [high-content imaging (HCI) and flow cytometry] and two key processes for drug discovery research (assay development and validation) following our own published industry quality standards. We will further focus on HCI as a useful tool for phenotypic screening and will provide a concrete example of HCI assay to detect Aurora-A or -B selective inhibitors discriminating the off-target effects related to the inhibition of other cell cycle or non-cell cycle key regulators. Finally, we will describe other assays that can help to characterize the in vitro pharmacology of the inhibitors.

Hepatic fibrosis: Concept to treatment.

PubMed

Trautwein, Christian; Friedman, Scott L; Schuppan, Detlef; Pinzani, Massimo

2015-04-01

Understanding the molecular mechanisms underlying liver fibrogenesis is fundamentally relevant to developing new treatments that are independent of the underlying etiology. The increasing success of antiviral treatments in blocking or reversing the fibrogenic progression of chronic liver disease has unearthed vital information about the natural history of fibrosis regression, and has established important principles and targets for antifibrotic drugs. Although antifibrotic activity has been demonstrated for many compounds in vitro and in animal models, none has been thoroughly validated in the clinic or commercialized as a therapy for fibrosis. In addition, it is likely that combination therapies that affect two or more key pathogenic targets and/or pathways will be needed. To accelerate the preclinical development of these combination therapies, reliable single target validation is necessary, followed by the rational selection and systematic testing of combination approaches. Improved noninvasive tools for the assessment of fibrosis content, fibrogenesis and fibrolysis must accompany in vivo validation in experimental fibrosis models, and especially in clinical trials. The rapidly changing landscape of clinical trial design for liver disease is recognized by regulatory agencies in the United States (FDA) and Western Europe (EMA), who are working together with the broad range of stakeholders to standardize approaches to testing antifibrotic drugs in cohorts of patients with chronic liver diseases. Copyright © 2015. Published by Elsevier B.V.

Novel Small Molecule Inhibitors of Choline Kinase Identified by Fragment-Based Drug Discovery.

PubMed

Zech, Stephan G; Kohlmann, Anna; Zhou, Tianjun; Li, Feng; Squillace, Rachel M; Parillon, Lois E; Greenfield, Matthew T; Miller, David P; Qi, Jiwei; Thomas, R Mathew; Wang, Yihan; Xu, Yongjin; Miret, Juan J; Shakespeare, William C; Zhu, Xiaotian; Dalgarno, David C

2016-01-28

Choline kinase α (ChoKα) is an enzyme involved in the synthesis of phospholipids and thereby plays key roles in regulation of cell proliferation, oncogenic transformation, and human carcinogenesis. Since several inhibitors of ChoKα display antiproliferative activity in both cellular and animal models, this novel oncogene has recently gained interest as a promising small molecule target for cancer therapy. Here we summarize our efforts to further validate ChoKα as an oncogenic target and explore the activity of novel small molecule inhibitors of ChoKα. Starting from weakly binding fragments, we describe a structure based lead discovery approach, which resulted in novel highly potent inhibitors of ChoKα. In cancer cell lines, our lead compounds exhibit a dose-dependent decrease of phosphocholine, inhibition of cell growth, and induction of apoptosis at low micromolar concentrations. The druglike lead series presented here is optimizable for improvements in cellular potency, drug target residence time, and pharmacokinetic parameters. These inhibitors may be utilized not only to further validate ChoKα as antioncogenic target but also as novel chemical matter that may lead to antitumor agents that specifically interfere with cancer cell metabolism.

Novel targets for HIV therapy.

PubMed

Greene, Warner C; Debyser, Zeger; Ikeda, Yasuhiro; Freed, Eric O; Stephens, Edward; Yonemoto, Wes; Buckheit, Robert W; Esté, José A; Cihlar, Tomas

2008-12-01

There are currently 25 drugs belonging to 6 different inhibitor classes approved for the treatment of human immunodeficiency virus (HIV) infection. However, new anti-HIV agents are still needed to confront the emergence of drug resistance and various adverse effects associated with long-term use of antiretroviral therapy. The 21st International Conference on Antiviral Research, held in April 2008 in Montreal, Canada, therefore featured a special session focused on novel targets for HIV therapy. The session included presentations by world-renowned experts in HIV virology and covered a diverse array of potential targets for the development of new classes of HIV therapies. This review contains concise summaries of discussed topics that included Vif-APOBEC3G, LEDGF/p75, TRIM 5alpha, virus assembly and maturation, and Vpu. The described viral and host factors represent some of the most noted examples of recent scientific breakthroughs that are opening unexplored avenues to novel anti-HIV target discovery and validation, and should feed the antiretroviral drug development pipeline in the near future.

Advances in identification and validation of protein targets of natural products without chemical modification.

PubMed

Chang, J; Kim, Y; Kwon, H J

2016-05-04

Covering: up to February 2016Identification of the target proteins of natural products is pivotal to understanding the mechanisms of action to develop natural products for use as molecular probes and potential therapeutic drugs. Affinity chromatography of immobilized natural products has been conventionally used to identify target proteins, and has yielded good results. However, this method has limitations, in that labeling or tagging for immobilization and affinity purification often result in reduced or altered activity of the natural product. New strategies have recently been developed and applied to identify the target proteins of natural products and synthetic small molecules without chemical modification of the natural product. These direct and indirect methods for target identification of label-free natural products include drug affinity responsive target stability (DARTS), stability of proteins from rates of oxidation (SPROX), cellular thermal shift assay (CETSA), thermal proteome profiling (TPP), and bioinformatics-based analysis of connectivity. This review focuses on and reports case studies of the latest advances in target protein identification methods for label-free natural products. The integration of newly developed technologies will provide new insights and highlight the value of natural products for use as biological probes and new drug candidates.

Improving anticancer drug development begins with cell culture: misinformation perpetrated by the misuse of cytotoxicity assays.

PubMed

Eastman, Alan

2017-01-31

The high failure rate of anticancer drug discovery and development has consumed billions of dollars annually. While many explanations have been provided, I believe that misinformation arising from inappropriate cell-based screens has been completely over-looked. Most cell culture experiments are irrelevant to how drugs are subsequently administered to patients. Usually, drug development focuses on growth inhibition rather than cell killing. Drugs are selected based on continuous incubation of cells, then frequently administered to the patient as a bolus. Target identification and validation is often performed by gene suppression that inevitably mimics continuous target inhibition. Drug concentrations in vitro frequently far exceed in vivo concentrations. Studies of drug synergy are performed at sub-optimal concentrations. And the focus on a limited number of cell lines can misrepresent the potential efficacy in a patient population. The intent of this review is to encourage more appropriate experimental design and data interpretation, and to improve drug development in the area of cell-based assays. Application of these principles should greatly enhance the successful translation of novel drugs to the patient.

Creation of a Novel Class of Potent and Selective MutT Homologue 1 (MTH1) Inhibitors Using Fragment-Based Screening and Structure-Based Drug Design.

PubMed

Rahm, Fredrik; Viklund, Jenny; Trésaugues, Lionel; Ellermann, Manuel; Giese, Anja; Ericsson, Ulrika; Forsblom, Rickard; Ginman, Tobias; Günther, Judith; Hallberg, Kenth; Lindström, Johan; Persson, Lars Boukharta; Silvander, Camilla; Talagas, Antoine; Díaz-Sáez, Laura; Fedorov, Oleg; Huber, Kilian V M; Panagakou, Ioanna; Siejka, Paulina; Gorjánácz, Mátyás; Bauser, Marcus; Andersson, Martin

2018-03-22

Recent literature has both suggested and questioned MTH1 as a novel cancer target. BAY-707 was just published as a target validation small molecule probe for assessing the effects of pharmacological inhibition of MTH1 on tumor cell survival, both in vitro and in vivo. (1) In this report, we describe the medicinal chemistry program creating BAY-707, where fragment-based methods were used to develop a series of highly potent and selective MTH1 inhibitors. Using structure-based drug design and rational medicinal chemistry approaches, the potency was increased over 10,000 times from the fragment starting point while maintaining high ligand efficiency and drug-like properties.

A Network Approach to Rare Disease Modeling

NASA Astrophysics Data System (ADS)

Ghiassian, Susan; Rabello, Sabrina; Sharma, Amitabh; Wiest, Olaf; Barabasi, Albert-Laszlo

2011-03-01

Network approaches have been widely used to better understand different areas of natural and social sciences. Network Science had a particularly great impact on the study of biological systems. In this project, using biological networks, candidate drugs as a potential treatment of rare diseases were identified. Developing new drugs for more than 2000 rare diseases (as defined by ORPHANET) is too expensive and beyond expectation. Disease proteins do not function in isolation but in cooperation with other interacting proteins. Research on FDA approved drugs have shown that most of the drugs do not target the disease protein but a protein which is 2 or 3 steps away from the disease protein in the Protein-Protein Interaction (PPI) network. We identified the already known drug targets in the disease gene's PPI subnetwork (up to the 3rd neighborhood) and among them those in the same sub cellular compartment and higher coexpression coefficient with the disease gene are expected to be stronger candidates. Out of 2177 rare diseases, 1092 were found not to have any drug target. Using the above method, we have found the strongest candidates among the rest in order to further experimental validations.

Computational repositioning of ethno medicine elucidated gB-gH-gL complex as novel anti herpes drug target

PubMed Central

2013-01-01

Background Herpes viruses are important human pathogens that can cause mild to severe lifelong infections with high morbidity. They remain latent in the host cells and can cause recurrent infections that might prove fatal. These viruses are known to infect the host cells by causing the fusion of viral and host cell membrane proteins. Fusion is achieved with the help of conserved fusion machinery components, glycoproteins gB, heterodimer gH-gL complex along with other non-conserved components. Whereas, another important glycoprotein gD without which viral entry to the cell is not possible, acts as a co-activator for the gB-gH-gL complex formation. Thus, this complex formation interface is the most promising drug target for the development of novel anti-herpes drug candidates. In the present study, we propose a model for binding of gH-gL to gB glycoprotein leading from pre to post conformational changes during gB-gH-gL complex formation and reported the key residues involved in this binding activity along with possible binding site locations. To validate the drug targetability of our proposed binding site, we have repositioned some of the most promising in vitro, in vivo validated anti-herpes molecules onto the proposed binding site of gH-gL complex in a computational approach. Methods Hex 6.3 standalone software was used for protein-protein docking studies. Arguslab 4.0.1 and Accelrys® Discovery Studio 3.1 Visualizer softwares were used for semi-flexible docking studies and visualizing the interactions respectively. Protein receptors and ethno compounds were retrieved from Protein Data Bank (PDB) and Pubchem databases respectively. Lipinski’s Filter, Osiris Property Explorer and Lazar online servers were used to check the pharmaceutical fidelity of the drug candidates. Results Through protein-protein docking studies, it was identified that the amino acid residues VAL342, GLU347, SER349, TYR355, SER388, ASN395, HIS398 and ALA387 of gH-gL complex play an active role in its binding activity with gB. Semi flexible docking analysis of the most promising in vitro, in vivo validated anti-herpes molecules targeting the above mentioned key residues of gH-gL complex showed that all the analyzed ethno medicinal compounds have successfully docked into the proposed binding site of gH-gL glycoprotein with binding energy range between -10.4 to -6.4 K.cal./mol. Conclusions Successful repositioning of the analyzed compounds onto the proposed binding site confirms the drug targetability of gH-gL complex. Based on the free binding energy and pharmacological properties, we propose (3-chloro phenyl) methyl-3,4,5 trihydroxybenzoate as worth a small ethno medicinal lead molecule for further development as potent anti-herpes drug candidate targeting gB-gH-gL complex formation interface. PMID:23587166

Modulation of Epigenetic Targets for Anticancer Therapy: Clinicopathological Relevance, Structural Data and Drug Discovery Perspectives

PubMed Central

Andreol, Federico; Barbosa, Arménio Jorge Moura; Daniele Parenti, Marco; Rio, Alberto Del

2013-01-01

Research on cancer epigenetics has flourished in the last decade. Nevertheless growing evidence point on the importance to understand the mechanisms by which epigenetic changes regulate the genesis and progression of cancer growth. Several epigenetic targets have been discovered and are currently under validation for new anticancer therapies. Drug discovery approaches aiming to target these epigenetic enzymes with small-molecules inhibitors have produced the first pre-clinical and clinical outcomes and many other compounds are now entering the pipeline as new candidate epidrugs. The most studied targets can be ascribed to histone deacetylases and DNA methyltransferases, although several other classes of enzymes are able to operate post-translational modifications to histone tails are also likely to represent new frontiers for therapeutic interventions. By acknowledging that the field of cancer epigenetics is evolving with an impressive rate of new findings, with this review we aim to provide a current overview of pre-clinical applications of small-molecules for cancer pathologies, combining them with the current knowledge of epigenetic targets in terms of available structural data and drug design perspectives. PMID:23016851

Fragment-based approaches to the discovery of kinase inhibitors.

PubMed

Mortenson, Paul N; Berdini, Valerio; O'Reilly, Marc

2014-01-01

Protein kinases are one of the most important families of drug targets, and aberrant kinase activity has been linked to a large number of disease areas. Although eminently targetable using small molecules, kinases present a number of challenges as drug targets, not least obtaining selectivity across such a large and relatively closely related target family. Fragment-based drug discovery involves screening simple, low-molecular weight compounds to generate initial hits against a target. These hits are then optimized to more potent compounds via medicinal chemistry, usually facilitated by structural biology. Here, we will present a number of recent examples of fragment-based approaches to the discovery of kinase inhibitors, detailing the construction of fragment-screening libraries, the identification and validation of fragment hits, and their optimization into potent and selective lead compounds. The advantages of fragment-based methodologies will be discussed, along with some of the challenges associated with using this route. Finally, we will present a number of key lessons derived both from our own experience running fragment screens against kinases and from a large number of published studies.

Glycyrrhetinic acid-functionalized mesoporous silica nanoparticles as hepatocellular carcinoma-targeted drug carrier

PubMed Central

Lv, Yongjiu; Li, Jingjing; Chen, Huali; Bai, Yan; Zhang, Liangke

2017-01-01

In this study, a glycyrrhetinic acid-functionalized mesoporous silica nanoparticle (MSN-GA) was prepared for active tumor targeting. MSN-GA exhibited satisfactory loading capacity for insoluble drugs, uniform size distribution, and specific tumor cell targeting. Glycyrrhetinic acid, a hepatocellular carcinoma-targeting group, was covalently decorated on the surface of MSN via an amido bond. The successful synthesis of MSN-GA was validated by the results of Fourier transform infrared spectroscopy, transmission electron microscopy (TEM), and zeta potential measurement. TEM images revealed the spherical morphology and uniform size distribution of the naked MSN and MSN-GA. Curcumin (CUR), an insoluble model drug, was loaded into MSN-GA (denoted as MSN-GA-CUR) with a high-loading capacity (8.78%±1.24%). The results of the in vitro cellular experiment demonstrated that MSN-GA-CUR significantly enhanced cytotoxicity and cellular uptake toward hepatocellular carcinoma (HepG2) cells via a specific GA receptor-mediated endocytosis mechanism. The results of this study provide a promising nanoplatform for the targeting of hepatocellular carcinoma. PMID:28652738

Application of the quality by design approach to the drug substance manufacturing process of an Fc fusion protein: towards a global multi-step design space.

PubMed

Eon-duval, Alex; Valax, Pascal; Solacroup, Thomas; Broly, Hervé; Gleixner, Ralf; Strat, Claire L E; Sutter, James

2012-10-01

The article describes how Quality by Design principles can be applied to the drug substance manufacturing process of an Fc fusion protein. First, the quality attributes of the product were evaluated for their potential impact on safety and efficacy using risk management tools. Similarly, process parameters that have a potential impact on critical quality attributes (CQAs) were also identified through a risk assessment. Critical process parameters were then evaluated for their impact on CQAs, individually and in interaction with each other, using multivariate design of experiment techniques during the process characterisation phase. The global multi-step Design Space, defining operational limits for the entire drug substance manufacturing process so as to ensure that the drug substance quality targets are met, was devised using predictive statistical models developed during the characterisation study. The validity of the global multi-step Design Space was then confirmed by performing the entire process, from cell bank thawing to final drug substance, at its limits during the robustness study: the quality of the final drug substance produced under different conditions was verified against predefined targets. An adaptive strategy was devised whereby the Design Space can be adjusted to the quality of the input material to ensure reliable drug substance quality. Finally, all the data obtained during the process described above, together with data generated during additional validation studies as well as manufacturing data, were used to define the control strategy for the drug substance manufacturing process using a risk assessment methodology. Copyright © 2012 Wiley-Liss, Inc.

Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.

PubMed

Hao, Ming; Bryant, Stephen H; Wang, Yanli

2018-02-06

While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred. Published by Oxford University Press 2018. This work is written by US Government employees and is in the public domain in the US.

Design and characterization of ebolavirus GP prehairpin intermediate mimics as drug targets

PubMed Central

Clinton, Tracy R; Weinstock, Matthew T; Jacobsen, Michael T; Szabo-Fresnais, Nicolas; Pandya, Maya J; Whitby, Frank G; Herbert, Andrew S; Prugar, Laura I; McKinnon, Rena; Hill, Christopher P; Welch, Brett D; Dye, John M; Eckert, Debra M; Kay, Michael S

2015-01-01

Ebolaviruses are highly lethal filoviruses that cause hemorrhagic fever in humans and nonhuman primates. With no approved treatments or preventatives, the development of an anti-ebolavirus therapy to protect against natural infections and potential weaponization is an urgent global health need. Here, we describe the design, biophysical characterization, and validation of peptide mimics of the ebolavirus N-trimer, a highly conserved region of the GP2 fusion protein, to be used as targets to develop broad-spectrum inhibitors of ebolavirus entry. The N-trimer region of GP2 is 90% identical across all ebolavirus species and forms a critical part of the prehairpin intermediate that is exposed during viral entry. Specifically, we fused designed coiled coils to the N-trimer to present it as a soluble trimeric coiled coil as it appears during membrane fusion. Circular dichroism, sedimentation equilibrium, and X-ray crystallography analyses reveal the helical, trimeric structure of the designed N-trimer mimic targets. Surface plasmon resonance studies validate that the N-trimer mimic binds its native ligand, the C-peptide region of GP2. The longest N-trimer mimic also inhibits virus entry, thereby confirming binding of the C-peptide region during viral entry and the presence of a vulnerable prehairpin intermediate. Using phage display as a model system, we validate the suitability of the N-trimer mimics as drug screening targets. Finally, we describe the foundational work to use the N-trimer mimics as targets in mirror-image phage display, which will be used to identify d-peptide inhibitors of ebolavirus entry. PMID:25287718

Design and characterization of ebolavirus GP prehairpin intermediate mimics as drug targets.

PubMed

Clinton, Tracy R; Weinstock, Matthew T; Jacobsen, Michael T; Szabo-Fresnais, Nicolas; Pandya, Maya J; Whitby, Frank G; Herbert, Andrew S; Prugar, Laura I; McKinnon, Rena; Hill, Christopher P; Welch, Brett D; Dye, John M; Eckert, Debra M; Kay, Michael S

2015-04-01

Ebolaviruses are highly lethal filoviruses that cause hemorrhagic fever in humans and nonhuman primates. With no approved treatments or preventatives, the development of an anti-ebolavirus therapy to protect against natural infections and potential weaponization is an urgent global health need. Here, we describe the design, biophysical characterization, and validation of peptide mimics of the ebolavirus N-trimer, a highly conserved region of the GP2 fusion protein, to be used as targets to develop broad-spectrum inhibitors of ebolavirus entry. The N-trimer region of GP2 is 90% identical across all ebolavirus species and forms a critical part of the prehairpin intermediate that is exposed during viral entry. Specifically, we fused designed coiled coils to the N-trimer to present it as a soluble trimeric coiled coil as it appears during membrane fusion. Circular dichroism, sedimentation equilibrium, and X-ray crystallography analyses reveal the helical, trimeric structure of the designed N-trimer mimic targets. Surface plasmon resonance studies validate that the N-trimer mimic binds its native ligand, the C-peptide region of GP2. The longest N-trimer mimic also inhibits virus entry, thereby confirming binding of the C-peptide region during viral entry and the presence of a vulnerable prehairpin intermediate. Using phage display as a model system, we validate the suitability of the N-trimer mimics as drug screening targets. Finally, we describe the foundational work to use the N-trimer mimics as targets in mirror-image phage display, which will be used to identify D-peptide inhibitors of ebolavirus entry. © 2014 The Protein Society.

Validation of CoaBC as a Bactericidal Target in the Coenzyme A Pathway of Mycobacterium tuberculosis.

PubMed

Evans, Joanna C; Trujillo, Carolina; Wang, Zhe; Eoh, Hyungjin; Ehrt, Sabine; Schnappinger, Dirk; Boshoff, Helena I M; Rhee, Kyu Y; Barry, Clifton E; Mizrahi, Valerie

2016-12-09

Mycobacterium tuberculosis relies on its own ability to biosynthesize coenzyme A to meet the needs of the myriad enzymatic reactions that depend on this cofactor for activity. As such, the essential pantothenate and coenzyme A biosynthesis pathways have attracted attention as targets for tuberculosis drug development. To identify the optimal step for coenzyme A pathway disruption in M. tuberculosis, we constructed and characterized a panel of conditional knockdown mutants in coenzyme A pathway genes. Here, we report that silencing of coaBC was bactericidal in vitro, whereas silencing of panB, panC, or coaE was bacteriostatic over the same time course. Silencing of coaBC was likewise bactericidal in vivo, whether initiated at infection or during either the acute or chronic stages of infection, confirming that CoaBC is required for M. tuberculosis to grow and persist in mice and arguing against significant CoaBC bypass via transport and assimilation of host-derived pantetheine in this animal model. These results provide convincing genetic validation of CoaBC as a new bactericidal drug target.

Development, validation and comparison of NIR and Raman methods for the identification and assay of poor-quality oral quinine drops.

PubMed

Mbinze, J K; Sacré, P-Y; Yemoa, A; Mavar Tayey Mbay, J; Habyalimana, V; Kalenda, N; Hubert, Ph; Marini, R D; Ziemons, E

2015-01-01

Poor quality antimalarial drugs are one of the public's major health problems in Africa. The depth of this problem may be explained in part by the lack of effective enforcement and the lack of efficient local drug analysis laboratories. To tackle part of this issue, two spectroscopic methods with the ability to detect and to quantify quinine dihydrochloride in children's oral drops formulations were developed and validated. Raman and near infrared (NIR) spectroscopy were selected for the drug analysis due to their low cost, non-destructive and rapid characteristics. Both of the methods developed were successfully validated using the total error approach in the range of 50-150% of the target concentration (20%W/V) within the 10% acceptance limits. Samples collected on the Congolese pharmaceutical market were analyzed by both techniques to detect potentially substandard drugs. After a comparison of the analytical performance of both methods, it has been decided to implement the method based on NIR spectroscopy to perform the routine analysis of quinine oral drop samples in the Quality Control Laboratory of Drugs at the University of Kinshasa (DRC). Copyright © 2015 Elsevier B.V. All rights reserved.

Low MITF/AXL ratio predicts early resistance to multiple targeted drugs in melanoma.

PubMed

Müller, Judith; Krijgsman, Oscar; Tsoi, Jennifer; Robert, Lidia; Hugo, Willy; Song, Chunying; Kong, Xiangju; Possik, Patricia A; Cornelissen-Steijger, Paulien D M; Geukes Foppen, Marnix H; Kemper, Kristel; Goding, Colin R; McDermott, Ultan; Blank, Christian; Haanen, John; Graeber, Thomas G; Ribas, Antoni; Lo, Roger S; Peeper, Daniel S

2014-12-15

Increased expression of the Microphthalmia-associated transcription factor (MITF) contributes to melanoma progression and resistance to BRAF pathway inhibition. Here we show that the lack of MITF is associated with more severe resistance to a range of inhibitors, while its presence is required for robust drug responses. Both in primary and acquired resistance, MITF levels inversely correlate with the expression of several activated receptor tyrosine kinases, most frequently AXL. The MITF-low/AXL-high/drug-resistance phenotype is common among mutant BRAF and NRAS melanoma cell lines. The dichotomous behaviour of MITF in drug response is corroborated in vemurafenib-resistant biopsies, including MITF-high and -low clones in a relapsed patient. Furthermore, drug cocktails containing AXL inhibitor enhance melanoma cell elimination by BRAF or ERK inhibition. Our results demonstrate that a low MITF/AXL ratio predicts early resistance to multiple targeted drugs, and warrant clinical validation of AXL inhibitors to combat resistance of BRAF and NRAS mutant MITF-low melanomas.

Optogenetic Approaches to Drug Discovery in Neuroscience and Beyond.

PubMed

Zhang, Hongkang; Cohen, Adam E

2017-07-01

Recent advances in optogenetics have opened new routes to drug discovery, particularly in neuroscience. Physiological cellular assays probe functional phenotypes that connect genomic data to patient health. Optogenetic tools, in particular tools for all-optical electrophysiology, now provide a means to probe cellular disease models with unprecedented throughput and information content. These techniques promise to identify functional phenotypes associated with disease states and to identify compounds that improve cellular function regardless of whether the compound acts directly on a target or through a bypass mechanism. This review discusses opportunities and unresolved challenges in applying optogenetic techniques throughout the discovery pipeline - from target identification and validation, to target-based and phenotypic screens, to clinical trials. Copyright © 2017 Elsevier Ltd. All rights reserved.

Animal models of addiction

PubMed Central

Spanagel, Rainer

2017-01-01

In recent years, animal models in psychiatric research have been criticized for their limited translational value to the clinical situation. Failures in clinical trials have thus often been attributed to the lack of predictive power of preclinical animal models. Here, I argue that animal models of voluntary drug intake—under nonoperant and operant conditions—and addiction models based on the Diagnostic and Statistical Manual of Mental Disorders are crucial and informative tools for the identification of pathological mechanisms, target identification, and drug development. These models provide excellent face validity, and it is assumed that the neurochemical and neuroanatomical substrates involved in drug-intake behavior are similar in laboratory rodents and humans. Consequently, animal models of drug consumption and addiction provide predictive validity. This predictive power is best illustrated in alcohol research, in which three approved medications—acamprosate, naltrexone, and nalmefene—were developed by means of animal models and then successfully translated into the clinical situation. PMID:29302222

Natural products used as a chemical library for protein-protein interaction targeted drug discovery.

PubMed

Jin, Xuemei; Lee, Kyungro; Kim, Nam Hee; Kim, Hyun Sil; Yook, Jong In; Choi, Jiwon; No, Kyoung Tai

2018-01-01

Protein-protein interactions (PPIs), which are essential for cellular processes, have been recognized as attractive therapeutic targets. Therefore, the construction of a PPI-focused chemical library is an inevitable necessity for future drug discovery. Natural products have been used as traditional medicines to treat human diseases for millennia; in addition, their molecular scaffolds have been used in diverse approved drugs and drug candidates. The recent discovery of the ability of natural products to inhibit PPIs led us to use natural products as a chemical library for PPI-targeted drug discovery. In this study, we collected natural products (NPDB) from non-commercial and in-house databases to analyze their similarities to small-molecule PPI inhibitors (iPPIs) and FDA-approved drugs by using eight molecular descriptors. Then, we evaluated the distribution of NPDB and iPPIs in the chemical space, represented by the molecular fingerprint and molecular scaffolds, to identify the promising scaffolds, which could interfere with PPIs. To investigate the ability of natural products to inhibit PPI targets, molecular docking was used. Then, we predicted a set of high-potency natural products by using the iPPI-likeness score based on a docking score-weighted model. These selected natural products showed high binding affinities to the PPI target, namely XIAP, which were validated in an in vitro experiment. In addition, the natural products with novel scaffolds might provide a promising starting point for further medicinal chemistry developments. Overall, our study shows the potency of natural products in targeting PPIs, which might help in the design of a PPI-focused chemical library for future drug discovery. Copyright © 2017 Elsevier Inc. All rights reserved.

The quest for fragile X biomarkers.

PubMed

Westmark, Cara J

2014-12-01

Fragile X is the most common form of inherited intellectual disability and the leading known genetic cause of autism. There is currently no cure or approved medication for fragile X although various drugs target specific disease symptoms and a large number of therapeutics are in various stages of clinical development. Multiple recent clinical trials have failed on their primary endpoints indicating that there is a compelling need for validated biomarkers and outcome measures in fragile X. There are currently no validated blood-based biomarkers to assess disease severity or to monitor drug efficacy in fragile X syndrome. Herein, we review candidate blood protein biomarkers including extracellular-regulated kinase, phosphoinositide 3-kinase, matrix metalloproteinase 9, amyloid-beta and amyloid-beta protein precursor. Bench-to-bedside plans for fragile X syndrome are severely limited by the lack of validated outcome measures. The reviewed candidate biomarkers are at early stages of validation and deserve further investigation.

The role of media and communication in improving the use of drugs and other technologies.

PubMed

Sitthi-amorn, C; Ngamvithayapongse, J

1998-01-01

Policy makers, health care providers, and the general public need valid information about the benefits and harmful effects of drugs and technologies to be able to make rational choices in their acquisition, distribution, and use. Effective communication is important for quality choices of drugs and other technologies. In effective communication, the choice of messages and media must correspond to the culture and beliefs of the target groups to make them comprehend and adopt the conclusions. Messages must be presented on a regular basis. Most regulatory agencies do not have enough resources to mount effective communication programs. Private advertising agencies and other stakeholders have definite roles. Valid knowledge must be the basis of dialogues to reduce emotional disputes among various benefit groups in society.

Ligand Based-Pharmacophore Modeling and Extended Bi oactivity Prediction for Salinosporamide A, B and C from Marine Actino mycetes Salinispora tropica.

PubMed

Dineshkumar, Kesavan; Vasudevan, Aparna; Hopper, Waheeta

2017-01-01

Actinomycetes produce structurally unique secondary metabolites with pharmaceutically essential bioactivities. Salinispora, an obligate marine actinomycete, produces structurally varied and unique secondary metabolites. There is plenty of scope for development of drugs from the novel compounds isolated from Salinispora. Anticancer, antibacterial and anti-protozoa activities have been shown for Salinosporamides A, B and C, the secondary metabolites identified from Salinispora, which make them interesting subjects for further extended biological activity prediction. An in silico ligand based-pharmacophore approach was used for the prediction of extended biological targets for salinosporamide A, B and C. Pharmacophore models of salinosporamide A, B and C were generated individually and screened against known drug databases. The drugs with best fitness score were shortlisted, and their respective targets pertaining to their bioactivity were retrieved. The predicted biological drug targets were docked with salinosporamide A, B and C for validation. The glucocorticoid receptor and methionine aminopeptidase 2 showed good docking score and binding energy with salinosporamide A, B and C. Molecular dynamics studies of the protein-ligand complexes showed stable interactions suggesting that the predicted new targets for salinosporamides might be promising. The glucocorticoid receptor and methionine aminopeptidase 2 could be possible new drug targets of bioactivity of salinosporamides. These proteins could be the druggable targets for antiinflammatory and anticancer activity of salinosporamides. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

The Tuberculosis Drug Discovery and Development Pipeline and Emerging Drug Targets

PubMed Central

Mdluli, Khisimuzi; Kaneko, Takushi; Upton, Anna

2015-01-01

The recent accelerated approval for use in extensively drug-resistant and multidrug-resistant-tuberculosis (MDR-TB) of two first-in-class TB drugs, bedaquiline and delamanid, has reinvigorated the TB drug discovery and development field. However, although several promising clinical development programs are ongoing to evaluate new TB drugs and regimens, the number of novel series represented is few. The global early-development pipeline is also woefully thin. To have a chance of achieving the goal of better, shorter, safer TB drug regimens with utility against drug-sensitive and drug-resistant disease, a robust and diverse global TB drug discovery pipeline is key, including innovative approaches that make use of recently acquired knowledge on the biology of TB. Fortunately, drug discovery for TB has resurged in recent years, generating compounds with varying potential for progression into developable leads. In parallel, advances have been made in understanding TB pathogenesis. It is now possible to apply the lessons learned from recent TB hit generation efforts and newly validated TB drug targets to generate the next wave of TB drug leads. Use of currently underexploited sources of chemical matter and lead-optimization strategies may also improve the efficiency of future TB drug discovery. Novel TB drug regimens with shorter treatment durations must target all subpopulations of Mycobacterium tuberculosis existing in an infection, including those responsible for the protracted TB treatment duration. This review summarizes the current TB drug development pipeline and proposes strategies for generating improved hits and leads in the discovery phase that could help achieve this goal. PMID:25635061

A magnetic bead-based ligand binding assay to facilitate human kynurenine 3-monooxygenase drug discovery.

PubMed

Wilson, Kris; Mole, Damian J; Homer, Natalie Z M; Iredale, John P; Auer, Manfred; Webster, Scott P

2015-02-01

Human kynurenine 3-monooxygenase (KMO) is emerging as an important drug target enzyme in a number of inflammatory and neurodegenerative disease states. Recombinant protein production of KMO, and therefore discovery of KMO ligands, is challenging due to a large membrane targeting domain at the C-terminus of the enzyme that causes stability, solubility, and purification difficulties. The purpose of our investigation was to develop a suitable screening method for targeting human KMO and other similarly challenging drug targets. Here, we report the development of a magnetic bead-based binding assay using mass spectrometry detection for human KMO protein. The assay incorporates isolation of FLAG-tagged KMO enzyme on protein A magnetic beads. The protein-bound beads are incubated with potential binding compounds before specific cleavage of the protein-compound complexes from the beads. Mass spectrometry analysis is used to identify the compounds that demonstrate specific binding affinity for the target protein. The technique was validated using known inhibitors of KMO. This assay is a robust alternative to traditional ligand-binding assays for challenging protein targets, and it overcomes specific difficulties associated with isolating human KMO. © 2014 Society for Laboratory Automation and Screening.

Microdosing and Other Phase 0 Clinical Trials: Facilitating Translation in Drug Development

DOE PAGES

Burt, T.; Yoshida, K.; Lappin, G.; ...

2016-02-26

A number of drivers and developments suggest that microdosing and other phase 0 applications will experience increased utilization in the near-to-medium future. Increasing costs of drug development and ethical concerns about the risks of exposing humans and animals to novel chemical entities are important drivers in favor of these approaches, and can be expected only to increase in their relevance. An increasing body of research supports the validity of extrapolation from the limited drug exposure of phase 0 approaches to the full, therapeutic exposure, with modeling and simulations capable of extrapolating even non-linear scenarios. An increasing number of applications andmore » design options demonstrate the versatility and flexibility these approaches offer to drug developers including the study of PK, bioavailability, DDI, and mechanistic PD effects. PET microdosing allows study of target localization, PK and receptor binding and occupancy, while Intra-Target Microdosing (ITM) allows study of local therapeutic-level acute PD coupled with systemic microdose-level exposure. Applications in vulnerable populations and extreme environments are attractive due to the unique risks of pharmacotherapy and increasing unmet healthcare needs. Lastly, all phase 0 approaches depend on the validity of extrapolation from the limited-exposure scenario to the full exposure of therapeutic intent, but in the final analysis the potential for controlled human data to reduce uncertainty about drug properties is bound to be a valuable addition to the drug development process.« less

Microdosing and Other Phase 0 Clinical Trials: Facilitating Translation in Drug Development

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burt, T.; Yoshida, K.; Lappin, G.

A number of drivers and developments suggest that microdosing and other phase 0 applications will experience increased utilization in the near-to-medium future. Increasing costs of drug development and ethical concerns about the risks of exposing humans and animals to novel chemical entities are important drivers in favor of these approaches, and can be expected only to increase in their relevance. An increasing body of research supports the validity of extrapolation from the limited drug exposure of phase 0 approaches to the full, therapeutic exposure, with modeling and simulations capable of extrapolating even non-linear scenarios. An increasing number of applications andmore » design options demonstrate the versatility and flexibility these approaches offer to drug developers including the study of PK, bioavailability, DDI, and mechanistic PD effects. PET microdosing allows study of target localization, PK and receptor binding and occupancy, while Intra-Target Microdosing (ITM) allows study of local therapeutic-level acute PD coupled with systemic microdose-level exposure. Applications in vulnerable populations and extreme environments are attractive due to the unique risks of pharmacotherapy and increasing unmet healthcare needs. Lastly, all phase 0 approaches depend on the validity of extrapolation from the limited-exposure scenario to the full exposure of therapeutic intent, but in the final analysis the potential for controlled human data to reduce uncertainty about drug properties is bound to be a valuable addition to the drug development process.« less

miR-133b down-regulates ABCC1 and enhances the sensitivity of CRC to anti-tumor drugs.

PubMed

Chen, Miao; Li, Daojiang; Gong, Ni; Wu, Hao; Su, Chen; Xie, Canbin; Xiang, Hong; Lin, Changwei; Li, Xiaorong

2017-08-08

Multidrug resistance (MDR) is the main cause of failed chemotherapy treatments. Therefore, preventing MDR is pivotal in treating colorectal cancer (CRC). In a previous study miR-133b was shown to be a tumor suppressor. Additionally, in CRC cells transfected with miR-133b, ATP-binding cassette (ABC) subfamily C member 1(ABCC1) was shown to be significantly down regulated. Whether miR-133b also enhances the chemosensitivity of drugs used to treat CRC by targeting ABCC1 is still unclear. Here, we utilized flow cytometry and high-performance liquid chromatography (HPLC) analysis to identify the ability of miR-133b to reserve MDR in CRC. We then used a dual-luciferase reporter assay to validate that miR-133b targets ABCC1. Further in vivo experiments were designed to validate the method in which miR-133b reversed MDR in CRC cells. The results demonstrated that the level of miR-133b was down-regulated and the expression of ABCC1 was up-regulated in drug-resistant CRC cells compared to non-drug-resistant CRC cells. The restoration of miR-133b expression in CRC drug-resistant cells in vitro resulted in reduced IC50s to chemotherapeutic drugs, significantly induced G1 accumulation, inhibited growth and promoted necrosis in combination with either 5-fluorouracil (5-FU) or vincristine (VCR), and decreased the expression of ABCC1. The dual-luciferase assay demonstrated that miR-133b directly targets ABCC1. The combination of agomiRNA-133b with chemotherapeutic drugs in vivo inhibited tumor growth induced by CRC drug-resistant cells. A xenograft from the in vivo model resulted in up-regulated levels of miR-133b and down-regulated levels of ABCC1. Therefore, miR-133b enhances the chemosensitivity of CRC cells to anti-tumor drugs by directly down-regulating ABCC1. This discovery provides a therapeutic strategy in which miR-133b is used as a potential sensitizer for drug-resistant CRC.

Rapid Identification of Chemoresistance Mechanisms Using Yeast DNA Mismatch Repair Mutants

PubMed Central

Ojini, Irene; Gammie, Alison

2015-01-01

Resistance to cancer therapy is a major obstacle in the long-term treatment of cancer. A greater understanding of drug resistance mechanisms will ultimately lead to the development of effective therapeutic strategies to prevent resistance from occurring. Here, we exploit the mutator phenotype of mismatch repair defective yeast cells combined with whole genome sequencing to identify drug resistance mutations in key pathways involved in the development of chemoresistance. The utility of this approach was demonstrated via the identification of the known CAN1 and TOP1 resistance targets for two compounds, canavanine and camptothecin, respectively. We have also experimentally validated the plasma membrane transporter HNM1 as the primary drug resistance target of mechlorethamine. Furthermore, the sequencing of mitoxantrone-resistant strains identified inactivating mutations within IPT1, a gene encoding inositolphosphotransferase, an enzyme involved in sphingolipid biosynthesis. In the case of bactobolin, a promising anticancer drug, the endocytosis pathway was identified as the drug resistance target responsible for conferring resistance. Finally, we show that that rapamycin, an mTOR inhibitor previously shown to alter the fitness of the ipt1 mutant, can effectively prevent the formation of mitoxantrone resistance. The rapid and robust nature of these techniques, using Saccharomyces cerevisiae as a model organism, should accelerate the identification of drug resistance targets and guide the development of novel therapeutic combination strategies to prevent the development of chemoresistance in various cancers. PMID:26199284

Nasal-to-CNS drug delivery: where are we now and where are we heading? An industrial perspective.

PubMed

Landis, Margaret S; Boyden, Tracey; Pegg, Simon

2012-02-01

Delivery of drug therapeutics across the blood-brain barrier is a challenging task for pharmaceutical scientists. Nasal-to-CNS drug delivery has shown promising results in preclinical efficacy models and investigatory human clinical trials. The further development of this technology with respect to the establishment of valid, predictable preclinical species models, translatable pharmacokinetic-pharmacodynamic relationships and definition of toxicology impact will help attract additional pharmaceutical investment in this drug-delivery approach. Further discoveries in nasal nanotechnology, targeted delivery devices and diagnostic olfactory imaging will serve to fuel the advancements in this area of drug delivery.

Cloud computing approaches to accelerate drug discovery value chain.

PubMed

Garg, Vibhav; Arora, Suchir; Gupta, Chitra

2011-12-01

Continued advancements in the area of technology have helped high throughput screening (HTS) evolve from a linear to parallel approach by performing system level screening. Advanced experimental methods used for HTS at various steps of drug discovery (i.e. target identification, target validation, lead identification and lead validation) can generate data of the order of terabytes. As a consequence, there is pressing need to store, manage, mine and analyze this data to identify informational tags. This need is again posing challenges to computer scientists to offer the matching hardware and software infrastructure, while managing the varying degree of desired computational power. Therefore, the potential of "On-Demand Hardware" and "Software as a Service (SAAS)" delivery mechanisms cannot be denied. This on-demand computing, largely referred to as Cloud Computing, is now transforming the drug discovery research. Also, integration of Cloud computing with parallel computing is certainly expanding its footprint in the life sciences community. The speed, efficiency and cost effectiveness have made cloud computing a 'good to have tool' for researchers, providing them significant flexibility, allowing them to focus on the 'what' of science and not the 'how'. Once reached to its maturity, Discovery-Cloud would fit best to manage drug discovery and clinical development data, generated using advanced HTS techniques, hence supporting the vision of personalized medicine.

Drug Targeting and Biomarkers in Head and Neck Cancers: Insights from Systems Biology Analyses.

PubMed

Islam, Tania; Rahman, Rezanur; Gov, Esra; Turanli, Beste; Gulfidan, Gizem; Haque, Anwarul; Arga, Kazım Yalçın; Haque Mollah, Nurul

2018-06-01

The head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers in the world, but robust biomarkers and diagnostics are still not available. This study provides in-depth insights from systems biology analyses to identify molecular biomarker signatures to inform systematic drug targeting in HNSCC. Gene expression profiles from tumors and normal tissues of 22 patients with histological confirmation of nonmetastatic HNSCC were subjected to integrative analyses with genome-scale biomolecular networks (i.e., protein-protein interaction and transcriptional and post-transcriptional regulatory networks). We aimed to discover molecular signatures at RNA and protein levels, which could serve as potential drug targets for therapeutic innovation in the future. Eleven proteins, 5 transcription factors, and 20 microRNAs (miRNAs) came into prominence as potential drug targets. The differential expression profiles of these reporter biomolecules were cross-validated by independent RNA-Seq and miRNA-Seq datasets, and risk discrimination performance of the reporter biomolecules, BLNK, CCL2, E4F1, FOSL1, ISG15, MMP9, MYCN, MYH11, miR-1252, miR-29b, miR-29c, miR-3610, miR-431, and miR-523, was also evaluated. Using the transcriptome guided drug repositioning tool, geneXpharma, several candidate drugs were repurposed, including antineoplastic agents (e.g., gemcitabine and irinotecan), antidiabetics (e.g., rosiglitazone), dermatological agents (e.g., clocortolone and acitretin), and antipsychotics (e.g., risperidone), and binding affinities of the drugs to their potential targets were assessed using molecular docking analyses. The molecular signatures and repurposed drugs presented in this study warrant further attention for experimental studies since they offer significant potential as biomarkers and candidate therapeutics for precision medicine approaches to clinical management of HNSCC.

Automatic signal extraction, prioritizing and filtering approaches in detecting post-marketing cardiovascular events associated with targeted cancer drugs from the FDA Adverse Event Reporting System (FAERS).

PubMed

Xu, Rong; Wang, Quanqiu

2014-02-01

Targeted drugs dramatically improve the treatment outcomes in cancer patients; however, these innovative drugs are often associated with unexpectedly high cardiovascular toxicity. Currently, cardiovascular safety represents both a challenging issue for drug developers, regulators, researchers, and clinicians and a concern for patients. While FDA drug labels have captured many of these events, spontaneous reporting systems are a main source for post-marketing drug safety surveillance in 'real-world' (outside of clinical trials) cancer patients. In this study, we present approaches to extracting, prioritizing, filtering, and confirming cardiovascular events associated with targeted cancer drugs from the FDA Adverse Event Reporting System (FAERS). The dataset includes records of 4,285,097 patients from FAERS. We first extracted drug-cardiovascular event (drug-CV) pairs from FAERS through named entity recognition and mapping processes. We then compared six ranking algorithms in prioritizing true positive signals among extracted pairs using known drug-CV pairs derived from FDA drug labels. We also developed three filtering algorithms to further improve precision. Finally, we manually validated extracted drug-CV pairs using 21 million published MEDLINE records. We extracted a total of 11,173 drug-CV pairs from FAERS. We showed that ranking by frequency is significantly more effective than by the five standard signal detection methods (246% improvement in precision for top-ranked pairs). The filtering algorithm we developed further improved overall precision by 91.3%. By manual curation using literature evidence, we show that about 51.9% of the 617 drug-CV pairs that appeared in both FAERS and MEDLINE sentences are true positives. In addition, 80.6% of these positive pairs have not been captured by FDA drug labeling. The unique drug-CV association dataset that we created based on FAERS could facilitate our understanding and prediction of cardiotoxic events associated with targeted cancer drugs. Copyright © 2013 Elsevier Inc. All rights reserved.

Computational drug discovery

PubMed Central

Ou-Yang, Si-sheng; Lu, Jun-yan; Kong, Xiang-qian; Liang, Zhong-jie; Luo, Cheng; Jiang, Hualiang

2012-01-01

Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved. In this review, we present an overview of these important computational methods, platforms and successful applications in this field. PMID:22922346

Improving compound-protein interaction prediction by building up highly credible negative samples.

PubMed

Liu, Hui; Sun, Jianjiang; Guan, Jihong; Zheng, Jie; Zhou, Shuigeng

2015-06-15

Computational prediction of compound-protein interactions (CPIs) is of great importance for drug design and development, as genome-scale experimental validation of CPIs is not only time-consuming but also prohibitively expensive. With the availability of an increasing number of validated interactions, the performance of computational prediction approaches is severely impended by the lack of reliable negative CPI samples. A systematic method of screening reliable negative sample becomes critical to improving the performance of in silico prediction methods. This article aims at building up a set of highly credible negative samples of CPIs via an in silico screening method. As most existing computational models assume that similar compounds are likely to interact with similar target proteins and achieve remarkable performance, it is rational to identify potential negative samples based on the converse negative proposition that the proteins dissimilar to every known/predicted target of a compound are not much likely to be targeted by the compound and vice versa. We integrated various resources, including chemical structures, chemical expression profiles and side effects of compounds, amino acid sequences, protein-protein interaction network and functional annotations of proteins, into a systematic screening framework. We first tested the screened negative samples on six classical classifiers, and all these classifiers achieved remarkably higher performance on our negative samples than on randomly generated negative samples for both human and Caenorhabditis elegans. We then verified the negative samples on three existing prediction models, including bipartite local model, Gaussian kernel profile and Bayesian matrix factorization, and found that the performances of these models are also significantly improved on the screened negative samples. Moreover, we validated the screened negative samples on a drug bioactivity dataset. Finally, we derived two sets of new interactions by training an support vector machine classifier on the positive interactions annotated in DrugBank and our screened negative interactions. The screened negative samples and the predicted interactions provide the research community with a useful resource for identifying new drug targets and a helpful supplement to the current curated compound-protein databases. Supplementary files are available at: http://admis.fudan.edu.cn/negative-cpi/. © The Author 2015. Published by Oxford University Press.

Advantages and application of label-free detection assays in drug screening.

PubMed

Cunningham, Brian T; Laing, Lance G

2008-08-01

Adoption is accelerating for a new family of label-free optical biosensors incorporated into standard format microplates owing to their ability to enable highly sensitive detection of small molecules, proteins and cells for high-throughput drug discovery applications. Label-free approaches are displacing other detection technologies owing to their ability to provide simple assay procedures for hit finding/validation, accessing difficult target classes, screening the interaction of cells with drugs and analyzing the affinity of small molecule inhibitors to target proteins. This review describes several new drug discovery applications that are under development for microplate-based photonic crystal optical biosensors and the key issues that will drive adoption of the technology. Microplate-based optical biosensors are enabling a variety of cell-based assays, inhibition assays, protein-protein binding assays and protein-small molecule binding assays to be performed with high-throughput and high sensitivity.

Targeting Tumor Associated Phosphatidylserine with New Zinc Dipicolylamine-Based Drug Conjugates.

PubMed

Liu, Yu-Wei; Shia, Kak-Shan; Wu, Chien-Huang; Liu, Kuan-Liang; Yeh, Yu-Cheng; Lo, Chen-Fu; Chen, Chiung-Tong; Chen, Yun-Yu; Yeh, Teng-Kuang; Chen, Wei-Han; Jan, Jiing-Jyh; Huang, Yu-Chen; Huang, Chen-Lung; Fang, Ming-Yu; Gray, Brian D; Pak, Koon Y; Hsu, Tsu-An; Huang, Kuan-Hsun; Tsou, Lun K

2017-07-19

A series of zinc(II) dipicolylamine (ZnDPA)-based drug conjugates have been synthesized to probe the potential of phosphatidylserine (PS) as a new antigen for small molecule drug conjugate (SMDC) development. Using in vitro cytotoxicity and plasma stability studies, PS-binding assay, in vivo pharmacokinetic studies, and maximum tolerated dose profiles, we provided a roadmap and the key parameters required for the development of the ZnDPA based drug conjugate. In particular, conjugate 24 induced tumor regression in the COLO 205 xenograft model and exhibited a more potent antitumor effect with a 70% reduction of cytotoxic payload compared to that of the marketed irinotecan when dosed at the same regimen. In addition to the validation of PS as an effective pharmacodelivery target for SMDC, our work also provided the foundation that, if applicable, a variety of therapeutic agents could be conjugated in the same manner to treat other PS-associated diseases.

Computer aided drug discovery of highly ligand efficient, low molecular weight imidazopyridine analogs as FLT3 inhibitors

PubMed Central

Frett, Brendan; McConnell, Nick; Smith, Catherine C.; Wang, Yuanxiang; Shah, Neil P.; Li, Hong-yu

2015-01-01

The FLT3 kinase represents an attractive target to effectively treat AML. Unfortunately, no FLT3 targeted therapeutic is currently approved. In line with our continued interests in treating kinase related disease for anti-FLT3 mutant activity, we utilized pioneering synthetic methodology in combination with computer aided drug discovery and identified low molecular weight, highly ligand efficient, FLT3 kinase inhibitors. Compounds were analyzed for biochemical inhibition, their ability to selectively inhibit cell proliferation, for FLT3 mutant activity, and preliminary aqueous solubility. Validated hits were discovered that can serve as starting platforms for lead candidates. PMID:25765758

Prospects for pharmacologic inhibition of hepatic glucose production.

PubMed

Kurukulasuriya, R; Link, J T; Madar, D J; Pei, Z; Rohde, J J; Richards, S J; Souers, A J; Szczepankiewicz, B G

2003-01-01

Type 2 diabetes is a widespread disease where effective pharmacologic therapies can have a profound beneficial public health impact. Increased hepatic glucose production (HGP) is observed in diabetics and its moderation by currently available agents provides therapeutic benefits. This review describes the challenges associated with the discovery of small molecules that inhibit HGP. Gluconeogenesis, glycogenolysis, liver architecture, and hepatocyte composition are described to provide background information on hepatic function. Current methods of target validation for drug discovery, HGP measurement, diabetes animal models, as well as current drug therapies are covered. In the accompanying review article the new drug targets being probed to produce the next generation of therapies are described. Significant pharmaceutical and academic efforts to pharmacologically inhibit HGP has the opportunity to provide new therapeutics for type 2 diabetics.

Rapid Countermeasure Discovery against Francisella tularensis Based on a Metabolic Network Reconstruction

PubMed Central

Chaudhury, Sidhartha; Abdulhameed, Mohamed Diwan M.; Singh, Narender; Tawa, Gregory J.; D’haeseleer, Patrik M.; Zemla, Adam T.; Navid, Ali; Zhou, Carol E.; Franklin, Matthew C.; Cheung, Jonah; Rudolph, Michael J.; Love, James; Graf, John F.; Rozak, David A.; Dankmeyer, Jennifer L.; Amemiya, Kei; Daefler, Simon; Wallqvist, Anders

2013-01-01

In the future, we may be faced with the need to provide treatment for an emergent biological threat against which existing vaccines and drugs have limited efficacy or availability. To prepare for this eventuality, our objective was to use a metabolic network-based approach to rapidly identify potential drug targets and prospectively screen and validate novel small-molecule antimicrobials. Our target organism was the fully virulent Francisella tularensis subspecies tularensis Schu S4 strain, a highly infectious intracellular pathogen that is the causative agent of tularemia and is classified as a category A biological agent by the Centers for Disease Control and Prevention. We proceeded with a staggered computational and experimental workflow that used a strain-specific metabolic network model, homology modeling and X-ray crystallography of protein targets, and ligand- and structure-based drug design. Selected compounds were subsequently filtered based on physiological-based pharmacokinetic modeling, and we selected a final set of 40 compounds for experimental validation of antimicrobial activity. We began screening these compounds in whole bacterial cell-based assays in biosafety level 3 facilities in the 20th week of the study and completed the screens within 12 weeks. Six compounds showed significant growth inhibition of F. tularensis, and we determined their respective minimum inhibitory concentrations and mammalian cell cytotoxicities. The most promising compound had a low molecular weight, was non-toxic, and abolished bacterial growth at 13 µM, with putative activity against pantetheine-phosphate adenylyltransferase, an enzyme involved in the biosynthesis of coenzyme A, encoded by gene coaD. The novel antimicrobial compounds identified in this study serve as starting points for lead optimization, animal testing, and drug development against tularemia. Our integrated in silico/in vitro approach had an overall 15% success rate in terms of active versus tested compounds over an elapsed time period of 32 weeks, from pathogen strain identification to selection and validation of novel antimicrobial compounds. PMID:23704901

Common pitfalls in preclinical cancer target validation.

PubMed

Kaelin, William G

2017-07-01

An alarming number of papers from laboratories nominating new cancer drug targets contain findings that cannot be reproduced by others or are simply not robust enough to justify drug discovery efforts. This problem probably has many causes, including an underappreciation of the danger of being misled by off-target effects when using pharmacological or genetic perturbants in complex biological assays. This danger is particularly acute when, as is often the case in cancer pharmacology, the biological phenotype being measured is a 'down' readout (such as decreased proliferation, decreased viability or decreased tumour growth) that could simply reflect a nonspecific loss of cellular fitness. These problems are compounded by multiple hypothesis testing, such as when candidate targets emerge from high-throughput screens that interrogate multiple targets in parallel, and by a publication and promotion system that preferentially rewards positive findings. In this Perspective, I outline some of the common pitfalls in preclinical cancer target identification and some potential approaches to mitigate them.

Pharmacophore modeling for identification of anti-IGF-1R drugs and in-vitro validation of fulvestrant as a potential inhibitor

PubMed Central

Hanif, Rumeza; Jabeen, Ishrat; Mansoor, Qaisar; Ismail, Muhammad

2018-01-01

Insulin-like growth factor 1 receptor (IGF-1R) is an important therapeutic target for breast cancer treatment. The alteration in the IGF-1R associated signaling network due to various genetic and environmental factors leads the system towards metastasis. The pharmacophore modeling and logical approaches have been applied to analyze the behaviour of complex regulatory network involved in breast cancer. A total of 23 inhibitors were selected to generate ligand based pharmacophore using the tool, Molecular Operating Environment (MOE). The best model consisted of three pharmacophore features: aromatic hydrophobic (HyD/Aro), hydrophobic (HyD) and hydrogen bond acceptor (HBA). This model was validated against World drug bank (WDB) database screening to identify 189 hits with the required pharmacophore features and was further screened by using Lipinski positive compounds. Finally, the most effective drug, fulvestrant, was selected. Fulvestrant is a selective estrogen receptor down regulator (SERD). This inhibitor was further studied by using both in-silico and in-vitro approaches that showed the targeted effect of fulvestrant in ER+ MCF-7 cells. Results suggested that fulvestrant has selective cytotoxic effect and a dose dependent response on IRS-1, IGF-1R, PDZK1 and ER-α in MCF-7 cells. PDZK1 can be an important inhibitory target using fulvestrant because it directly regulates IGF-1R. PMID:29787591

Pharmacophore modeling for identification of anti-IGF-1R drugs and in-vitro validation of fulvestrant as a potential inhibitor.

PubMed

Khalid, Samra; Hanif, Rumeza; Jabeen, Ishrat; Mansoor, Qaisar; Ismail, Muhammad

2018-01-01

Insulin-like growth factor 1 receptor (IGF-1R) is an important therapeutic target for breast cancer treatment. The alteration in the IGF-1R associated signaling network due to various genetic and environmental factors leads the system towards metastasis. The pharmacophore modeling and logical approaches have been applied to analyze the behaviour of complex regulatory network involved in breast cancer. A total of 23 inhibitors were selected to generate ligand based pharmacophore using the tool, Molecular Operating Environment (MOE). The best model consisted of three pharmacophore features: aromatic hydrophobic (HyD/Aro), hydrophobic (HyD) and hydrogen bond acceptor (HBA). This model was validated against World drug bank (WDB) database screening to identify 189 hits with the required pharmacophore features and was further screened by using Lipinski positive compounds. Finally, the most effective drug, fulvestrant, was selected. Fulvestrant is a selective estrogen receptor down regulator (SERD). This inhibitor was further studied by using both in-silico and in-vitro approaches that showed the targeted effect of fulvestrant in ER+ MCF-7 cells. Results suggested that fulvestrant has selective cytotoxic effect and a dose dependent response on IRS-1, IGF-1R, PDZK1 and ER-α in MCF-7 cells. PDZK1 can be an important inhibitory target using fulvestrant because it directly regulates IGF-1R.

Proteasome inhibition for treatment of leishmaniasis, Chagas disease and sleeping sickness

PubMed Central

Khare, Shilpi; Nagle, Advait S.; Biggart, Agnes; Lai, Yin H.; Liang, Fang; Davis, Lauren C.; Barnes, S. Whitney; Mathison, Casey J. N.; Myburgh, Elmarie; Gao, Mu-Yun; Gillespie, J. Robert; Liu, Xianzhong; Tan, Jocelyn L.; Stinson, Monique; Rivera, Ianne C.; Ballard, Jaime; Yeh, Vince; Groessl, Todd; Federe, Glenn; Koh, Hazel X. Y.; Venable, John D.; Bursulaya, Badry; Shapiro, Michael; Mishra, Pranab K.; Spraggon, Glen; Brock, Ansgar; Mottram, Jeremy C.; Buckner, Frederick S.; Rao, Srinivasa P. S.; Wen, Ben G.; Walker, John R.; Tuntland, Tove; Molteni, Valentina; Glynne, Richard J.; Supek, Frantisek

2016-01-01

Chagas disease, leishmaniasis, and sleeping sickness affect 20 million people worldwide and lead to more than 50,000 deaths annually1. The diseases are caused by infection with the kinetoplastid parasites Trypanosoma cruzi, Leishmania spp. and Trypanosoma brucei spp., respectively. These parasites have similar biology and genomic sequence, suggesting that all three diseases could be cured with drug(s) modulating the activity of a conserved parasite target2. However, no such molecular targets or broad spectrum drugs have been identified to date. Here we describe a selective inhibitor of the kinetoplastid proteasome (GNF6702) with unprecedented in vivo efficacy, which cleared parasites from mice in all three models of infection. GNF6702 inhibits the kinetoplastid proteasome through a non-competitive mechanism, does not inhibit the mammalian proteasome or growth of mammalian cells, and is well-tolerated in mice. Our data provide genetic and chemical validation of the parasite proteasome as a promising therapeutic target for treatment of kinetoplastid infections, and underscore the possibility of developing a single class of drugs for these neglected diseases. PMID:27501246

Disruption of de Novo Adenosine Triphosphate (ATP) Biosynthesis Abolishes Virulence in Cryptococcus neoformans.

PubMed

Blundell, Ross D; Williams, Simon J; Arras, Samantha D M; Chitty, Jessica L; Blake, Kirsten L; Ericsson, Daniel J; Tibrewal, Nidhi; Rohr, Jurgen; Koh, Y Q Andre E; Kappler, Ulrike; Robertson, Avril A B; Butler, Mark S; Cooper, Matthew A; Kobe, Bostjan; Fraser, James A

2016-09-09

Opportunistic fungal pathogens such as Cryptococcus neoformans are a growing cause of morbidity and mortality among immunocompromised populations worldwide. To address the current paucity of antifungal therapeutic agents, further research into fungal-specific drug targets is required. Adenylosuccinate synthetase (AdSS) is a crucial enzyme in the adeosine triphosphate (ATP) biosynthetic pathway, catalyzing the formation of adenylosuccinate from inosine monophosphate and aspartate. We have investigated the potential of this enzyme as an antifungal drug target, finding that loss of function results in adenine auxotrophy in C. neoformans, as well as complete loss of virulence in a murine model. Cryptococcal AdSS was expressed and purified in Escherichia coli and the enzyme's crystal structure determined, the first example of a structure of this enzyme from fungi. Together with enzyme kinetic studies, this structural information enabled comparison of the fungal enzyme with the human orthologue and revealed species-specific differences potentially exploitable via rational drug design. These results validate AdSS as a promising antifungal drug target and lay a foundation for future in silico and in vitro screens for novel antifungal compounds.

Systematic Identification of Combinatorial Drivers and Targets in Cancer Cell Lines

PubMed Central

Tabchy, Adel; Eltonsy, Nevine; Housman, David E.; Mills, Gordon B.

2013-01-01

There is an urgent need to elicit and validate highly efficacious targets for combinatorial intervention from large scale ongoing molecular characterization efforts of tumors. We established an in silico bioinformatic platform in concert with a high throughput screening platform evaluating 37 novel targeted agents in 669 extensively characterized cancer cell lines reflecting the genomic and tissue-type diversity of human cancers, to systematically identify combinatorial biomarkers of response and co-actionable targets in cancer. Genomic biomarkers discovered in a 141 cell line training set were validated in an independent 359 cell line test set. We identified co-occurring and mutually exclusive genomic events that represent potential drivers and combinatorial targets in cancer. We demonstrate multiple cooperating genomic events that predict sensitivity to drug intervention independent of tumor lineage. The coupling of scalable in silico and biologic high throughput cancer cell line platforms for the identification of co-events in cancer delivers rational combinatorial targets for synthetic lethal approaches with a high potential to pre-empt the emergence of resistance. PMID:23577104

Systematic identification of combinatorial drivers and targets in cancer cell lines.

PubMed

Tabchy, Adel; Eltonsy, Nevine; Housman, David E; Mills, Gordon B

2013-01-01

There is an urgent need to elicit and validate highly efficacious targets for combinatorial intervention from large scale ongoing molecular characterization efforts of tumors. We established an in silico bioinformatic platform in concert with a high throughput screening platform evaluating 37 novel targeted agents in 669 extensively characterized cancer cell lines reflecting the genomic and tissue-type diversity of human cancers, to systematically identify combinatorial biomarkers of response and co-actionable targets in cancer. Genomic biomarkers discovered in a 141 cell line training set were validated in an independent 359 cell line test set. We identified co-occurring and mutually exclusive genomic events that represent potential drivers and combinatorial targets in cancer. We demonstrate multiple cooperating genomic events that predict sensitivity to drug intervention independent of tumor lineage. The coupling of scalable in silico and biologic high throughput cancer cell line platforms for the identification of co-events in cancer delivers rational combinatorial targets for synthetic lethal approaches with a high potential to pre-empt the emergence of resistance.

Drug repurposing: translational pharmacology, chemistry, computers and the clinic.

PubMed

Issa, Naiem T; Byers, Stephen W; Dakshanamurthy, Sivanesan

2013-01-01

The process of discovering a pharmacological compound that elicits a desired clinical effect with minimal side effects is a challenge. Prior to the advent of high-performance computing and large-scale screening technologies, drug discovery was largely a serendipitous endeavor, as in the case of thalidomide for erythema nodosum leprosum or cancer drugs in general derived from flora located in far-reaching geographic locations. More recently, de novo drug discovery has become a more rationalized process where drug-target-effect hypotheses are formulated on the basis of already known compounds/protein targets and their structures. Although this approach is hypothesis-driven, the actual success has been very low, contributing to the soaring costs of research and development as well as the diminished pharmaceutical pipeline in the United States. In this review, we discuss the evolution in computational pharmacology as the next generation of successful drug discovery and implementation in the clinic where high-performance computing (HPC) is used to generate and validate drug-target-effect hypotheses completely in silico. The use of HPC would decrease development time and errors while increasing productivity prior to in vitro, animal and human testing. We highlight approaches in chemoinformatics, bioinformatics as well as network biopharmacology to illustrate potential avenues from which to design clinically efficacious drugs. We further discuss the implications of combining these approaches into an integrative methodology for high-accuracy computational predictions within the context of drug repositioning for the efficient streamlining of currently approved drugs back into clinical trials for possible new indications.

Structure-based drug design: aiming for a perfect fit

PubMed Central

van Montfort, Rob L.M.; Workman, Paul

2017-01-01

Knowledge of the three-dimensional structure of therapeutically relevant targets has informed drug discovery since the first protein structures were determined using X-ray crystallography in the 1950s and 1960s. In this editorial we provide a brief overview of the powerful impact of structure-based drug design (SBDD), which has its roots in computational and structural biology, with major contributions from both academia and industry. We describe advances in the application of SBDD for integral membrane protein targets that have traditionally proved very challenging. We emphasize the major progress made in fragment-based approaches for which success has been exemplified by over 30 clinical drug candidates and importantly three FDA-approved drugs in oncology. We summarize the articles in this issue that provide an excellent snapshot of the current state of the field of SBDD and fragment-based drug design and which offer key insights into exciting new developments, such as the X-ray free-electron laser technology, cryo-electron microscopy, open science approaches and targeted protein degradation. We stress the value of SBDD in the design of high-quality chemical tools that are used to interrogate biology and disease pathology, and to inform target validation. We emphasize the need to maintain the scientific rigour that has been traditionally associated with structural biology and extend this to other methods used in drug discovery. This is particularly important because the quality and robustness of any form of contributory data determines its usefulness in accelerating drug design, and therefore ultimately in providing patient benefit. PMID:29118091

A Specific Two-pore Domain Potassium Channel Blocker Defines the Structure of the TASK-1 Open Pore*

PubMed Central

Streit, Anne K.; Netter, Michael F.; Kempf, Franca; Walecki, Magdalena; Rinné, Susanne; Bollepalli, Murali K.; Preisig-Müller, Regina; Renigunta, Vijay; Daut, Jürgen; Baukrowitz, Thomas; Sansom, Mark S. P.; Stansfeld, Phillip J.; Decher, Niels

2011-01-01

Two-pore domain potassium (K2P) channels play a key role in setting the membrane potential of excitable cells. Despite their role as putative targets for drugs and general anesthetics, little is known about the structure and the drug binding site of K2P channels. We describe A1899 as a potent and highly selective blocker of the K2P channel TASK-1. As A1899 acts as an open-channel blocker and binds to residues forming the wall of the central cavity, the drug was used to further our understanding of the channel pore. Using alanine mutagenesis screens, we have identified residues in both pore loops, the M2 and M4 segments, and the halothane response element to form the drug binding site of TASK-1. Our experimental data were used to validate a K2P open-pore homology model of TASK-1, providing structural insights for future rational design of drugs targeting K2P channels. PMID:21362619

Integrated analysis of drug-induced gene expression profiles predicts novel hERG inhibitors.

PubMed

Babcock, Joseph J; Du, Fang; Xu, Kaiping; Wheelan, Sarah J; Li, Min

2013-01-01

Growing evidence suggests that drugs interact with diverse molecular targets mediating both therapeutic and toxic effects. Prediction of these complex interactions from chemical structures alone remains challenging, as compounds with different structures may possess similar toxicity profiles. In contrast, predictions based on systems-level measurements of drug effect may reveal pharmacologic similarities not evident from structure or known therapeutic indications. Here we utilized drug-induced transcriptional responses in the Connectivity Map (CMap) to discover such similarities among diverse antagonists of the human ether-à-go-go related (hERG) potassium channel, a common target of promiscuous inhibition by small molecules. Analysis of transcriptional profiles generated in three independent cell lines revealed clusters enriched for hERG inhibitors annotated using a database of experimental measurements (hERGcentral) and clinical indications. As a validation, we experimentally identified novel hERG inhibitors among the unannotated drugs in these enriched clusters, suggesting transcriptional responses may serve as predictive surrogates of cardiotoxicity complementing existing functional assays.

Integrated Analysis of Drug-Induced Gene Expression Profiles Predicts Novel hERG Inhibitors

PubMed Central

Babcock, Joseph J.; Du, Fang; Xu, Kaiping; Wheelan, Sarah J.; Li, Min

2013-01-01

Growing evidence suggests that drugs interact with diverse molecular targets mediating both therapeutic and toxic effects. Prediction of these complex interactions from chemical structures alone remains challenging, as compounds with different structures may possess similar toxicity profiles. In contrast, predictions based on systems-level measurements of drug effect may reveal pharmacologic similarities not evident from structure or known therapeutic indications. Here we utilized drug-induced transcriptional responses in the Connectivity Map (CMap) to discover such similarities among diverse antagonists of the human ether-à-go-go related (hERG) potassium channel, a common target of promiscuous inhibition by small molecules. Analysis of transcriptional profiles generated in three independent cell lines revealed clusters enriched for hERG inhibitors annotated using a database of experimental measurements (hERGcentral) and clinical indications. As a validation, we experimentally identified novel hERG inhibitors among the unannotated drugs in these enriched clusters, suggesting transcriptional responses may serve as predictive surrogates of cardiotoxicity complementing existing functional assays. PMID:23936032

Proof of Concept: A review on how network and systems biology approaches aid in the discovery of potent anticancer drug combinations

PubMed Central

Azmi, Asfar S.; Wang, Zhiwei; Philip, Philip A.; Mohammad, Ramzi M.; Sarkar, Fazlul H.

2010-01-01

Cancer therapies that target key molecules have not fulfilled expected promises for most common malignancies. Major challenges include the incomplete understanding and validation of these targets in patients, the multiplicity and complexity of genetic and epigenetic changes in the majority of cancers, and the redundancies and cross-talk found in key signaling pathways. Collectively, the uses of single-pathway targeted approaches are not effective therapies for human malignances. To overcome these barriers, it is important to understand the molecular cross-talk among key signaling pathways and how they may be altered by targeted agents. This requires innovative approaches such as understanding the global physiological environment of target proteins and the effects of modifying them without losing key molecular details. Such strategies will aid the design of novel therapeutics and their combinations against multifaceted diseases where efficacious combination therapies will focus on altering multiple pathways rather than single proteins. Integrated network modeling and systems biology has emerged as a powerful tool benefiting our understanding of drug mechanism of action in real time. This mini-review highlights the significance of the network and systems biology-based strategy and presents a “proof-of-concept” recently validated in our laboratory using the example of a combination treatment of oxaliplatin and the MDM2 inhibitor MI-219 in genetically complex and incurable pancreatic adenocarcinoma. PMID:21041384

Safe and Effective Sarcoma Therapy through Bispecific Targeting of EGFR and uPAR

PubMed Central

Borgatti, Antonella; Koopmeiners, Joseph S.; Sarver, Aaron L.; Winter, Amber L.; Stuebner, Kathleen; Todhunter, Deborah; Rizzardi, Anthony E.; Henriksen, Jonathan C.; Schmechel, Stephen; Forster, Colleen L.; Kim, Jong-Hyuk; Froelich, Jerry; Walz, Jillian; Henson, Michael S.; Breen, Matthew; Lindblad-Toh, Kerstin; Oh, Felix; Pilbeam, Kristy; Modiano, Jaime F.; Vallera, Daniel A.

2017-01-01

Sarcomas differ from carcinomas in their mesenchymal origin. Therapeutic advancements have come slowly so alternative drugs and models are urgently needed. These studies report a new drug for sarcomas that simultaneously targets both tumor and tumor neovasculature. eBAT is a bispecific angiotoxin consisting of truncated, deimmunized Pseudomonas exotoxin fused to epidermal growth factor (EGF) and the amino terminal fragment (ATF) of urokinase. Here, we study the drug in an in vivo “ontarget” companion dog trial since eBAT effectively kills canine hemangiosarcoma (HSA) and human sarcoma cells in vitro. We reasoned the model has value due to the common occurrence of spontaneous sarcomas in dogs and a limited lifespan allowing for rapid accrual and data collection. Splenectomized dogs with minimal residual disease were given one cycle of eBAT followed by adjuvant doxorubicin in an adaptive dose-finding, phase I–II study of 23 dogs with spontaneous, stage I–II, splenic HSA. eBAT improved 6-month survival from <40% in a comparison population to ~70% in dogs treated at a biologically active dose (50 μg/kg). Six dogs were long-term survivors, living >450 days. eBAT abated expected toxicity associated with EGFR-targeting, a finding supported by mouse studies. Urokinase plasminogen activator receptor (uPAR) and EGFR are targets for human sarcomas, so thorough evaluation is crucial for validation of the dog model. Thus, we validated these markers for human sarcoma targeting in the study of 212 human and 97 canine sarcoma samples. Our results support further translation of eBAT for human patients with sarcomas and perhaps other EGFR-expressing malignancies. PMID:28193671

CRISPR Genome-Wide Screening Identifies Dependence on the Proteasome Subunit PSMC6 for Bortezomib Sensitivity in Multiple Myeloma.

PubMed

Shi, Chang-Xin; Kortüm, K Martin; Zhu, Yuan Xiao; Bruins, Laura A; Jedlowski, Patrick; Votruba, Patrick G; Luo, Moulun; Stewart, Robert A; Ahmann, Jonathan; Braggio, Esteban; Stewart, A Keith

2017-12-01

Bortezomib is highly effective in the treatment of multiple myeloma; however, emergent drug resistance is common. Consequently, we employed CRISPR targeting 19,052 human genes to identify unbiased targets that contribute to bortezomib resistance. Specifically, we engineered an RPMI8226 multiple myeloma cell line to express Cas9 infected by lentiviral vector CRISPR library and cultured derived cells in doses of bortezomib lethal to parental cells. Sequencing was performed on surviving cells to identify inactivated genes responsible for drug resistance. From two independent whole-genome screens, we selected 31 candidate genes and constructed a second CRISPR sgRNA library, specifically targeting each of these 31 genes with four sgRNAs. After secondary screening for bortezomib resistance, the top 20 "resistance" genes were selected for individual validation. Of these 20 targets, the proteasome regulatory subunit PSMC6 was the only gene validated to reproducibly confer bortezomib resistance. We confirmed that inhibition of chymotrypsin-like proteasome activity by bortezomib was significantly reduced in cells lacking PSMC6. We individually investigated other members of the PSMC group (PSMC1 to 5) and found that deficiency in each of those subunits also imparts bortezomib resistance. We found 36 mutations in 19S proteasome subunits out of 895 patients in the IA10 release of the CoMMpass study (https://themmrf.org). Our findings demonstrate that the PSMC6 subunit is the most prominent target required for bortezomib sensitivity in multiple myeloma cells and should be examined in drug-refractory populations. Mol Cancer Ther; 16(12); 2862-70. ©2017 AACR . ©2017 American Association for Cancer Research.

Transcription Factor NRF2 as a Therapeutic Target for Chronic Diseases: A Systems Medicine Approach.

PubMed

Cuadrado, Antonio; Manda, Gina; Hassan, Ahmed; Alcaraz, María José; Barbas, Coral; Daiber, Andreas; Ghezzi, Pietro; León, Rafael; López, Manuela G; Oliva, Baldo; Pajares, Marta; Rojo, Ana I; Robledinos-Antón, Natalia; Valverde, Angela M; Guney, Emre; Schmidt, Harald H H W

2018-04-01

Systems medicine has a mechanism-based rather than a symptom- or organ-based approach to disease and identifies therapeutic targets in a nonhypothesis-driven manner. In this work, we apply this to transcription factor nuclear factor (erythroid-derived 2)-like 2 (NRF2) by cross-validating its position in a protein-protein interaction network (the NRF2 interactome) functionally linked to cytoprotection in low-grade stress, chronic inflammation, metabolic alterations, and reactive oxygen species formation. Multiscale network analysis of these molecular profiles suggests alterations of NRF2 expression and activity as a common mechanism in a subnetwork of diseases (the NRF2 diseasome). This network joins apparently heterogeneous phenotypes such as autoimmune, respiratory, digestive, cardiovascular, metabolic, and neurodegenerative diseases, along with cancer. Importantly, this approach matches and confirms in silico several applications for NRF2-modulating drugs validated in vivo at different phases of clinical development. Pharmacologically, their profile is as diverse as electrophilic dimethyl fumarate, synthetic triterpenoids like bardoxolone methyl and sulforaphane, protein-protein or DNA-protein interaction inhibitors, and even registered drugs such as metformin and statins, which activate NRF2 and may be repurposed for indications within the NRF2 cluster of disease phenotypes. Thus, NRF2 represents one of the first targets fully embraced by classic and systems medicine approaches to facilitate both drug development and drug repurposing by focusing on a set of disease phenotypes that appear to be mechanistically linked. The resulting NRF2 drugome may therefore rapidly advance several surprising clinical options for this subset of chronic diseases. Copyright © 2018 by The Author(s).

Therapeutic Targeting of Eosinophil Adhesion and Accumulation in Allergic Conjunctivitis

PubMed Central

Baiula, Monica; Bedini, Andrea; Carbonari, Gioia; Dattoli, Samantha Deianira; Spampinato, Santi

2012-01-01

Considerable evidence indicates that eosinophils are important effectors of ocular allergy. Increased worldwide prevalence of allergic eye pathologies has stimulated the identification of novel drug targets, including eosinophils and adhesion molecules. Accumulation of eosinophils in the eye is a key event in the onset and maintenance of allergic inflammation and is mediated by different adhesion molecules. Antihistamines with multiple mechanisms of action can be effective during the early and late phases of allergic conjunctivitis by blocking the interaction between β1 integrins and vascular cell adhesion molecule (VCAM)-1. Small molecule antagonists that target key elements in the process of eosinophil recruitment have been identified and reinforce the validity of α4β1 integrin as a therapeutic target. Glucocorticoids are among the most effective drugs for ocular allergy, but their use is limited by adverse effects. Novel dissociated glucocorticoids can prevent eosinophil accumulation and induce apoptosis of eosinophils, making them promising candidates for ophthalmic drugs. This article reviews recent understanding of the role of adhesion molecules in eosinophil recruitment in the inflamed conjunctiva along with effective treatments for allergic conjunctivitis. PMID:23271999

Opportunities and challenges of disease biomarkers: a new section in the Journal of Translational Medicine.

PubMed

Wang, Xiangdong; Ward, Peter A

2012-12-05

Disease biomarkers are defined to diagnose various phases of diseases, monitor severities of diseases and responses to therapies, or predict prognosis of patients. Disease-specific biomarkers should benefit drug discovery and development, integrate multidisciplinary sciences, be validated by molecular imaging. The opportunities and challenges in biomarker development are emphasized and considered. The Journal of Translational Medicine opens a new Section of Disease Biomarkers to bridge identification and validation of gene or protein-based biomarkers, network biomarkers, dynamic network biomarkers in human diseases, patient phenotypes, and clinical applications. Disease biomarkers are also important for determining drug effects, target specificities and binding, dynamic metabolism and pharmacological kinetics, or toxicity profiles.

Mappability of drug-like space: towards a polypharmacologically competent map of drug-relevant compounds

NASA Astrophysics Data System (ADS)

Sidorov, Pavel; Gaspar, Helena; Marcou, Gilles; Varnek, Alexandre; Horvath, Dragos

2015-12-01

Intuitive, visual rendering—mapping—of high-dimensional chemical spaces (CS), is an important topic in chemoinformatics. Such maps were so far dedicated to specific compound collections—either limited series of known activities, or large, even exhaustive enumerations of molecules, but without associated property data. Typically, they were challenged to answer some classification problem with respect to those same molecules, admired for their aesthetical virtues and then forgotten—because they were set-specific constructs. This work wishes to address the question whether a general, compound set-independent map can be generated, and the claim of "universality" quantitatively justified, with respect to all the structure-activity information available so far—or, more realistically, an exploitable but significant fraction thereof. The "universal" CS map is expected to project molecules from the initial CS into a lower-dimensional space that is neighborhood behavior-compliant with respect to a large panel of ligand properties. Such map should be able to discriminate actives from inactives, or even support quantitative neighborhood-based, parameter-free property prediction (regression) models, for a wide panel of targets and target families. It should be polypharmacologically competent, without requiring any target-specific parameter fitting. This work describes an evolutionary growth procedure of such maps, based on generative topographic mapping, followed by the validation of their polypharmacological competence. Validation was achieved with respect to a maximum of exploitable structure-activity information, covering all of Homo sapiens proteins of the ChEMBL database, antiparasitic and antiviral data, etc. Five evolved maps satisfactorily solved hundreds of activity-based ligand classification challenges for targets, and even in vivo properties independent from training data. They also stood chemogenomics-related challenges, as cumulated responsibility vectors obtained by mapping of target-specific ligand collections were shown to represent validated target descriptors, complying with currently accepted target classification in biology. Therefore, they represent, in our opinion, a robust and well documented answer to the key question "What is a good CS map?"

Mappability of drug-like space: towards a polypharmacologically competent map of drug-relevant compounds.

PubMed

Sidorov, Pavel; Gaspar, Helena; Marcou, Gilles; Varnek, Alexandre; Horvath, Dragos

2015-12-01

Intuitive, visual rendering--mapping--of high-dimensional chemical spaces (CS), is an important topic in chemoinformatics. Such maps were so far dedicated to specific compound collections--either limited series of known activities, or large, even exhaustive enumerations of molecules, but without associated property data. Typically, they were challenged to answer some classification problem with respect to those same molecules, admired for their aesthetical virtues and then forgotten--because they were set-specific constructs. This work wishes to address the question whether a general, compound set-independent map can be generated, and the claim of "universality" quantitatively justified, with respect to all the structure-activity information available so far--or, more realistically, an exploitable but significant fraction thereof. The "universal" CS map is expected to project molecules from the initial CS into a lower-dimensional space that is neighborhood behavior-compliant with respect to a large panel of ligand properties. Such map should be able to discriminate actives from inactives, or even support quantitative neighborhood-based, parameter-free property prediction (regression) models, for a wide panel of targets and target families. It should be polypharmacologically competent, without requiring any target-specific parameter fitting. This work describes an evolutionary growth procedure of such maps, based on generative topographic mapping, followed by the validation of their polypharmacological competence. Validation was achieved with respect to a maximum of exploitable structure-activity information, covering all of Homo sapiens proteins of the ChEMBL database, antiparasitic and antiviral data, etc. Five evolved maps satisfactorily solved hundreds of activity-based ligand classification challenges for targets, and even in vivo properties independent from training data. They also stood chemogenomics-related challenges, as cumulated responsibility vectors obtained by mapping of target-specific ligand collections were shown to represent validated target descriptors, complying with currently accepted target classification in biology. Therefore, they represent, in our opinion, a robust and well documented answer to the key question "What is a good CS map?"

Efforts Aimed To Reduce Attrition in Antimalarial Drug Discovery: A Systematic Evaluation of the Current Antimalarial Targets Portfolio.

PubMed

Chaparro, María Jesús; Calderón, Félix; Castañeda, Pablo; Fernández-Alvaro, Elena; Gabarró, Raquel; Gamo, Francisco Javier; Gómez-Lorenzo, María G; Martín, Julio; Fernández, Esther

2018-04-13

Malaria remains a major global health problem. In 2015 alone, more than 200 million cases of malaria were reported, and more than 400,000 deaths occurred. Since 2010, emerging resistance to current front-line ACTs (artemisinin combination therapies) has been detected in endemic countries. Therefore, there is an urgency for new therapies based on novel modes of action, able to relieve symptoms as fast as the artemisinins and/or block malaria transmission. During the past few years, the antimalarial community has focused their efforts on phenotypic screening as a pragmatic approach to identify new hits. Optimization efforts on several chemical series have been successful, and clinical candidates have been identified. In addition, recent advances in genetics and proteomics have led to the target deconvolution of phenotypic clinical candidates. New mechanisms of action will also be critical to overcome resistance and reduce attrition. Therefore, a complementary strategy focused on identifying well-validated targets to start hit identification programs is essential to reinforce the clinical pipeline. Leveraging published data, we have assessed the status quo of the current antimalarial target portfolio with a focus on the blood stage clinical disease. From an extensive list of reported Plasmodium targets, we have defined triage criteria. These criteria consider genetic, pharmacological, and chemical validation, as well as tractability/doability, and safety implications. These criteria have provided a quantitative score that has led us to prioritize those targets with the highest probability to deliver successful and differentiated new drugs.

CRISPR-Cas9-based target validation for p53-reactivating model compounds

PubMed Central

Wanzel, Michael; Vischedyk, Jonas B; Gittler, Miriam P; Gremke, Niklas; Seiz, Julia R; Hefter, Mirjam; Noack, Magdalena; Savai, Rajkumar; Mernberger, Marco; Charles, Joël P; Schneikert, Jean; Bretz, Anne Catherine; Nist, Andrea; Stiewe, Thorsten

2015-01-01

Inactivation of the p53 tumor suppressor by Mdm2 is one of the most frequent events in cancer, so compounds targeting the p53-Mdm2 interaction are promising for cancer therapy. Mechanisms conferring resistance to p53-reactivating compounds are largely unknown. Here we show using CRISPR-Cas9–based target validation in lung and colorectal cancer that the activity of nutlin, which blocks the p53-binding pocket of Mdm2, strictly depends on functional p53. In contrast, sensitivity to the drug RITA, which binds the Mdm2-interacting N terminus of p53, correlates with induction of DNA damage. Cells with primary or acquired RITA resistance display cross-resistance to DNA crosslinking compounds such as cisplatin and show increased DNA cross-link repair. Inhibition of FancD2 by RNA interference or pharmacological mTOR inhibitors restores RITA sensitivity. The therapeutic response to p53-reactivating compounds is therefore limited by compound-specific resistance mechanisms that can be resolved by CRISPR-Cas9-based target validation and should be considered when allocating patients to p53-reactivating treatments. PMID:26595461

Current HPLC Methods for Assay of Nano Drug Delivery Systems.

PubMed

Tekkeli, Serife Evrim Kepekci; Kiziltas, Mustafa Volkan

2017-01-01

In nano drug formulations the mechanism of release is a critical process to recognize controlled and targeted drug delivery systems. In order to gain high bioavailability and specificity from the drug to reach its therapeutic goal, the active substance must be loaded into the nanoparticles efficiently. Therefore, the amount in biological fluids or tissues and the remaining amount in nano carriers are very important parameters to understand the potential of the nano drug delivery systems. For this aim, suitable and validated quantitation methods are required to determine released drug concentrations from nano pharmaceutical formulations. HPLC (High Performance Liquid Chromatography) is one of the most common techniques used for determination of released drug content out of nano drug formulations, in different physical conditions, over different periods of time. Since there are many types of HPLC methods depending on detector and column types, it is a challenge for the researchers to choose a suitable method that is simple, fast and validated HPLC techniques for their nano drug delivery systems. This review's goal is to compare HPLC methods that are currently used in different nano drug delivery systems in order to provide detailed and useful information for researchers. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Eukaryotic elongation factor 2 is a prognostic marker and its kinase a potential therapeutic target in HCC

PubMed Central

Pott, Leona L; Hagemann, Sascha; Reis, Henning; Lorenz, Kristina; Bracht, Thilo; Herold, Thomas; Skryabin, Boris V; Megger, Dominik A; Kälsch, Julia; Weber, Frank; Sitek, Barbara; Baba, Hideo A

2017-01-01

Hepatocellular carcinoma is a cancer with increasing incidence and largely refractory to current anticancer drugs. Since Sorafenib, a multikinase inhibitor has shown modest efficacy in advanced hepatocellular carcinoma additional treatments are highly needed. Protein phosphorylation via kinases is an important post-translational modification to regulate cell homeostasis including proliferation and apoptosis. Therefore kinases are valuable targets in cancer therapy. To this end we performed 2D differential gel electrophoresis and mass spectrometry analysis of phosphoprotein-enriched lysates of tumor and corresponding non-tumorous liver samples to detect differentially abundant phosphoproteins to screen for novel kinases as potential drug targets. We identified 34 differentially abundant proteins in phosphoprotein enriched lysates. Expression and distribution of the candidate protein eEF2 and its phosphorylated isoform was validated immunohistochemically on 78 hepatocellular carcinoma and non-tumorous tissue samples. Validation showed that total eEF2 and phosphorylated eEF2 at threonine 56 are prognostic markers for overall survival of HCC-patients. The activity of the regulating eEF2 kinase, compared between tumor and non-tumorous tissue lysates by in vitro kinase assays, is more than four times higher in tumor tissues. Functional analyzes regarding eEF2 kinase were performed in JHH5 cells with CRISPR/Cas9 mediated eEF2 kinase knock out. Proliferation and growth is decreased in eEF2 kinase knock out cells. Conclusion eEF2 and phosphorylated eEF2 are prognostic markers for survival of hepatocellular carcinoma patients and the regulating eEF2 kinase is a potential drug target for tumor therapy. PMID:28060762

Eukaryotic elongation factor 2 is a prognostic marker and its kinase a potential therapeutic target in HCC.

PubMed

Pott, Leona L; Hagemann, Sascha; Reis, Henning; Lorenz, Kristina; Bracht, Thilo; Herold, Thomas; Skryabin, Boris V; Megger, Dominik A; Kälsch, Julia; Weber, Frank; Sitek, Barbara; Baba, Hideo A

2017-02-14

Hepatocellular carcinoma is a cancer with increasing incidence and largely refractory to current anticancer drugs. Since Sorafenib, a multikinase inhibitor has shown modest efficacy in advanced hepatocellular carcinoma additional treatments are highly needed. Protein phosphorylation via kinases is an important post-translational modification to regulate cell homeostasis including proliferation and apoptosis. Therefore kinases are valuable targets in cancer therapy. To this end we performed 2D differential gel electrophoresis and mass spectrometry analysis of phosphoprotein-enriched lysates of tumor and corresponding non-tumorous liver samples to detect differentially abundant phosphoproteins to screen for novel kinases as potential drug targets. We identified 34 differentially abundant proteins in phosphoprotein enriched lysates. Expression and distribution of the candidate protein eEF2 and its phosphorylated isoform was validated immunohistochemically on 78 hepatocellular carcinoma and non-tumorous tissue samples. Validation showed that total eEF2 and phosphorylated eEF2 at threonine 56 are prognostic markers for overall survival of HCC-patients. The activity of the regulating eEF2 kinase, compared between tumor and non-tumorous tissue lysates by in vitro kinase assays, is more than four times higher in tumor tissues. Functional analyzes regarding eEF2 kinase were performed in JHH5 cells with CRISPR/Cas9 mediated eEF2 kinase knock out. Proliferation and growth is decreased in eEF2 kinase knock out cells. eEF2 and phosphorylated eEF2 are prognostic markers for survival of hepatocellular carcinoma patients and the regulating eEF2 kinase is a potential drug target for tumor therapy.

Surrogate endpoints in clinical trials of chronic kidney disease progression: moving from single to multiple risk marker response scores.

PubMed

Schievink, Bauke; Mol, Peter G M; Lambers Heerspink, Hiddo J

2015-11-01

There is increased interest in developing surrogate endpoints for clinical trials of chronic kidney disease progression, as the established clinically meaningful endpoint end-stage renal disease requires large and lengthy trials to assess drug efficacy. We describe recent developments in the search for novel surrogate endpoints. Declines in estimated glomerular filtration rate (eGFR) of 30% or 40% and albuminuria have been proposed as surrogates for end-stage renal disease. However, changes in eGFR or albuminuria may not be valid under all circumstances as drugs always have effects on multiple renal risk markers. Changes in each of these other 'off-target' risk markers can alter renal risk (either beneficially or adversely), and can thereby confound the relationship between surrogates that are based on single risk markers and renal outcome. Risk algorithms that integrate the short-term drug effects on multiple risk markers to predict drug effects on hard renal outcomes may therefore be more accurate. The validity of these risk algorithms is currently investigated. Given that drugs affect multiple renal risk markers, risk scores that integrate these effects are a promising alternative to using eGFR decline or albuminuria. Proper validation is required before these risk scores can be implemented.

Screening for illicit drugs in pooled human urine and urinated soil samples and studies on the stability of urinary excretion products of cocaine, MDMA, and MDEA in wastewater by hyphenated mass spectrometry techniques.

PubMed

Mardal, Marie; Kinyua, Juliet; Ramin, Pedram; Miserez, Bram; Van Nuijs, Alexander L N; Covaci, Adrian; Meyer, Markus R

2017-01-01

Monitoring population drug use through wastewater-based epidemiology (WBE) is a useful method to quantitatively follow trends and estimate total drug consumption in communities. Concentrations of drug biomarkers might be low in wastewater due to dilution; and therefore analysis of pooled urine (PU) is useful to detect consumed drugs and identify targets of illicit drugs use. The aims of the study were (1) to screen PU and urinated soil (US) samples collected at festivals for illicit drug excretion products using hyphenated techniques; (2) to develop and validate a hydrophilic interaction liquid chromatography - mass spectrometry / mass spectrometry (HILIC-MS/MS) method of quantifying urinary targets of identified drugs in wastewater; and (3) to conduct a 24 h stability study, using PU and US to better reflect the chemical environment for targets in wastewater. Cocaine (COC) and ecstasy-like compounds were the most frequently detected illicit drugs; an analytical method was developed to quantify their excretion products. Hydroxymethoxymethamphetamine (HMMA), 3,4-methylenedioxymethamphetamine (MDMA), 3,4-methylenedioxyamphetamine (MDA), HMMA sulfate (HMMA-S), benzoylecgonine (BE), and cocaethylene (CE) had 85-102% of initial concentration after 8 h of incubation, whereas COC and ecgonine methyl ester (EME) had 74 and 67% after 8 h, respectively. HMMA showed a net increase during 24 h of incubation (107% ± 27, n = 8), possibly due to the cleavage of HMMA conjugates, and biotransformation of MDMA. The results suggest HMMA as analytical target for MDMA consumption in WBE, due to its stability in wastewater and its excretion as the main phase I metabolite of MDMA. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Proteasome inhibition for treatment of leishmaniasis, Chagas disease and sleeping sickness.

PubMed

Khare, Shilpi; Nagle, Advait S; Biggart, Agnes; Lai, Yin H; Liang, Fang; Davis, Lauren C; Barnes, S Whitney; Mathison, Casey J N; Myburgh, Elmarie; Gao, Mu-Yun; Gillespie, J Robert; Liu, Xianzhong; Tan, Jocelyn L; Stinson, Monique; Rivera, Ianne C; Ballard, Jaime; Yeh, Vince; Groessl, Todd; Federe, Glenn; Koh, Hazel X Y; Venable, John D; Bursulaya, Badry; Shapiro, Michael; Mishra, Pranab K; Spraggon, Glen; Brock, Ansgar; Mottram, Jeremy C; Buckner, Frederick S; Rao, Srinivasa P S; Wen, Ben G; Walker, John R; Tuntland, Tove; Molteni, Valentina; Glynne, Richard J; Supek, Frantisek

2016-09-08

Chagas disease, leishmaniasis and sleeping sickness affect 20 million people worldwide and lead to more than 50,000 deaths annually. The diseases are caused by infection with the kinetoplastid parasites Trypanosoma cruzi, Leishmania spp. and Trypanosoma brucei spp., respectively. These parasites have similar biology and genomic sequence, suggesting that all three diseases could be cured with drugs that modulate the activity of a conserved parasite target. However, no such molecular targets or broad spectrum drugs have been identified to date. Here we describe a selective inhibitor of the kinetoplastid proteasome (GNF6702) with unprecedented in vivo efficacy, which cleared parasites from mice in all three models of infection. GNF6702 inhibits the kinetoplastid proteasome through a non-competitive mechanism, does not inhibit the mammalian proteasome or growth of mammalian cells, and is well-tolerated in mice. Our data provide genetic and chemical validation of the parasite proteasome as a promising therapeutic target for treatment of kinetoplastid infections, and underscore the possibility of developing a single class of drugs for these neglected diseases.

[Caenorhabditis elegans: a powerful tool for drug discovery].

PubMed

Jia, Xi-Hua; Cao, Cheng

2009-07-01

A simple model organism Caenorhabditis elegans has contributed substantially to the fundamental researches in biology. In an era of functional genomics, nematode worm has been developed into a multi-purpose tool that can be exploited to identify disease-causing or disease-associated genes, validate potential drug targets. This, coupled with its genetic amenability, low cost experimental manipulation and compatibility with high throughput screening in an intact physiological condition, makes the model organism into an effective toolbox for drug discovery. This review shows the unique features of C. elegans, how it can play a valuable role in our understanding of the molecular mechanism of human diseases and finding drug leads in drug development process.

Drug discovery in tuberculosis. New drug targets and antimycobacterial agents.

PubMed

Campaniço, André; Moreira, Rui; Lopes, Francisca

2018-04-25

Tuberculosis (TB) remains a major health problem worldwide. The infectious agent, Mycobacterium tuberculosis, has a unique ability to survive within the host, alternating between active and latent disease states, and escaping the immune system defences. The extended duration of anti-TB regimens and the increasing prevalence of multidrug- (MDR) and extensively drug-resistant (XDR) M. tuberculosis strains have created an urgent need for new antibiotics active against drug-resistant organisms and that can shorten standard therapy. However, despite success in identifying active compounds through phenotypic screens, the conversion of hits into novel chemical series and ultimately into clinical candidates is hampered by the poor efficacy in eliminating M. tuberculosis within different host compartments, including macrophages, as well as a lack of knowledge about the specific target(s) inhibited and/or upregulated. The current status of anti-TB lead generation has much improved over the last decade, as exemplified by the recent approval of bedaquiline and delamanid to treat MDR-TB and XDR-TB. This review provides a critical analysis on the strategies used to progress hit compounds into viable lead candidates, and how emerging targets may play a role in TB drug discovery in the near future. Four new relevant targets are addressed: the enoyl-acyl carrier protein reductase, InhA; the transmembrane transport protein large, MmpL3; the decaprenylphospho-beta-d-ribofuranose 2-oxidase, DprE1; and the ubiquinol-cytochrome C reductase, QcrB. Validated hit compounds for each target are presented and explored, and the medicinal chemistry strategies to expand SAR around novel chemotypes analyzed. In addition, very recent emerging targets are also discussed. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Poly(NIPAm-AMPS) nanoparticles for targeted delivery of anti-inflammatory cell penetrating peptides

NASA Astrophysics Data System (ADS)

Bartlett, Rush Lloyd, II

Inflammatory diseases such as osteoarthritis and rheumatoid arthritis cause $127.8 billion in US healthcare expenditures each year and are the cause of disability for 27% of disabled persons in the United States. Current treatment options rarely halt disease progression and often result in significant unwanted and debilitating side effects. Our laboratory has previously developed a family of cell penetrating peptides (CPPs) which inhibit the activity of mitogen activated protein kinase activate protein kinase 2 (MK2). MK2 mediates the inflammatory response by activating Tristetraprline (TTP). Once activated, TTP rapidly stabilizes AU rich regions of pro-inflammatory cytokine mRNA which allows translation of pro-inflammatory cytokines to occur. Blocking MK2 with our labs CPPs yields a decrease in inflammatory activity but CPPs by are highly non specific and prone to rapid enzymatic degradation in vivo.. In order to increase the potency of MK2 inhibiting CPPs we have developed a novel nanoparticle drug carrier composed of poly(N-isopropylacrylamide-co-2-acrylamido-2-methyl-1-propanesulfonic acid). This drug carrier has been shown to have preliminary efficacy in vitro and ex vivo for suppressing pro-inflammatory cytokine production when releasing CPPs. This thesis will present progress made on three aims: Specific Aim 1) Create and validate a NIPAm based drug delivery system that mimics the binding and release previously observed between cell penetrating peptides and glycosaminoglycans. Specific Aim 2) Engineer degradability into poly(NIPAm-AMPS) nanoparticles to enable more drug to be released and qualify that system in vitro. Specific Aim 3) Validate poly(NIPAm-AMPS) nanoparticles for targeted drug delivery in an ex vivo inflammatory model. Overall we have developed a novel anionic nanoparticle system that is biocompatible and efficient at loading and releasing cell penetrating peptides to inflamed tissue. Once loaded with a CPP the nanoparticle drug complex is capable of targeting diseased tissue and preventing the production of pro-inflammatory cytokines in both in vitro and ex vivo models.

Herceptin conjugated PLGA-PHis-PEG pH sensitive nanoparticles for targeted and controlled drug delivery.

PubMed

Zhou, Zilan; Badkas, Apurva; Stevenson, Max; Lee, Joo-Youp; Leung, Yuet-Kin

2015-06-20

A dual functional nano-scaled drug carrier, comprising of a targeting ligand and pH sensitivity, has been made in order to increase the specificity and efficacy of the drug delivery system. The nanoparticles are made of a tri-block copolymer, poly(d,l lactide-co-glycolide) (PLGA)-b-poly(l-histidine) (PHis)-b-polyethylene glycol (PEG), via nano-precipitation. To provide the nanoparticle feature of endolysosomal escape and pH sensitivity, poly(l-histidine) was chosen as a proton sponge polymer. Herceptin, which specifically binds to HER2 antigen, was conjugated to the nanoparticles through click chemistry. The nanoparticles were characterized via dynamic light scattering (DLS) and transmission electron microscopy (TEM). Both methods showed the sizes of about 100nm with a uniform size distribution. The pH sensitivity was assessed by drug releases and size changes at different pH conditions. As pH decreased from 7.4 to 5.2, the drug release rate accelerated and the size significantly increased. During in vitro tests against human breast cancer cell lines, MCF-7 and SK-BR-3 showed significantly increased uptake for Herceptin-conjugated nanoparticles, as compared to non-targeted nanoparticles. Herceptin-conjugated pH-sensitive nanoparticles showed the highest therapeutic effect, and thus validated the efficacy of a combined approach of pH sensitivity and active targeting. Copyright © 2015 Elsevier B.V. All rights reserved.

Mapping sites of aspirin-induced acetylations in live cells by quantitative acid-cleavable activity-based protein profiling (QA-ABPP)

PubMed Central

Wang, Jigang; Zhang, Chong-Jing; Zhang, Jianbin; He, Yingke; Lee, Yew Mun; Chen, Songbi; Lim, Teck Kwang; Ng, Shukie; Shen, Han-Ming; Lin, Qingsong

2015-01-01

Target-identification and understanding of mechanism-of-action (MOA) are challenging for development of small-molecule probes and their application in biology and drug discovery. For example, although aspirin has been widely used for more than 100 years, its molecular targets have not been fully characterized. To cope with this challenge, we developed a novel technique called quantitative acid-cleavable activity-based protein profiling (QA-ABPP) with combination of the following two parts: (i) activity-based protein profiling (ABPP) and iTRAQ™ quantitative proteomics for identification of target proteins and (ii) acid-cleavable linker-based ABPP for identification of peptides with specific binding sites. It is known that reaction of aspirin with its target proteins leads to acetylation. We thus applied the above technique using aspirin-based probes in human cancer HCT116 cells. We identified 1110 target proteins and 2775 peptides with exact acetylation sites. By correlating these two sets of data, 523 proteins were identified as targets of aspirin. We used various biological assays to validate the effects of aspirin on inhibition of protein synthesis and induction of autophagy which were elicited from the pathway analysis of Aspirin target profile. This technique is widely applicable for target identification in the field of drug discovery and biology, especially for the covalent drugs. PMID:25600173

Targeting the ubiquitin-proteasome system for cancer treatment: discovering novel inhibitors from nature and drug repurposing.

PubMed

Soave, Claire L; Guerin, Tracey; Liu, Jinbao; Dou, Q Ping

2017-12-01

In the past 15 years, the proteasome has been validated as an anti-cancer drug target and 20S proteasome inhibitors (such as bortezomib and carfilzomib) have been approved by the FDA for the treatment of multiple myeloma and some other liquid tumors. However, there are shortcomings of clinical proteasome inhibitors, including severe toxicity, drug resistance, and no effect in solid tumors. At the same time, extensive research has been conducted in the areas of natural compounds and old drug repositioning towards the goal of discovering effective, economical, low toxicity proteasome-inhibitory anti-cancer drugs. A variety of dietary polyphenols, medicinal molecules, metallic complexes, and metal-binding compounds have been found to be able to selectively inhibit tumor cellular proteasomes and induce apoptotic cell death in vitro and in vivo, supporting the clinical success of specific 20S proteasome inhibitors bortezomib and carfilzomib. Therefore, the discovery of natural proteasome inhibitors and researching old drugs with proteasome-inhibitory properties may provide an alternative strategy for improving the current status of cancer treatment and even prevention.

The Cyclic Peptide Ecumicin Targeting ClpC1 Is Active against Mycobacterium tuberculosis In Vivo

PubMed Central

Gao, Wei; Kim, Jin-Yong; Anderson, Jeffrey R.; Akopian, Tatos; Hong, Seungpyo; Jin, Ying-Yu; Kandror, Olga; Kim, Jong-Woo; Lee, In-Ae; Lee, Sun-Young; McAlpine, James B.; Mulugeta, Surafel; Sunoqrot, Suhair; Wang, Yuehong; Yang, Seung-Hwan; Yoon, Tae-Mi; Goldberg, Alfred L.; Pauli, Guido F.; Cho, Sanghyun

2014-01-01

Drug-resistant tuberculosis (TB) has lent urgency to finding new drug leads with novel modes of action. A high-throughput screening campaign of >65,000 actinomycete extracts for inhibition of Mycobacterium tuberculosis viability identified ecumicin, a macrocyclic tridecapeptide that exerts potent, selective bactericidal activity against M. tuberculosis in vitro, including nonreplicating cells. Ecumicin retains activity against isolated multiple-drug-resistant (MDR) and extensively drug-resistant (XDR) strains of M. tuberculosis. The subcutaneous administration to mice of ecumicin in a micellar formulation at 20 mg/kg body weight resulted in plasma and lung exposures exceeding the MIC. Complete inhibition of M. tuberculosis growth in the lungs of mice was achieved following 12 doses at 20 or 32 mg/kg. Genome mining of lab-generated, spontaneous ecumicin-resistant M. tuberculosis strains identified the ClpC1 ATPase complex as the putative target, and this was confirmed by a drug affinity response test. ClpC1 functions in protein breakdown with the ClpP1P2 protease complex. Ecumicin markedly enhanced the ATPase activity of wild-type (WT) ClpC1 but prevented activation of proteolysis by ClpC1. Less stimulation was observed with ClpC1 from ecumicin-resistant mutants. Thus, ClpC1 is a valid drug target against M. tuberculosis, and ecumicin may serve as a lead compound for anti-TB drug development. PMID:25421483

Mapping the pathways of resistance to targeted therapies

PubMed Central

Wood, Kris C.

2015-01-01

Resistance substantially limits the depth and duration of clinical responses to targeted anticancer therapies. Through the use of complementary experimental approaches, investigators have revealed that cancer cells can achieve resistance through adaptation or selection driven by specific genetic, epigenetic, or microenvironmental alterations. Ultimately, these diverse alterations often lead to the activation of signaling pathways that, when co-opted, enable cancer cells to survive drug treatments. Recently developed methods enable the direct and scalable identification of the signaling pathways capable of driving resistance in specific contexts. Using these methods, novel pathways of resistance to clinically approved drugs have been identified and validated. By combining systematic resistance pathway mapping methods with studies revealing biomarkers of specific resistance pathways and pharmacological approaches to block these pathways, it may be possible to rationally construct drug combinations that yield more penetrant and lasting responses in patients. PMID:26392071

Computer aided drug discovery of highly ligand efficient, low molecular weight imidazopyridine analogs as FLT3 inhibitors.

PubMed

Frett, Brendan; McConnell, Nick; Smith, Catherine C; Wang, Yuanxiang; Shah, Neil P; Li, Hong-yu

2015-04-13

The FLT3 kinase represents an attractive target to effectively treat AML. Unfortunately, no FLT3 targeted therapeutic is currently approved. In line with our continued interests in treating kinase related disease for anti-FLT3 mutant activity, we utilized pioneering synthetic methodology in combination with computer aided drug discovery and identified low molecular weight, highly ligand efficient, FLT3 kinase inhibitors. Compounds were analyzed for biochemical inhibition, their ability to selectively inhibit cell proliferation, for FLT3 mutant activity, and preliminary aqueous solubility. Validated hits were discovered that can serve as starting platforms for lead candidates. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

A network pharmacology study of Sendeng-4, a Mongolian medicine.

PubMed

Zi, Tian; Yu, Dong

2015-02-01

We collected the data on the Sendeng-4 chemical composition corresponding targets through the literature and from DrugBank, SuperTarget, TTD (Therapeutic Targets Database) and other databases and the relevant signaling pathways from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database and established models of the chemical composition-target network and chemical composition-target-disease network using Cytoscape software, the analysis indicated that the chemical composition had at least nine different types of targets that acted together to exert effects on the diseases, suggesting a "multi-component, multi-target" feature of the traditional Mongolian medicine. We also employed the rat model of rheumatoid arthritis induced by Collgen Type II to validate the key targets of the chemical components of Sendeng-4, and three of the key targets were validated through laboratory experiments, further confirming the anti-inflammatory effects of Sendeng-4. In all, this study predicted the active ingredients and targets of Sendeng-4, and explored its mechanism of action, which provided new strategies and methods for further research and development of Sendeng-4 and other traditional Mongolian medicines as well. Copyright © 2015 China Pharmaceutical University. Published by Elsevier B.V. All rights reserved.

Structure-Based Design of Highly Selective Inhibitors of the CREB Binding Protein Bromodomain.

PubMed

Denny, R Aldrin; Flick, Andrew C; Coe, Jotham; Langille, Jonathan; Basak, Arindrajit; Liu, Shenping; Stock, Ingrid; Sahasrabudhe, Parag; Bonin, Paul; Hay, Duncan A; Brennan, Paul E; Pletcher, Mathew; Jones, Lyn H; Chekler, Eugene L Piatnitski

2017-07-13

Chemical probes are required for preclinical target validation to interrogate novel biological targets and pathways. Selective inhibitors of the CREB binding protein (CREBBP)/EP300 bromodomains are required to facilitate the elucidation of biology associated with these important epigenetic targets. Medicinal chemistry optimization that paid particular attention to physiochemical properties delivered chemical probes with desirable potency, selectivity, and permeability attributes. An important feature of the optimization process was the successful application of rational structure-based drug design to address bromodomain selectivity issues (particularly against the structurally related BRD4 protein).

The impact of intermittent versus continuous exposure to EGFR tyrosine kinase inhibitor on selection of EGFR T790M-mutant drug-resistant clones in a lung cancer cell line carrying activating EGFR mutation

PubMed Central

Lee, Youngjoo; Choi, Yu-Ra; Kim, Kyoung-Yeon; Shin, Dong Hoon

2016-01-01

Drug-resistant cell lines are essential tools for investigating the mechanisms of resistance to molecular-targeted anti-cancer drugs. However, little is known about how to establish clinically relevant drug-resistant cell lines. Our study examined the impact of a drug-free period on the establishment of a cell line with clinically relevant resistance to molecular-targeted drugs. We used PC9 cells, a lung cancer cell line carrying EGFR mutation, because this is a validated target for EGFR tyrosine kinase inhibitors (TKI). PC9 cells were intermittently or continuously exposed to increasing concentrations of gefitinib (0.01 μM to 1.0 μM) and the emergence of the most common acquired resistance mutation in EGFR, T790M, was determined. T790M was detected at a 25-fold lower drug concentration in cells continuously exposed to gefitinib (PC9/GRc) than in cells intermittently exposed to gefitinib (PC9/GRi) (0.04 μM vs 1.0 μM, respectively). The mutation frequencies at those drug concentrations were 19.8% and 8.0% in PC9/GRc and PC9/GRi cells, respectively. After drug-free culture for 8 weeks, resistance to gefitinib decreased in the PC9/GRi cells but not in the PC9/GRc cells. In the PC9/GRc cells, the frequency of the T790M mutation was consistently about 20% from 0.04 μM to 1.0 μM of gefitinib. In the PC9/GRc cells, the T790M mutation was detected in all single-cell clones, at frequencies ranging from 7.0% to 37.0%, with a median of 19.5% (95% confidence interval, 17.3%–20.9%). In conclusion, compared with intermittent drug exposure, continuous exposure might select better minor drug-resistant clones when creating cell lines resistant to molecular-targeted drugs. PMID:27270313

The impact of intermittent versus continuous exposure to EGFR tyrosine kinase inhibitor on selection of EGFR T790M-mutant drug-resistant clones in a lung cancer cell line carrying activating EGFR mutation.

PubMed

Lee, Youngjoo; Choi, Yu-Ra; Kim, Kyoung-Yeon; Shin, Dong Hoon

2016-07-12

Drug-resistant cell lines are essential tools for investigating the mechanisms of resistance to molecular-targeted anti-cancer drugs. However, little is known about how to establish clinically relevant drug-resistant cell lines. Our study examined the impact of a drug-free period on the establishment of a cell line with clinically relevant resistance to molecular-targeted drugs. We used PC9 cells, a lung cancer cell line carrying EGFR mutation, because this is a validated target for EGFR tyrosine kinase inhibitors (TKI). PC9 cells were intermittently or continuously exposed to increasing concentrations of gefitinib (0.01 μM to 1.0 μM) and the emergence of the most common acquired resistance mutation in EGFR, T790M, was determined. T790M was detected at a 25-fold lower drug concentration in cells continuously exposed to gefitinib (PC9/GRc) than in cells intermittently exposed to gefitinib (PC9/GRi) (0.04 μM vs 1.0 μM, respectively). The mutation frequencies at those drug concentrations were 19.8% and 8.0% in PC9/GRc and PC9/GRi cells, respectively. After drug-free culture for 8 weeks, resistance to gefitinib decreased in the PC9/GRi cells but not in the PC9/GRc cells. In the PC9/GRc cells, the frequency of the T790M mutation was consistently about 20% from 0.04 μM to 1.0 μM of gefitinib. In the PC9/GRc cells, the T790M mutation was detected in all single-cell clones, at frequencies ranging from 7.0% to 37.0%, with a median of 19.5% (95% confidence interval, 17.3%-20.9%). In conclusion, compared with intermittent drug exposure, continuous exposure might select better minor drug-resistant clones when creating cell lines resistant to molecular-targeted drugs.

Revisiting inconsistency in large pharmacogenomic studies

PubMed Central

Safikhani, Zhaleh; Smirnov, Petr; Freeman, Mark; El-Hachem, Nehme; She, Adrian; Rene, Quevedo; Goldenberg, Anna; Birkbak, Nicolai J.; Hatzis, Christos; Shi, Leming; Beck, Andrew H.; Aerts, Hugo J.W.L.; Quackenbush, John; Haibe-Kains, Benjamin

2017-01-01

In 2013, we published a comparative analysis of mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. We present a new analysis using these expanded data, where we address the most significant suggestions for improvements on our published analysis — that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should be compared across cell lines, and that the software analysis tools provided should have been easier to run, particularly as the GDSC and CCLE released additional data. Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. Using new statistics to assess data consistency allowed identification of two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens. PMID:28928933

DNA topoisomerase I and DNA gyrase as targets for TB therapy.

PubMed

Nagaraja, Valakunja; Godbole, Adwait A; Henderson, Sara R; Maxwell, Anthony

2017-03-01

Tuberculosis (TB) is the deadliest bacterial disease in the world. New therapeutic agents are urgently needed to replace existing drugs for which resistance is a significant problem. DNA topoisomerases are well-validated targets for antimicrobial and anticancer chemotherapies. Although bacterial topoisomerase I has yet to be exploited as a target for clinical antibiotics, DNA gyrase has been extensively targeted, including the highly clinically successful fluoroquinolones, which have been utilized in TB therapy. Here, we review the exploitation of topoisomerases as antibacterial targets and summarize progress in developing new agents to target DNA topoisomerase I and DNA gyrase from Mycobacterium tuberculosis. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Glycyrrhetinic Acid-Mediated Polymeric Drug Delivery Targeting the Acidic Microenvironment of Hepatocellular Carcinoma.

PubMed

Zhang, Jinming; Zhang, Min; Ji, Juan; Fang, Xiefan; Pan, Xin; Wang, Yitao; Wu, Chuanbin; Chen, Meiwan

2015-10-01

The major hurdle of current drug carrier against hepatocellular carcinoma (HCC) is the lack of specific and selective drug delivery to HCC. In this study, a novel glycyrrhetinic acid (GA) and poly(L-Histidine) (PHIS) mediated polymeric drug delivery system was developed to target HCC that have GA binding receptors and release its encapsulated anticancer drug in the acidic microenvironment of HCC. Firstly, GA and PHIS were conjugated to form poly (ethylene glycol)-poly(lactic-co-glycolic acid) (GA-PEG-PHIS-PLGA, GA-PPP) micelles by grafting reaction between active terminal groups. Secondly, andrographolide (AGP) was encapsulated to GA-PPP to make AGP/GA-PPP using the solvent evaporation method. The pH-responsive property of AGP/GA-PPP micelles was validated by monitoring its stability and drug release behavior in different pH conditions. Furthermore, selective hepatocellular uptake of GA-PPP micelles in vitro, liver specific drug accumulation in vivo, as well as the enhanced antitumor effects of AGP/GA-PPP micelles confirmed the HCC targeting property of our novel drug delivery system. Average size of AGP/GA-PPP micelles increased significantly and the encapsulated AGP released faster in vitro at pH 5.0, while micelles keeping stable in pH 7.4. AGP/GA-PPP micelles were uptaken more efficiently by human Hep3B liver cells than that by human MDA-MB-231 breast cancer cells. GA-PPP micelles accumulated specifically in the liver and possessed long retention time in vivo. AGP/GA-PPP micelles significantly inhibited tumor growth and provided better therapeutic outcomes compared to free AGP and AGP/PEG-PLGA(AGP/PP) micelles without GA and PHIS decoration. This novel GA-PPP polymeric carrier is promising for targeted treatment of HCC.

Targeting Lysine Deacetylases (KDACs) in Parasites

PubMed Central

Wang, Qi; Rosa, Bruce A.; Nare, Bakela; Powell, Kerrie; Valente, Sergio; Rotili, Dante; Mai, Antonello; Marshall, Garland R.; Mitreva, Makedonka

2015-01-01

Due to an increasing problem of drug resistance among almost all parasites species ranging from protists to worms, there is an urgent need to explore new drug targets and their inhibitors to provide new and effective parasitic therapeutics. In this regard, there is growing interest in exploring known drug leads of human epigenetic enzymes as potential starting points to develop novel treatments for parasitic diseases. This approach of repurposing (starting with validated targets and inhibitors) is quite attractive since it has the potential to reduce the expense of drug development and accelerate the process of developing novel drug candidates for parasite control. Lysine deacetylases (KDACs) are among the most studied epigenetic drug targets of humans, and a broad range of small-molecule inhibitors for these enzymes have been reported. In this work, we identify the KDAC protein families in representative species across important classes of parasites, screen a compound library of 23 hydroxamate- or benzamide-based small molecules KDAC inhibitors, and report their activities against a range of parasitic species, including the pathogen of malaria (Plasmodium falciparum), kinetoplastids (Trypanosoma brucei and Leishmania donovani), and nematodes (Brugia malayi, Dirofilaria immitis and Haemonchus contortus). Compound activity against parasites is compared to that observed against the mammalian cell line (L929 mouse fibroblast) in order to determine potential parasite-versus-host selectivity). The compounds showed nanomolar to sub-nanomolar potency against various parasites, and some selectivity was observed within the small panel of compounds tested. The possible binding modes of the active compounds at the different protein target sites within different species were explored by docking to homology models to help guide the discovery of more selective, parasite-specific inhibitors. This current work supports previous studies that explored the use of KDAC inhibitors in targeting Plasmodium to develop new anti-malarial treatments, and also pioneers experiments with these KDAC inhibitors as potential new anthelminthics. The selectivity observed begins to address the challenges of targeting specific parasitic diseases while limiting host toxicity. PMID:26402733

Tricyclic GyrB/ParE (TriBE) Inhibitors: A New Class of Broad-Spectrum Dual-Targeting Antibacterial Agents

PubMed Central

Tari, Leslie W.; Li, Xiaoming; Trzoss, Michael; Bensen, Daniel C.; Chen, Zhiyong; Lam, Thanh; Zhang, Junhu; Lee, Suk Joong; Hough, Grayson; Phillipson, Doug; Akers-Rodriguez, Suzanne; Cunningham, Mark L.; Kwan, Bryan P.; Nelson, Kirk J.; Castellano, Amanda; Locke, Jeff B.; Brown-Driver, Vickie; Murphy, Timothy M.; Ong, Voon S.; Pillar, Chris M.; Shinabarger, Dean L.; Nix, Jay; Lightstone, Felice C.; Wong, Sergio E.; Nguyen, Toan B.; Shaw, Karen J.; Finn, John

2013-01-01

Increasing resistance to every major class of antibiotics and a dearth of novel classes of antibacterial agents in development pipelines has created a dwindling reservoir of treatment options for serious bacterial infections. The bacterial type IIA topoisomerases, DNA gyrase and topoisomerase IV, are validated antibacterial drug targets with multiple prospective drug binding sites, including the catalytic site targeted by the fluoroquinolone antibiotics. However, growing resistance to fluoroquinolones, frequently mediated by mutations in the drug-binding site, is increasingly limiting the utility of this antibiotic class, prompting the search for other inhibitor classes that target different sites on the topoisomerase complexes. The highly conserved ATP-binding subunits of DNA gyrase (GyrB) and topoisomerase IV (ParE) have long been recognized as excellent candidates for the development of dual-targeting antibacterial agents with broad-spectrum potential. However, to date, no natural product or small molecule inhibitors targeting these sites have succeeded in the clinic, and no inhibitors of these enzymes have yet been reported with broad-spectrum antibacterial activity encompassing the majority of Gram-negative pathogens. Using structure-based drug design (SBDD), we have created a novel dual-targeting pyrimidoindole inhibitor series with exquisite potency against GyrB and ParE enzymes from a broad range of clinically important pathogens. Inhibitors from this series demonstrate potent, broad-spectrum antibacterial activity against Gram-positive and Gram-negative pathogens of clinical importance, including fluoroquinolone resistant and multidrug resistant strains. Lead compounds have been discovered with clinical potential; they are well tolerated in animals, and efficacious in Gram-negative infection models. PMID:24386374

The Isolation and Characterization of Human Prostate Cancer Stem Cells

DTIC Science & Technology

2012-02-01

established cell lines and primary patient samples) with human prostate fibroblasts hold promise as models of tumor initiation/cancer stem cell activity...We continue to optimize and validate our in vitro model of prostate cancer initiation to facilitate cancer stem cell discovery as well as drug targeting.

Erythrocyte membrane-coated gold nanocages for targeted photothermal and chemical cancer therapy

NASA Astrophysics Data System (ADS)

Zhu, Dao-Ming; Xie, Wei; Xiao, Yu-Sha; Suo, Meng; Zan, Ming-Hui; Liao, Qing-Quan; Hu, Xue-Jia; Chen, Li-Ben; Chen, Bei; Wu, Wen-Tao; Ji, Li-Wei; Huang, Hui-Ming; Guo, Shi-Shang; Zhao, Xing-Zhong; Liu, Quan-Yan; Liu, Wei

2018-02-01

Recently, red blood cell (RBC) membrane-coated nanoparticles have attracted much attention because of their excellent immune escapability; meanwhile, gold nanocages (AuNs) have been extensively used for cancer therapy due to their photothermal effect and drug delivery capability. The combination of the RBC membrane coating and AuNs may provide an effective approach for targeted cancer therapy. However, few reports have shown the utilization of combining these two technologies. Here, we design erythrocyte membrane-coated gold nanocages for targeted photothermal and chemical cancer therapy. First, anti-EpCam antibodies were used to modify the RBC membranes to target 4T1 cancer cells. Second, the antitumor drug paclitaxel (PTX) was encapsulated into AuNs. Then, the AuNs were coated with the modified RBC membranes. These new nanoparticles were termed EpCam-RPAuNs. We characterized the capability of the EpCam-RPAuNs for selective tumor targeting via exposure to near-infrared irradiation. The experimental results demonstrate that EpCam-RPAuNs can effectively generate hyperthermia and precisely deliver the antitumor drug PTX to targeted cells. We also validated the biocompatibility of the EpCam-RAuNs in vitro. By combining the molecularly modified targeting RBC membrane and AuNs, our approach provides a new way to design biomimetic nanoparticles to enhance the surface functionality of nanoparticles. We believe that EpCam-RPAuNs can be potentially applied for cancer diagnoses and therapies.

Predictive validity of behavioural animal models for chronic pain

PubMed Central

Berge, Odd-Geir

2011-01-01

Rodent models of chronic pain may elucidate pathophysiological mechanisms and identify potential drug targets, but whether they predict clinical efficacy of novel compounds is controversial. Several potential analgesics have failed in clinical trials, in spite of strong animal modelling support for efficacy, but there are also examples of successful modelling. Significant differences in how methods are implemented and results are reported means that a literature-based comparison between preclinical data and clinical trials will not reveal whether a particular model is generally predictive. Limited reports on negative outcomes prevents reliable estimate of specificity of any model. Animal models tend to be validated with standard analgesics and may be biased towards tractable pain mechanisms. But preclinical publications rarely contain drug exposure data, and drugs are usually given in high doses and as a single administration, which may lead to drug distribution and exposure deviating significantly from clinical conditions. The greatest challenge for predictive modelling is, however, the heterogeneity of the target patient populations, in terms of both symptoms and pharmacology, probably reflecting differences in pathophysiology. In well-controlled clinical trials, a majority of patients shows less than 50% reduction in pain. A model that responds well to current analgesics should therefore predict efficacy only in a subset of patients within a diagnostic group. It follows that successful translation requires several models for each indication, reflecting critical pathophysiological processes, combined with data linking exposure levels with effect on target. LINKED ARTICLES This article is part of a themed issue on Translational Neuropharmacology. To view the other articles in this issue visit http://dx.doi.org/10.1111/bph.2011.164.issue-4 PMID:21371010

AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease.

PubMed

Fang, Jiansong; Wang, Ling; Li, Yecheng; Lian, Wenwen; Pang, Xiaocong; Wang, Hong; Yuan, Dongsheng; Wang, Qi; Liu, Ai-Lin; Du, Guan-Hua

2017-01-01

Alzheimer's disease (AD) is a complicated progressive neurodegeneration disorder. To confront AD, scientists are searching for multi-target-directed ligands (MTDLs) to delay disease progression. The in silico prediction of chemical-protein interactions (CPI) can accelerate target identification and drug discovery. Previously, we developed 100 binary classifiers to predict the CPI for 25 key targets against AD using the multi-target quantitative structure-activity relationship (mt-QSAR) method. In this investigation, we aimed to apply the mt-QSAR method to enlarge the model library to predict CPI towards AD. Another 104 binary classifiers were further constructed to predict the CPI for 26 preclinical AD targets based on the naive Bayesian (NB) and recursive partitioning (RP) algorithms. The internal 5-fold cross-validation and external test set validation were applied to evaluate the performance of the training sets and test set, respectively. The area under the receiver operating characteristic curve (ROC) for the test sets ranged from 0.629 to 1.0, with an average of 0.903. In addition, we developed a web server named AlzhCPI to integrate the comprehensive information of approximately 204 binary classifiers, which has potential applications in network pharmacology and drug repositioning. AlzhCPI is available online at http://rcidm.org/AlzhCPI/index.html. To illustrate the applicability of AlzhCPI, the developed system was employed for the systems pharmacology-based investigation of shichangpu against AD to enhance the understanding of the mechanisms of action of shichangpu from a holistic perspective.

Combining Machine Learning Systems and Multiple Docking Simulation Packages to Improve Docking Prediction Reliability for Network Pharmacology

PubMed Central

Hsin, Kun-Yi; Ghosh, Samik; Kitano, Hiroaki

2013-01-01

Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learning systems is a re-scoring function to evaluate binding modes generated by docking tools. The second is a binding mode selection function to identify the most predictive binding mode. Results from a series of benchmark validations and a case study show that this approach surpasses the prediction reliability of other techniques and that it also identifies either primary or off-targets of kinase inhibitors. Integrating this approach with molecular network maps makes it possible to address drug safety issues by comprehensively investigating network-dependent effects of a drug or drug candidate. PMID:24391846

Target-Independent Prediction of Drug Synergies Using Only Drug Lipophilicity

PubMed Central

2015-01-01

Physicochemical properties of compounds have been instrumental in selecting lead compounds with increased drug-likeness. However, the relationship between physicochemical properties of constituent drugs and the tendency to exhibit drug interaction has not been systematically studied. We assembled physicochemical descriptors for a set of antifungal compounds (“drugs”) previously examined for interaction. Analyzing the relationship between molecular weight, lipophilicity, H-bond donor, and H-bond acceptor values for drugs and their propensity to show pairwise antifungal drug synergy, we found that combinations of two lipophilic drugs had a greater tendency to show drug synergy. We developed a more refined decision tree model that successfully predicted drug synergy in stringent cross-validation tests based on only lipophilicity of drugs. Our predictions achieved a precision of 63% and allowed successful prediction for 58% of synergistic drug pairs, suggesting that this phenomenon can extend our understanding for a substantial fraction of synergistic drug interactions. We also generated and analyzed a large-scale synergistic human toxicity network, in which we observed that combinations of lipophilic compounds show a tendency for increased toxicity. Thus, lipophilicity, a simple and easily determined molecular descriptor, is a powerful predictor of drug synergy. It is well established that lipophilic compounds (i) are promiscuous, having many targets in the cell, and (ii) often penetrate into the cell via the cellular membrane by passive diffusion. We discuss the positive relationship between drug lipophilicity and drug synergy in the context of potential drug synergy mechanisms. PMID:25026390

Developments in SPR Fragment Screening.

PubMed

Chavanieu, Alain; Pugnière, Martine

2016-01-01

Fragment-based approaches have played an increasing role alongside high-throughput screening in drug discovery for 15 years. The label-free biosensor technology based on surface plasmon resonance (SPR) is now sensitive and informative enough to serve during primary screens and validation steps. In this review, the authors discuss the role of SPR in fragment screening. After a brief description of the underlying principles of the technique and main device developments, they evaluate the advantages and adaptations of SPR for fragment-based drug discovery. SPR can also be applied to challenging targets such as membrane receptors and enzymes. The high-level of immobilization of the protein target and its stability are key points for a relevant screening that can be optimized using oriented immobilized proteins and regenerable sensors. Furthermore, to decrease the rate of false negatives, a selectivity test may be performed in parallel on the main target bearing the binding site mutated or blocked with a low-off-rate ligand. Fragment-based drug design, integrated in a rational workflow led by SPR, will thus have a predominant role for the next wave of drug discovery which could be greatly enhanced by new improvements in SPR devices.

DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches.

PubMed

Olayan, Rawan S; Ashoor, Haitham; Bajic, Vladimir B

2018-04-01

Finding computationally drug-target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate. We developed DDR, a novel method that improves the DTI prediction accuracy. DDR is based on the use of a heterogeneous graph that contains known DTIs with multiple similarities between drugs and multiple similarities between target proteins. DDR applies non-linear similarity fusion method to combine different similarities. Before fusion, DDR performs a pre-processing step where a subset of similarities is selected in a heuristic process to obtain an optimized combination of similarities. Then, DDR applies a random forest model using different graph-based features extracted from the DTI heterogeneous graph. Using 5-repeats of 10-fold cross-validation, three testing setups, and the weighted average of area under the precision-recall curve (AUPR) scores, we show that DDR significantly reduces the AUPR score error relative to the next best start-of-the-art method for predicting DTIs by 34% when the drugs are new, by 23% when targets are new and by 34% when the drugs and the targets are known but not all DTIs between them are not known. Using independent sources of evidence, we verify as correct 22 out of the top 25 DDR novel predictions. This suggests that DDR can be used as an efficient method to identify correct DTIs. The data and code are provided at https://bitbucket.org/RSO24/ddr/. vladimir.bajic@kaust.edu.sa. Supplementary data are available at Bioinformatics online.

IFPTarget: A Customized Virtual Target Identification Method Based on Protein-Ligand Interaction Fingerprinting Analyses.

PubMed

Li, Guo-Bo; Yu, Zhu-Jun; Liu, Sha; Huang, Lu-Yi; Yang, Ling-Ling; Lohans, Christopher T; Yang, Sheng-Yong

2017-07-24

Small-molecule target identification is an important and challenging task for chemical biology and drug discovery. Structure-based virtual target identification has been widely used, which infers and prioritizes potential protein targets for the molecule of interest (MOI) principally via a scoring function. However, current "universal" scoring functions may not always accurately identify targets to which the MOI binds from the retrieved target database, in part due to a lack of consideration of the important binding features for an individual target. Here, we present IFPTarget, a customized virtual target identification method, which uses an interaction fingerprinting (IFP) method for target-specific interaction analyses and a comprehensive index (Cvalue) for target ranking. Evaluation results indicate that the IFP method enables substantially improved binding pose prediction, and Cvalue has an excellent performance in target ranking for the test set. When applied to screen against our established target library that contains 11,863 protein structures covering 2842 unique targets, IFPTarget could retrieve known targets within the top-ranked list and identified new potential targets for chemically diverse drugs. IFPTarget prediction led to the identification of the metallo-β-lactamase VIM-2 as a target for quercetin as validated by enzymatic inhibition assays. This study provides a new in silico target identification tool and will aid future efforts to develop new target-customized methods for target identification.

Drug effects on drug targets: inhibition of enzymes by neuroleptics, antimycotics, antibiotics and other drugs on human pathogenic amoebas and their anti-proliferative effects.

PubMed

Ondarza, Raúl N

2007-11-01

This paper reviews the inhibition of various enzymes by neuroleptics, anti-mycotics, antibiotics and other drugs on three species of human pathogenic amoebas, mainly Entamoeba histolytica, Acanthamoeba polyphaga and Naegleria fowleri, and their antiproliferative effects. A recent patent registered by Philip relates to the combination of an antibacterial formulation and antifungal agent for producing a therapeutically effective quantity of an antimicrobial that is suitable for suppressing or treating fungal growth. The rationale behind this patent focused on essential and valid targets with a description of the main pathogenic characteristics of these amoebas. The study of new targets, such as trypanothione and trypanothione reductase, and the drug effects of selected agents were arranged into six main groups: A) Inhibition of disulfide reducing enzymes by neuroleptics, antimycotics and antibiotics; B) Comparative evaluation of the efficacies of several drugs with antiproliferative activities; C) Inhibition of the enzymes for the synthesis of trypanothione, such as ornithine decarboxylase, spermidine synthase and trypanothione synthetase; D) Inhibition of the glycolytic enzyme PPi-dependent phosphofructokinase (PFK) from Entamoeba and Naegleria by pyrophosphate analogues, different from the host enzyme; E) Inhibition of enzymes secreted by these parasites to invade the human host, for example cysteine proteinases; and F) Inhibition of encystment pathways and cyst-wall assembly proteins.

Rational, computer-enabled peptide drug design: principles, methods, applications and future directions.

PubMed

Diller, David J; Swanson, Jon; Bayden, Alexander S; Jarosinski, Mark; Audie, Joseph

2015-01-01

Peptides provide promising templates for developing drugs to occupy a middle space between small molecules and antibodies and for targeting 'undruggable' intracellular protein-protein interactions. Importantly, rational or in cerebro design, especially when coupled with validated in silico tools, can be used to efficiently explore chemical space and identify islands of 'drug-like' peptides to satisfy diverse drug discovery program objectives. Here, we consider the underlying principles of and recent advances in rational, computer-enabled peptide drug design. In particular, we consider the impact of basic physicochemical properties, potency and ADME/Tox opportunities and challenges, and recently developed computational tools for enabling rational peptide drug design. Key principles and practices are spotlighted by recent case studies. We close with a hypothetical future case study.

Target identification, lead optimization and antitumor evaluation of some new 1,2,4-triazines as c-Met kinase inhibitors.

PubMed

El-Wakil, Marwa H; Ashour, Hayam M; Saudi, Manal N; Hassan, Ahmed M; Labouta, Ibrahim M

2017-08-01

In silico target fishing approach using PharmMapper server identified c-Met kinase as the selective target for our previously synthesized compound NCI 748494/1. This approach was validated by in vitro kinase assay which showed that NCI 748494/1 possessed promising inhibitory activity against c-Met kinase (IC 50 =31.70μM). Assessment of ADMET profiling, drug-likeness, drug score as well as docking simulation for the binding pose of that compound in the active site of c-Met kinase domain revealed that NCI 748494/1 could be considered as a promising drug lead. Based on target identification and validation, it was observed that there is structure similarity between NCI 748494/1 and the reported type II c-Met kinase inhibitor BMS-777607. Optimization of our lead NCI 748494/1 furnished newly synthesized 1,2,4-triazine derivatives based on well-established structure-activity relationships, whereas three compounds namely; 4d, 7a and 8c displayed excellent in vitro cytotoxicity against three c-Met addicted cancer cell lines; A549 (lung adenocarcinoma), HT-29 (colon cancer) and MKN-45 (gastric carcinoma); with IC 50 values in the range 0.01-1.86µM. In vitro c-Met kinase assay showed 8c to possess the highest c-Met kinase inhibition profile (IC 50 =4.31µM). Docking of the active compounds in c-Met kinase active site revealed strong binding interactions comparable to the lead NCI 748494/1 and BMS-777607, suggesting that c-Met inhibition is very likely to be the mechanism of the antitumor effect of these derivatives. Copyright © 2017 Elsevier Inc. All rights reserved.

Numerical simulations of targeted delivery of magnetic drug aerosols in the human upper and central respiratory system: a validation study.

PubMed

Kenjereš, Saša; Tjin, Jimmy Leroy

2017-12-01

In the present study, we investigate the concept of the targeted delivery of pharmaceutical drug aerosols in an anatomically realistic geometry of the human upper and central respiratory system. The geometry considered extends from the mouth inlet to the eighth generation of the bronchial bifurcations and is identical to the phantom model used in the experimental studies of Banko et al. (2015 Exp. Fluids 56 , 1-12 (doi:10.1007/s00348-015-1966-y)). In our computer simulations, we combine the transitional Reynolds-averaged Navier-Stokes (RANS) and the wall-resolved large eddy simulation (LES) methods for the air phase with the Lagrangian approach for the particulate (aerosol) phase. We validated simulations against recently obtained magnetic resonance velocimetry measurements of Banko et al. (2015 Exp. Fluids 56 , 1-12. (doi:10.1007/s00348-015-1966-y)) that provide a full three-dimensional mean velocity field for steady inspiratory conditions. Both approaches produced good agreement with experiments, and the transitional RANS approach is selected for the multiphase simulations of aerosols transport, because of significantly lower computational costs. The local and total deposition efficiency are calculated for different classes of pharmaceutical particles (in the 0.1 μm≤ d p ≤10 μm range) without and with a paramagnetic core (the shell-core particles). For the latter, an external magnetic field is imposed. The source of the imposed magnetic field was placed in the proximity of the first bronchial bifurcation. We demonstrated that both total and local depositions of aerosols at targeted locations can be significantly increased by an applied magnetization force. This finding confirms the possible potential for further advancement of the magnetic drug targeting technique for more efficient treatments for respiratory diseases.

Systems Pharmacology-Based Approach of Connecting Disease Genes in Genome-Wide Association Studies with Traditional Chinese Medicine.

PubMed

Kim, Jihye; Yoo, Minjae; Shin, Jimin; Kim, Hyunmin; Kang, Jaewoo; Tan, Aik Choon

2018-01-01

Traditional Chinese medicine (TCM) originated in ancient China has been practiced over thousands of years for treating various symptoms and diseases. However, the molecular mechanisms of TCM in treating these diseases remain unknown. In this study, we employ a systems pharmacology-based approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. We studied 102 TCM components and their target genes by analyzing microarray gene expression experiments. We constructed disease-gene networks from 2558 GWAS studies. We applied a systems pharmacology approach to prioritize disease-target genes. Using this bioinformatics approach, we analyzed 14,713 GWAS disease-TCM-target gene pairs and identified 115 disease-gene pairs with q value < 0.2. We validated several of these GWAS disease-TCM-target gene pairs with literature evidence, demonstrating that this computational approach could reveal novel indications for TCM. We also develop TCM-Disease web application to facilitate the traditional Chinese medicine drug repurposing efforts. Systems pharmacology is a promising approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. The computational approaches described in this study could be easily expandable to other disease-gene network analysis.

Systems Pharmacology-Based Approach of Connecting Disease Genes in Genome-Wide Association Studies with Traditional Chinese Medicine

PubMed Central

Kim, Jihye; Yoo, Minjae; Shin, Jimin; Kim, Hyunmin; Kang, Jaewoo

2018-01-01

Traditional Chinese medicine (TCM) originated in ancient China has been practiced over thousands of years for treating various symptoms and diseases. However, the molecular mechanisms of TCM in treating these diseases remain unknown. In this study, we employ a systems pharmacology-based approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. We studied 102 TCM components and their target genes by analyzing microarray gene expression experiments. We constructed disease-gene networks from 2558 GWAS studies. We applied a systems pharmacology approach to prioritize disease-target genes. Using this bioinformatics approach, we analyzed 14,713 GWAS disease-TCM-target gene pairs and identified 115 disease-gene pairs with q value < 0.2. We validated several of these GWAS disease-TCM-target gene pairs with literature evidence, demonstrating that this computational approach could reveal novel indications for TCM. We also develop TCM-Disease web application to facilitate the traditional Chinese medicine drug repurposing efforts. Systems pharmacology is a promising approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. The computational approaches described in this study could be easily expandable to other disease-gene network analysis. PMID:29765977

Identification of alsterpaullone as a novel small molecule inhibitor to target group 3 medulloblastoma.

PubMed

Faria, Claudia C; Agnihotri, Sameer; Mack, Stephen C; Golbourn, Brian J; Diaz, Roberto J; Olsen, Samantha; Bryant, Melissa; Bebenek, Matthew; Wang, Xin; Bertrand, Kelsey C; Kushida, Michelle; Head, Renee; Clark, Ian; Dirks, Peter; Smith, Christian A; Taylor, Michael D; Rutka, James T

2015-08-28

Advances in the molecular biology of medulloblastoma revealed four genetically and clinically distinct subgroups. Group 3 medulloblastomas are characterized by frequent amplifications of the oncogene MYC, a high incidence of metastasis, and poor prognosis despite aggressive therapy. We investigated several potential small molecule inhibitors to target Group 3 medulloblastomas based on gene expression data using an in silico drug screen. The Connectivity Map (C-MAP) analysis identified piperlongumine as the top candidate drug for non-WNT medulloblastomas and the cyclin-dependent kinase (CDK) inhibitor alsterpaullone as the compound predicted to have specific antitumor activity against Group 3 medulloblastomas. To validate our findings we used these inhibitors against established Group 3 medulloblastoma cell lines. The C-MAP predicted drugs reduced cell proliferation in vitro and increased survival in Group 3 medulloblastoma xenografts. Alsterpaullone had the highest efficacy in Group 3 medulloblastoma cells. Genomic profiling of Group 3 medulloblastoma cells treated with alsterpaullone confirmed inhibition of cell cycle-related genes, and down-regulation of MYC. Our results demonstrate the preclinical efficacy of using a targeted therapy approach for Group 3 medulloblastomas. Specifically, we provide rationale for advancing alsterpaullone as a targeted therapy in Group 3 medulloblastoma.

Application of a novel microtitre plate-based assay for the discovery of new inhibitors of DNA gyrase and DNA topoisomerase VI.

PubMed

Taylor, James A; Mitchenall, Lesley A; Rejzek, Martin; Field, Robert A; Maxwell, Anthony

2013-01-01

DNA topoisomerases are highly exploited targets for antimicrobial drugs. The spread of antibiotic resistance represents a significant threat to public health and necessitates the discovery of inhibitors that target topoisomerases in novel ways. However, the traditional assays for topoisomerase activity are not suitable for the high-throughput approaches necessary for drug discovery. In this study we validate a novel assay for screening topoisomerase inhibitors. A library of 960 compounds was screened against Escherichia coli DNA gyrase and archaeal Methanosarcina mazei DNA topoisomerase VI. Several novel inhibitors were identified for both enzymes, and subsequently characterised in vitro and in vivo. Inhibitors from the M. mazei topoisomerase VI screen were tested for their ability to inhibit Arabidopsis topoisomerase VI in planta. The data from this work present new options for antibiotic drug discovery and provide insight into the mechanism of topoisomerase VI.

Application of a Novel Microtitre Plate-Based Assay for the Discovery of New Inhibitors of DNA Gyrase and DNA Topoisomerase VI

PubMed Central

Taylor, James A.; Mitchenall, Lesley A.; Rejzek, Martin; Field, Robert A.; Maxwell, Anthony

2013-01-01

DNA topoisomerases are highly exploited targets for antimicrobial drugs. The spread of antibiotic resistance represents a significant threat to public health and necessitates the discovery of inhibitors that target topoisomerases in novel ways. However, the traditional assays for topoisomerase activity are not suitable for the high-throughput approaches necessary for drug discovery. In this study we validate a novel assay for screening topoisomerase inhibitors. A library of 960 compounds was screened against Escherichia coli DNA gyrase and archaeal Methanosarcina mazei DNA topoisomerase VI. Several novel inhibitors were identified for both enzymes, and subsequently characterised in vitro and in vivo. Inhibitors from the M. mazei topoisomerase VI screen were tested for their ability to inhibit Arabidopsis topoisomerase VI in planta. The data from this work present new options for antibiotic drug discovery and provide insight into the mechanism of topoisomerase VI. PMID:23469129

European League against rheumatism - selected presentation and poster a highlights June 11th to 13th 2013.

PubMed

Braddock, Martin

2014-02-01

The heterogeneous pathology of many autoimmune diseases warrants the continual discovery and development of new drugs. Drawing on selected oral presentations and selected poster displays, this article highlights some new developments in the pharmacological validation of molecular targets implicated in inflammatory autoimmune disease and may be of direct importance to scientists working in this field. This report describes the current state of the pharmacology of selected drugs and targets which may have utility in modulating immune function and autoimmune inflammatory disease. Many new molecules are progressing through clinical development for the treatment of rheumatological diseases. The value of the basic nonclinical and clinical research presented is to further pharmacological knowledge of the molecule, better understand the benefit-risk associated with clinical development and to assist in supporting the potential position of a new drug in the current treatment paradigm.

Angiogenesis in Spontaneous Tumors and Implications for Comparative Tumor Biology

PubMed Central

Benazzi, C.; Al-Dissi, A.; Chau, C. H.; Figg, W. D.; Sarli, G.; de Oliveira, J. T.; Gärtner, F.

2014-01-01

Blood supply is essential for development and growth of tumors and angiogenesis is the fundamental process of new blood vessel formation from preexisting ones. Angiogenesis is a prognostic indicator for a variety of tumors, and it coincides with increased shedding of neoplastic cells into the circulation and metastasis. Several molecules such as cell surface receptors, growth factors, and enzymes are involved in this process. While antiangiogenic therapy for cancer has been proposed over 20 years ago, it has garnered much controversy in recent years within the scientific community. The complex relationships between the angiogenic signaling cascade and antiangiogenic substances have indicated the angiogenic pathway as a valid target for anticancer drug development and VEGF has become the primary antiangiogenic drug target. This review discusses the basic and clinical perspectives of angiogenesis highlighting the importance of comparative biology in understanding tumor angiogenesis and the integration of these model systems for future drug development. PMID:24563633

Analysis of a simulation algorithm for direct brain drug delivery

PubMed Central

Rosenbluth, Kathryn Hammond; Eschermann, Jan Felix; Mittermeyer, Gabriele; Thomson, Rowena; Mittermeyer, Stephan; Bankiewicz, Krystof S.

2011-01-01

Convection enhanced delivery (CED) achieves targeted delivery of drugs with a pressure-driven infusion through a cannula placed stereotactically in the brain. This technique bypasses the blood brain barrier and gives precise distributions of drugs, minimizing off-target effects of compounds such as viral vectors for gene therapy or toxic chemotherapy agents. The exact distribution is affected by the cannula positioning, flow rate and underlying tissue structure. This study presents an analysis of a simulation algorithm for predicting the distribution using baseline MRI images acquired prior to inserting the cannula. The MRI images included diffusion tensor imaging (DTI) to estimate the tissue properties. The algorithm was adapted for the devices and protocols identified for upcoming trials and validated with direct MRI visualization of Gadolinium in 20 infusions in non-human primates. We found strong agreement between the size and location of the simulated and gadolinium volumes, demonstrating the clinical utility of this surgical planning algorithm. PMID:21945468

Developing therapeutic microRNAs for cancer

PubMed Central

Bader, AG; Brown, D; Stoudemire, J; Lammers, P

2014-01-01

Despite substantial progress in understanding the cancer-signaling network, effective therapies remain scarce due to insufficient disruption of oncogenic pathways, drug resistance and drug-induced toxicity. This complexity of cancer defines an urgent goal for researchers and clinicians to develop novel therapeutic strategies. The discovery of microRNAs (miRNAs) provides new hope for accomplishing this task. Supported by solid evidence for a critical role in cancer and bolstered by a unique mechanism of action, miRNAs are likely to yield a new class of targeted therapeutics. In contrast to current cancer medicines, miRNA-based therapies function by subtle repression of gene expression on a yet large number of oncogenic factors and are, therefore, anticipated to be highly efficacious. After the completion of target validation for several candidates, the development of therapeutic miRNAs is now moving to a new stage that involves pharmacological drug delivery, preclinical toxicology and regulatory guidelines. PMID:21633392

Literature Mining for the Discovery of Hidden Connections between Drugs, Genes and Diseases

PubMed Central

Frijters, Raoul; van Vugt, Marianne; Smeets, Ruben; van Schaik, René; de Vlieg, Jacob; Alkema, Wynand

2010-01-01

The scientific literature represents a rich source for retrieval of knowledge on associations between biomedical concepts such as genes, diseases and cellular processes. A commonly used method to establish relationships between biomedical concepts from literature is co-occurrence. Apart from its use in knowledge retrieval, the co-occurrence method is also well-suited to discover new, hidden relationships between biomedical concepts following a simple ABC-principle, in which A and C have no direct relationship, but are connected via shared B-intermediates. In this paper we describe CoPub Discovery, a tool that mines the literature for new relationships between biomedical concepts. Statistical analysis using ROC curves showed that CoPub Discovery performed well over a wide range of settings and keyword thesauri. We subsequently used CoPub Discovery to search for new relationships between genes, drugs, pathways and diseases. Several of the newly found relationships were validated using independent literature sources. In addition, new predicted relationships between compounds and cell proliferation were validated and confirmed experimentally in an in vitro cell proliferation assay. The results show that CoPub Discovery is able to identify novel associations between genes, drugs, pathways and diseases that have a high probability of being biologically valid. This makes CoPub Discovery a useful tool to unravel the mechanisms behind disease, to find novel drug targets, or to find novel applications for existing drugs. PMID:20885778

Is there a rational approach for increasing drug specificity? Considerations on CNS target choice and validation.

PubMed

Resende, Rodrigo R; Ulrich, Henning; Faria, Marcella

2007-01-01

The description of mental illness states brings into light a referential paradox on the absence of grounds for normality. Furthermore, the semiology itself poses a problem throughout the intricate consensual relations between psychiatrists. New molecules with activity on the CNS are ever more specific as to molecular cognitive capabilities, reaching limits of individual genetic variability. Cultural mechanisms of neuronal adaptation also contribute significantly to representations and its correlation with feelings. Neuropeptides increase excitability in various different brain regions, with networks underlying optimal behaviour patterns. Therefore, the sole specification of target molecules yet does not lead directly to specific results, as insights from a systematic approach should conceal. Current validation methods generate insufficient data for discriminating successful treatable candidates. Instead of regarding the heuristics of empirically classified disease models, a new tendency to compromise scientia rationale with technical capabilities should be regarded. Some of the drugs that have obtained patents recently will be discussed in the framework of their rational and actual specificity. The molecular basis underlining function will be contrasted with an alternative approach, namely: how functional organization constrains molecular action. The categories comprising neurogenarative pathologies at one hand and the mood disorders at the other hand will be analysed separately as the procedures guiding drug design in each case seem to be different.

Interactions of antiparasitic sterols with sterol 14α-demethylase (CYP51) of human pathogens.

PubMed

Warfield, Jasmine; Setzer, William N; Ogungbe, Ifedayo Victor

2014-01-01

Sterol 14α-demethylase is a validated and an attractive drug target in human protozoan parasites. Pharmacological inactivation of this important enzyme has proven very effective against fungal infections, and it is a target that is being exploited for new antitrypanosomal and antileishmanial chemotherapy. We have used in silico calculations to identify previously reported antiparasitic sterol-like compounds and their structural congeners that have preferential and high docking affinity for CYP51. The sterol 14α-demethylase from Trypanosoma cruzi and Leishmania infantum, in particular, preferentially dock to taraxerol, epi-oleanolic acid, and α/β-amyrim structural scaffolds. These structural information and predicted interactions can be exploited for fragment/structure-based antiprotozoal drug design.

Discovering modes of action for therapeutic compounds using a genome-wide screen of yeast heterozygotes.

PubMed

Lum, Pek Yee; Armour, Christopher D; Stepaniants, Sergey B; Cavet, Guy; Wolf, Maria K; Butler, J Scott; Hinshaw, Jerald C; Garnier, Philippe; Prestwich, Glenn D; Leonardson, Amy; Garrett-Engele, Philip; Rush, Christopher M; Bard, Martin; Schimmack, Greg; Phillips, John W; Roberts, Christopher J; Shoemaker, Daniel D

2004-01-09

Modern medicine faces the challenge of developing safer and more effective therapies to treat human diseases. Many drugs currently in use were discovered without knowledge of their underlying molecular mechanisms. Understanding their biological targets and modes of action will be essential to design improved second-generation compounds. Here, we describe the use of a genome-wide pool of tagged heterozygotes to assess the cellular effects of 78 compounds in Saccharomyces cerevisiae. Specifically, lanosterol synthase in the sterol biosynthetic pathway was identified as a target of the antianginal drug molsidomine, which may explain its cholesterol-lowering effects. Further, the rRNA processing exosome was identified as a potential target of the cell growth inhibitor 5-fluorouracil. This genome-wide screen validated previously characterized targets or helped identify potentially new modes of action for over half of the compounds tested, providing proof of this principle for analyzing the modes of action of clinically relevant compounds.

3D MI-DRAGON: new model for the reconstruction of US FDA drug- target network and theoretical-experimental studies of inhibitors of rasagiline derivatives for AChE.

PubMed

Prado-Prado, Francisco; García-Mera, Xerardo; Escobar, Manuel; Alonso, Nerea; Caamaño, Olga; Yañez, Matilde; González-Díaz, Humberto

2012-01-01

The number of neurodegenerative diseases has been increasing in recent years. Many of the drug candidates to be used in the treatment of neurodegenerative diseases present specific 3D structural features. An important protein in this sense is the acetylcholinesterase (AChE), which is the target of many Alzheimer's dementia drugs. Consequently, the prediction of Drug-Protein Interactions (DPIs/nDPIs) between new drug candidates and specific 3D structure and targets is of major importance. To this end, we can use Quantitative Structure-Activity Relationships (QSAR) models to carry out a rational DPIs prediction. Unfortunately, many previous QSAR models developed to predict DPIs take into consideration only 2D structural information and codify the activity against only one target. To solve this problem we can develop some 3D multi-target QSAR (3D mt-QSAR) models. In this study, using the 3D MI-DRAGON technique, we have introduced a new predictor for DPIs based on two different well-known software. We have used the MARCH-INSIDE (MI) and DRAGON software to calculate 3D structural parameters for drugs and targets respectively. Both classes of 3D parameters were used as input to train Artificial Neuronal Network (ANN) algorithms using as benchmark dataset the complex network (CN) made up of all DPIs between US FDA approved drugs and their targets. The entire dataset was downloaded from the DrugBank database. The best 3D mt-QSAR predictor found was an ANN of Multi-Layer Perceptron-type (MLP) with profile MLP 37:37-24-1:1. This MLP classifies correctly 274 out of 321 DPIs (Sensitivity = 85.35%) and 1041 out of 1190 nDPIs (Specificity = 87.48%), corresponding to training Accuracy = 87.03%. We have validated the model with external predicting series with Sensitivity = 84.16% (542/644 DPIs; Specificity = 87.51% (2039/2330 nDPIs) and Accuracy = 86.78%. The new CNs of DPIs reconstructed from US FDA can be used to explore large DPI databases in order to discover both new drugs and/or targets. We have carried out some theoretical-experimental studies to illustrate the practical use of 3D MI-DRAGON. First, we have reported the prediction and pharmacological assay of 22 different rasagiline derivatives with possible AChE inhibitory activity. In this work, we have reviewed different computational studies on Drug- Protein models. First, we have reviewed 10 studies on DP computational models. Next, we have reviewed 2D QSAR, 3D QSAR, CoMFA, CoMSIA and Docking with different compounds to find Drug-Protein QSAR models. Last, we have developped a 3D multi-target QSAR (3D mt-QSAR) models for the prediction of the activity of new compounds against different targets or the discovery of new targets.

A safe lithium mimetic for bipolar disorder

PubMed Central

Singh, Nisha; Halliday, Amy C.; Thomas, Justyn M.; Kuznetsova, Olga; Baldwin, Rhiannon; Woon, Esther C. Y.; Aley, Parvinder K.; Antoniadou, Ivi; Sharp, Trevor; Vasudevan, Sridhar R.; Churchill, Grant C.

2012-01-01

Lithium is the most effective mood stabilizer for the treatment of bipolar disorder, but it is toxic at only twice the therapeutic dosage and has many undesirable side effects. It is likely that a small molecule could be found with lithium-like efficacy but without toxicity through target-based drug discovery; however, lithium’s therapeutic target remains equivocal. Inositol monophosphatase is a possible target but no bioavailable inhibitors exist. Here we report that the antioxidant ebselen inhibits inositol monophosphatase and induces lithium-like effects on mouse behaviour, which are reversed with inositol, consistent with a mechanism involving inhibition of inositol recycling. Ebselen is part of the National Institutes of Health Clinical Collection, a chemical library of bioavailable drugs considered clinically safe but without proven use. Therefore, ebselen represents a lithium mimetic with the potential both to validate inositol monophosphatase inhibition as a treatment for bipolar disorder and to serve as a treatment itself. PMID:23299882

Concordance of IHC, FISH and RT-PCR for EML4-ALK rearrangements.

PubMed

Teixidó, Cristina; Karachaliou, Niki; Peg, Vicente; Gimenez-Capitan, Ana; Rosell, Rafael

2014-04-01

The echinoderm microtubule-associated protein-like 4 anaplastic lymphoma kinase (EML4-ALK) has emerged as the second most important driver oncogene in lung cancer and the first targetable fusion oncokinase to be identified in 4-6% of lung adenocarcinomas. Crizotinib, along with a diagnostic test-the Vysis ALK Break Apart fluorescence in situ hybridization (FISH) Probe Kit-is approved for the treatment of ALK positive advanced non-small cell lung cancer (NSCLC). However, the success of a targeted drug is critically dependent on a sensitive and specific screening assay to detect the molecular drug target. In our experience, reverse transcription polymerase chain reaction (RT-PCR)-based detection of EML4-ALK is a more sensitive and reliable approach compared to FISH and immunohistochemistry (IHC). Although ALK FISH is clinically validated, the assay can be technically challenging and other diagnostic modalities, including IHC and RT-PCR should be further explored.

Present and future breast cancer management--bench to bedside and back: a positioning paper of academia, regulatory authorities and pharmaceutical industry.

PubMed

Bartsch, R; Frings, S; Marty, M; Awada, A; Berghoff, A S; Conte, P; Dickin, S; Enzmann, H; Gnant, M; Hasmann, M; Hendriks, H R; Llombart, A; Massacesi, C; von Minckwitz, G; Penault-Llorca, F; Scaltriti, M; Yarden, Y; Zwierzina, H; Zielinski, C C

2014-04-01

Insights into tumour biology of breast cancer have led the path towards the introduction of targeted treatment approaches; still, breast cancer-related mortality remains relatively high. Efforts in the field of basic research revealed new druggable targets which now await validation within the context of clinical trials. Therefore, questions concerning the optimal design of future studies are becoming even more pertinent. Aspects such as the ideal end point, availability of predictive markers to identify the optimal cohort for drug testing, or potential mechanisms of resistance need to be resolved. An expert panel representing the academic community, the pharmaceutical industry, as well as European Regulatory Authorities met in Vienna, Austria, in November 2012, in order to discuss breast cancer biology, identification of novel biological targets and optimal drug development with the aim of treatment individualization. This article summarizes statements and perspectives provided by the meeting participants.

Bioenergetics of Mycobacterium: An Emerging Landscape for Drug Discovery

PubMed Central

Iqbal, Iram Khan; Bajeli, Sapna; Akela, Ajit Kumar

2018-01-01

Mycobacterium tuberculosis (Mtb) exhibits remarkable metabolic flexibility that enables it to survive a plethora of host environments during its life cycle. With the advent of bedaquiline for treatment of multidrug-resistant tuberculosis, oxidative phosphorylation has been validated as an important target and a vulnerable component of mycobacterial metabolism. Exploiting the dependence of Mtb on oxidative phosphorylation for energy production, several components of this pathway have been targeted for the development of new antimycobacterial agents. This includes targeting NADH dehydrogenase by phenothiazine derivatives, menaquinone biosynthesis by DG70 and other compounds, terminal oxidase by imidazopyridine amides and ATP synthase by diarylquinolines. Importantly, oxidative phosphorylation also plays a critical role in the survival of persisters. Thus, inhibitors of oxidative phosphorylation can synergize with frontline TB drugs to shorten the course of treatment. In this review, we discuss the oxidative phosphorylation pathway and development of its inhibitors in detail. PMID:29473841

Diversity-oriented synthetic strategy for developing a chemical modulator of protein-protein interaction

NASA Astrophysics Data System (ADS)

Kim, Jonghoon; Jung, Jinjoo; Koo, Jaeyoung; Cho, Wansang; Lee, Won Seok; Kim, Chanwoo; Park, Wonwoo; Park, Seung Bum

2016-10-01

Diversity-oriented synthesis (DOS) can provide a collection of diverse and complex drug-like small molecules, which is critical in the development of new chemical probes for biological research of undruggable targets. However, the design and synthesis of small-molecule libraries with improved biological relevance as well as maximized molecular diversity represent a key challenge. Herein, we employ functional group-pairing strategy for the DOS of a chemical library containing privileged substructures, pyrimidodiazepine or pyrimidine moieties, as chemical navigators towards unexplored bioactive chemical space. To validate the utility of this DOS library, we identify a new small-molecule inhibitor of leucyl-tRNA synthetase-RagD protein-protein interaction, which regulates the amino acid-dependent activation of mechanistic target of rapamycin complex 1 signalling pathway. This work highlights that privileged substructure-based DOS strategy can be a powerful research tool for the construction of drug-like compounds to address challenging biological targets.

Selective inhibition of Sarcocystis neurona calcium-dependent protein kinase 1 for equine protozoal myeloencephalitis therapy

USDA-ARS?s Scientific Manuscript database

Sarcocystis neurona is the most frequent cause of Equine Protozoal Myeloencephalitis (EPM), a debilitating neurologic disease of horses that can be difficult to treat. We identified SnCDPK1, the S. neurona homologue of calcium dependent protein kinase 1 (CDPK1), a validated drug target in Toxoplasma...

Integrating Transcriptomics with Metabolic Modeling Predicts Biomarkers and Drug Targets for Alzheimer's Disease

PubMed Central

Stempler, Shiri; Yizhak, Keren; Ruppin, Eytan

2014-01-01

Accumulating evidence links numerous abnormalities in cerebral metabolism with the progression of Alzheimer's disease (AD), beginning in its early stages. Here, we integrate transcriptomic data from AD patients with a genome-scale computational human metabolic model to characterize the altered metabolism in AD, and employ state-of-the-art metabolic modelling methods to predict metabolic biomarkers and drug targets in AD. The metabolic descriptions derived are first tested and validated on a large scale versus existing AD proteomics and metabolomics data. Our analysis shows a significant decrease in the activity of several key metabolic pathways, including the carnitine shuttle, folate metabolism and mitochondrial transport. We predict several metabolic biomarkers of AD progression in the blood and the CSF, including succinate and prostaglandin D2. Vitamin D and steroid metabolism pathways are enriched with predicted drug targets that could mitigate the metabolic alterations observed. Taken together, this study provides the first network wide view of the metabolic alterations associated with AD progression. Most importantly, it offers a cohort of new metabolic leads for the diagnosis of AD and its treatment. PMID:25127241

Targeting Insulin Signaling for the Treatment of Alzheimer's Disease.

PubMed

Chen, Yanxing; Zhang, Jianfang; Zhang, Baorong; Gong, Cheng-Xin

2016-01-01

Sporadic Alzheimer's disease (AD) is caused by multiple etiological factors, among which impaired brain insulin signaling and decreased brain glucose metabolism are important metabolic factors. Contrary to previous belief that insulin would not act in the brain, studies in the last three decades have proven important roles of insulin and insulin signaling in various biological functions in the brain. Impaired brain insulin signaling or brain insulin resistance and its role in the molecular pathogenesis of sporadic AD have been demonstrated. Thus, targeting brain insulin signaling for the treatment of cognitive impairment and AD has now attracted much attention in the field of AD drug discovery. This article reviews recent studies that target brain insulin signaling, especially those investigations on intranasal insulin administration and drugs that improve insulin sensitivity, including incretins, dipeptidyl peptidase IV inhibitors, thiazolidinediones, and metformin. These drugs have been previously approved for the treatment of diabetes mellitus, which could expedite their development for the treatment of AD. Although larger clinical trials are needed for validating their efficacy for the treatment of cognitive impairment and AD, results of animal studies and clinical trials available to date are encouraging.

Discovery and Characterization of a Highly Potent and Selective Aminopyrazoline-Based in Vivo Probe (BAY-598) for the Protein Lysine Methyltransferase SMYD2.

PubMed

Eggert, Erik; Hillig, Roman C; Koehr, Silke; Stöckigt, Detlef; Weiske, Jörg; Barak, Naomi; Mowat, Jeffrey; Brumby, Thomas; Christ, Clara D; Ter Laak, Antonius; Lang, Tina; Fernandez-Montalvan, Amaury E; Badock, Volker; Weinmann, Hilmar; Hartung, Ingo V; Barsyte-Lovejoy, Dalia; Szewczyk, Magdalena; Kennedy, Steven; Li, Fengling; Vedadi, Masoud; Brown, Peter J; Santhakumar, Vijayaratnam; Arrowsmith, Cheryl H; Stellfeld, Timo; Stresemann, Carlo

2016-05-26

Protein lysine methyltransferases have recently emerged as a new target class for the development of inhibitors that modulate gene transcription or signaling pathways. SET and MYND domain containing protein 2 (SMYD2) is a catalytic SET domain containing methyltransferase reported to monomethylate lysine residues on histone and nonhistone proteins. Although several studies have uncovered an important role of SMYD2 in promoting cancer by protein methylation, the biology of SMYD2 is far from being fully understood. Utilization of highly potent and selective chemical probes for target validation has emerged as a concept which circumvents possible limitations of knockdown experiments and, in particular, could result in an improved exploration of drug targets with a complex underlying biology. Here, we report the development of a potent, selective, and cell-active, substrate-competitive inhibitor of SMYD2, which is the first reported inhibitor suitable for in vivo target validation studies in rodents.

Nonclinical dose formulation analysis method validation and sample analysis.

PubMed

Whitmire, Monica Lee; Bryan, Peter; Henry, Teresa R; Holbrook, John; Lehmann, Paul; Mollitor, Thomas; Ohorodnik, Susan; Reed, David; Wietgrefe, Holly D

2010-12-01

Nonclinical dose formulation analysis methods are used to confirm test article concentration and homogeneity in formulations and determine formulation stability in support of regulated nonclinical studies. There is currently no regulatory guidance for nonclinical dose formulation analysis method validation or sample analysis. Regulatory guidance for the validation of analytical procedures has been developed for drug product/formulation testing; however, verification of the formulation concentrations falls under the framework of GLP regulations (not GMP). The only current related regulatory guidance is the bioanalytical guidance for method validation. The fundamental parameters for bioanalysis and formulation analysis validations that overlap include: recovery, accuracy, precision, specificity, selectivity, carryover, sensitivity, and stability. Divergence in bioanalytical and drug product validations typically center around the acceptance criteria used. As the dose formulation samples are not true "unknowns", the concept of quality control samples that cover the entire range of the standard curve serving as the indication for the confidence in the data generated from the "unknown" study samples may not always be necessary. Also, the standard bioanalytical acceptance criteria may not be directly applicable, especially when the determined concentration does not match the target concentration. This paper attempts to reconcile the different practices being performed in the community and to provide recommendations of best practices and proposed acceptance criteria for nonclinical dose formulation method validation and sample analysis.

N-Myristoyltransferase inhibitors as new leads to treat sleeping sickness

PubMed Central

Frearson, Julie A.; Brand, Stephen; McElroy, Stuart P.; Cleghorn, Laura A.T.; Smid, Ondrej; Stojanovski, Laste; Price, Helen P.; Guther, M. Lucia S.; Torrie, Leah S.; Robinson, David A.; Hallyburton, Irene; Mpamhanga, Chidochangu P.; Brannigan, James A.; Wilkinson, Anthony J.; Hodgkinson, Michael; Hui, Raymond; Qiu, Wei; Raimi, Olawale G.; van Aalten, Daan M. F.; Brenk, Ruth; Gilbert, Ian H.; Read, Kevin D.; Fairlamb, Alan H.; Ferguson, Michael A. J.; Smith, Deborah F.; Wyatt, Paul G.

2010-01-01

African sleeping sickness or human African trypanosomiasis (HAT), caused by Trypanosoma brucei spp., is responsible for ~30,000 deaths each year. Available treatments for this neglected disease are poor, with unacceptable efficacy and safety profiles, particularly in the late stage of the disease, when the parasite has infected the central nervous system. Here, we report the validation of a molecular target and discovery of associated lead compounds with potential to address this unmet need. Inhibition of this target, T. brucei N-myristoyltransferase (TbNMT), leads to rapid killing of trypanosomes both in vitro and in vivo and cures trypanosomiasis in mice. These high affinity inhibitors bind into the peptide substrate pocket of the enzyme and inhibit protein N-myristoylation in trypanosomes. The compounds identified have very promising pharmaceutical properties and represent an exciting opportunity to develop oral drugs to treat this devastating disease. Our studies validate TbNMT as a promising therapeutic target for HAT. PMID:20360736

Targeting Ligandable Pockets on Plant Homeodomain (PHD) Zinc Finger Domains by a Fragment-Based Approach

PubMed Central

2018-01-01

Plant homeodomain (PHD) zinc fingers are histone reader domains that are often associated with human diseases. Despite this, they constitute a poorly targeted class of readers, suggesting low ligandability. Here, we describe a successful fragment-based campaign targeting PHD fingers from the proteins BAZ2A and BAZ2B as model systems. We validated a pool of in silico fragments both biophysically and structurally and solved the first crystal structures of PHD zinc fingers in complex with fragments bound to an anchoring pocket at the histone binding site. The best-validated hits were found to displace a histone H3 tail peptide in competition assays. This work identifies new chemical scaffolds that provide suitable starting points for future ligand optimization using structure-guided approaches. The demonstrated ligandability of the PHD reader domains could pave the way for the development of chemical probes to drug this family of epigenetic readers. PMID:29529862

Targeting Ligandable Pockets on Plant Homeodomain (PHD) Zinc Finger Domains by a Fragment-Based Approach.

PubMed

Amato, Anastasia; Lucas, Xavier; Bortoluzzi, Alessio; Wright, David; Ciulli, Alessio

2018-04-20

Plant homeodomain (PHD) zinc fingers are histone reader domains that are often associated with human diseases. Despite this, they constitute a poorly targeted class of readers, suggesting low ligandability. Here, we describe a successful fragment-based campaign targeting PHD fingers from the proteins BAZ2A and BAZ2B as model systems. We validated a pool of in silico fragments both biophysically and structurally and solved the first crystal structures of PHD zinc fingers in complex with fragments bound to an anchoring pocket at the histone binding site. The best-validated hits were found to displace a histone H3 tail peptide in competition assays. This work identifies new chemical scaffolds that provide suitable starting points for future ligand optimization using structure-guided approaches. The demonstrated ligandability of the PHD reader domains could pave the way for the development of chemical probes to drug this family of epigenetic readers.

Implementing Genome-Driven Oncology

PubMed Central

Hyman, David M.; Taylor, Barry S.; Baselga, José

2017-01-01

Early successes in identifying and targeting individual oncogenic drivers, together with the increasing feasibility of sequencing tumor genomes, have brought forth the promise of genome-driven oncology care. As we expand the breadth and depth of genomic analyses, the biological and clinical complexity of its implementation will be unparalleled. Challenges include target credentialing and validation, implementing drug combinations, clinical trial designs, targeting tumor heterogeneity, and deploying technologies beyond DNA sequencing, among others. We review how contemporary approaches are tackling these challenges and will ultimately serve as an engine for biological discovery and increase our insight into cancer and its treatment. PMID:28187282

Deorphaning the Macromolecular Targets of the Natural Anticancer Compound Doliculide.

PubMed

Schneider, Gisbert; Reker, Daniel; Chen, Tao; Hauenstein, Kurt; Schneider, Petra; Altmann, Karl-Heinz

2016-09-26

The cyclodepsipeptide doliculide is a marine natural product with strong actin-polymerizing and anticancer activities. Evidence for doliculide acting as a potent and subtype-selective antagonist of prostanoid E receptor 3 (EP3) is presented. Computational target prediction suggested that this membrane receptor is a likely macromolecular target and enabled immediate in vitro validation. This proof-of-concept study demonstrates the in silico deorphanization of phenotypic screening hits as a viable concept for future natural-product-inspired chemical biology and drug discovery efforts. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Rapid Python-Based Methodology for Target-Focused Combinatorial Library Design.

PubMed

Li, Shiliang; Song, Yuwei; Liu, Xiaofeng; Li, Honglin

2016-01-01

The chemical space is so vast that only a small portion of it has been examined. As a complementary approach to systematically probe the chemical space, virtual combinatorial library design has extended enormous impacts on generating novel and diverse structures for drug discovery. Despite the favorable contributions, high attrition rates in drug development that mainly resulted from lack of efficacy and side effects make it increasingly challenging to discover good chemical starting points. In most cases, focused libraries, which are restricted to particular regions of the chemical space, are deftly exploited to maximize hit rate and improve efficiency at the beginning of the drug discovery and drug development pipeline. This paper presented a valid methodology for fast target-focused combinatorial library design in both reaction-based and production-based ways with the library creating rates of approximately 70,000 molecules per second. Simple, quick and convenient operating procedures are the specific features of the method. SHAFTS, a hybrid 3D similarity calculation software, was embedded to help refine the size of the libraries and improve hit rates. Two target-focused (p38-focused and COX2-focused) libraries were constructed efficiently in this study. This rapid library enumeration method is portable and applicable to any other targets for good chemical starting points identification collaborated with either structure-based or ligand-based virtual screening.

Targeting Thromboxane A2 Receptor for Anti-Metastasis Therapy of Breast Cancer

DTIC Science & Technology

2011-09-01

of cell function by Rho GTPases." Drug News Perspect 14(7): 389-95. Erickson, J. W., R. A. Cerione, et al. (1997). "Identification of an actin...Focusing Tumor Microenvironment, Stem Cells and Metastasis 570 (MTOC) and Golgi apparatus to the front of the nucleus, oriented toward the direction of...define the function of TP in tumor cell motility and to validate TP as a target for anti-metastasis therapy of breast cancer. In the first aim, the

Structure-guided development of selective TbcatB inhibitors

PubMed Central

Mallari, Jeremy P.; Shelat, Anang A.; Kosinski, Aaron; Caffrey, Conor R.; Connelly, Michele; Zhu, Fangyi; McKerrow, James H.; Guy, R. Kiplin

2009-01-01

The trypanosomal cathepsin TbcatB is essential for parasite survival and is an attractive therapeutic target. Herein we report the structure-guided development of TbcatB inhibitors with specificity relative to rhodesain and human cathepsins B and L. Inhibitors were tested for enzymatic activity, trypanocidal activity, and general cytotoxicity. These data chemically validate TbcatB as a drug target, and demonstrate that it is possible to potently and selectively inhibit TbcatB relative to trypanosomal and human homologues. PMID:19769357

A combined ligand-based and target-based drug design approach for G-protein coupled receptors: application to salvinorin A, a selective kappa opioid receptor agonist

NASA Astrophysics Data System (ADS)

Singh, Nidhi; Chevé, Gwénaël; Ferguson, David M.; McCurdy, Christopher R.

2006-08-01

Combined ligand-based and target-based drug design approaches provide a synergistic advantage over either method individually. Therefore, we set out to develop a powerful virtual screening model to identify novel molecular scaffolds as potential leads for the human KOP (hKOP) receptor employing a combined approach. Utilizing a set of recently reported derivatives of salvinorin A, a structurally unique KOP receptor agonist, a pharmacophore model was developed that consisted of two hydrogen bond acceptor and three hydrophobic features. The model was cross-validated by randomizing the data using the CatScramble technique. Further validation was carried out using a test set that performed well in classifying active and inactive molecules correctly. Simultaneously, a bovine rhodopsin based "agonist-bound" hKOP receptor model was also generated. The model provided more accurate information about the putative binding site of salvinorin A based ligands. Several protein structure-checking programs were used to validate the model. In addition, this model was in agreement with the mutation experiments carried out on KOP receptor. The predictive ability of the model was evaluated by docking a set of known KOP receptor agonists into the active site of this model. The docked scores correlated reasonably well with experimental p K i values. It is hypothesized that the integration of these two independently generated models would enable a swift and reliable identification of new lead compounds that could reduce time and cost of hit finding within the drug discovery and development process, particularly in the case of GPCRs.

Tricyclic GyrB/ParE (TriBE) Inhibitors. A new class of broad-spectrum dual-targeting antibacterial agents

DOE PAGES

Tari, Leslie W.; Li, Xiaoming; Trzoss, Michael; ...

2013-12-26

Increasing resistance to every major class of antibiotics and a dearth of novel classes of antibacterial agents in development pipelines has created a dwindling reservoir of treatment options for serious bacterial infections. The bacterial type IIA topoisomerases, DNA gyrase and topoisomerase IV, are validated antibacterial drug targets with multiple prospective drug binding sites, including the catalytic site targeted by the fluoroquinolone antibiotics. Growing resistance to fluoroquinolones, frequently mediated by mutations in the drug-binding site, is increasingly limiting the utility of this antibiotic class, prompting the search for other inhibitor classes that target different sites on the topoisomerase complexes. The highlymore » conserved ATP-binding subunits of DNA gyrase (GyrB) and topoisomerase IV (ParE) have long been recognized as excellent candidates for the development of dual-targeting antibacterial agents with broad-spectrum potential. However, to date, no natural product or small molecule inhibitors targeting these sites have succeeded in the clinic, and no inhibitors of these enzymes have yet been reported with broad-spectrum antibacterial activity encompassing the majority of Gram-negative pathogens. Using structure-based drug design (SBDD), we have created a novel dual-targeting pyrimidoindole inhibitor series with exquisite potency against GyrB and ParE enzymes from a broad range of clinically important pathogens. Inhibitors from this series demonstrate potent, broad-spectrum antibacterial activity against Gram-positive and Gram-negative pathogens of clinical importance, including fluoroquinolone resistant and multidrug resistant strains. Moreover, lead compounds have been discovered with clinical potential; they are well tolerated in animals, and efficacious in Gram-negative infection models.« less

Tricyclic GyrB/ParE (TriBE) Inhibitors. A new class of broad-spectrum dual-targeting antibacterial agents

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tari, Leslie W.; Li, Xiaoming; Trzoss, Michael

Increasing resistance to every major class of antibiotics and a dearth of novel classes of antibacterial agents in development pipelines has created a dwindling reservoir of treatment options for serious bacterial infections. The bacterial type IIA topoisomerases, DNA gyrase and topoisomerase IV, are validated antibacterial drug targets with multiple prospective drug binding sites, including the catalytic site targeted by the fluoroquinolone antibiotics. Growing resistance to fluoroquinolones, frequently mediated by mutations in the drug-binding site, is increasingly limiting the utility of this antibiotic class, prompting the search for other inhibitor classes that target different sites on the topoisomerase complexes. The highlymore » conserved ATP-binding subunits of DNA gyrase (GyrB) and topoisomerase IV (ParE) have long been recognized as excellent candidates for the development of dual-targeting antibacterial agents with broad-spectrum potential. However, to date, no natural product or small molecule inhibitors targeting these sites have succeeded in the clinic, and no inhibitors of these enzymes have yet been reported with broad-spectrum antibacterial activity encompassing the majority of Gram-negative pathogens. Using structure-based drug design (SBDD), we have created a novel dual-targeting pyrimidoindole inhibitor series with exquisite potency against GyrB and ParE enzymes from a broad range of clinically important pathogens. Inhibitors from this series demonstrate potent, broad-spectrum antibacterial activity against Gram-positive and Gram-negative pathogens of clinical importance, including fluoroquinolone resistant and multidrug resistant strains. Moreover, lead compounds have been discovered with clinical potential; they are well tolerated in animals, and efficacious in Gram-negative infection models.« less

Simultaneous Screening and Quantification of Basic, Neutral and Acidic Drugs in Blood Using UPLC-QTOF-MS.

PubMed

Bidny, Sergei; Gago, Kim; Chung, Phuong; Albertyn, Desdemona; Pasin, Daniel

2017-04-01

An analytical method using ultra performance liquid chromatography (UPLC) quadrupole time-of-flight mass spectrometry (QTOF-MS) was developed and validated for the targeted toxicological screening and quantification of commonly used pharmaceuticals and drugs of abuse in postmortem blood using 100 µL sample. It screens for more than 185 drugs and metabolites and quantifies more than 90 drugs. The selected compounds include classes of pharmaceuticals and drugs of abuse such as: antidepressants, antipsychotics, analgesics (including narcotic analgesics), anti-inflammatory drugs, benzodiazepines, beta-blockers, amphetamines, new psychoactive substances (NPS), cocaine and metabolites. Compounds were extracted into acetonitrile using a salting-out assisted liquid-liquid extraction (SALLE) procedure. The extracts were analyzed using a Waters ACQUITY UPLC coupled with a XEVO QTOF mass spectrometer. Separation of the analytes was achieved by gradient elution using Waters ACQUITY HSS C18 column (2.1 mm x 150 mm, 1.8 μm). The mass spectrometer was operated in both positive and negative electrospray ionization modes. The high-resolution mass spectrometry (HRMS) data was acquired using a patented Waters MSE acquisition mode which collected low and high energy spectra alternatively during the same acquisition. Positive identification of target analytes was based on accurate mass measurements of the molecular ion, product ion, peak area ratio and retention times. Calibration curves were linear over the concentration range 0.05-2 mg/L for basic and neutral analytes and 0.1-6 mg/L for acidic analytes with the correlation coefficients (r2) > 0.96 for most analytes. The limits of detection (LOD) were between 0.001-0.05 mg/L for all analytes. Good recoveries were achieved ranging from 80% to 100% for most analytes using the SALLE method. The method was validated for sensitivity, selectivity, accuracy, precision, stability, carryover and matrix effects. The developed method was tested on a number of authentic forensic samples producing consistent results that correlated with results obtained from other validated methods. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Meeting report from the Prostate Cancer Foundation PSMA-directed radionuclide scientific working group.

PubMed

Miyahira, Andrea K; Pienta, Kenneth J; Morris, Michael J; Bander, Neil H; Baum, Richard P; Fendler, Wolfgang P; Goeckeler, William; Gorin, Michael A; Hennekes, Hartwig; Pomper, Martin G; Sartor, Oliver; Tagawa, Scott T; Williams, Scott; Soule, Howard R

2018-05-01

The Prostate Cancer Foundation (PCF) convened a PSMA-Directed Radionuclide Scientific Working Group on November 14, 2017, at Weill Cornell Medicine, New York, NY. The meeting was attended by 35 global investigators with expertise in prostate cancer biology, radionuclide therapy, molecular imaging, prostate-specific membrane antigen (PSMA)-targeted agents, drug development, and prostate cancer clinical trials. The goal of this meeting was to discuss the potential for using PSMA-targeted radionuclide agents for the treatment of advanced prostate cancer and to define the studies and clinical trials necessary for validating and optimizing the use of these agents. Several major topic areas were discussed including the overview of PSMA biology, lessons and applications of PSMA-targeted PET imaging, the nuances of designing PSMA-targeted radionuclide agents, clinical experiences with PSMA-targeted radionuclides, PCF-funded projects to accelerate PSMA-targeted radionuclide therapy, and barriers to the use of radionuclide treatments in widespread clinical practice. This article reviews the major topics discussed at the meeting with the goal of promoting research that will validate and optimize the use of PSMA-targeted radionuclide therapies for the treatment of advanced prostate cancer. © 2018 Wiley Periodicals, Inc.

In silico analysis and experimental validation of azelastine hydrochloride (N4) targeting sodium taurocholate co-transporting polypeptide (NTCP) in HBV therapy.

PubMed

Fu, L-L; Liu, J; Chen, Y; Wang, F-T; Wen, X; Liu, H-Q; Wang, M-Y; Ouyang, L; Huang, J; Bao, J-K; Wei, Y-Q

2014-08-01

The aim of this study was to explore sodium taurocholate co-transporting polypeptide (NTCP) exerting its function with hepatitis B virus (HBV) and its targeted candidate compounds, in HBV therapy. Identification of NTCP as a novel HBV target for screening candidate small molecules, was used by phylogenetic analysis, network construction, molecular modelling, molecular docking and molecular dynamics (MD) simulation. In vitro virological examination, q-PCR, western blotting and cytotoxicity studies were used for validating efficacy of the candidate compound. We used the phylogenetic analysis of NTCP and constructed its protein-protein network. Also, we screened compounds from Drugbank and ZINC, among which five were validated for their authentication in HepG 2.2.15 cells. Then, we selected compound N4 (azelastine hydrochloride) as the most potent of them. This showed good inhibitory activity against HBsAg (IC50 = 7.5 μm) and HBeAg (IC50 = 3.7 μm), as well as high SI value (SI = 4.68). Further MD simulation results supported good interaction between compound N4 and NTCP. In silico analysis and experimental validation together demonstrated that compound N4 can target NTCP in HepG2.2.15 cells, which may shed light on exploring it as a potential anti-HBV drug. © 2014 John Wiley & Sons Ltd.

Animal models of serotonergic psychedelics.

PubMed

Hanks, James B; González-Maeso, Javier

2013-01-16

The serotonin 5-HT(2A) receptor is the major target of psychedelic drugs such as lysergic acid diethylamide (LSD), mescaline, and psilocybin. Serotonergic psychedelics induce profound effects on cognition, emotion, and sensory processing that often seem uniquely human. This raises questions about the validity of animal models of psychedelic drug action. Nonetheless, recent findings suggest behavioral abnormalities elicited by psychedelics in rodents that predict such effects in humans. Here we review the behavioral effects induced by psychedelic drugs in rodent models, discuss the translational potential of these findings, and define areas where further research is needed to better understand the molecular mechanisms and neuronal circuits underlying their neuropsychological effects.

Animal Models of Serotonergic Psychedelics

PubMed Central

2012-01-01

The serotonin 5-HT2A receptor is the major target of psychedelic drugs such as lysergic acid diethylamide (LSD), mescaline, and psilocybin. Serotonergic psychedelics induce profound effects on cognition, emotion, and sensory processing that often seem uniquely human. This raises questions about the validity of animal models of psychedelic drug action. Nonetheless, recent findings suggest behavioral abnormalities elicited by psychedelics in rodents that predict such effects in humans. Here we review the behavioral effects induced by psychedelic drugs in rodent models, discuss the translational potential of these findings, and define areas where further research is needed to better understand the molecular mechanisms and neuronal circuits underlying their neuropsychological effects. PMID:23336043

Virtual drug discovery: beyond computational chemistry?

PubMed

Gilardoni, Francois; Arvanites, Anthony C

2010-02-01

This editorial looks at how a fully integrated structure that performs all aspects in the drug discovery process, under one company, is slowly disappearing. The steps in the drug discovery paradigm have been slowly increasing toward virtuality or outsourcing at various phases of product development in a company's candidate pipeline. Each step in the process, such as target identification and validation and medicinal chemistry, can be managed by scientific teams within a 'virtual' company. Pharmaceutical companies to biotechnology start-ups have been quick in adopting this new research and development business strategy in order to gain flexibility, access the best technologies and technical expertise, and decrease product developmental costs. In today's financial climate, the term virtual drug discovery has an organizational meaning. It represents the next evolutionary step in outsourcing drug development.

Targeting RNA structure in SMN2 reverses spinal muscular atrophy molecular phenotypes.

PubMed

Garcia-Lopez, Amparo; Tessaro, Francesca; Jonker, Hendrik R A; Wacker, Anna; Richter, Christian; Comte, Arnaud; Berntenis, Nikolaos; Schmucki, Roland; Hatje, Klas; Petermann, Olivier; Chiriano, Gianpaolo; Perozzo, Remo; Sciarra, Daniel; Konieczny, Piotr; Faustino, Ignacio; Fournet, Guy; Orozco, Modesto; Artero, Ruben; Metzger, Friedrich; Ebeling, Martin; Goekjian, Peter; Joseph, Benoît; Schwalbe, Harald; Scapozza, Leonardo

2018-05-23

Modification of SMN2 exon 7 (E7) splicing is a validated therapeutic strategy against spinal muscular atrophy (SMA). However, a target-based approach to identify small-molecule E7 splicing modifiers has not been attempted, which could reveal novel therapies with improved mechanistic insight. Here, we chose as a target the stem-loop RNA structure TSL2, which overlaps with the 5' splicing site of E7. A small-molecule TSL2-binding compound, homocarbonyltopsentin (PK4C9), was identified that increases E7 splicing to therapeutic levels and rescues downstream molecular alterations in SMA cells. High-resolution NMR combined with molecular modelling revealed that PK4C9 binds to pentaloop conformations of TSL2 and promotes a shift to triloop conformations that display enhanced E7 splicing. Collectively, our study validates TSL2 as a target for small-molecule drug discovery in SMA, identifies a novel mechanism of action for an E7 splicing modifier, and sets a precedent for other splicing-mediated diseases where RNA structure could be similarly targeted.

Core signaling pathways in ovarian cancer stem cell revealed by integrative analysis of multi-marker genomics data.

PubMed

Zhang, Tianyu; Xu, Jielin; Deng, Siyuan; Zhou, Fengqi; Li, Jin; Zhang, Liwei; Li, Lang; Wang, Qi-En; Li, Fuhai

2018-01-01

Tumor recurrence occurs in more than 70% of ovarian cancer patients, and the majority eventually becomes refractory to treatments. Ovarian Cancer Stem Cells (OCSCs) are believed to be responsible for the tumor relapse and drug resistance. Therefore, eliminating ovarian CSCs is important to improve the prognosis of ovarian cancer patients. However, there is a lack of effective drugs to eliminate OCSCs because the core signaling pathways regulating OCSCs remain unclear. Also it is often hard for biologists to identify a few testable targets and infer driver signaling pathways regulating CSCs from a large number of differentially expression genes in an unbiased manner. In this study, we propose a straightforward and integrative analysis to identify potential core signaling pathways of OCSCs by integrating transcriptome data of OCSCs isolated based on two distinctive markers, ALDH and side population, with regulatory network (Transcription Factor (TF) and Target Interactome) and signaling pathways. We first identify the common activated TFs in two OCSC populations integrating the gene expression and TF-target Interactome; and then uncover up-stream signaling cascades regulating the activated TFs. In specific, 22 activated TFs are identified. Through literature search validation, 15 of them have been reported in association with cancer stem cells. Additionally, 10 TFs are found in the KEGG signaling pathways, and their up-stream signaling cascades are extracted, which also provide potential treatment targets. Moreover, 40 FDA approved drugs are identified to target on the up-stream signaling cascades, and 15 of them have been reported in literatures in cancer stem cell treatment. In conclusion, the proposed approach can uncover the activated up-stream signaling, activated TFs and up-regulated target genes that constitute the potential core signaling pathways of ovarian CSC. Also drugs and drug combinations targeting on the core signaling pathways might be able to eliminate OCSCs. The proposed approach can also be applied for identifying potential activated signaling pathways of other types of cancers.

DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue.

PubMed

Wang, QuanQiu; Xu, Rong

2015-01-01

Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algorithm to prioritize FDA-approved drugs from dengue-related diseases to treat dengue. When tested in a de-novo validation setting, DenguePredict found the only two drugs tested in clinical trials for treating dengue and ranked them highly: chloroquine ranked at top 0.96% and ivermectin at top 22.75%. We showed that drugs targeting immune systems and arachidonic acid metabolism-related apoptotic pathways might represent innovative drugs to treat dengue. In summary, DenguePredict, by combining comprehensive disease- and drug-related data and novel algorithms, may greatly facilitate drug discovery for dengue.

Effective implementation of novel MET pharmacodynamic assays in translational studies.

PubMed

Srivastava, Apurva K; Navas, Tony; Herrick, William G; Hollingshead, Melinda G; Bottaro, Donald P; Doroshow, James H; Parchment, Ralph E

2017-01-01

MET tyrosine kinase (TK) dysregulation is significantly implicated in many types of cancer. Despite over 20 years of drug development to target MET in cancers, a pure anti-MET therapeutic has not yet received market approval. The failure of two recently concluded phase III trials point to a major weakness in biomarker strategies to identify patients who will benefit most from MET therapies. The capability to interrogate oncogenic mutations in MET via circulating tumor DNA (ctDNA) provides an important advancement in identification and stratification of patients for MET therapy. However, a wide range in type and frequency of these mutations suggest there is a need to carefully link these mutations to MET dysregulation, at least in proof-of-concept studies. In this review, we elaborate how we can utilize recently developed and validated pharmacodynamic biomarkers of MET not only to show target engagement, but more importantly to quantitatively measure MET dysregulation in tumor tissues. The MET assay endpoints provide evidence of both canonical and non-canonical MET signaling, can be used as "effect markers" to define biologically effective doses (BEDs) for molecularly targeted drugs, confirm mechanism-of-action in testing combination of drugs, and establish whether a diagnostic test is reporting MET dysregulation. We have established standard operating procedures for tumor biopsy collections to control pre-analytical variables that have produced valid results in proof-of-concept studies. The reagents and procedures are made available to the research community for potential implementation on multiple platforms such as ELISA, quantitative immunofluorescence assay (qIFA), and immuno-MRM assays.

EMUDRA: Ensemble of Multiple Drug Repositioning Approaches to Improve Prediction Accuracy.

PubMed

Zhou, Xianxiao; Wang, Minghui; Katsyv, Igor; Irie, Hanna; Zhang, Bin

2018-04-24

Availability of large-scale genomic, epigenetic and proteomic data in complex diseases makes it possible to objectively and comprehensively identify therapeutic targets that can lead to new therapies. The Connectivity Map has been widely used to explore novel indications of existing drugs. However, the prediction accuracy of the existing methods, such as Kolmogorov-Smirnov statistic remains low. Here we present a novel high-performance drug repositioning approach that improves over the state-of-the-art methods. We first designed an expression weighted cosine method (EWCos) to minimize the influence of the uninformative expression changes and then developed an ensemble approach termed EMUDRA (Ensemble of Multiple Drug Repositioning Approaches) to integrate EWCos and three existing state-of-the-art methods. EMUDRA significantly outperformed individual drug repositioning methods when applied to simulated and independent evaluation datasets. We predicted using EMUDRA and experimentally validated an antibiotic rifabutin as an inhibitor of cell growth in triple negative breast cancer. EMUDRA can identify drugs that more effectively target disease gene signatures and will thus be a useful tool for identifying novel therapies for complex diseases and predicting new indications for existing drugs. The EMUDRA R package is available at doi:10.7303/syn11510888. bin.zhang@mssm.edu or zhangb@hotmail.com. Supplementary data are available at Bioinformatics online.

Toward theragnostics.

PubMed

Pene, Frédéric; Courtine, Emilie; Cariou, Alain; Mira, Jean-Paul

2009-01-01

Theragnostics is a treatment strategy that combines therapeutics with diagnostics. It associates both a diagnostic test that identifies patients most likely to be helped or harmed by a new medication, and targeted drug therapy based on the test results. Bioinformatics, genomics, proteomics, and functional genomics are molecular biology tools essential for the progress of molecular theragnostics. These tools generate the genetic and protein information required for the development of diagnostic assays. Theragnostics includes a wide range of subjects, including personalized medicine, pharmacogenomics, and molecular imaging to develop efficient new targeted therapies with adequate benefit/risk to patients and a better molecular understanding of how to optimize drug selection. Furthermore, theragnostics aims to monitor the response to the treatment, to increase drug efficacy and safety. In addition, theragnostics could eliminate the unnecessary treatment of patients for whom therapy is not appropriate, resulting in significant drug cost savings for the healthcare system. However, the introduction of theragnostic tests into routine health care requires both a demonstration of cost-effectiveness and the availability of appropriate accessible testing systems. This review reports validation studies in oncology and infectious diseases that have demonstrated the benefits of such approach in well-defined subpopulations of patients, moving the field from the drug development process toward clinical practice and routine application. Theragnostics may change the usual business model of pharmaceutical companies from the classic blockbuster model toward targeted therapies.

The effectiveness of computerized drug-lab alerts: a systematic review and meta-analysis.

PubMed

Bayoumi, Imaan; Al Balas, Mosab; Handler, Steven M; Dolovich, Lisa; Hutchison, Brian; Holbrook, Anne

2014-06-01

Inadequate lab monitoring of drugs is a potential cause of ADEs (adverse drug events) which is remediable. To determine the effectiveness of computerized drug-lab alerts to improve medication-related outcomes. Citations from the Computerized Clinical Decision Support System Systematic Review (CCDSSR) and MMIT (Medications Management through Health Information Technology) databases, which had searched MEDLINE, EMBASE, CINAHL, Cochrane Database of Systematic Reviews, International Pharmaceutical Abstracts from 1974 to March 27, 2013. Randomized controlled trials (RCTs) of clinician-targeted computerized drug lab alerts conducted in any healthcare setting. Two reviewers performed full text review to determine study eligibility. A single reviewer abstracted data and evaluated validity of included studies using Cochrane handbook domains. Thirty-six studies met the inclusion criteria (25 single drug studies with 22,504 participants, 14 targeting anticoagulation; 11 multi-drug studies with 56,769 participants). ADEs were reported as an outcome in only four trials, all targeting anticoagulants. Computerized drug-lab alerts did not reduce ADEs (OR 0.89, 95% CI 0.79-1.00, p=0.05), length of hospital stay (SMD 0.00, 95%CI -0.93 to 0.93, p=0.055, 1 study), likelihood of hypoglycemia (OR 1.29, 95% CI 0.31-5.37) or likelihood of bleeding, but were associated with increased likelihood of prescribing changes (OR 1.73, 95% CI 1.21-2.47) or lab monitoring (OR 1.47, 95% confidence interval 1.12-1.94) in accordance with the alert. There is no evidence that computerized drug-lab alerts are associated with important clinical benefits, but there is evidence of improvement in selected clinical surrogate outcomes (time in therapeutic range for vitamin K antagonists), and changes in process outcomes (lab monitoring and prescribing decisions). Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Multi-drug UPLC-MS/MS method to quantify antimicrobials in feedingstuffs at carry-over level.

PubMed

Civitareale, C; Mestria, S; Gallo, P; Giannetti, L; Neri, B; Stacchini, P; Fiori, M

2018-06-25

Carry-over is an undesirable contamination from medicated to non-medicated feedingstuffs during feedingstuffs production. In 2009, the European Commission recognised this phenomenon as unavoidable and set tolerable levels of coccidiostats and histomonostats as a result of carry-over in feedingstuffs intended for non-target animal species. Cross-contamination can also take place from other pharmaceuticals employed in medicated feed. No harmonized approach has been adopted by the Member States so far to evaluate non-target feedingstuffs containing veterinary drugs due to carry-over. However, the European Council and the Parliament are drafting a new regulation, fixing tolerable levels of drugs in non-target feedingstuffs. This work presents a simple and fast multi-drug method by liquid chromatography-tandem mass spectrometry (LC-MS/MS) for the detection, confirmation and quantification of thirty-seven drugs belonging to different classes of antimicrobials (sulphonamides, tetracyclines, macrolides, quinolones, pleuromutilins and streptogramins) in feedingstuffs. The method was in-house validated at carry-over levels, in the concentration range 0.25-2.0 mg kg -1 , according to the Regulation (UE) 2017/625 requirements and to Commission Decision 2002/657/EC. The capability of the method to detect all drugs at lower levels than the lowest proposed by the last draft of the perspective regulation guarantees its applicability for confirmatory purposes in official control activities. The developed method has been applied to non-compliant feed samples. This article is protected by copyright. All rights reserved.

Targeted Nanocurcumin Therapy Using Annexin A2 Anitbody Improves Tumor Accumulation and Therapeutic Efficacy Against Highly Metastatic Breast Cancer.

PubMed

Mukerjee, Anindita; Ranjan, Anmalendu P; Vishwanatha, Jamboor K

2016-07-01

A major challenge in pharmaceutical research is effective targeting strategies to their sites of action. Emerging knowledge and the current progress in nanotechnology based delivery systems has opened up exciting ways towards successful targeted nanodelivery systems. For cancer therapy, nanoparticle-based drug formulations hold several advantages over free drugs, including improved pharmacokinetics, enhanced tumor accumulation, reduced systemic exposure and side effects and better patient compliance. The goal of this study was to validate the in vivo targeting potential and evaluate the combinatorial therapeutic potential of novel Annexin A2 (AnxA2) antibody-conjugated curcumin loaded poly(lactic-co-glycolic acid) (PLGA) nanoparticles (AnxA2-CPNP) against metastatic breast cancer. As a first step, we demonstrated that the cell-surface expression of AnxA2 is increases during breast cancer progression with very high expression in highly malignant cancer cells and basal expression in non-malignant cells. This confirmed AnxA2 as an excellent target for targeting our curcumin nanoparticles. Our results indicate that AnxA2-CPNP showed increased uptake in highly metastatic breast cancer cells than untargeted nanoparticles due to the differential AnxA2 expression. Cell viability, plasmin generation and wound healing assays reveal that AnxA2-CPNPs effectively inhibited cell proliferation, invasion and migration, key elements for cancer growth and metastasis. Further, angiogenesis assay illustrated that AnxA2-CPNPs decreased the formation of tube capillaries, thus inhibiting neoangiogenesis, a critical element in tumor growth. Live animal imaging demonstrated that AnxA2-PNPs and AnxA2-CPNPs effectively targeted and accumulated in the tumor as seen by the increased fluorescence intensity on the live scans. Xenograft studies in mice showed significant regression of breast tumor as a result of both effective targeting, accumulation and sustained release of curcumin in the tumor. In conclusion, AnxA2-CPNPs were successfully validated for their breast tumor targeting potential and its improved therapeutic efficacy against metastatic breast cancer.

Safety Lead Optimization and Candidate Identification: Integrating New Technologies into Decision-Making.

PubMed

Dambach, Donna M; Misner, Dinah; Brock, Mathew; Fullerton, Aaron; Proctor, William; Maher, Jonathan; Lee, Dong; Ford, Kevin; Diaz, Dolores

2016-04-18

Discovery toxicology focuses on the identification of the most promising drug candidates through the development and implementation of lead optimization strategies and hypothesis-driven investigation of issues that enable rational and informed decision-making. The major goals are to [a] identify and progress the drug candidate with the best overall drug safety profile for a therapeutic area, [b] remove the most toxic drugs from the portfolio prior to entry into humans to reduce clinical attrition due to toxicity, and [c] establish a well-characterized hazard and translational risk profile to enable clinical trial designs. This is accomplished through a framework that balances the multiple considerations to identify a drug candidate with the overall best drug characteristics and provides a cogent understanding of mechanisms of toxicity. The framework components include establishing a target candidate profile for each program that defines the qualities of a successful candidate based on the intended therapeutic area, including the risk tolerance for liabilities; evaluating potential liabilities that may result from engaging the therapeutic target (pharmacology-mediated or on-target) and that are chemical structure-mediated (off-target); and characterizing identified liabilities. Lead optimization and investigation relies upon the integrated use of a variety of technologies and models (in silico, in vitro, and in vivo) that have achieved a sufficient level of qualification or validation to provide confidence in their use. We describe the strategic applications of various nonclinical models (established and new) for a holistic and integrated risk assessment that is used for rational decision-making. While this review focuses on strategies for small molecules, the overall concepts, approaches, and technologies are generally applicable to biotherapeutics.

Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I.

PubMed

Ekins, Sean; Godbole, Adwait Anand; Kéri, György; Orfi, Lászlo; Pato, János; Bhat, Rajeshwari Subray; Verma, Rinkee; Bradley, Erin K; Nagaraja, Valakunja

2017-03-01

There is a shortage of compounds that are directed towards new targets apart from those targeted by the FDA approved drugs used against Mycobacterium tuberculosis. Topoisomerase I (Mttopo I) is an essential mycobacterial enzyme and a promising target in this regard. However, it suffers from a shortage of known inhibitors. We have previously used computational approaches such as homology modeling and docking to propose 38 FDA approved drugs for testing and identified several active molecules. To follow on from this, we now describe the in vitro testing of a library of 639 compounds. These data were used to create machine learning models for Mttopo I which were further validated. The combined Mttopo I Bayesian model had a 5 fold cross validation receiver operator characteristic of 0.74 and sensitivity, specificity and concordance values above 0.76 and was used to select commercially available compounds for testing in vitro. The recently described crystal structure of Mttopo I was also compared with the previously described homology model and then used to dock the Mttopo I actives norclomipramine and imipramine. In summary, we describe our efforts to identify small molecule inhibitors of Mttopo I using a combination of machine learning modeling and docking studies in conjunction with screening of the selected molecules for enzyme inhibition. We demonstrate the experimental inhibition of Mttopo I by small molecule inhibitors and show that the enzyme can be readily targeted for lead molecule development. Copyright © 2017 Elsevier Ltd. All rights reserved.

NMR screening in fragment-based drug design: a practical guide.

PubMed

Kim, Hai-Young; Wyss, Daniel F

2015-01-01

Fragment-based drug design (FBDD) comprises both fragment-based screening (FBS) to find hits and elaboration of these hits to lead compounds. Typical fragment hits have lower molecular weight (<300-350 Da) and lower initial potency but higher ligand efficiency when compared to those from high-throughput screening. NMR spectroscopy has been widely used for FBDD since it identifies and localizes the binding site of weakly interacting hits on the target protein. Here we describe ligand-based NMR methods for hit identification from fragment libraries and for functional cross-validation of primary hits.

Ensemble-based docking: From hit discovery to metabolism and toxicity predictions

DOE PAGES

Evangelista, Wilfredo; Weir, Rebecca; Ellingson, Sally; ...

2016-07-29

The use of ensemble-based docking for the exploration of biochemical pathways and toxicity prediction of drug candidates is described. We describe the computational engineering work necessary to enable large ensemble docking campaigns on supercomputers. We show examples where ensemble-based docking has significantly increased the number and the diversity of validated drug candidates. Finally, we illustrate how ensemble-based docking can be extended beyond hit discovery and toward providing a structural basis for the prediction of metabolism and off-target binding relevant to pre-clinical and clinical trials.

The processivity factor complex of feline herpes virus-1 is a new drug target.

PubMed

Zhukovskaya, Natalia L; Guan, Hancheng; Saw, Yih Ling; Nuth, Manunya; Ricciardi, Robert P

2015-03-01

Feline herpes virus-1 (FHV-1) is ubiquitous in the cat population and is a major cause of blindness for which antiviral drugs, including acyclovir, are not completely effective. Recurrent infections, due to reactivation of latent FHV-1 residing in the trigeminal ganglia, can lead to epithelial keratitis and stromal keratitis and eventually loss of sight. This has prompted the medical need for an antiviral drug that will specifically inhibit FHV-1 infection. A new antiviral target is the DNA polymerase and its associated processivity factor, which forms a complex that is essential for extended DNA strand synthesis. In this study we have cloned and expressed the FHV-1 DNA polymerase (f-UL30) and processivity factor (f-UL42) and demonstrated that both proteins are required to completely synthesize the 7249 nucleotide full-length DNA from the M13 primed-DNA template in vitro. Significantly, a known inhibitor of human herpes simplex virus-1 (HSV-1) processivity complex was shown to inhibit FHV-1 processive DNA synthesis in vitro and block infection of cells. This validates using f-UL42/f-UL30 as a new antiviral drug target to treat feline ocular herpes infection. Copyright © 2015 Elsevier B.V. All rights reserved.

Beyond Standard Therapy: Drugs Under Investigation for The Treatment of Gastrointestinal Stromal Tumor

PubMed Central

Alturkmani, Hani J; Pessetto, Ziyan Y; Godwin, Andrew K

2015-01-01

Introduction Gastrointestinal stromal tumor (GIST) is the most common non-epithelial malignancy of the GI tract. With the discovery of KIT and later PDGFRA gain-of-function mutations as factors in the pathogenesis of the disease, GIST was the quintessential model for targeted therapy. Despite the successful clinical use of imatinib mesylate, a selective receptor tyrosine kinase (RTK) inhibitor that targets KIT, PDGFRA and BCR-ABL, we still do not have treatment for the long-term control of advanced GIST. Areas covered This review summarizes the drugs that are under investigation or have been assessed in trials for GIST treatment. The article focuses on their mechanisms of actions, the preclinical evidence of efficacy, and the clinical trials concerning safety and efficacy in humans. Expert opinion It is known that KIT and PDGFRA mutations in GIST patients influence the response to treatment. This observation should be taken into consideration when investigating new drugs. RECIST was developed to help uniformly report efficacy trials in oncology. Despite the usefulness of this system, many questions are being addressed about its validity in evaluating the true efficacy of drugs knowing that new targeted therapies do not affect the tumor size as much as they halt progression and prolong survival. PMID:26098203

A Global Genomic and Genetic Strategy to Identify, Validate and Use Gene Signatures of Xenobiotic-Responsive Transcription Factors in Prediction of Pathway Activation in the Mouse Liver

EPA Science Inventory

Many drugs and environmentally-relevant chemicals activate xenobiotic-responsive transcription factors. Identification of target genes of these factors would be useful in predicting pathway activation in in vitro chemical screening as well as their involvement in disease states. ...

Effective electron-density map improvement and structure validation on a Linux multi-CPU web cluster: The TB Structural Genomics Consortium Bias Removal Web Service.

PubMed

Reddy, Vinod; Swanson, Stanley M; Segelke, Brent; Kantardjieff, Katherine A; Sacchettini, James C; Rupp, Bernhard

2003-12-01

Anticipating a continuing increase in the number of structures solved by molecular replacement in high-throughput crystallography and drug-discovery programs, a user-friendly web service for automated molecular replacement, map improvement, bias removal and real-space correlation structure validation has been implemented. The service is based on an efficient bias-removal protocol, Shake&wARP, and implemented using EPMR and the CCP4 suite of programs, combined with various shell scripts and Fortran90 routines. The service returns improved maps, converted data files and real-space correlation and B-factor plots. User data are uploaded through a web interface and the CPU-intensive iteration cycles are executed on a low-cost Linux multi-CPU cluster using the Condor job-queuing package. Examples of map improvement at various resolutions are provided and include model completion and reconstruction of absent parts, sequence correction, and ligand validation in drug-target structures.

Large-Scale Chemical Similarity Networks for Target Profiling of Compounds Identified in Cell-Based Chemical Screens

PubMed Central

Lo, Yu-Chen; Senese, Silvia; Li, Chien-Ming; Hu, Qiyang; Huang, Yong; Damoiseaux, Robert; Torres, Jorge Z.

2015-01-01

Target identification is one of the most critical steps following cell-based phenotypic chemical screens aimed at identifying compounds with potential uses in cell biology and for developing novel disease therapies. Current in silico target identification methods, including chemical similarity database searches, are limited to single or sequential ligand analysis that have limited capabilities for accurate deconvolution of a large number of compounds with diverse chemical structures. Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling. Our benchmark study showed that CSNAP can achieve an overall higher accuracy (>80%) of target prediction with respect to representative chemotypes in large (>200) compound sets, in comparison to the SEA approach (60–70%). Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms (proteomic, genetic, etc) for system-wise drug target validation. To demonstrate the utility of the CSNAP approach, we combined CSNAP's target prediction with experimental ligand evaluation to identify the major mitotic targets of hit compounds from a cell-based chemical screen and we highlight novel compounds targeting microtubules, an important cancer therapeutic target. The CSNAP method is freely available and can be accessed from the CSNAP web server (http://services.mbi.ucla.edu/CSNAP/). PMID:25826798

ANN multiscale model of anti-HIV drugs activity vs AIDS prevalence in the US at county level based on information indices of molecular graphs and social networks.

PubMed

González-Díaz, Humberto; Herrera-Ibatá, Diana María; Duardo-Sánchez, Aliuska; Munteanu, Cristian R; Orbegozo-Medina, Ricardo Alfredo; Pazos, Alejandro

2014-03-24

This work is aimed at describing the workflow for a methodology that combines chemoinformatics and pharmacoepidemiology methods and at reporting the first predictive model developed with this methodology. The new model is able to predict complex networks of AIDS prevalence in the US counties, taking into consideration the social determinants and activity/structure of anti-HIV drugs in preclinical assays. We trained different Artificial Neural Networks (ANNs) using as input information indices of social networks and molecular graphs. We used a Shannon information index based on the Gini coefficient to quantify the effect of income inequality in the social network. We obtained the data on AIDS prevalence and the Gini coefficient from the AIDSVu database of Emory University. We also used the Balaban information indices to quantify changes in the chemical structure of anti-HIV drugs. We obtained the data on anti-HIV drug activity and structure (SMILE codes) from the ChEMBL database. Last, we used Box-Jenkins moving average operators to quantify information about the deviations of drugs with respect to data subsets of reference (targets, organisms, experimental parameters, protocols). The best model found was a Linear Neural Network (LNN) with values of Accuracy, Specificity, and Sensitivity above 0.76 and AUROC > 0.80 in training and external validation series. This model generates a complex network of AIDS prevalence in the US at county level with respect to the preclinical activity of anti-HIV drugs in preclinical assays. To train/validate the model and predict the complex network we needed to analyze 43,249 data points including values of AIDS prevalence in 2,310 counties in the US vs ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4,856 protocols, and 10 possible experimental measures.

Enteropeptidase: A Gene Associated with a Starvation Human Phenotype and a Novel Target for Obesity Treatment

PubMed Central

Braud, Sandrine; Ciufolini, Marco A.; Harosh, Itzik

2012-01-01

Background Obesity research focuses essentially on gene targets associated with the obese phenotype. None of these targets have yet provided a viable drug therapy. Focusing instead on genes that are involved in energy absorption and that are associated with a “human starvation phenotype”, we have identified enteropeptidase (EP), a gene associated with congenital enteropeptidase deficiency, as a novel target for obesity treatment. The advantages of this target are that the gene is expressed exclusively in the brush border of the intestine; it is peripheral and not redundant. Methodology/Principal Findings Potent and selective EP inhibitors were designed around a boroarginine or borolysine motif. Oral administration of these compounds to mice restricted the bioavailability of dietary energy, and in a long-term treatment it significantly diminished the rate of increase in body weight, despite ad libitum food intake. No adverse reactions of the type seen with lipase inhibitors, such as diarrhea or steatorrhea, were observed. This validates EP as a novel, druggable target for obesity treatment. Conclusions In vivo testing of novel boroarginine or borolysine-based EP inhibitors validates a novel approach to the treatment of obesity. PMID:23185382

FDA 2011 process validation guidance: lifecycle compliance model.

PubMed

Campbell, Cliff

2014-01-01

This article has been written as a contribution to the industry's efforts in migrating from a document-driven to a data-driven compliance mindset. A combination of target product profile, control engineering, and general sum principle techniques is presented as the basis of a simple but scalable lifecycle compliance model in support of modernized process validation. Unit operations and significant variables occupy pole position within the model, documentation requirements being treated as a derivative or consequence of the modeling process. The quality system is repositioned as a subordinate of system quality, this being defined as the integral of related "system qualities". The article represents a structured interpretation of the U.S. Food and Drug Administration's 2011 Guidance for Industry on Process Validation and is based on the author's educational background and his manufacturing/consulting experience in the validation field. The U.S. Food and Drug Administration's Guidance for Industry on Process Validation (2011) provides a wide-ranging and rigorous outline of compliant drug manufacturing requirements relative to its 20(th) century predecessor (1987). Its declared focus is patient safety, and it identifies three inter-related (and obvious) stages of the compliance lifecycle. Firstly, processes must be designed, both from a technical and quality perspective. Secondly, processes must be qualified, providing evidence that the manufacturing facility is fully "roadworthy" and fit for its intended purpose. Thirdly, processes must be verified, meaning that commercial batches must be monitored to ensure that processes remain in a state of control throughout their lifetime.

New strategies in drug discovery.

PubMed

Ohlstein, Eliot H; Johnson, Anthony G; Elliott, John D; Romanic, Anne M

2006-01-01

Gene identification followed by determination of the expression of genes in a given disease and understanding of the function of the gene products is central to the drug discovery process. The ability to associate a specific gene with a disease can be attributed primarily to the extraordinary progress that has been made in the areas of gene sequencing and information technologies. Selection and validation of novel molecular targets have become of great importance in light of the abundance of new potential therapeutic drug targets that have emerged from human gene sequencing. In response to this revolution within the pharmaceutical industry, the development of high-throughput methods in both biology and chemistry has been necessitated. Further, the successful translation of basic scientific discoveries into clinical experimental medicine and novel therapeutics is an increasing challenge. As such, a new paradigm for drug discovery has emerged. This process involves the integration of clinical, genetic, genomic, and molecular phenotype data partnered with cheminformatics. Central to this process, the data generated are managed, collated, and interpreted with the use of informatics. This review addresses the use of new technologies that have arisen to deal with this new paradigm.

Thiophenecarboxamide Derivatives Activated by EthA Kill Mycobacterium tuberculosis by Inhibiting the CTP Synthetase PyrG

PubMed Central

Mori, Giorgia; Chiarelli, Laurent R.; Esposito, Marta; Makarov, Vadim; Bellinzoni, Marco; Hartkoorn, Ruben C.; Degiacomi, Giulia; Boldrin, Francesca; Ekins, Sean; de Jesus Lopes Ribeiro, Ana Luisa; Marino, Leonardo B.; Centárová, Ivana; Svetlíková, Zuzana; Blaško, Jaroslav; Kazakova, Elena; Lepioshkin, Alexander; Barilone, Nathalie; Zanoni, Giuseppe; Porta, Alessio; Fondi, Marco; Fani, Renato; Baulard, Alain R.; Mikušová, Katarína; Alzari, Pedro M.; Manganelli, Riccardo; de Carvalho, Luiz Pedro S.; Riccardi, Giovanna; Cole, Stewart T.; Pasca, Maria Rosalia

2015-01-01

Summary To combat the emergence of drug-resistant strains of Mycobacterium tuberculosis, new antitubercular agents and novel drug targets are needed. Phenotypic screening of a library of 594 hit compounds uncovered two leads that were active against M. tuberculosis in its replicating, non-replicating, and intracellular states: compounds 7947882 (5-methyl-N-(4-nitrophenyl)thiophene-2-carboxamide) and 7904688 (3-phenyl-N-[(4-piperidin-1-ylphenyl)carbamothioyl]propanamide). Mutants resistant to both compounds harbored mutations in ethA (rv3854c), the gene encoding the monooxygenase EthA, and/or in pyrG (rv1699) coding for the CTP synthetase, PyrG. Biochemical investigations demonstrated that EthA is responsible for the activation of the compounds, and by mass spectrometry we identified the active metabolite of 7947882, which directly inhibits PyrG activity. Metabolomic studies revealed that pharmacological inhibition of PyrG strongly perturbs DNA and RNA biosynthesis, and other metabolic processes requiring nucleotides. Finally, the crystal structure of PyrG was solved, paving the way for rational drug design with this newly validated drug target. PMID:26097035

Investigational drugs for pruritus.

PubMed

Benecke, Heike; Lotts, Tobias; Ständer, Sonja

2013-09-01

Chronic pruritus (CP), defined as itch lasting for > 6 weeks, is a burdensome symptom of several different diseases, dermatological and systemic, with a high negative impact on the quality of life of patients. Given the manifold aetiologies of CP, therapy is often difficult. In recent years, however, novel substances have been developed for treatment of certain CP entities and identified targets. In this review, the authors present a survey of targets currently believed to be promising (H4R, IL-31, MOR, KOR, GRPR, NGF, NK-1R, TRP channels) and related investigational drugs that are in the preclinical or clinical stage of development. Some substances have already undergone clinical testing, but only one of them (nalfurafine) has been licensed so far. Many of them are most likely to exert their effects on the skin and interfere there with the cutaneous neurobiology of CP. Currently, the most promising candidates for new therapeutic agents in CP are neurokinin-1 receptor antagonists and substances targeting the kappa- or mu-opioid receptor, or both. They have the potential to target the neuronal pathway of CP and are thus of interest for several CP entities. The goal for the coming years is to validate these concepts and move forward in developing new drugs for the therapy of CP.

Characterization of parasite-specific indels and their proposed relevance for selective anthelminthic drug targeting

PubMed Central

Wang, Qi; Heizer, Esley; Rosa, Bruce A.; Wildman, Scott A.; Janetka, James W.; Mitreva, Makedonka

2016-01-01

Insertions and deletions (indels) are important sequence variants that are considered as phylogenetic markers that reflect evolutionary adaptations in different species. In an effort to systematically study indels specific to the phylum Nematoda and their structural impact on the proteins bearing them, we examined over 340,000 polypeptides from 21 nematode species spanning the phylum, compared them to non-nematodes and identified indels unique to nematode proteins in more than 3,000 protein families. Examination of the amino acid composition revealed uneven usage of amino acids for insertions and deletions. The amino acid composition and cost, along with the secondary structure constitution of the indels, were analyzed in the context of their biological pathway associations. Species-specific indels could enable indel-based targeting for drug design in pathogens/parasites. Therefore, we screened the spatial locations of the indels in the parasite’s protein 3D structures, determined the location of the indel and identified potential unique drug targeting sites. These indels could be confirmed by RNA-Seq data. Examples are presented that illustrate the close proximity of the indel to established small-molecule binding pockets that can potentially facilitate selective targeting to the parasites and bypassing their host, thus reducing or eliminating the toxicity of the potential drugs. The study presents an approach for understanding the adaptation of pathogens/parasites at a molecular level, and outlines a strategy to identify such nematode-selective targets that remain essential to the organism. With further experimental characterization and validation, it opens a possible channel for the development of novel treatments with high target specificity, addressing both host toxicity and resistance concerns. PMID:26829384

Characterization of parasite-specific indels and their proposed relevance for selective anthelminthic drug targeting.

PubMed

Wang, Qi; Heizer, Esley; Rosa, Bruce A; Wildman, Scott A; Janetka, James W; Mitreva, Makedonka

2016-04-01

Insertions and deletions (indels) are important sequence variants that are considered as phylogenetic markers that reflect evolutionary adaptations in different species. In an effort to systematically study indels specific to the phylum Nematoda and their structural impact on the proteins bearing them, we examined over 340,000 polypeptides from 21 nematode species spanning the phylum, compared them to non-nematodes and identified indels unique to nematode proteins in more than 3000 protein families. Examination of the amino acid composition revealed uneven usage of amino acids for insertions and deletions. The amino acid composition and cost, along with the secondary structure constitution of the indels, were analyzed in the context of their biological pathway associations. Species-specific indels could enable indel-based targeting for drug design in pathogens/parasites. Therefore, we screened the spatial locations of the indels in the parasite's protein 3D structures, determined the location of the indel and identified potential unique drug targeting sites. These indels could be confirmed by RNA-Seq data. Examples are presented illustrating the close proximity of some indels to established small-molecule binding pockets that can potentially facilitate selective targeting to the parasites and bypassing their host, thus reducing or eliminating the toxicity of the potential drugs. This study presents an approach for understanding the adaptation of pathogens/parasites at a molecular level, and outlines a strategy to identify such nematode-selective targets that remain essential to the organism. With further experimental characterization and validation, it opens a possible channel for the development of novel treatments with high target specificity, addressing both host toxicity and resistance concerns. Copyright © 2016 Elsevier B.V. All rights reserved.

Liposome-based glioma targeted drug delivery enabled by stable peptide ligands.

PubMed

Wei, Xiaoli; Gao, Jie; Zhan, Changyou; Xie, Cao; Chai, Zhilan; Ran, Danni; Ying, Man; Zheng, Ping; Lu, Weiyue

2015-11-28

The treatment of glioma is one of the most challenging tasks in clinic. As an intracranial tumor, glioma exhibits many distinctive characteristics from other tumors. In particular, various barriers including enzymatic barriers in the blood and brain capillary endothelial cells, blood-brain barrier (BBB) and blood-brain tumor barrier (BBTB) rigorously prevent drug and drug delivery systems from reaching the tumor site. To tackle this dilemma, we developed a liposomal formulation to circumvent multiple-barriers by modifying the liposome surface with proteolytically stable peptides, (D)CDX and c(RGDyK). (D)CDX is a D-peptide ligand of nicotine acetylcholine receptors (nAChRs) on the BBB, and c(RGDyK) is a ligand of integrin highly expressed on the BBTB and glioma cells. Lysosomal compartments of brain capillary endothelial cells are implicated in the transcytosis of those liposomes. However, both peptide ligands displayed exceptional stability in lysosomal homogenate, ensuring that intact ligands could exert subsequent exocytosis from brain capillary endothelial cells and glioma targeting. In the cellular uptake studies, dually labeled liposomes could target both brain capillary endothelial cells and tumor cells, effectively traversing the BBB and BBTB monolayers, overcoming enzymatic barrier and targeting three-dimensional tumor spheroids. Its targeting ability to intracranial glioma was further verified in vivo by ex vivo imaging and histological studies. As a result, doxorubicin liposomes modified with both (D)CDX and c(RGDyK) presented better anti-glioma effect with prolonged median survival of nude mice bearing glioma than did unmodified liposomes and liposomes modified with individual peptide ligand. In conclusion, the liposome suggested in the present study could effectively overcome multi-barriers and accomplish glioma targeted drug delivery, validating its potential value in improving the therapeutic efficacy of doxorubicin for glioma. Copyright © 2015 Elsevier B.V. All rights reserved.

ProSelection: A Novel Algorithm to Select Proper Protein Structure Subsets for in Silico Target Identification and Drug Discovery Research.

PubMed

Wang, Nanyi; Wang, Lirong; Xie, Xiang-Qun

2017-11-27

Molecular docking is widely applied to computer-aided drug design and has become relatively mature in the recent decades. Application of docking in modeling varies from single lead compound optimization to large-scale virtual screening. The performance of molecular docking is highly dependent on the protein structures selected. It is especially challenging for large-scale target prediction research when multiple structures are available for a single target. Therefore, we have established ProSelection, a docking preferred-protein selection algorithm, in order to generate the proper structure subset(s). By the ProSelection algorithm, protein structures of "weak selectors" are filtered out whereas structures of "strong selectors" are kept. Specifically, the structure which has a good statistical performance of distinguishing active ligands from inactive ligands is defined as a strong selector. In this study, 249 protein structures of 14 autophagy-related targets are investigated. Surflex-dock was used as the docking engine to distinguish active and inactive compounds against these protein structures. Both t test and Mann-Whitney U test were used to distinguish the strong from the weak selectors based on the normality of the docking score distribution. The suggested docking score threshold for active ligands (SDA) was generated for each strong selector structure according to the receiver operating characteristic (ROC) curve. The performance of ProSelection was further validated by predicting the potential off-targets of 43 U.S. Federal Drug Administration approved small molecule antineoplastic drugs. Overall, ProSelection will accelerate the computational work in protein structure selection and could be a useful tool for molecular docking, target prediction, and protein-chemical database establishment research.

Chemical Proteomics Reveals Ferrochelatase as a Common Off-target of Kinase Inhibitors.

PubMed

Klaeger, Susan; Gohlke, Bjoern; Perrin, Jessica; Gupta, Vipul; Heinzlmeir, Stephanie; Helm, Dominic; Qiao, Huichao; Bergamini, Giovanna; Handa, Hiroshi; Savitski, Mikhail M; Bantscheff, Marcus; Médard, Guillaume; Preissner, Robert; Kuster, Bernhard

2016-05-20

Many protein kinases are valid drug targets in oncology because they are key components of signal transduction pathways. The number of clinical kinase inhibitors is on the rise, but these molecules often exhibit polypharmacology, potentially eliciting desired and toxic effects. Therefore, a comprehensive assessment of a compound's target space is desirable for a better understanding of its biological effects. The enzyme ferrochelatase (FECH) catalyzes the conversion of protoporphyrin IX into heme and was recently found to be an off-target of the BRAF inhibitor Vemurafenib, likely explaining the phototoxicity associated with this drug in melanoma patients. This raises the question of whether FECH binding is a more general feature of kinase inhibitors. To address this, we applied a chemical proteomics approach using kinobeads to evaluate 226 clinical kinase inhibitors for their ability to bind FECH. Surprisingly, low or submicromolar FECH binding was detected for 29 of all compounds tested and isothermal dose response measurements confirmed target engagement in cells. We also show that Vemurafenib, Linsitinib, Neratinib, and MK-2461 reduce heme levels in K562 cells, verifying that drug binding leads to a loss of FECH activity. Further biochemical and docking experiments identified the protoporphyrin pocket in FECH as one major drug binding site. Since the genetic loss of FECH activity leads to photosensitivity in humans, our data strongly suggest that FECH inhibition by kinase inhibitors is the molecular mechanism triggering photosensitivity in patients. We therefore suggest that a FECH assay should generally be part of the preclinical molecular toxicology package for the development of kinase inhibitors.

Towards a Drug Development Path that Targets Metastatic Progression in Osteosarcoma

PubMed Central

Khanna, Chand; Fan, Timothy M.; Gorlick, Richard; Helman, Lee J; Kleinerman, Eugenie S.; Adamson, Peter C.; Houghton, Peter J.; Tap, William D.; Welch, Danny R.; Steeg, Patricia S.; Merlino, Glenn; Sorensen, Poul HB; Kirsch, David G.; Janeway, Katherine A.; Weigel, Brenda; Randall, R. Lor; Meltzer, Paul; Withrow, Stephen J; Paoloni, Melissa; Kaplan, Rosandra N.; Teicher, Beverly A.; Seibel, Nita L.; Üren, Aykut; Patel, Shreyaskumar R.; Trent, Jeffrey; Savage, Sharon A.; Mirabello, Lisa; Reinke, Denise; Barkauskas, Donald A.; Krailo, Mark; Smith, Malcolm A.; Bernstein, Mark

2014-01-01

Despite successful primary tumor treatment, the development of pulmonary metastasis continues to be the most common cause of mortality in osteosarcoma patients. A conventional drug development path requiring drugs to induce regression of established lesions has not led to improvements for osteosarcoma patients in over 30 years. Based on our growing understanding of metastasis biology, it is now reasonable and essential that we focus on developing therapeutics that target metastatic progression. To advance this agenda a meeting of key opinion leaders and experts in the metastasis and osteosarcoma communities was convened in Bethesda Maryland. The goal of this meeting was to provide a “Perspective” that would establish a preclinical translational path that could support the early evaluation of potential therapeutic agents that uniquely target the metastatic phenotype. Although focused on osteosarcoma the need for this perspective is shared among many cancer types. The consensus achieved from the meeting included the following: That the biology of metastatic progression is associated with metastasis-specific targets/processes that may not influence grossly detectable lesions; targeting of metastasis-specific processes is feasible; rigorous preclinical data is needed to support translation of metastasis-specific agents into human trials where regression of measurable disease is not an expected outcome; preclinical data should include an understanding of mechanism of action, validation of pharmacodynamic markers of effective exposure and response, the use of several murine models of effectiveness, and where feasible the inclusion of the dog with naturally occurring osteosarcoma to define the activity of new drugs in the micro-metastatic disease setting. PMID:24803583

Current Strategies for Inhibition of Chikungunya Infection.

PubMed

Subudhi, Bharat Bhusan; Chattopadhyay, Soma; Mishra, Priyadarsee; Kumar, Abhishek

2018-05-03

Increasing incidences of Chikungunya virus (CHIKV) infection and co-infections with Dengue/Zika virus have highlighted the urgency for CHIKV management. Failure in developing effective vaccines or specific antivirals has fuelled further research. This review discusses updated strategies of CHIKV inhibition and provides possible future directions. In addition, it analyzes advances in CHIKV lifecycle, drug-target development, and potential hits obtained by in silico and experimental methods. Molecules identified with anti-CHIKV properties using traditional/rational drug design and their potential to succeed in subsequent stages of drug development have also been discussed. Possibilities of repurposing existing drugs based on their in vitro findings have also been elucidated. Probable modes of interference of these compounds at various stages of infection, including entry and replication, have been highlighted. The use of host factors as targets to identify antivirals against CHIKV has been addressed. While most of the earlier antivirals were effective in the early phases of the CHIKV life cycle, this review is also focused on drug candidates that are effective at multiple stages of its life cycle. Since most of these antivirals require validation in preclinical and clinical models, the challenges regarding this have been discussed and will provide critical information for further research.

Current Strategies for Inhibition of Chikungunya Infection

PubMed Central

Subudhi, Bharat Bhusan; Chattopadhyay, Soma; Mishra, Priyadarsee

2018-01-01

Increasing incidences of Chikungunya virus (CHIKV) infection and co-infections with Dengue/Zika virus have highlighted the urgency for CHIKV management. Failure in developing effective vaccines or specific antivirals has fuelled further research. This review discusses updated strategies of CHIKV inhibition and provides possible future directions. In addition, it analyzes advances in CHIKV lifecycle, drug-target development, and potential hits obtained by in silico and experimental methods. Molecules identified with anti-CHIKV properties using traditional/rational drug design and their potential to succeed in subsequent stages of drug development have also been discussed. Possibilities of repurposing existing drugs based on their in vitro findings have also been elucidated. Probable modes of interference of these compounds at various stages of infection, including entry and replication, have been highlighted. The use of host factors as targets to identify antivirals against CHIKV has been addressed. While most of the earlier antivirals were effective in the early phases of the CHIKV life cycle, this review is also focused on drug candidates that are effective at multiple stages of its life cycle. Since most of these antivirals require validation in preclinical and clinical models, the challenges regarding this have been discussed and will provide critical information for further research. PMID:29751486

New Structural Templates for Clinically Validated and Novel Targets in Antimicrobial Drug Research and Development.

PubMed

Klahn, Philipp; Brönstrup, Mark

The development of bacterial resistance against current antibiotic drugs necessitates a continuous renewal of the arsenal of efficacious drugs. This imperative has not been met by the output of antibiotic research and development of the past decades for various reasons, including the declining efforts of large pharma companies in this area. Moreover, the majority of novel antibiotics are chemical derivatives of existing structures that represent mostly step innovations, implying that the available chemical space may be exhausted. This review negates this impression by showcasing recent achievements in lead finding and optimization of antibiotics that have novel or unexplored chemical structures. Not surprisingly, many of the novel structural templates like teixobactins, lysocin, griselimycin, or the albicidin/cystobactamid pair were discovered from natural sources. Additional compounds were obtained from the screening of synthetic libraries and chemical synthesis, including the gyrase-inhibiting NTBI's and spiropyrimidinetrione, the tarocin and targocil inhibitors of wall teichoic acid synthesis, or the boronates and diazabicyclo[3.2.1]octane as novel β-lactamase inhibitors. A motif that is common to most clinically validated antibiotics is that they address hotspots in complex biosynthetic machineries, whose functioning is essential for the bacterial cell. Therefore, an introduction to the biological targets-cell wall synthesis, topoisomerases, the DNA sliding clamp, and membrane-bound electron transport-is given for each of the leads presented here.

Mapping of Drug-like Chemical Universe with Reduced Complexity Molecular Frameworks.

PubMed

Kontijevskis, Aleksejs

2017-04-24

The emergence of the DNA-encoded chemical libraries (DEL) field in the past decade has attracted the attention of the pharmaceutical industry as a powerful mechanism for the discovery of novel drug-like hits for various biological targets. Nuevolution Chemetics technology enables DNA-encoded synthesis of billions of chemically diverse drug-like small molecule compounds, and the efficient screening and optimization of these, facilitating effective identification of drug candidates at an unprecedented speed and scale. Although many approaches have been developed by the cheminformatics community for the analysis and visualization of drug-like chemical space, most of them are restricted to the analysis of a maximum of a few millions of compounds and cannot handle collections of 10 8 -10 12 compounds typical for DELs. To address this big chemical data challenge, we developed the Reduced Complexity Molecular Frameworks (RCMF) methodology as an abstract and very general way of representing chemical structures. By further introducing RCMF descriptors, we constructed a global framework map of drug-like chemical space and demonstrated how chemical space occupied by multi-million-member drug-like Chemetics DNA-encoded libraries and virtual combinatorial libraries with >10 12 members could be analyzed and mapped without a need for library enumeration. We further validate the approach by performing RCMF-based searches in a drug-like chemical universe and mapping Chemetics library selection outputs for LSD1 targets on a global framework chemical space map.

Quantitative structure-activity relationship: promising advances in drug discovery platforms.

PubMed

Wang, Tao; Wu, Mian-Bin; Lin, Jian-Ping; Yang, Li-Rong

2015-12-01

Quantitative structure-activity relationship (QSAR) modeling is one of the most popular computer-aided tools employed in medicinal chemistry for drug discovery and lead optimization. It is especially powerful in the absence of 3D structures of specific drug targets. QSAR methods have been shown to draw public attention since they were first introduced. In this review, the authors provide a brief discussion of the basic principles of QSAR, model development and model validation. They also highlight the current applications of QSAR in different fields, particularly in virtual screening, rational drug design and multi-target QSAR. Finally, in view of recent controversies, the authors detail the challenges faced by QSAR modeling and the relevant solutions. The aim of this review is to show how QSAR modeling can be applied in novel drug discovery, design and lead optimization. QSAR should intentionally be used as a powerful tool for fragment-based drug design platforms in the field of drug discovery and design. Although there have been an increasing number of experimentally determined protein structures in recent years, a great number of protein structures cannot be easily obtained (i.e., membrane transport proteins and G-protein coupled receptors). Fragment-based drug discovery, such as QSAR, could be applied further and have a significant role in dealing with these problems. Moreover, along with the development of computer software and hardware, it is believed that QSAR will be increasingly important.

iEzy-Drug: A Web Server for Identifying the Interaction between Enzymes and Drugs in Cellular Networking

PubMed Central

Min, Jian-Liang; Chou, Kuo-Chen

2013-01-01

With the features of extremely high selectivity and efficiency in catalyzing almost all the chemical reactions in cells, enzymes play vitally important roles for the life of an organism and hence have become frequent targets for drug design. An essential step in developing drugs by targeting enzymes is to identify drug-enzyme interactions in cells. It is both time-consuming and costly to do this purely by means of experimental techniques alone. Although some computational methods were developed in this regard based on the knowledge of the three-dimensional structure of enzyme, unfortunately their usage is quite limited because three-dimensional structures of many enzymes are still unknown. Here, we reported a sequence-based predictor, called “iEzy-Drug,” in which each drug compound was formulated by a molecular fingerprint with 258 feature components, each enzyme by the Chou's pseudo amino acid composition generated via incorporating sequential evolution information and physicochemical features derived from its sequence, and the prediction engine was operated by the fuzzy K-nearest neighbor algorithm. The overall success rate achieved by iEzy-Drug via rigorous cross-validations was about 91%. Moreover, to maximize the convenience for the majority of experimental scientists, a user-friendly web server was established, by which users can easily obtain their desired results. PMID:24371828

Combination of Anti-angiogenesis with Chemotherapy for More Effective Cancer Treatment*

PubMed Central

Ma, Jie; Waxman, David J.

2008-01-01

Angiogenesis is a hallmark of tumor development and metastasis and is now a validated target for cancer treatment. Overall, however, the survival benefits of anti-angiogenic drugs have, thus far, been rather modest, stimulating interest in developing more effective ways to combine anti-angiogenic drugs with established chemotherapies. This review discusses recent progress and emerging challenges in this field; interactions between anti-angiogenic drugs and conventional chemotherapeutic agents are examined, and strategies for the optimization of combination therapies are discussed. Anti-angiogenic drugs such as the anti-VEGF antibody bevacizumab can induce a functional normalization of the tumor vasculature that is transient and can potentiate the activity of co-administered chemoradiotherapies. However, chronic angiogenesis inhibition typically reduces tumor uptake of co-administered chemotherapeutics, indicating a need to explore new approaches, including intermittent treatment schedules and provascular strategies to increase chemotherapeutic drug exposure. In cases where anti-angiogenesis-induced tumor cell starvation augments the intrinsic cytotoxic effects of a conventional chemotherapeutic drug, combination therapy may increase anti-tumor activity despite a decrease in cytotoxic drug exposure. As new angiogenesis inhibitors enter the clinic, reliable surrogate markers are needed to monitor the progress of anti-angiogenic therapies and to identify responsive patients. New targets for anti-angiogenesis continue to be discovered, increasing the opportunities to interdict tumor angiogenesis and circumvent resistance mechanisms that may emerge with chronic use of these drugs. PMID:19074844

Predicting Drug Concentration‐Time Profiles in Multiple CNS Compartments Using a Comprehensive Physiologically‐Based Pharmacokinetic Model

PubMed Central

Yamamoto, Yumi; Välitalo, Pyry A.; Huntjens, Dymphy R.; Proost, Johannes H.; Vermeulen, An; Krauwinkel, Walter; Beukers, Margot W.; van den Berg, Dirk‐Jan; Hartman, Robin; Wong, Yin Cheong; Danhof, Meindert; van Hasselt, John G. C.

2017-01-01

Drug development targeting the central nervous system (CNS) is challenging due to poor predictability of drug concentrations in various CNS compartments. We developed a generic physiologically based pharmacokinetic (PBPK) model for prediction of drug concentrations in physiologically relevant CNS compartments. System‐specific and drug‐specific model parameters were derived from literature and in silico predictions. The model was validated using detailed concentration‐time profiles from 10 drugs in rat plasma, brain extracellular fluid, 2 cerebrospinal fluid sites, and total brain tissue. These drugs, all small molecules, were selected to cover a wide range of physicochemical properties. The concentration‐time profiles for these drugs were adequately predicted across the CNS compartments (symmetric mean absolute percentage error for the model prediction was <91%). In conclusion, the developed PBPK model can be used to predict temporal concentration profiles of drugs in multiple relevant CNS compartments, which we consider valuable information for efficient CNS drug development. PMID:28891201

Application of Quantitative Structure–Activity Relationship Models of 5-HT1A Receptor Binding to Virtual Screening Identifies Novel and Potent 5-HT1A Ligands

PubMed Central

2015-01-01

The 5-hydroxytryptamine 1A (5-HT1A) serotonin receptor has been an attractive target for treating mood and anxiety disorders such as schizophrenia. We have developed binary classification quantitative structure–activity relationship (QSAR) models of 5-HT1A receptor binding activity using data retrieved from the PDSP Ki database. The prediction accuracy of these models was estimated by external 5-fold cross-validation as well as using an additional validation set comprising 66 structurally distinct compounds from the World of Molecular Bioactivity database. These validated models were then used to mine three major types of chemical screening libraries, i.e., drug-like libraries, GPCR targeted libraries, and diversity libraries, to identify novel computational hits. The five best hits from each class of libraries were chosen for further experimental testing in radioligand binding assays, and nine of the 15 hits were confirmed to be active experimentally with binding affinity better than 10 μM. The most active compound, Lysergol, from the diversity library showed very high binding affinity (Ki) of 2.3 nM against 5-HT1A receptor. The novel 5-HT1A actives identified with the QSAR-based virtual screening approach could be potentially developed as novel anxiolytics or potential antischizophrenic drugs. PMID:24410373

Metallochaperone UreG serves as a new target for design of urease inhibitor: A novel strategy for development of antimicrobials.

PubMed

Yang, Xinming; Koohi-Moghadam, Mohamad; Wang, Runming; Chang, Yuen-Yan; Woo, Patrick C Y; Wang, Junwen; Li, Hongyan; Sun, Hongzhe

2018-01-01

Urease as a potential target of antimicrobial drugs has received considerable attention given its versatile roles in microbial infection. Development of effective urease inhibitors, however, is a significant challenge due to the deeply buried active site and highly specific substrate of a bacterial urease. Conventionally, urease inhibitors are designed by either targeting the active site or mimicking substrate of urease, which is not efficient. Up to now, only one effective inhibitor-acetohydroxamic acid (AHA)-is clinically available, but it has adverse side effects. Herein, we demonstrate that a clinically used drug, colloidal bismuth subcitrate, utilizes an unusual way to inhibit urease activity, i.e., disruption of urease maturation process via functional perturbation of a metallochaperone, UreG. Similar phenomena were also observed in various pathogenic bacteria, suggesting that UreG may serve as a general target for design of new types of urease inhibitors. Using Helicobacter pylori UreG as a showcase, by virtual screening combined with experimental validation, we show that two compounds targeting UreG also efficiently inhibited urease activity with inhibitory concentration (IC)50 values of micromolar level, resulting in attenuated virulence of the pathogen. We further demonstrate the efficacy of the compounds in a mammalian cell infection model. This study opens up a new opportunity for the design of more effective urease inhibitors and clearly indicates that metallochaperones involved in the maturation of important microbial metalloenzymes serve as new targets for devising a new type of antimicrobial drugs.

Human genetics as a model for target validation: finding new therapies for diabetes.

PubMed

Thomsen, Soren K; Gloyn, Anna L

2017-06-01

Type 2 diabetes is a global epidemic with major effects on healthcare expenditure and quality of life. Currently available treatments are inadequate for the prevention of comorbidities, yet progress towards new therapies remains slow. A major barrier is the insufficiency of traditional preclinical models for predicting drug efficacy and safety. Human genetics offers a complementary model to assess causal mechanisms for target validation. Genetic perturbations are 'experiments of nature' that provide a uniquely relevant window into the long-term effects of modulating specific targets. Here, we show that genetic discoveries over the past decades have accurately predicted (now known) therapeutic mechanisms for type 2 diabetes. These findings highlight the potential for use of human genetic variation for prospective target validation, and establish a framework for future applications. Studies into rare, monogenic forms of diabetes have also provided proof-of-principle for precision medicine, and the applicability of this paradigm to complex disease is discussed. Finally, we highlight some of the limitations that are relevant to the use of genome-wide association studies (GWAS) in the search for new therapies for diabetes. A key outstanding challenge is the translation of GWAS signals into disease biology and we outline possible solutions for tackling this experimental bottleneck.

Cardiac Arrhythmia: In vivo screening in the zebrafish to overcome complexity in drug discovery.

PubMed

Macrae, Calum A

2010-07-01

IMPORTANCE OF THE FIELD: Cardiac arrhythmias remain a major challenge for modern drug discovery. Clinical events are paroxysmal, often rare and may be asymptomatic until a highly morbid complication. Target selection is often based on limited information and though highly specific agents are identified in screening, the final efficacy is often compromised by unanticipated systemic responses, a narrow therapeutic index and substantial toxicities. AREAS COVERED IN THIS REVIEW: Our understanding of complexity of arrhythmogenesis has grown dramatically over the last two decades, and the range of potential disease mechanisms now includes pathways previously thought only tangentially involved in arrhythmia. This review surveys the literature on arrhythmia mechanisms from 1965 to the present day, outlines the complex biology underlying potentially each and every rhythm disturbance, and highlights the problems for rational target identification. The rationale for in vivo screening is described and the utility of the zebrafish for this approach and for complementary work in functional genomics is discussed. Current limitations of the model in this setting and the need for careful validation in new disease areas are also described. WHAT THE READER WILL GAIN: An overview of the complex mechanisms underlying most clinical arrhythmias, and insight into the limits of ion channel conductances as drug targets. An introduction to the zebrafish as a model organism, in particular for cardiovascular biology. Potential approaches to overcoming the hurdles to drug discovery in the face of complex biology including in vivo screening of zebrafish genetic disease models. TAKE HOME MESSAGE: In vivo screening in faithful disease models allows the effects of drugs on integrative physiology and disease biology to be captured during the screening process, in a manner agnostic to potential drug target or targets. This systematic strategy bypasses current gaps in our understanding of disease biology, but emphasizes the importance of the rigor of the disease model.

Magnetic resonance imaging of folic acid-coated magnetite nanoparticles reflects tissue biodistribution of long-acting antiretroviral therapy.

PubMed

Li, Tianyuzi; Gendelman, Howard E; Zhang, Gang; Puligujja, Pavan; McMillan, JoEllyn M; Bronich, Tatiana K; Edagwa, Benson; Liu, Xin-Ming; Boska, Michael D

2015-01-01

Regimen adherence, systemic toxicities, and limited drug penetrance to viral reservoirs are obstacles limiting the effectiveness of antiretroviral therapy (ART). Our laboratory's development of the monocyte-macrophage-targeted long-acting nanoformulated ART (nanoART) carriage provides a novel opportunity to simplify drug-dosing regimens. Progress has nonetheless been slowed by cumbersome, but required, pharmacokinetic (PK), pharmacodynamics, and biodistribution testing. To this end, we developed a small magnetite ART (SMART) nanoparticle platform to assess antiretroviral drug tissue biodistribution and PK using magnetic resonance imaging (MRI) scans. Herein, we have taken this technique a significant step further by determining nanoART PK with folic acid (FA) decorated magnetite (ultrasmall superparamagnetic iron oxide [USPIO]) particles and by using SMART particles. FA nanoparticles enhanced the entry and particle retention to the reticuloendothelial system over nondecorated polymers after systemic administration into mice. These data were seen by MRI testing and validated by comparison with SMART particles and direct evaluation of tissue drug levels after nanoART. The development of alendronate (ALN)-coated magnetite thus serves as a rapid initial screen for the ability of targeting ligands to enhance nanoparticle-antiretroviral drug biodistribution, underscoring the value of decorated magnetite particles as a theranostic tool for improved drug delivery.

Virtual screening for development of new effective compounds against Staphylococcus aureus.

PubMed

Diniz, Roseane Costa; Soares, Lucas Weba; da Silva, Luis Claudio Nascimento

2018-03-26

Staphylococcus aureus is a notorious pathogenic bacterium causing a wide range of diseases from soft-tissue contamination, to more serious and deep-seated infections. This species is highlighted by its ability to express several kinds of virulence factors and to acquire genes related to drug resistance. Target this number of factors to design any drug is not an easy task. In this review we discuss the importance of computational methods to impulse the development of new drugs against S. aureus. The application of docking methods to screen large library of natural or synthetic compounds and to provide insights into action mechanisms is demonstrated. Particularly, highlighted the studies that validated in silico results with biochemical and microbiological assays. We also comment the computer-aided design of new molecules using some known inhibitors. The confirmation of in silico results with biochemical and microbiological assays allowed the identification of lead molecules that could be used for drug design such as rhodomyrtone, quinuclidine, berberine (and their derivative compounds). The fast development in the computational methods is essential to improve our ability to discovery new drugs, as well as to expand understanding about drug-target interactions. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Magnetic resonance imaging of folic acid-coated magnetite nanoparticles reflects tissue biodistribution of long-acting antiretroviral therapy

PubMed Central

Li, Tianyuzi; Gendelman, Howard E; Zhang, Gang; Puligujja, Pavan; McMillan, JoEllyn M; Bronich, Tatiana K; Edagwa, Benson; Liu, Xin-Ming; Boska, Michael D

2015-01-01

Regimen adherence, systemic toxicities, and limited drug penetrance to viral reservoirs are obstacles limiting the effectiveness of antiretroviral therapy (ART). Our laboratory’s development of the monocyte-macrophage-targeted long-acting nanoformulated ART (nanoART) carriage provides a novel opportunity to simplify drug-dosing regimens. Progress has nonetheless been slowed by cumbersome, but required, pharmacokinetic (PK), pharmacodynamics, and biodistribution testing. To this end, we developed a small magnetite ART (SMART) nanoparticle platform to assess antiretroviral drug tissue biodistribution and PK using magnetic resonance imaging (MRI) scans. Herein, we have taken this technique a significant step further by determining nanoART PK with folic acid (FA) decorated magnetite (ultrasmall superparamagnetic iron oxide [USPIO]) particles and by using SMART particles. FA nanoparticles enhanced the entry and particle retention to the reticuloendothelial system over nondecorated polymers after systemic administration into mice. These data were seen by MRI testing and validated by comparison with SMART particles and direct evaluation of tissue drug levels after nanoART. The development of alendronate (ALN)-coated magnetite thus serves as a rapid initial screen for the ability of targeting ligands to enhance nanoparticle-antiretroviral drug biodistribution, underscoring the value of decorated magnetite particles as a theranostic tool for improved drug delivery. PMID:26082630

DNA synthesis inhibitors for the treatment of gastrointestinal cancer.

PubMed

Yasui, Hiroshi; Tsurita, Giichiro; Imai, Kohzoh

2014-11-01

Intensive laboratory, preclinical and clinical studies have identified and validated molecular targets in cancers, leading to a shift toward the development of novel, rationally designed and specific therapeutic agents. However, gastrointestinal cancers continue to have a poor prognosis, largely due to drug resistance. Here, we discuss the current understanding of DNA synthesis inhibitors and their mechanisms of action for the treatment of gastrointestinal malignancies. Conventional agents, including DNA synthesis inhibitors such as fluoropyrimidines and platinum analogs, remain the most effective therapeutics and are the standards against which new drugs are compared. Novel DNA synthesis inhibitors for the treatment of gastrointestinal malignancies include a combination of the antimetabolite TAS-102, which consists of trifluorothymidine with a thymidine phosphorylase inhibitor, and a novel micellar formulation of cisplatin NC-6004 that uses a nanotechnology-based drug delivery system. The challenges of translational cancer research using DNA synthesis inhibitors include the identification of drugs that are specific to tumor cells to reduce toxicity and increase antitumor efficacy, biomarkers to predict pharmacological responses to chemotherapeutic drugs, identification of ways to overcome drug resistance and development of novel combination therapies with DNA synthesis inhibitors and other cancer therapies, such as targeted molecular therapeutics. Here, we discuss the current understanding of DNA synthesis inhibitors and their mechanisms of action for the treatment of gastrointestinal malignancies.

Optimization of a Diaphragm for a Micro-Shock Tube-Based Drug Delivery Method

PubMed Central

Rathod, Vivek T.; Mahapatra, Debiprosad Roy

2017-01-01

This paper presents the design optimization of diaphragms for a micro-shock tube-based drug delivery device. The function of the diaphragm is to impart the required velocity and direction to the loosely held drug particles on the diaphragm through van der Waals interaction. The finite element model-based studies involved diaphragms made up of copper, brass and aluminium. The study of the influence of material and geometric parameters serves as a vital tool in optimizing the magnitude and direction of velocity distribution on the diaphragm surface. Experiments carried out using a micro-shock tube validate the final deformed shape of the diaphragms determined from the finite element simulation. The diaphragm yields a maximum velocity of 335 m/s for which the maximum deviation of the velocity vector is 0.62°. Drug particles that travel to the destination target tissue are simulated using the estimated velocity distribution and angular deviation. Further, a theoretical model of penetration helps in the prediction of the drug particle penetration in the skin tissue like a target, which is found to be 0.126 mm. The design and calibration procedure of a micro-shock tube device to alter drug particle penetration considering the skin thickness and property are presented. PMID:28952503

Alkaloids and leishmania donovani UDP-galactopyarnose mutase: Anovel approach in drug designing against Visceral leishmaniasis.

PubMed

Srivastava, Ankita; Chandra, Deepak

2017-06-05

The unsatisfactory treatment options for Visceral Leishmaniasis (VL), needs identification of new drug targets. Among natural products, Alkaloids have been proved to be highly effective against number of diseases. In Leishmania UDP-galactopyranose mutase (UGM) is a critical enzyme required for cell wall synthesis and thus a drug target for structure based drug designing against L. donovani. To build the homology model of UDP galactopyranse mutase and investigate the interaction of selected alkaloids with this modeled UDP galactopyranose mutase by molecular docking. Since there is no crystal structure record has been found with this protein, a homology modeling was performed and a three dimensional structure of L. donovani UGM was created using MODELLER v9.9, structure quality was validated using PROCHECK and QMEAN programs which confirms that the structure is reliable. Further Molecular docking was performed with previously reported 15 alkaloids. It was found that Protopine shows a binding energy of -12.39Kcal/mole, binds at Flavin adenine dinucleotide (FAD) biding site. Concluding that Protopine, an alkaloid could interrupt the functional aspect of L. donovani UGM and thus may be useful for drug designing studies. These finding would contribute to the understanding of effect of drug on the parasite. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Genetic Determinants of Drug Resistance in Mycobacterium tuberculosis and Their Diagnostic Value.

PubMed

Farhat, Maha R; Sultana, Razvan; Iartchouk, Oleg; Bozeman, Sam; Galagan, James; Sisk, Peter; Stolte, Christian; Nebenzahl-Guimaraes, Hanna; Jacobson, Karen; Sloutsky, Alexander; Kaur, Devinder; Posey, James; Kreiswirth, Barry N; Kurepina, Natalia; Rigouts, Leen; Streicher, Elizabeth M; Victor, Tommie C; Warren, Robin M; van Soolingen, Dick; Murray, Megan

2016-09-01

The development of molecular diagnostics that detect both the presence of Mycobacterium tuberculosis in clinical samples and drug resistance-conferring mutations promises to revolutionize patient care and interrupt transmission by ensuring early diagnosis. However, these tools require the identification of genetic determinants of resistance to the full range of antituberculosis drugs. To determine the optimal molecular approach needed, we sought to create a comprehensive catalog of resistance mutations and assess their sensitivity and specificity in diagnosing drug resistance. We developed and validated molecular inversion probes for DNA capture and deep sequencing of 28 drug-resistance loci in M. tuberculosis. We used the probes for targeted sequencing of a geographically diverse set of 1,397 clinical M. tuberculosis isolates with known drug resistance phenotypes. We identified a minimal set of mutations to predict resistance to first- and second-line antituberculosis drugs and validated our predictions in an independent dataset. We constructed and piloted a web-based database that provides public access to the sequence data and prediction tool. The predicted resistance to rifampicin and isoniazid exceeded 90% sensitivity and specificity but was lower for other drugs. The number of mutations needed to diagnose resistance is large, and for the 13 drugs studied it was 238 across 18 genetic loci. These data suggest that a comprehensive M. tuberculosis drug resistance diagnostic will need to allow for a high dimension of mutation detection. They also support the hypothesis that currently unknown genetic determinants, potentially discoverable by whole-genome sequencing, encode resistance to second-line tuberculosis drugs.

Genetic Determinants of Drug Resistance in Mycobacterium tuberculosis and Their Diagnostic Value

PubMed Central

Sultana, Razvan; Iartchouk, Oleg; Bozeman, Sam; Galagan, James; Sisk, Peter; Stolte, Christian; Nebenzahl-Guimaraes, Hanna; Jacobson, Karen; Sloutsky, Alexander; Kaur, Devinder; Posey, James; Kreiswirth, Barry N.; Kurepina, Natalia; Rigouts, Leen; Streicher, Elizabeth M.; Victor, Tommie C.; Warren, Robin M.; van Soolingen, Dick; Murray, Megan

2016-01-01

Rationale: The development of molecular diagnostics that detect both the presence of Mycobacterium tuberculosis in clinical samples and drug resistance–conferring mutations promises to revolutionize patient care and interrupt transmission by ensuring early diagnosis. However, these tools require the identification of genetic determinants of resistance to the full range of antituberculosis drugs. Objectives: To determine the optimal molecular approach needed, we sought to create a comprehensive catalog of resistance mutations and assess their sensitivity and specificity in diagnosing drug resistance. Methods: We developed and validated molecular inversion probes for DNA capture and deep sequencing of 28 drug-resistance loci in M. tuberculosis. We used the probes for targeted sequencing of a geographically diverse set of 1,397 clinical M. tuberculosis isolates with known drug resistance phenotypes. We identified a minimal set of mutations to predict resistance to first- and second-line antituberculosis drugs and validated our predictions in an independent dataset. We constructed and piloted a web-based database that provides public access to the sequence data and prediction tool. Measurements and Main Results: The predicted resistance to rifampicin and isoniazid exceeded 90% sensitivity and specificity but was lower for other drugs. The number of mutations needed to diagnose resistance is large, and for the 13 drugs studied it was 238 across 18 genetic loci. Conclusions: These data suggest that a comprehensive M. tuberculosis drug resistance diagnostic will need to allow for a high dimension of mutation detection. They also support the hypothesis that currently unknown genetic determinants, potentially discoverable by whole-genome sequencing, encode resistance to second-line tuberculosis drugs. PMID:26910495

Systematic Identification of MCU Modulators by Orthogonal Interspecies Chemical Screening.

PubMed

Arduino, Daniela M; Wettmarshausen, Jennifer; Vais, Horia; Navas-Navarro, Paloma; Cheng, Yiming; Leimpek, Anja; Ma, Zhongming; Delrio-Lorenzo, Alba; Giordano, Andrea; Garcia-Perez, Cecilia; Médard, Guillaume; Kuster, Bernhard; García-Sancho, Javier; Mokranjac, Dejana; Foskett, J Kevin; Alonso, M Teresa; Perocchi, Fabiana

2017-08-17

The mitochondrial calcium uniporter complex is essential for calcium (Ca 2+ ) uptake into mitochondria of all mammalian tissues, where it regulates bioenergetics, cell death, and Ca 2+ signal transduction. Despite its involvement in several human diseases, we currently lack pharmacological agents for targeting uniporter activity. Here we introduce a high-throughput assay that selects for human MCU-specific small-molecule modulators in primary drug screens. Using isolated yeast mitochondria, reconstituted with human MCU, its essential regulator EMRE, and aequorin, and exploiting a D-lactate- and mannitol/sucrose-based bioenergetic shunt that greatly minimizes false-positive hits, we identify mitoxantrone out of more than 600 clinically approved drugs as a direct selective inhibitor of human MCU. We validate mitoxantrone in orthogonal mammalian cell-based assays, demonstrating that our screening approach is an effective and robust tool for MCU-specific drug discovery and, more generally, for the identification of compounds that target mitochondrial functions. Copyright © 2017 Elsevier Inc. All rights reserved.

Structural insights into simocyclinone as an antibiotic, effector ligand and substrate

PubMed Central

Buttner, Mark J; Schäfer, Martin; Lawson, David M

2017-01-01

Abstract Simocyclinones are antibiotics produced by Streptomyces and Kitasatospora species that inhibit the validated drug target DNA gyrase in a unique way, and they are thus of therapeutic interest. Structural approaches have revealed their mode of action, the inducible-efflux mechanism in the producing organism, and given insight into one step in their biosynthesis. The crystal structures of simocyclinones bound to their target (gyrase), the transcriptional repressor SimR and the biosynthetic enzyme SimC7 reveal fascinating insight into how molecular recognition is achieved with these three unrelated proteins. PMID:29126195

Structural insights into simocyclinone as an antibiotic, effector ligand and substrate.

PubMed

Buttner, Mark J; Schäfer, Martin; Lawson, David M; Maxwell, Anthony

2018-01-01

Simocyclinones are antibiotics produced by Streptomyces and Kitasatospora species that inhibit the validated drug target DNA gyrase in a unique way, and they are thus of therapeutic interest. Structural approaches have revealed their mode of action, the inducible-efflux mechanism in the producing organism, and given insight into one step in their biosynthesis. The crystal structures of simocyclinones bound to their target (gyrase), the transcriptional repressor SimR and the biosynthetic enzyme SimC7 reveal fascinating insight into how molecular recognition is achieved with these three unrelated proteins. © FEMS 2017.

Class I Microcins: Their Structures, Activities, and Mechanisms of Resistance

NASA Astrophysics Data System (ADS)

Severinov, Konstantin; Semenova, Ekaterina; Kazakov, Teymur

Microcin J25, microcin B17, and microcin C7-C51 are the three known members of class I posttranslationally modified microcins (heavily posttranslationally modified antibacterial peptides produced by Enterobacteriaceae with molecular weights of less than 5 kDa). The three microcins are unrelated to each other; they have structures that are highly atypical for ribosomally synthesized peptides and target essential molecular machines that are validated drug targets. In this chapter, available data on mechanisms of action, structure-activity relationships, and immunity mechanisms for class I microcins and related compounds are discussed.

2D MI-DRAGON: a new predictor for protein-ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins.

PubMed

Prado-Prado, Francisco; García-Mera, Xerardo; Escobar, Manuel; Sobarzo-Sánchez, Eduardo; Yañez, Matilde; Riera-Fernandez, Pablo; González-Díaz, Humberto

2011-12-01

There are many pairs of possible Drug-Proteins Interactions that may take place or not (DPIs/nDPIs) between drugs with high affinity/non-affinity for different proteins. This fact makes expensive in terms of time and resources, for instance, the determination of all possible ligands-protein interactions for a single drug. In this sense, we can use Quantitative Structure-Activity Relationships (QSAR) models to carry out rational DPIs prediction. Unfortunately, almost all QSAR models predict activity against only one target. To solve this problem we can develop multi-target QSAR (mt-QSAR) models. In this work, we introduce the technique 2D MI-DRAGON a new predictor for DPIs based on two different well-known software. We use the software MARCH-INSIDE (MI) to calculate 3D structural parameters for targets and the software DRAGON was used to calculated 2D molecular descriptors all drugs showing known DPIs present in the Drug Bank (US FDA benchmark dataset). Both classes of parameters were used as input of different Artificial Neural Network (ANN) algorithms to seek an accurate non-linear mt-QSAR predictor. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 21:21-31-1:1. This MLP classifies correctly 303 out of 339 DPIs (Sensitivity = 89.38%) and 480 out of 510 nDPIs (Specificity = 94.12%), corresponding to training Accuracy = 92.23%. The validation of the model was carried out by means of external predicting series with Sensitivity = 92.18% (625/678 DPIs; Specificity = 90.12% (730/780 nDPIs) and Accuracy = 91.06%. 2D MI-DRAGON offers a good opportunity for fast-track calculation of all possible DPIs of one drug enabling us to re-construct large drug-target or DPIs Complex Networks (CNs). For instance, we reconstructed the CN of the US FDA benchmark dataset with 855 nodes 519 drugs+336 targets). We predicted CN with similar topology (observed and predicted values of average distance are equal to 6.7 vs. 6.6). These CNs can be used to explore large DPIs databases in order to discover both new drugs and/or targets. Finally, we illustrated in one theoretic-experimental study the practical use of 2D MI-DRAGON. We reported the prediction, synthesis, and pharmacological assay of 10 different oxoisoaporphines with MAO-A inhibitory activity. The more active compound OXO5 presented IC(50) = 0.00083 μM, notably better than the control drug Clorgyline. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

The Inosine Monophosphate Dehydrogenase, GuaB2, Is a Vulnerable New Bactericidal Drug Target for Tuberculosis

PubMed Central

2016-01-01

VCC234718, a molecule with growth inhibitory activity against Mycobacterium tuberculosis (Mtb), was identified by phenotypic screening of a 15344-compound library. Sequencing of a VCC234718-resistant mutant identified a Y487C substitution in the inosine monophosphate dehydrogenase, GuaB2, which was subsequently validated to be the primary molecular target of VCC234718 in Mtb. VCC234718 inhibits Mtb GuaB2 with a Ki of 100 nM and is uncompetitive with respect to IMP and NAD+. This compound binds at the NAD+ site, after IMP has bound, and makes direct interactions with IMP; therefore, the inhibitor is by definition uncompetitive. VCC234718 forms strong pi interactions with the Y487 residue side chain from the adjacent protomer in the tetramer, explaining the resistance-conferring mutation. In addition to sensitizing Mtb to VCC234718, depletion of GuaB2 was bactericidal in Mtb in vitro and in macrophages. When supplied at a high concentration (≥125 μM), guanine alleviated the toxicity of VCC234718 treatment or GuaB2 depletion via purine salvage. However, transcriptional silencing of guaB2 prevented Mtb from establishing an infection in mice, confirming that Mtb has limited access to guanine in this animal model. Together, these data provide compelling validation of GuaB2 as a new tuberculosis drug target. PMID:27726334

Pyrrole-indolinone SU11652 targets the nucleoside diphosphate kinase from Leishmania parasites.

PubMed

Vieira, Plínio Salmazo; Souza, Tatiana de Arruda Campos Brasil; Honorato, Rodrigo Vargas; Zanphorlin, Letícia Maria; Severiano, Kelven Ulisses; Rocco, Silvana Aparecida; de Oliveira, Arthur Henrique Cavalcante; Cordeiro, Artur Torres; Oliveira, Paulo Sérgio Lopes; de Giuseppe, Priscila Oliveira; Murakami, Mário Tyago

2017-07-01

Nucleoside diphosphate kinases (NDKs) are key enzymes in the purine-salvage pathway of trypanosomatids and have been associated with the maintenance of host-cell integrity for the benefit of the parasite, being potential targets for rational drug discovery and design. The NDK from Leishmania major (LmNDK) and mutants were expressed and purified to homogeneity. Thermal shift assays were employed to identify potential inhibitors for LmNDK. Calorimetric experiments, site-directed mutagenesis and molecular docking analysis were performed to validate the interaction and to evaluate the structural basis of ligand recognition. Furthermore, the anti-leishmanial activity of the newly identified and validated compound was tested in vitro against different Leishmania species. The molecule SU11652, a Sunitinib analog, was identified as a potential inhibitor for LmNDK and structural studies indicated that this molecule binds to the active site of LmNDK in a similar conformation to nucleotides, mimicking natural substrates. Isothermal titration calorimetry experiments combined with site-directed mutagenesis revealed that the residues H50 and H117, considered essential for catalysis, play an important role in ligand binding. In vitro cell studies showed that SU11652 had similar efficacy to Amphotericin b against some Leishmania species. Together, our results indicate the pyrrole-indolinone SU11652 as a promising scaffold for the rational design of new drugs targeting the enzyme NDK from Leishmania parasites. Copyright © 2017 Elsevier Inc. All rights reserved.

Integrative molecular network analysis identifies emergent enzalutamide resistance mechanisms in prostate cancer

PubMed Central

King, Carly J.; Woodward, Josha; Schwartzman, Jacob; Coleman, Daniel J.; Lisac, Robert; Wang, Nicholas J.; Van Hook, Kathryn; Gao, Lina; Urrutia, Joshua; Dane, Mark A.; Heiser, Laura M.; Alumkal, Joshi J.

2017-01-01

Recent work demonstrates that castration-resistant prostate cancer (CRPC) tumors harbor countless genomic aberrations that control many hallmarks of cancer. While some specific mutations in CRPC may be actionable, many others are not. We hypothesized that genomic aberrations in cancer may operate in concert to promote drug resistance and tumor progression, and that organization of these genomic aberrations into therapeutically targetable pathways may improve our ability to treat CRPC. To identify the molecular underpinnings of enzalutamide-resistant CRPC, we performed transcriptional and copy number profiling studies using paired enzalutamide-sensitive and resistant LNCaP prostate cancer cell lines. Gene networks associated with enzalutamide resistance were revealed by performing an integrative genomic analysis with the PAthway Representation and Analysis by Direct Reference on Graphical Models (PARADIGM) tool. Amongst the pathways enriched in the enzalutamide-resistant cells were those associated with MEK, EGFR, RAS, and NFKB. Functional validation studies of 64 genes identified 10 candidate genes whose suppression led to greater effects on cell viability in enzalutamide-resistant cells as compared to sensitive parental cells. Examination of a patient cohort demonstrated that several of our functionally-validated gene hits are deregulated in metastatic CRPC tumor samples, suggesting that they may be clinically relevant therapeutic targets for patients with enzalutamide-resistant CRPC. Altogether, our approach demonstrates the potential of integrative genomic analyses to clarify determinants of drug resistance and rational co-targeting strategies to overcome resistance. PMID:29340039

The Inosine Monophosphate Dehydrogenase, GuaB2, Is a Vulnerable New Bactericidal Drug Target for Tuberculosis.

PubMed

Singh, Vinayak; Donini, Stefano; Pacitto, Angela; Sala, Claudia; Hartkoorn, Ruben C; Dhar, Neeraj; Keri, Gyorgy; Ascher, David B; Mondésert, Guillaume; Vocat, Anthony; Lupien, Andréanne; Sommer, Raphael; Vermet, Hélène; Lagrange, Sophie; Buechler, Joe; Warner, Digby F; McKinney, John D; Pato, Janos; Cole, Stewart T; Blundell, Tom L; Rizzi, Menico; Mizrahi, Valerie

2017-01-13

VCC234718, a molecule with growth inhibitory activity against Mycobacterium tuberculosis (Mtb), was identified by phenotypic screening of a 15344-compound library. Sequencing of a VCC234718-resistant mutant identified a Y487C substitution in the inosine monophosphate dehydrogenase, GuaB2, which was subsequently validated to be the primary molecular target of VCC234718 in Mtb. VCC234718 inhibits Mtb GuaB2 with a K i of 100 nM and is uncompetitive with respect to IMP and NAD + . This compound binds at the NAD + site, after IMP has bound, and makes direct interactions with IMP; therefore, the inhibitor is by definition uncompetitive. VCC234718 forms strong pi interactions with the Y487 residue side chain from the adjacent protomer in the tetramer, explaining the resistance-conferring mutation. In addition to sensitizing Mtb to VCC234718, depletion of GuaB2 was bactericidal in Mtb in vitro and in macrophages. When supplied at a high concentration (≥125 μM), guanine alleviated the toxicity of VCC234718 treatment or GuaB2 depletion via purine salvage. However, transcriptional silencing of guaB2 prevented Mtb from establishing an infection in mice, confirming that Mtb has limited access to guanine in this animal model. Together, these data provide compelling validation of GuaB2 as a new tuberculosis drug target.

A Phenotypic Based Target Screening Approach Delivers New Antitubercular CTP Synthetase Inhibitors.

PubMed

Esposito, Marta; Szadocka, Sára; Degiacomi, Giulia; Orena, Beatrice S; Mori, Giorgia; Piano, Valentina; Boldrin, Francesca; Zemanová, Júlia; Huszár, Stanislav; Barros, David; Ekins, Sean; Lelièvre, Joel; Manganelli, Riccardo; Mattevi, Andrea; Pasca, Maria Rosalia; Riccardi, Giovanna; Ballell, Lluis; Mikušová, Katarína; Chiarelli, Laurent R

2017-06-09

Despite its great potential, the target-based approach has been mostly unsuccessful in tuberculosis drug discovery, while whole cell phenotypic screening has delivered several active compounds. However, for many of these hits, the cellular target has not yet been identified, thus preventing further target-based optimization of the compounds. In this context, the newly validated drug target CTP synthetase PyrG was exploited to assess a target-based approach of already known, but untargeted, antimycobacterial compounds. To this purpose the publically available GlaxoSmithKline antimycobacterial compound set was assayed, uncovering a series of 4-(pyridin-2-yl)thiazole derivatives which efficiently inhibit the Mycobacterium tuberculosis PyrG enzyme activity, one of them showing low activity against the human CTP synthetase. The three best compounds were ATP binding site competitive inhibitors, with K i values ranging from 3 to 20 μM, but did not show any activity against a small panel of different prokaryotic and eukaryotic kinases, thus demonstrating specificity for the CTP synthetases. Metabolic labeling experiments demonstrated that the compounds directly interfere not only with CTP biosynthesis, but also with other CTP dependent biochemical pathways, such as lipid biosynthesis. Moreover, using a M. tuberculosis pyrG conditional knock-down strain, it was shown that the activity of two compounds is dependent on the intracellular concentration of the CTP synthetase. All these results strongly suggest a role of PyrG as a target of these compounds, thus strengthening the value of this kind of approach for the identification of new scaffolds for drug development.

Emerging therapeutic targets currently under investigation for the treatment of systemic amyloidosis.

PubMed

Nuvolone, Mario; Merlini, Giampaolo

2017-12-01

Systemic amyloidosis occurs when one of a growing list of circulating proteins acquires an abnormal fold, aggregates and gives rise to extracellular amyloid deposits in different body sites, leading to organ dysfunction and eventually death. Current approaches are mainly aimed at lowering the supply of the amyloidogenic precursor or at stabilizing it in a non-amyloidogenic state, thus interfering with the initial phases of amyloid formation and toxicity. Areas covered: Improved understanding of the pathophysiology is indicating novel steps and molecules that could be therapeutically targeted. Here, we will review emerging molecular targets and therapeutic approaches against the main forms of systemic amyloidosis at the early preclinical level. Expert opinion: Conspicuous efforts in drug design and drug discovery have provided an unprecedented list of potential new drugs or therapeutic strategies, from gene-based therapies to small molecules and peptides, from novel monoclonal antibodies to engineered cell-based therapies. The challenge will now be to validate and optimize the most promising candidates, cross the bridge from the preclinical phase to the clinics and identify, through innovative trials design, the safest and most effective combination therapies, striving for a better care, possibly a definitive cure for these diseases.

Biodegradation and bio-sorption of antibiotics and non-steroidal anti-inflammatory drugs using immobilized cell process.

PubMed

Yu, Tsung-Hsien; Lin, Angela Yu-Chen; Panchangam, Sri Chandana; Hong, Pui-Kwan Andy; Yang, Ping-Yi; Lin, Cheng-Fang

2011-08-01

In the present study, the removal mechanisms of four antibiotics (sulfamethoxazole, sulfadimethoxine, sulfamethazine, and trimethoprim) and four non-steroidal anti-inflammatory drugs (acetaminophen, ibuprofen, ketoprofen, and naproxen) in immobilized cell process were investigated using batch reactors. This work principally explores the individual or collective roles of biodegradation and bio-sorption as removal routes of the target pharmaceuticals and the results were validated by various experimental and analytical tools. Biodegradation and bio-sorption were found as dominant mechanisms for the drug removal, while volatilization and hydrolysis were negligible for all target pharmaceuticals. The target pharmaceuticals responded to the two observed removal mechanisms in different ways, typically: (1) strong biodegradability and bio-sorption by acetaminophen, (2) strong biodegradability and weak bio-sorption by sulfamethoxazole, sulfadimethoxine, ibuprofen and naproxen, (3) low biodegradability and weak bio-sorption by sulfamethazine and ketoprofen, and (4) low biodegradability and medium bio-sorption by trimethoprim. In the sorption/desorption experiment, acetaminophen, sulfamethoxazole and sulfadimethoxine were characterized by strong sorption and weak desorption. A phenomenon of moderate sorption and well desorption was observed for sulfamethazine, trimethoprim and naproxen. Both ibuprofen and ketoprofen were weakly sorbed and strongly desorbed. Copyright © 2011 Elsevier Ltd. All rights reserved.

Liquid chromatography tandem mass spectrometry determination of selected synthetic cathinones and two piperazines in oral fluid. Cross reactivity study with an on-site immunoassay device.

PubMed

de Castro, Ana; Lendoiro, Elena; Fernández-Vega, Hadriana; Steinmeyer, Stefan; López-Rivadulla, Manuel; Cruz, Angelines

2014-12-29

Since the past few years, several synthetic cathinones and piperazines have been introduced into the drug market to substitute illegal stimulant drugs such as amphetamine and derivatives or cocaine due to their unregulated situation. These emerging drugs are not usually included in routine toxicological analysis. We developed and validated a LC-MS/MS method for the determination of methedrone, methylone, mephedrone, 3,4-methylenedioxypyrovalerone (MDPV), fluoromethcathinone, fluoromethamphetamine, 1-(3-chlorophenyl)piperazine (mCPP) and 3-trifluoromethylphenylpiperazine (TFMPP) in oral fluid. Sample extraction was performed using Strata X cartridges. Chromatographic separation was achieved in 10min using an Atlantis(®) T3 column (100mm×2.1mm, 3μm), and formic acid 0.1% and acetonitrile as mobile phase. The method was satisfactorily validated, including selectivity, linearity (0.2-0.5 to 200ng/mL), limits of detection (0.025-0.1ng/mL) and quantification (0.2-0.5ng/mL), imprecision and accuracy in neat oral fluid (%CV=0.0-12.7% and 84.8-103.6% of target concentration, respectively) and in oral fluid mixed with Quantisal™ buffer (%CV=7.2-10.3% and 80.2-106.5% of target concentration, respectively), matrix effect in neat oral fluid (-11.6 to 399.7%) and in oral fluid with Quantisal™ buffer (-69.9 to 131.2%), extraction recovery (87.9-134.3%) and recovery from the Quantisal™ (79.6-107.7%), dilution integrity (75-99% of target concentration) and stability at different conditions (-14.8 to 30.8% loss). In addition, cross reactivity produced by the studied synthetic cathinones in oral fluid using the Dräger DrugTest 5000 was assessed. All the analytes produced a methamphetamine positive result at high concentrations (100 or 10μg/mL), and fluoromethamphetamine also at low concentration (0.075μg/mL). Copyright © 2014 Elsevier B.V. All rights reserved.

Matrix solid phase dispersion assisted enzymatic hydrolysis as a novel approach for cocaine and opiates isolation from human hair.

PubMed

Míguez-Framil, Martha; Cabarcos, Pamela; Tabernero, María Jesús; Bermejo, Ana María; Bermejo-Barrera, Pilar; Moreda-Piñeiro, Antonio

2013-11-05

The possibility of assisting enzymatic hydrolysis (EH) procedures by sample disruption mechanisms inherent to matrix solid phase dispersion (MSPD) has been explored in the current study. EH of hair specimens from poly-drug abusers was assisted by dispersing/blending the sample (0.05 g) with alumina (2.25 g) before loading the dissolved enzyme (6 mL of 1 mg mL(-1) Pronase E in 1.4 M/1.4 M Tris/HCl, pH 7.3) through the hair-alumina solid phase packaged inside a disposable MSPD syringe. The MSPD-EH method was developed, and it proved to offer quantitative results when isolating cocaine, benzoylecgonine (BZE), codeine, morphine and 6-monoacethylmorphine (6-MAM) from human hair samples. The procedure allows an on column clean-up/pre-concentration procedure of the isolated targets by attaching a previously conditioned Oasis HLB cartridge to the end of the MSPD syringe. The EH procedure of human hair with Pronase E can therefore be shortened to approximately 30 min. Within this time, sample blending/dispersion, MSPD syringe package, elution (EH when dissolved Pronase E is passing through the sample-dispersant bed), and extract clean-up and target pre-concentration stages are achieved. Gas chromatography-mass spectrometry (GC-MS) was used for determining each target after elution from the Oasis HLB cartridges with 2 mL of 2% (v/v) acetic acid in methanol, concentration by N2 stream evaporation, and dried extract derivatization with N-methyl-tert-butylsilyltrifluoroacetamide (BSTFA) and chlorotrimethylsilane (TMCS). The method was validated according to the guidance for bioanalytical method validation of the US Department of Health and Human Services, Food and Drug Administration. The simplicity of the proposed approach makes it a useful procedure for screening/quantifying drugs of abuse in hair specimens from poly-drug abusers. Copyright © 2013 Elsevier B.V. All rights reserved.

Discovery of novel InhA reductase inhibitors: application of pharmacophore- and shape-based screening approach.

PubMed

Kumar, Uday Chandra; Bvs, Suneel Kumar; Mahmood, Shaik; D, Sriram; Kumar-Sahu, Prashanta; Pulakanam, Sridevi; Ballell, Lluís; Alvarez-Gomez, Daniel; Malik, Siddharth; Jarp, Sarma

2013-03-01

InhA is a promising and attractive target in antimycobacterial drug development. InhA is involved in the reduction of long-chain trans-2-enoyl-ACP in the type II fatty acid biosynthesis pathway of Mycobacterium tuberculosis. Recent studies have demonstrated that InhA is one of the targets for the second line antitubercular drug ethionamide. In the current study, we have generated quantitative pharmacophore models using known InhA inhibitors and validated using a large test set. The validated pharmacophore model was used as a query to screen an in-house database of 400,000 compounds and retrieved 25,000 hits. These hits were further ranked based on its shape and feature similarity with potent InhA inhibitor using rapid overlay of chemical structures (OpenEye™) and subsequent hits were subjected for docking. Based on the pharmacophore, rapid overlay of chemical structures model and docking interactions, 32 compounds with more than eight chemotypes were selected, purchased and assayed for InhA inhibitory activity. Out of the 32 compounds, 28 demonstrated 10-38% inhibition against InhA at 10 µM. Further optimization of these analogues is in progress and will update in due course.

Ligand cluster-based protein network and ePlatton, a multi-target ligand finder.

PubMed

Du, Yu; Shi, Tieliu

2016-01-01

Small molecules are information carriers that make cells aware of external changes and couple internal metabolic and signalling pathway systems with each other. In some specific physiological status, natural or artificial molecules are used to interact with selective biological targets to activate or inhibit their functions to achieve expected biological and physiological output. Millions of years of evolution have optimized biological processes and pathways and now the endocrine and immune system cannot work properly without some key small molecules. In the past thousands of years, the human race has managed to find many medicines against diseases by trail-and-error experience. In the recent decades, with the deepening understanding of life and the progress of molecular biology, researchers spare no effort to design molecules targeting one or two key enzymes and receptors related to corresponding diseases. But recent studies in pharmacogenomics have shown that polypharmacology may be necessary for the effects of drugs, which challenge the paradigm, 'one drug, one target, one disease'. Nowadays, cheminformatics and structural biology can help us reasonably take advantage of the polypharmacology to design next-generation promiscuous drugs and drug combination therapies. 234,591 protein-ligand interactions were extracted from ChEMBL. By the 2D structure similarity, 13,769 ligand emerged from 156,151 distinct ligands which were recognized by 1477 proteins. Ligand cluster- and sequence-based protein networks (LCBN, SBN) were constructed, compared and analysed. For assisting compound designing, exploring polypharmacology and finding possible drug combination, we integrated the pathway, disease, drug adverse reaction and the relationship of targets and ligand clusters into the web platform, ePlatton, which is available at http://www.megabionet.org/eplatton. Although there were some disagreements between the LCBN and SBN, communities in both networks were largely the same with normalized mutual information at 0.9. The study of target and ligand cluster promiscuity underlying the LCBN showed that light ligand clusters were more promiscuous than the heavy one and that highly connected nodes tended to be protein kinases and involved in phosphorylation. ePlatton considerably reduced the redundancy of the ligand set of targets and made it easy to deduce the possible relationship between compounds and targets, pathways and side effects. ePlatton behaved reliably in validation experiments and also fast in virtual screening and information retrieval.Graphical abstractCluster exemplars and ePlatton's mechanism.

Fragment Linking and Optimization of Inhibitors of the Aspartic Protease Endothiapepsin: Fragment‐Based Drug Design Facilitated by Dynamic Combinatorial Chemistry

PubMed Central

Mondal, Milon; Radeva, Nedyalka; Fanlo‐Virgós, Hugo; Otto, Sijbren; Klebe, Gerhard

2016-01-01

Abstract Fragment‐based drug design (FBDD) affords active compounds for biological targets. While there are numerous reports on FBDD by fragment growing/optimization, fragment linking has rarely been reported. Dynamic combinatorial chemistry (DCC) has become a powerful hit‐identification strategy for biological targets. We report the synergistic combination of fragment linking and DCC to identify inhibitors of the aspartic protease endothiapepsin. Based on X‐ray crystal structures of endothiapepsin in complex with fragments, we designed a library of bis‐acylhydrazones and used DCC to identify potent inhibitors. The most potent inhibitor exhibits an IC50 value of 54 nm, which represents a 240‐fold improvement in potency compared to the parent hits. Subsequent X‐ray crystallography validated the predicted binding mode, thus demonstrating the efficiency of the combination of fragment linking and DCC as a hit‐identification strategy. This approach could be applied to a range of biological targets, and holds the potential to facilitate hit‐to‐lead optimization. PMID:27400756

Biomarkers and patient selection for PI3K/Akt/mTOR targeted therapies: current status and future directions.

PubMed

Bartlett, John M S

2010-11-01

The phosphatidylinositol 3-kinase (PI3K)/Akt/ mammalian target of rapamycin (mTOR) pathway regulates a broad spectrum of physiologic and pathologic processes. In breast cancer mutation, amplification, deletion, methylation, and posttranslational modifications lead to significant dysregulation of this pathway leading to more aggressive and potentially drug-resistant disease. Multiple novel agents, targeting different nodes within the pathway are currently under development by both commercial and academic partners. The key to the successful validation of these markers is selection of the appropriate patient groups using biomarkers. This article reviews current progress in this area, highlighting the key molecular alterations described in genes within the PI3K/Akt/mTOR pathway that may have an effect on response to current and future therapeutic interventions. Herein, gaps in current knowledge are highlighted and suggestions for future research directions given that may facilitate biomarker development in partnership with current drug development.

Fabrication of Gold Nanoparticles for targeted therapy in pancreatic cancer**

PubMed Central

Patra, Chitta Ranjan; Bhattacharya, Resham; Mukhopadhyay, Debabrata; Mukherjee, Priyabrata

2009-01-01

The targeted delivery of a drug should result in enhanced therapeutic efficacy with low to minimal side effects. This is a widely accepted concept, but limited in application due to lack of available technologies and process of validation. Biomedical nanotechnology can play an important role in this respect. Biomedical nanotechnology is a burgeoning field with myriads of opportunities and possibilities for advancing medical science and disease treatment. Cancer nanotechnology (1–100 nm size range) is expected to change the very foundations of cancer treatment, diagnosis and detection. Nanomaterials, especially gold nanoparticles (AuNPs) have unique physicochemical properties, such as ultra small size, large surface area to mass ratio, and high surface reactivity, presence of surface plasmon resonance (SPR) bands, biocompatibility and ease of surface functionalization. In this review, we will discuss how the unique physico-chemical properties of gold nanoparticles may be utilized for targeted drug delivery in pancreatic cancer leading to increased efficacy of traditional chemotherapeutics. PMID:19914317

Inducing cell death in vitro in cancer cells by targeted delivery of cytochrome c via a transferrin conjugate

PubMed Central

Delgado, Yamixa; Sharma, Rohit Kumar; Sharma, Shweta; Guzmán, Solimar Liz Ponce De León; Tinoco, Arthur D.; Griebenow, Kai

2018-01-01

One of the major drawbacks of many of the currently used cancer drugs are off-target effects. Targeted delivery is one method to minimize such unwanted and detrimental events. To actively target lung cancer cells, we have developed a conjugate of the apoptosis inducing protein cytochrome c with transferrin because the transferrin receptor is overexpressed by many rapidly dividing cancer cells. Cytochrome c and transferrin were cross-linked with a redox sensitive disulfide bond for the intra-cellular release of the protein upon endocytosis by the transferrin receptor. Confocal results demonstrated the cellular uptake of the cytochrome c-transferrin conjugate by transferrin receptor overexpressing A549 lung cancer cells. Localization studies further validated that this conjugate escaped the endosome. Additionally, an in vitro assay showed that the conjugate could induce apoptosis by activating caspase-3. The neo-conjugate not only maintained an IC50 value similar to the well known drug cisplatin (50 μM) in A549 cancer cells but also was nontoxic to the normal lung (MRC5) cells. Our neo-conjugate holds promise for future development to target cancers with enhanced transferrin receptor expression. PMID:29649293

Adaptable setups for magnetic drug targeting in human muscular arteries: Design and implementation

NASA Astrophysics Data System (ADS)

Hajiaghajani, Amirhossein; Hashemi, Soheil; Abdolali, Ali

2017-09-01

Magnetic drug targeting has been used to steer magnetic therapeutic agents and has received much attention for capillaries and human brain arteries. In this paper, we focus on noninvasive targeting of nanoparticles in muscular arteries, in where the vessel diameter and blood flow are much challengingly higher than brain capillaries. We aim to design a low intensity magnetic field which avoids potential side effects on blood cells while steers particles with high targeting rate. The setup design procedure is considerably flexible to be used in a wide variety of large vessels. Using particle tracing, a new method is proposed to connect the geometry of the vessel under the action of targeting to the required magnetic force. Specifications of the coil which is placed outside the body are derived based on this required force. Mutual effects of coil dimensions on the produced magnetic force are elaborated and summarized in a design flowchart to be used for arbitrary muscular vessel sizes. The performance of the optimized coil is validated by in vitro experiments and it is shown that particles are steered with the average efficiency of 80.2% for various conditions.

Detection of First-Line Drug Resistance Mutations and Drug-Protein Interaction Dynamics from Tuberculosis Patients in South India.

PubMed

Nachappa, Somanna Ajjamada; Neelambike, Sumana M; Amruthavalli, Chokkanna; Ramachandra, Nallur B

2018-05-01

Diagnosis of drug-resistant tuberculosis predominantly relies on culture-based drug susceptibility testing, which take weeks to produce a result and a more time-efficient alternative method is multiplex allele-specific PCR (MAS-PCR). Also, understanding the role of mutations in causing resistance helps better drug designing. To evaluate the ability of MAS-PCR in the detection of drug resistance and to understand the mechanism of interaction of drugs with mutant proteins in Mycobacterium tuberculosis. Detection of drug-resistant mutations using MAS-PCR and validation through DNA sequencing. MAS-PCR targeted five loci on three genes, katG 315 and inhA -15 for the drug isoniazid (INH), and rpoB 516, 526, and 531 for rifampicin (RIF). Furthermore, the sequence data were analyzed to study the effect on interaction of the anti-TB drug molecule with the target protein using in silico docking. We identified drug-resistant mutations in 8 out of 114 isolates with 2 of them as multidrug-resistant TB using MAS-PCR. DNA sequencing confirmed only six of these, recording a sensitivity of 85.7% and specificity of 99.3% for MAS-PCR. Molecular docking showed estimated free energy of binding (ΔG) being higher for RIF binding with RpoB S531L mutant. Codon 315 in KatG does not directly interact with INH but blocks the drug access to active site. We propose DNA sequencing-based drug resistance detection for TB, which is more accurate than MAS-PCR. Understanding the action of resistant mutations in disrupting the normal drug-protein interaction aids in designing effective drug alternatives.

Prediction of Drug-Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures.

PubMed

Meng, Fan-Rong; You, Zhu-Hong; Chen, Xing; Zhou, Yong; An, Ji-Yong

2017-07-05

Knowledge of drug-target interaction (DTI) plays an important role in discovering new drug candidates. Unfortunately, there are unavoidable shortcomings; including the time-consuming and expensive nature of the experimental method to predict DTI. Therefore, it motivates us to develop an effective computational method to predict DTI based on protein sequence. In the paper, we proposed a novel computational approach based on protein sequence, namely PDTPS (Predicting Drug Targets with Protein Sequence) to predict DTI. The PDTPS method combines Bi-gram probabilities (BIGP), Position Specific Scoring Matrix (PSSM), and Principal Component Analysis (PCA) with Relevance Vector Machine (RVM). In order to evaluate the prediction capacity of the PDTPS, the experiment was carried out on enzyme, ion channel, GPCR, and nuclear receptor datasets by using five-fold cross-validation tests. The proposed PDTPS method achieved average accuracy of 97.73%, 93.12%, 86.78%, and 87.78% on enzyme, ion channel, GPCR and nuclear receptor datasets, respectively. The experimental results showed that our method has good prediction performance. Furthermore, in order to further evaluate the prediction performance of the proposed PDTPS method, we compared it with the state-of-the-art support vector machine (SVM) classifier on enzyme and ion channel datasets, and other exiting methods on four datasets. The promising comparison results further demonstrate that the efficiency and robust of the proposed PDTPS method. This makes it a useful tool and suitable for predicting DTI, as well as other bioinformatics tasks.

Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory.

PubMed

Durán, Claudio; Daminelli, Simone; Thomas, Josephine M; Haupt, V Joachim; Schroeder, Michael; Cannistraci, Carlo Vittorio

2017-04-26

The bipartite network representation of the drug-target interactions (DTIs) in a biosystem enhances understanding of the drugs' multifaceted action modes, suggests therapeutic switching for approved drugs and unveils possible side effects. As experimental testing of DTIs is costly and time-consuming, computational predictors are of great aid. Here, for the first time, state-of-the-art DTI supervised predictors custom-made in network biology were compared-using standard and innovative validation frameworks-with unsupervised pure topological-based models designed for general-purpose link prediction in bipartite networks. Surprisingly, our results show that the bipartite topology alone, if adequately exploited by means of the recently proposed local-community-paradigm (LCP) theory-initially detected in brain-network topological self-organization and afterwards generalized to any complex network-is able to suggest highly reliable predictions, with comparable performance with the state-of-the-art-supervised methods that exploit additional (non-topological, for instance biochemical) DTI knowledge. Furthermore, a detailed analysis of the novel predictions revealed that each class of methods prioritizes distinct true interactions; hence, combining methodologies based on diverse principles represents a promising strategy to improve drug-target discovery. To conclude, this study promotes the power of bio-inspired computing, demonstrating that simple unsupervised rules inspired by principles of topological self-organization and adaptiveness arising during learning in living intelligent systems (like the brain) can efficiently equal perform complicated algorithms based on advanced, supervised and knowledge-based engineering. © The Author 2017. Published by Oxford University Press.

Clinical trials and late-stage drug development for Alzheimer’s disease: an appraisal from 1984 to 2014

PubMed Central

Schneider, Lon S.; Mangialasche, Francesca; Andreasen, Niels; Feldman, Howard; Giacobini, Ezio; Jones, Roy; Mantua, Valentina; Mecocci, Patrizia; Pani, Luca; Winblad, Bengt; Kivipelto, Miia

2014-01-01

The modern era of drug development for Alzheimer’s disease began with the proposal of the cholinergic hypothesis of memory impairment and the 1984 research criteria for Alzheimer’s disease. Since then, despite the evaluation of numerous potential treatments in clinical trials, only four cholinesterase inhibitors and memantine have shown sufficient safety and efficacy to allow marketing approval at an international level. Although this is probably because the other drugs tested were ineffective, inadequate clinical development methods have also been blamed for the failures. Here we review the development of treatments for Alzheimer’s disease during the past 30 years, considering the drugs, potential targets, late-stage clinical trials, development methods, emerging use of biomarkers and evolution of regulatory considerations in order to summarize advances and anticipate future developments. We have considered late-stage Alzheimer’s disease drug development from 1984 to 2013, including individual clinical trials, systematic and qualitative reviews, meta-analyses, methods, commentaries, position papers and guidelines. We then review the evolution of drugs in late clinical development, methods, biomarkers and regulatory issues. Although a range of small molecules and biological products against many targets have been investigated in clinical trials, the predominant drug targets have been the cholinergic system and the amyloid cascade. Trial methods have evolved incrementally: inclusion criteria have largely remained focused on mild to moderate Alzheimer’s disease criteria, recently extending to early or prodromal Alzheimer disease or ‘mild cognitive impairment due to Alzheimer’s disease’, for drugs considered to be disease modifying. The duration of trials has remained at 6 to 12 months for drugs intended to improve symptoms; 18- to 24-month trials have been established for drugs expected to attenuate clinical course. Cognitive performance, activities of daily living, global change and severity ratings have persisted as the primary clinically relevant outcomes. Regulatory guidance and oversight have evolved to allow for enrichment of early-stage Alzheimer’s disease trial samples by using biomarkers and phase-specific outcomes. In conclusion, validated drug targets for Alzheimer’s disease remain to be developed. Only drugs that affect an aspect of cholinergic function have shown consistent, but modest, clinical effects in late-phase trials. There is opportunity for substantial improvements in drug discovery and clinical development methods. PMID:24605808

Mitoketoscins: Novel mitochondrial inhibitors for targeting ketone metabolism in cancer stem cells (CSCs)

PubMed Central

Ozsvari, Bela; Sotgia, Federica; Simmons, Katie; Trowbridge, Rachel; Foster, Richard; Lisanti, Michael P.

2017-01-01

Previous studies have now well-established that epithelial cancer cells can utilize ketone bodies (3-hydroxybutyrate and aceto-acetate) as mitochondrial fuels, to actively promote tumor growth and metastatic dissemination. The two critical metabolic enzymes implicated in this process are OXCT1 and ACAT1, which are both mitochondrial proteins. Importantly, over-expression of OXCT1 or ACAT1 in human breast cancer cells is sufficient to genetically drive tumorigenesis and/or lung metastasis, validating that they indeed behave as metabolic “tumor promoters”. Here, we decided to target these two enzymes, which give cancer cells the ability to recycle ketone bodies into Acetyl-CoA and, therefore, to produce increased ATP. Briefly, we used computational chemistry (in silico drug design) to select a sub-set of potentially promising compounds that spatially fit within the active site of these enzymes, based on their known 3D crystal structures. These libraries of compounds were then phenotypically screened for their effects on total cellular ATP levels. Positive hits were further validated by metabolic flux analysis. Our results indicated that four of these compounds effectively inhibited mitochondrial oxygen consumption. Two of these compounds also induced a reactive glycolytic phenotype in cancer cells. Most importantly, using the mammosphere assay, we showed that these compounds can be used to functionally inhibit cancer stem cell (CSC) activity and propagation. Finally, our molecular modeling studies directly show how these novel compounds are predicted to bind to the active catalytic sites of OXCT1 and ACAT1, within their Coenzyme A binding site. As such, we speculate that these mitochondrial inhibitors are partially mimicking the structure of Coenzyme A. Thus, we conclude that OXCT1 and ACAT1 are important new therapeutic targets for further drug development and optimization. We propose that this new class of drugs should be termed “mitoketoscins”, to reflect that they were designed to target ketone re-utilization and mitochondrial function. PMID:29108233

A novel multi-target regression framework for time-series prediction of drug efficacy.

PubMed

Li, Haiqing; Zhang, Wei; Chen, Ying; Guo, Yumeng; Li, Guo-Zheng; Zhu, Xiaoxin

2017-01-18

Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods to predict the efficacy of traditional Chinese medicine (TCM) prescription. The main purpose of our study is to obtain some knowledge of the correlation in TCM prescription. Here, a novel method named Multi-target Regression Framework to deal with the problem of efficacy prediction is proposed. We employ the correlation between the values of different time sequences and add predictive targets of previous time as features to predict the value of current time. Several experiments are conducted to test the validity of our method and the results of leave-one-out cross-validation clearly manifest the competitiveness of our framework. Compared with linear regression, artificial neural networks, and partial least squares, support vector regression combined with our framework demonstrates the best performance, and appears to be more suitable for this task.

A novel multi-target regression framework for time-series prediction of drug efficacy

PubMed Central

Li, Haiqing; Zhang, Wei; Chen, Ying; Guo, Yumeng; Li, Guo-Zheng; Zhu, Xiaoxin

2017-01-01

Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods to predict the efficacy of traditional Chinese medicine (TCM) prescription. The main purpose of our study is to obtain some knowledge of the correlation in TCM prescription. Here, a novel method named Multi-target Regression Framework to deal with the problem of efficacy prediction is proposed. We employ the correlation between the values of different time sequences and add predictive targets of previous time as features to predict the value of current time. Several experiments are conducted to test the validity of our method and the results of leave-one-out cross-validation clearly manifest the competitiveness of our framework. Compared with linear regression, artificial neural networks, and partial least squares, support vector regression combined with our framework demonstrates the best performance, and appears to be more suitable for this task. PMID:28098186

A Sensitive in Vitro High-Throughput Screen To Identify Pan-filoviral Replication Inhibitors Targeting the VP35–NP Interface

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Gai; Nash, Peter J.; Johnson, Britney

The 2014 Ebola outbreak in West Africa, the largest outbreak on record, highlighted the need for novel approaches to therapeutics targeting Ebola virus (EBOV). Within the EBOV replication complex, the interaction between polymerase cofactor, viral protein 35 (VP35), and nucleoprotein (NP) is critical for viral RNA synthesis. We recently identified a peptide at the N-terminus of VP35 (termed NPBP) that is sufficient for interaction with NP and suppresses EBOV replication, suggesting that the NPBP binding pocket can serve as a potential drug target. Here we describe the development and validation of a sensitive high-throughput screen (HTS) using a fluorescence polarizationmore » assay. Initial hits from this HTS include the FDA-approved compound tolcapone, whose potency against EBOV infection was validated in a nonfluorescent secondary assay. High conservation of the NP–VP35 interface among filoviruses suggests that this assay has the capacity to identify pan-filoviral inhibitors for development as antivirals.« less

N-myristoyltransferase inhibitors as new leads to treat sleeping sickness

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frearson, Julie A.; Brand, Stephen; McElroy, Stuart P.

2010-11-05

African sleeping sickness or human African trypanosomiasis, caused by Trypanosoma brucei spp., is responsible for {approx}30,000 deaths each year. Available treatments for this disease are poor, with unacceptable efficacy and safety profiles, particularly in the late stage of the disease when the parasite has infected the central nervous system. Here we report the validation of a molecular target and the discovery of associated lead compounds with the potential to address this lack of suitable treatments. Inhibition of this target - T. brucei N-myristoyltransferase - leads to rapid killing of trypanosomes both in vitro and in vivo and cures trypanosomiasis inmore » mice. These high-affinity inhibitors bind into the peptide substrate pocket of the enzyme and inhibit protein N-myristoylation in trypanosomes. The compounds identified have promising pharmaceutical properties and represent an opportunity to develop oral drugs to treat this devastating disease. Our studies validate T. brucei N-myristoyltransferase as a promising therapeutic target for human African trypanosomiasis.« less

Targeting BCR-ABL-Independent TKI Resistance in Chronic Myeloid Leukemia by mTOR and Autophagy Inhibition.

PubMed

Mitchell, Rebecca; Hopcroft, Lisa E M; Baquero, Pablo; Allan, Elaine K; Hewit, Kay; James, Daniel; Hamilton, Graham; Mukhopadhyay, Arunima; O'Prey, Jim; Hair, Alan; Melo, Junia V; Chan, Edmond; Ryan, Kevin M; Maguer-Satta, Véronique; Druker, Brian J; Clark, Richard E; Mitra, Subir; Herzyk, Pawel; Nicolini, Franck E; Salomoni, Paolo; Shanks, Emma; Calabretta, Bruno; Holyoake, Tessa L; Helgason, G Vignir

2018-05-01

Imatinib and second-generation tyrosine kinase inhibitors (TKIs) nilotinib and dasatinib have statistically significantly improved the life expectancy of chronic myeloid leukemia (CML) patients; however, resistance to TKIs remains a major clinical challenge. Although ponatinib, a third-generation TKI, improves outcomes for patients with BCR-ABL-dependent mechanisms of resistance, including the T315I mutation, a proportion of patients may have or develop BCR-ABL-independent resistance and fail ponatinib treatment. By modeling ponatinib resistance and testing samples from these CML patients, it is hoped that an alternative drug target can be identified and inhibited with a novel compound. Two CML cell lines with acquired BCR-ABL-independent resistance were generated following culture in ponatinib. RNA sequencing and gene ontology (GO) enrichment were used to detect aberrant transcriptional response in ponatinib-resistant cells. A validated oncogene drug library was used to identify US Food and Drug Administration-approved drugs with activity against TKI-resistant cells. Validation was performed using bone marrow (BM)-derived cells from TKI-resistant patients (n = 4) and a human xenograft mouse model (n = 4-6 mice per group). All statistical tests were two-sided. We show that ponatinib-resistant CML cells can acquire BCR-ABL-independent resistance mediated through alternative activation of mTOR. Following transcriptomic analysis and drug screening, we highlight mTOR inhibition as an alternative therapeutic approach in TKI-resistant CML cells. Additionally, we show that catalytic mTOR inhibitors induce autophagy and demonstrate that genetic or pharmacological inhibition of autophagy sensitizes ponatinib-resistant CML cells to death induced by mTOR inhibition in vitro (% number of colonies of control[SD], NVP-BEZ235 vs NVP-BEZ235+HCQ: 45.0[17.9]% vs 24.0[8.4]%, P = .002) and in vivo (median survival of NVP-BEZ235- vs NVP-BEZ235+HCQ-treated mice: 38.5 days vs 47.0 days, P = .04). Combined mTOR and autophagy inhibition may provide an attractive approach to target BCR-ABL-independent mechanism of resistance.

TS-Chemscore, a Target-Specific Scoring Function, Significantly Improves the Performance of Scoring in Virtual Screening.

PubMed

Wang, Wen-Jing; Huang, Qi; Zou, Jun; Li, Lin-Li; Yang, Sheng-Yong

2015-07-01

Most of the scoring functions currently used in structure-based drug design belong to 'universal' scoring functions, which often give a poor correlation between the calculated scores and experimental binding affinities. In this investigation, we proposed a simple strategy to construct target-specific scoring functions based on known 'universal' scoring functions. This strategy was applied to Chemscore, a widely used empirical scoring function, which led to a new scoring function, termed TS-Chemscore. TS-Chemscore was validated on 14 protein targets, which cover a wide range of biological target categories. The results showed that TS-Chemscore significantly improved the correlation between the calculated scores and experimental binding affinities compared with the original Chemscore. TS-Chemscore was then applied in virtual screening to retrieve novel JAK3 and YopH inhibitors. Top 30 compounds for each target were selected for experimental validation. Six active compounds for JAK3 and four for YopH were obtained. These compounds were out of the lists of top 30 compounds sorted by Chemscore. Collectively, TS-Chemscore established in this study showed a better performance in virtual screening than its counterpart Chemscore. © 2014 John Wiley & Sons A/S.

In silico analysis of a therapeutic target in Leishmania infantum: the guanosine-diphospho-D-mannose pyrophosphorylase.

PubMed

Pomel, S; Rodrigo, J; Hendra, F; Cavé, C; Loiseau, P M

2012-02-01

Leishmaniases are tropical and sub-tropical diseases for which classical drugs (i.e. antimonials) exhibit toxicity and drug resistance. Such a situation requires to find new chemical series with antileishmanial activity. This work consists in analyzing the structure of a validated target in Leishmania: the GDP-mannose pyrophosphorylase (GDP-MP), an enzyme involved in glycosylation and essential for amastigote survival. By comparing both human and L. infantum GDP-MP 3D homology models, we identified (i) a common motif of amino acids that binds to the mannose moiety of the substrate and, interestingly, (ii) a motif that is specific to the catalytic site of the parasite enzyme. This motif could then be used to design compounds that specifically inhibit the leishmanial GDP-MP, without any effect on the human homolog.

NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning

PubMed Central

Chen, Ming; Wang, Quanxin; Zhang, Lixin; Yan, Guiying

2016-01-01

Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations. PMID:27415801

Computational modeling-based discovery of novel classes of anti-inflammatory drugs that target lanthionine synthetase C-like protein 2.

PubMed

Lu, Pinyi; Hontecillas, Raquel; Horne, William T; Carbo, Adria; Viladomiu, Monica; Pedragosa, Mireia; Bevan, David R; Lewis, Stephanie N; Bassaganya-Riera, Josep

2012-01-01

Lanthionine synthetase component C-like protein 2 (LANCL2) is a member of the eukaryotic lanthionine synthetase component C-Like protein family involved in signal transduction and insulin sensitization. Recently, LANCL2 is a target for the binding and signaling of abscisic acid (ABA), a plant hormone with anti-diabetic and anti-inflammatory effects. The goal of this study was to determine the role of LANCL2 as a potential therapeutic target for developing novel drugs and nutraceuticals against inflammatory diseases. Previously, we performed homology modeling to construct a three-dimensional structure of LANCL2 using the crystal structure of lanthionine synthetase component C-like protein 1 (LANCL1) as a template. Using this model, structure-based virtual screening was performed using compounds from NCI (National Cancer Institute) Diversity Set II, ChemBridge, ZINC natural products, and FDA-approved drugs databases. Several potential ligands were identified using molecular docking. In order to validate the anti-inflammatory efficacy of the top ranked compound (NSC61610) in the NCI Diversity Set II, a series of in vitro and pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the lead compound, NSC61610, activated peroxisome proliferator-activated receptor gamma in a LANCL2- and adenylate cyclase/cAMP dependent manner in vitro and ameliorated experimental colitis by down-modulating colonic inflammatory gene expression and favoring regulatory T cell responses. LANCL2 is a novel therapeutic target for inflammatory diseases. High-throughput, structure-based virtual screening is an effective computational-based drug design method for discovering anti-inflammatory LANCL2-based drug candidates.

Computational Modeling-Based Discovery of Novel Classes of Anti-Inflammatory Drugs That Target Lanthionine Synthetase C-Like Protein 2

PubMed Central

Lu, Pinyi; Hontecillas, Raquel; Horne, William T.; Carbo, Adria; Viladomiu, Monica; Pedragosa, Mireia; Bevan, David R.; Lewis, Stephanie N.; Bassaganya-Riera, Josep

2012-01-01

Background Lanthionine synthetase component C-like protein 2 (LANCL2) is a member of the eukaryotic lanthionine synthetase component C-Like protein family involved in signal transduction and insulin sensitization. Recently, LANCL2 is a target for the binding and signaling of abscisic acid (ABA), a plant hormone with anti-diabetic and anti-inflammatory effects. Methodology/Principal Findings The goal of this study was to determine the role of LANCL2 as a potential therapeutic target for developing novel drugs and nutraceuticals against inflammatory diseases. Previously, we performed homology modeling to construct a three-dimensional structure of LANCL2 using the crystal structure of lanthionine synthetase component C-like protein 1 (LANCL1) as a template. Using this model, structure-based virtual screening was performed using compounds from NCI (National Cancer Institute) Diversity Set II, ChemBridge, ZINC natural products, and FDA-approved drugs databases. Several potential ligands were identified using molecular docking. In order to validate the anti-inflammatory efficacy of the top ranked compound (NSC61610) in the NCI Diversity Set II, a series of in vitro and pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the lead compound, NSC61610, activated peroxisome proliferator-activated receptor gamma in a LANCL2- and adenylate cyclase/cAMP dependent manner in vitro and ameliorated experimental colitis by down-modulating colonic inflammatory gene expression and favoring regulatory T cell responses. Conclusions/Significance LANCL2 is a novel therapeutic target for inflammatory diseases. High-throughput, structure-based virtual screening is an effective computational-based drug design method for discovering anti-inflammatory LANCL2-based drug candidates. PMID:22509338

De novo design of chiral organotin cancer drug candidates: validation of enantiopreferential binding to molecular target DNA and 5'-GMP by UV-visible, fluorescence, (1)H and (31)P NMR.

PubMed

Arjmand, Farukh; Sharma, Girish Chandra; Sayeed, Fatima; Muddassir, Mohd; Tabassum, Sartaj

2011-12-02

N,N-bis[(R-/S-)-1-benzyl-2-ethoxyethane] tin (IV) complexes were synthesized by applying de novo design strategy by the condensation reaction of (R-/S-)2-amino-2-phenylethanol and dibromoethane in presence of dimethyltin dichloride and thoroughly characterized by elemental analysis, conductivity measurements, IR, ESI-MS, (1)H, (13)C and (119)Sn, multinuclear NMR spectroscopy and XRD study. Enantioselective and specific binding profile of R-enantiomer 1 in comparison to S-enantiomer 2 with ultimate molecular target CT-DNA was validated by UV-visible, fluorescence, circular dichroism, (1)H and (31)P NMR techniques. This was further corroborated well by interaction of 1 and 2 with 5'-GMP. Copyright © 2011 Elsevier B.V. All rights reserved.

Multiwell cell culture plate format with integrated microfluidic perfusion system

NASA Astrophysics Data System (ADS)

Domansky, Karel; Inman, Walker; Serdy, Jim; Griffith, Linda G.

2006-01-01

A new cell culture analog has been developed. It is based on the standard multiwell cell culture plate format but it provides perfused three-dimensional cell culture capability. The new capability is achieved by integrating microfluidic valves and pumps into the plate. The system provides a means to conduct high throughput assays for target validation and predictive toxicology in the drug discovery and development process. It can be also used for evaluation of long-term exposure to drugs or environmental agents or as a model to study viral hepatitis, cancer metastasis, and other diseases and pathological conditions.

Molecular Targets in Advanced Therapeutics of Cancers: The Role of Pharmacogenetics.

PubMed

Abubakar, Murtala B; Gan, Siew Hua

2016-01-01

The advent of advanced molecular targeted therapy has resulted in improved prognoses for patients with advanced malignancies. However, despite the significant success and specificity of this advocated targeted therapy, significant on- and off-target adverse effects and inter-individual variability in treatment responses have been reported. The interpatient variability in drug response has been suggested to be partly due to variations in patient genomes. Therefore, the identification of genetic biomarkers by conducting pharmacogenetics studies can help predict patient responses to targeted therapy and may serve as a basis for individualized treatment. In this review, both clinically established and potential molecular targets are highlighted. Overall, current literature suggests that individualization of targeted therapy is promising; however, integrating the clinical benefits of identified biomarkers into clinical practice for personalized medicine remains a major challenge, and further studies to validate these markers and identify novel therapeutic approaches are needed. © 2016 S. Karger AG, Basel.

Caenorhabditis elegans as a Model to Study the Molecular and Genetic Mechanisms of Drug Addiction

PubMed Central

Engleman, Eric A.; Katner, Simon N.; Neal-Beliveau, Bethany S.

2016-01-01

Drug addiction takes a massive toll on society. Novel animal models are needed to test new treatments and understand the basic mechanisms underlying addiction. Rodent models have identified the neurocircuitry involved in addictive behavior and indicate that rodents possess some of the same neurobiologic mechanisms that mediate addiction in humans. Recent studies indicate that addiction is mechanistically and phylogenetically ancient and many mechanisms that underlie human addiction are also present in invertebrates. The nematode Caenorhabditis elegans has conserved neurobiologic systems with powerful molecular and genetic tools and a rapid rate of development that enables cost-effective translational discovery. Emerging evidence suggests that C. elegans is an excellent model to identify molecular mechanisms that mediate drug-induced behavior and potential targets for medications development for various addictive compounds. C. elegans emit many behaviors that can be easily quantitated including some that involve interactions with the environment. Ethanol (EtOH) is the best-studied drug-of-abuse in C. elegans and at least 50 different genes/targets have been identified as mediating EtOH’s effects and polymorphisms in some orthologs in humans are associated with alcohol use disorders. C. elegans has also been shown to display dopamine and cholinergic system–dependent attraction to nicotine and demonstrate preference for cues previously associated with nicotine. Cocaine and methamphetamine have been found to produce dopamine-dependent reward-like behaviors in C. elegans. These behavioral tests in combination with genetic/molecular manipulations have led to the identification of dozens of target genes/systems in C. elegans that mediate drug effects. The one target/gene identified as essential for drug-induced behavioral responses across all drugs of abuse was the cat-2 gene coding for tyrosine hydroxylase, which is consistent with the role of dopamine neurotransmission in human addiction. Overall, C. elegans can be used to model aspects of drug addiction and identify systems and molecular mechanisms that mediate drug effects. The findings are surprisingly consistent with analogous findings in higher-level organisms. Further, model refinement is warranted to improve model validity and increase utility for medications development. PMID:26810004

Caenorhabditis elegans as a Model to Study the Molecular and Genetic Mechanisms of Drug Addiction.

PubMed

Engleman, Eric A; Katner, Simon N; Neal-Beliveau, Bethany S

2016-01-01

Drug addiction takes a massive toll on society. Novel animal models are needed to test new treatments and understand the basic mechanisms underlying addiction. Rodent models have identified the neurocircuitry involved in addictive behavior and indicate that rodents possess some of the same neurobiologic mechanisms that mediate addiction in humans. Recent studies indicate that addiction is mechanistically and phylogenetically ancient and many mechanisms that underlie human addiction are also present in invertebrates. The nematode Caenorhabditis elegans has conserved neurobiologic systems with powerful molecular and genetic tools and a rapid rate of development that enables cost-effective translational discovery. Emerging evidence suggests that C. elegans is an excellent model to identify molecular mechanisms that mediate drug-induced behavior and potential targets for medications development for various addictive compounds. C. elegans emit many behaviors that can be easily quantitated including some that involve interactions with the environment. Ethanol (EtOH) is the best-studied drug-of-abuse in C. elegans and at least 50 different genes/targets have been identified as mediating EtOH's effects and polymorphisms in some orthologs in humans are associated with alcohol use disorders. C. elegans has also been shown to display dopamine and cholinergic system-dependent attraction to nicotine and demonstrate preference for cues previously associated with nicotine. Cocaine and methamphetamine have been found to produce dopamine-dependent reward-like behaviors in C. elegans. These behavioral tests in combination with genetic/molecular manipulations have led to the identification of dozens of target genes/systems in C. elegans that mediate drug effects. The one target/gene identified as essential for drug-induced behavioral responses across all drugs of abuse was the cat-2 gene coding for tyrosine hydroxylase, which is consistent with the role of dopamine neurotransmission in human addiction. Overall, C. elegans can be used to model aspects of drug addiction and identify systems and molecular mechanisms that mediate drug effects. The findings are surprisingly consistent with analogous findings in higher-level organisms. Further, model refinement is warranted to improve model validity and increase utility for medications development. Copyright © 2016. Published by Elsevier Inc.

Wide-Scope Screening Method for Multiclass Veterinary Drug Residues in Fish, Shrimp, and Eel Using Liquid Chromatography-Quadrupole High-Resolution Mass Spectrometry.

PubMed

Turnipseed, Sherri B; Storey, Joseph M; Lohne, Jack J; Andersen, Wendy C; Burger, Robert; Johnson, Aaron S; Madson, Mark R

2017-08-30

A screening method for veterinary drug residues in fish, shrimp, and eel using LC with a high-resolution MS instrument has been developed and validated. The method was optimized for over 70 test compounds representing a variety of veterinary drug classes. Tissues were extracted by vortex mixing with acetonitrile acidified with 2% acetic acid and 0.2% p-toluenesulfonic acid. A centrifuged portion of the extract was passed through a novel solid phase extraction cartridge designed to remove interfering matrix components from tissue extracts. The eluent was then evaporated and reconstituted for analysis. Data were collected with a quadrupole-Orbitrap high-resolution mass spectrometer using both nontargeted and targeted acquisition methods. Residues were detected on the basis of the exact mass of the precursor and a product ion along with isotope pattern and retention time matching. Semiquantitative data analysis compared MS 1 signal to a one-point extracted matrix standard at a target testing level. The test compounds were detected and identified in salmon, tilapia, catfish, shrimp, and eel extracts fortified at the target testing levels. Fish dosed with selected analytes and aquaculture samples previously found to contain residues were also analyzed. The screening method can be expanded to monitor for an additional >260 veterinary drugs on the basis of exact mass measurements and retention times.

Multiple grid arrangement improves ligand docking with unknown binding sites: Application to the inverse docking problem.

PubMed

Ban, Tomohiro; Ohue, Masahito; Akiyama, Yutaka

2018-04-01

The identification of comprehensive drug-target interactions is important in drug discovery. Although numerous computational methods have been developed over the years, a gold standard technique has not been established. Computational ligand docking and structure-based drug design allow researchers to predict the binding affinity between a compound and a target protein, and thus, they are often used to virtually screen compound libraries. In addition, docking techniques have also been applied to the virtual screening of target proteins (inverse docking) to predict target proteins of a drug candidate. Nevertheless, a more accurate docking method is currently required. In this study, we proposed a method in which a predicted ligand-binding site is covered by multiple grids, termed multiple grid arrangement. Notably, multiple grid arrangement facilitates the conformational search for a grid-based ligand docking software and can be applied to the state-of-the-art commercial docking software Glide (Schrödinger, LLC). We validated the proposed method by re-docking with the Astex diverse benchmark dataset and blind binding site situations, which improved the correct prediction rate of the top scoring docking pose from 27.1% to 34.1%; however, only a slight improvement in target prediction accuracy was observed with inverse docking scenarios. These findings highlight the limitations and challenges of current scoring functions and the need for more accurate docking methods. The proposed multiple grid arrangement method was implemented in Glide by modifying a cross-docking script for Glide, xglide.py. The script of our method is freely available online at http://www.bi.cs.titech.ac.jp/mga_glide/. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Intranasal delivery of quercetin-loaded mucoadhesive nanoemulsion for treatment of cerebral ischaemia.

PubMed

Ahmad, Niyaz; Ahmad, Rizwan; Naqvi, Atta Abbas; Alam, Md Aftab; Ashafaq, Mohammad; Abdur Rub, Rehan; Ahmad, Farhan Jalees

2018-06-01

Quercetin (QUR), as an antioxidant flavonoid, exhibits potential role in the amelioration of cerebral ischaemia; however, poor solubility as well as oral absorption results low serum and tissue levels for this drug. To enhance bioavailability, this study aims to prepare QUR nanoemulsions and administer via non-invasive nasal route in order to evaluate the drug targeting in brain. Quercetin mucoadhesive nanoemulsion (QMNE) was prepared (ionic gelation method) and optimized using various parameters, that is, particle size, entrapment efficiency, zeta potential and ex vivo permeation study. The results observed for optimized QMNE were as follows: mean globule size (91.63 ± 4.36 nm), zeta potential (-17.26 ± 1.04 mV), drug content (99.84 ± 0.34%) and viscosity (121 ± 13 cp). To evaluate the extent of bioavailability for QMNE via post-intranasal (i.n.) administration, Ultra performance liquid chromatography-mass spectroscopy (UPLC-ESI-Q-TOF-MS/MS)-based bioanalytical method was developed and validated for pharmacokinetics, biodistribution, brain-targeting efficiency (9333.33 ± 39.39%) and brain drug-targeting potential (2181.83 ± 5.69%) which revealed enhanced QUR brain bioavailability as compared to intravenous administration (i.v.). Furthermore, improved neurobehavioral activity (locomotor and grip strength), histopathology and reduced infarction volume effects were observed in middle cerebral artery occlusion (MCAO)-induced cerebral ischemic rats model after i.n. administration of QMNE. This study supports a significant role for QMNE in terms of high brain-targeting potential and formulation efficiency due to ease of access and effective targeting in brain.

Translational systems pharmacology‐based predictive assessment of drug‐induced cardiomyopathy

PubMed Central

Messinis, Dimitris E.; Melas, Ioannis N.; Hur, Junguk; Varshney, Navya; Alexopoulos, Leonidas G.

2018-01-01

Drug‐induced cardiomyopathy contributes to drug attrition. We compared two pipelines of predictive modeling: (1) applying elastic net (EN) to differentially expressed genes (DEGs) of drugs; (2) applying integer linear programming (ILP) to construct each drug's signaling pathway starting from its targets to downstream proteins, to transcription factors, and to its DEGs in human cardiomyocytes, and then subjecting the genes/proteins in the drugs' signaling networks to EN regression. We classified 31 drugs with availability of DEGs into 13 toxic and 18 nontoxic drugs based on a clinical cardiomyopathy incidence cutoff of 0.1%. The ILP‐augmented modeling increased prediction accuracy from 79% to 88% (sensitivity: 88%; specificity: 89%) under leave‐one‐out cross validation. The ILP‐constructed signaling networks of drugs were better predictors than DEGs. Per literature, the microRNAs that reportedly regulate expression of our six top predictors are of diagnostic value for natural heart failure or doxorubicin‐induced cardiomyopathy. This translational predictive modeling might uncover potential biomarkers. PMID:29341478

Targeted drug delivery across the blood brain barrier in Alzheimer's disease.

PubMed

Rocha, Sandra

2013-01-01

The discovery of drugs for Alzheimer's disease (AD) therapy that can also permeate the blood brain barrier (BBB) is very difficult owing to its specificity and restrictive nature. The BBB disruption or the administration of the drug directly into the brain is not an option due to toxic effects and low diffusion of the therapeutic molecule in the brain parenchyma. A promising approach for drug systemic delivery to the central nervous system is the use of nanosized carriers. The therapeutic potential of certain nanopharmaceuticals for AD has already been demonstrated in vivo after systemic delivery. They are based on i) conjugates of drug and monoclonal antibodies against BBB endogenous receptors; ii) cationized or end terminal protected proteins/peptides; iii) liposomes and polymeric nanoparticles coated with polysorbate 80, cationic macromolecules or antibodies against BBB receptors/amyloid beta-peptides. Optimization and further validation of these systems are needed.

Targeting of tumor endothelium by RGD-grafted PLGA-nanoparticles.

PubMed

Danhier, Fabienne; Pourcelle, Vincent; Marchand-Brynaert, Jacqueline; Jérôme, Christine; Feron, Olivier; Préat, Véronique

2012-01-01

The destruction of the neovessels in solid tumors can cause the death of tumor cells resulting from the lack of oxygen and nutrients. Peculiarities of the tumor vasculature, however, also position angiogenic endothelial cells as obvious targets to address cytotoxic drugs into the tumor. In particular, the identification of a three-amino acids sequence, arginine-glycine-aspartate (RGD), as a fundamental recognition site for proliferating endothelial attachment to the extracellular matrix leads to the development of tumor-targeting ligands for nanoparticles. The RGD peptide can target the α(v)β(3) integrin overexpressed by the tumor endothelium, and thereby increases the accumulation of drug-loaded RGD-grafted nanoparticles. RGD-nanoparticles may thus extravasate more efficiently and enter the tumor via the enhanced permeability and retention (EPR) effect. This combination of active and passive processes leads to the penetration of nanoparticles into the tumor tissue, followed by cellular uptake and intracellular delivery of the cytotoxic payload. Since cancer cells may also express α(v)β(3) integrin, the entrapping of RGD-nanoparticles into the tumor interstitial fluid may yet be facilitated through direct binding to cancer cells. Here, we describe methods used for the preparation of RGD-nanoparticles and for the validation of their potential of tumor endothelium targeting both in vitro and in vivo. We also illustrate how RGD-nanoparticles may be more suited than nontargeted modalities for the tumor delivery of poorly soluble and/or highly cytotoxic drugs, using different mouse tumor xenograft models. Copyright © 2012 Elsevier Inc. All rights reserved.

Validation of internal controls for gene expression analysis in the intestine of rats infected with Hymenolepis diminuta.

PubMed

Hoque, Tafazzal; Bhogal, Meetu; Boghal, Meetu; Webb, Rodney A

2007-12-01

The non-invasive parasitic cestode Hymenolepis diminuta induces hypertrophy, hyperplasia and other changes in cell activity in the intestine of rats which are indicated in the expression of mRNA. We have investigated various house-keeping genes (GAPDH, beta-actin, 18S and HPRT) and other internal controls (total RNA/unit biomass, total RNA/unit length of intestine) to validate gene expression in the rat intestine after cestode infection and drug-induced neuromodulation. Variation in GAPDH, beta-actin, 18S and HPRT expression was observed in rat jejunal tissue according to treatment. Total RNA/unit length of intestine was found to be the most suitable internal control for normalizing target gene mRNA expression in both infected and/or drug-induced rat intestine. This normalization method may be applied to studies of gene expression levels in intestinal tissue where hypertrophy, hyperplasia, rapid growth and cell differentiation generally occur.

Treponema pallidum Putative Novel Drug Target Identification and Validation: Rethinking Syphilis Therapeutics with Plant-Derived Terpenoids

PubMed Central

Tiwari, Sameeksha; Singh, Priyanka; Singh, Swati; Awasthi, Manika; Pandey, Veda P.

2015-01-01

Abstract Syphilis, a slow progressive and the third most common sexually transmitted disease found worldwide, is caused by a spirochete gram negative bacteria Treponema pallidum. Emergence of antibiotic resistant T. pallidum has led to a search for novel drugs and their targets. Subtractive genomics analyses of pathogen T. pallidum and host Homo sapiens resulted in identification of 126 proteins essential for survival and viability of the pathogen. Metabolic pathway analyses of these essential proteins led to discovery of nineteen proteins distributed among six metabolic pathways unique to T. pallidum. One hundred plant-derived terpenoids, as potential therapeutic molecules against T. pallidum, were screened for their drug likeness and ADMET (absorption, distribution, metabolism, and toxicity) properties. Subsequently the resulting nine terpenoids were docked with five unique T. pallidum targets through molecular modeling approaches. Out of five targets analyzed, D-alanine:D-alanine ligase was found to be the most promising target, while terpenoid salvicine was the most potent inhibitor. A comparison of the inhibitory potential of the best docked readily available natural compound, namely pomiferin (flavonoid) with that of the best docked terpenoid salvicine, revealed that salvicine was a more potent inhibitor than that of pomiferin. To the best of our knowledge, this is the first report of a terpenoid as a potential therapeutic molecule against T. pallidum with D-alanine:D-alanine ligase as a novel target. Further studies are warranted to evaluate and explore the potential clinical ramifications of these findings in relation to syphilis that has public health importance worldwide. PMID:25683888

Novel Platform for MRI-Guided Convection-Enhanced Delivery of Therapeutics: Preclinical Validation in Nonhuman Primate Brain

PubMed Central

Richardson, R. Mark; Kells, Adrian P.; Martin, Alastair J.; Larson, Paul S.; Starr, Philip A.; Piferi, Peter G.; Bates, Geoffrey; Tansey, Lisa; Rosenbluth, Kathryn H.; Bringas, John R.; Berger, Mitchel S.; Bankiewicz, Krystof S.

2011-01-01

Background/Aims A skull-mounted aiming device and integrated software platform has been developed for MRI-guided neurological interventions. In anticipation of upcoming gene therapy clinical trials, we adapted this device for real-time convection-enhanced delivery of therapeutics via a custom-designed infusion cannula. The targeting accuracy of this delivery system and the performance of the infusion cannula were validated in nonhuman primates. Methods Infusions of gadoteridol were delivered to multiple brain targets and the targeting error was determined for each cannula placement. Cannula performance was assessed by analyzing gadoteridol distributions and by histological analysis of tissue damage. Results The average targeting error for all targets (n = 11) was 0.8 mm (95% CI = 0.14). For clinically relevant volumes, the distribution volume of gadoteridol increased as a linear function (R2 = 0.97) of the infusion volume (average slope = 3.30, 95% CI = 0.2). No infusions in any target produced occlusion, cannula reflux or leakage from adjacent tracts, and no signs of unexpected tissue damage were observed. Conclusions This integrated delivery platform allows real-time convection-enhanced delivery to be performed with a high level of precision, predictability and safety. This approach may improve the success rate for clinical trials involving intracerebral drug delivery by direct infusion. PMID:21494065

A liquid chromatography-tandem mass spectrometry-based targeted proteomics assay for monitoring P-glycoprotein levels in human breast tissue.

PubMed

Yang, Ting; Chen, Fei; Xu, Feifei; Wang, Fengliang; Xu, Qingqing; Chen, Yun

2014-09-25

P-glycoprotein (P-gp) can efflux drugs from cancer cells, and its overexpression is commonly associated with multi-drug resistance (MDR). Thus, the accurate quantification of P-gp would help predict the response to chemotherapy and for prognosis of breast cancer patients. An advanced liquid chromatography-tandem mass spectrometry (LC/MS/MS)-based targeted proteomics assay was developed and validated for monitoring P-gp levels in breast tissue. Tryptic peptide 368IIDNKPSIDSYSK380 was selected as a surrogate analyte for quantification, and immuno-depleted tissue extract was used as a surrogate matrix. Matched pairs of breast tissue samples from 60 patients who were suspected to have drug resistance were subject to analysis. The levels of P-gp were quantified. Using data from normal tissue, we suggested a P-gp reference interval. The experimental values of tumor tissue samples were compared with those obtained from Western blotting and immunohistochemistry (IHC). The result indicated that the targeted proteomics approach was comparable to IHC but provided a lower limit of quantification (LOQ) and could afford more reliable results at low concentrations than the other two methods. LC/MS/MS-based targeted proteomics may allow the quantification of P-gp in breast tissue in a more accurate manner. Copyright © 2014 Elsevier B.V. All rights reserved.

GRP78 enabled micelle-based glioma targeted drug delivery.

PubMed

Ran, Danni; Mao, Jiani; Shen, Qing; Xie, Cao; Zhan, Changyou; Wang, Ruifeng; Lu, Weiyue

2017-06-10

GRP78, a specific cancer cell-surface marker, is implicated in cancer cells proliferation, apoptosis resistance, metastasis and drug resistance. l-VAP (SNTRVAP) is a tumor homing peptide exhibiting high binding affinity in vitro to GRP78 protein overexpressed on glioma, glioma stem cells, vasculogenic mimicry and neovasculature. Even though short peptides are often non-immunogenic and demonstrate high affinity to tumor cells, their targeting efficacy is always undermined by rapid blood clearance and enzymatic degradation. In the present study, two d peptides RI-VAP (retro inverso isomer of l-VAP) and d-VAP (retro isomer of l-VAP) were developed by structure-guided peptide design and retro-inverso isomerization technique for glioma targeting. RI-VAP and d-VAP were predicted to bind their receptor GRP78 protein with similar binding affinity, which was experimentally confirmed. The results of in vivo imaging demonstrated that RI-VAP and d-VAP had remarkably advantage over l-VAP for tumor accumulation. In addition, RI-VAP and d-VAP modified paclitaxel-loaded polymeric micelle had better anti-tumor efficacy in comparison to taxol, paclitaxel-loaded plain micelles and l-VAP modified micelles. Overall, the VAP modified micelles suggested in the present study could effectively achieve glioma-targeted drug delivery, validating the potential of the stable VAP peptides in improving the therapeutic efficacy of paclitaxel for glioma. Copyright © 2017 Elsevier B.V. All rights reserved.

A personalized medicine approach to the design of dry powder inhalers: Selecting the optimal amount of bypass.

PubMed

Kopsch, Thomas; Murnane, Darragh; Symons, Digby

2017-08-30

In dry powder inhalers (DPIs) the patient's inhalation manoeuvre strongly influences the release of drug. Drug release from a DPI may also be influenced by the size of any air bypass incorporated in the device. If the amount of bypass is high less air flows through the entrainment geometry and the release rate is lower. In this study we propose to reduce the intra- and inter-patient variations of drug release by controlling the amount of air bypass in a DPI. A fast computational method is proposed that can predict how much bypass is needed for a specified drug delivery rate for a particular patient. This method uses a meta-model which was constructed using multiphase computational fluid dynamic (CFD) simulations. The meta-model is applied in an optimization framework to predict the required amount of bypass needed for drug delivery that is similar to a desired target release behaviour. The meta-model was successfully validated by comparing its predictions to results from additional CFD simulations. The optimization framework has been applied to identify the optimal amount of bypass needed for fictitious sample inhalation manoeuvres in order to deliver a target powder release profile for two patients. Copyright © 2017 Elsevier B.V. All rights reserved.

Development and Validation of a Novel Dual Luciferase Reporter Gene Assay to Quantify Ebola Virus VP24 Inhibition of IFN Signaling

PubMed Central

Frau, Aldo; Sgarbanti, Marco; Orsatti, Roberto

2018-01-01

The interferon (IFN) system is the first line of defense against viral infections. Evasion of IFN signaling by Ebola viral protein 24 (VP24) is a critical event in the pathogenesis of the infection and, hence, VP24 is a potential target for drug development. Since no drugs target VP24, the identification of molecules able to inhibit VP24, restoring and possibly enhancing the IFN response, is a goal of concern. Accordingly, we developed a dual signal firefly and Renilla luciferase cell-based drug screening assay able to quantify IFN-mediated induction of Interferon Stimulated Genes (ISGs) and its inhibition by VP24. Human Embryonic Kidney 293T (HEK293T) cells were transiently transfected with a luciferase reporter gene construct driven by the promoter of ISGs, Interferon-Stimulated Response Element (ISRE). Stimulation of cells with IFN-α activated the IFN cascade leading to the expression of ISRE. Cotransfection of cells with a plasmid expressing VP24 cloned from a virus isolated during the last 2014 outbreak led to the inhibition of ISRE transcription, quantified by a luminescent signal. To adapt this system to test a large number of compounds, we performed it in 96-well plates; optimized the assay analyzing different parameters; and validated the system by calculating the Z′- and Z-factor, which showed values of 0.62 and 0.53 for IFN-α stimulation assay and VP24 inhibition assay, respectively, indicative of robust assay performance. PMID:29495311

Development and Validation of a Novel Dual Luciferase Reporter Gene Assay to Quantify Ebola Virus VP24 Inhibition of IFN Signaling.

PubMed

Fanunza, Elisa; Frau, Aldo; Sgarbanti, Marco; Orsatti, Roberto; Corona, Angela; Tramontano, Enzo

2018-02-24

The interferon (IFN) system is the first line of defense against viral infections. Evasion of IFN signaling by Ebola viral protein 24 (VP24) is a critical event in the pathogenesis of the infection and, hence, VP24 is a potential target for drug development. Since no drugs target VP24, the identification of molecules able to inhibit VP24, restoring and possibly enhancing the IFN response, is a goal of concern. Accordingly, we developed a dual signal firefly and Renilla luciferase cell-based drug screening assay able to quantify IFN-mediated induction of Interferon Stimulated Genes (ISGs) and its inhibition by VP24. Human Embryonic Kidney 293T (HEK293T) cells were transiently transfected with a luciferase reporter gene construct driven by the promoter of ISGs, Interferon-Stimulated Response Element (ISRE). Stimulation of cells with IFN-α activated the IFN cascade leading to the expression of ISRE. Cotransfection of cells with a plasmid expressing VP24 cloned from a virus isolated during the last 2014 outbreak led to the inhibition of ISRE transcription, quantified by a luminescent signal. To adapt this system to test a large number of compounds, we performed it in 96-well plates; optimized the assay analyzing different parameters; and validated the system by calculating the Z'- and Z-factor, which showed values of 0.62 and 0.53 for IFN-α stimulation assay and VP24 inhibition assay, respectively, indicative of robust assay performance.

Alzheimer's disease master regulators analysis: search for potential molecular targets and drug repositioning candidates.

PubMed

Vargas, D M; De Bastiani, M A; Zimmer, E R; Klamt, F

2018-06-23

Alzheimer's disease (AD) is a multifactorial and complex neuropathology that involves impairment of many intricate molecular mechanisms. Despite recent advances, AD pathophysiological characterization remains incomplete, which hampers the development of effective treatments. In fact, currently, there are no effective pharmacological treatments for AD. Integrative strategies such as transcription regulatory network and master regulator analyses exemplify promising new approaches to study complex diseases and may help in the identification of potential pharmacological targets. In this study, we used transcription regulatory network and master regulator analyses on transcriptomic data of human hippocampus to identify transcription factors (TFs) that can potentially act as master regulators in AD. All expression profiles were obtained from the Gene Expression Omnibus database using the GEOquery package. A normal hippocampus transcription factor-centered regulatory network was reconstructed using the ARACNe algorithm. Master regulator analysis and two-tail gene set enrichment analysis were employed to evaluate the inferred regulatory units in AD case-control studies. Finally, we used a connectivity map adaptation to prospect new potential therapeutic interventions by drug repurposing. We identified TFs with already reported involvement in AD, such as ATF2 and PARK2, as well as possible new targets for future investigations, such as CNOT7, CSRNP2, SLC30A9, and TSC22D1. Furthermore, Connectivity Map Analysis adaptation suggested the repositioning of six FDA-approved drugs that can potentially modulate master regulator candidate regulatory units (Cefuroxime, Cyproterone, Dydrogesterone, Metrizamide, Trimethadione, and Vorinostat). Using a transcription factor-centered regulatory network reconstruction we were able to identify several potential molecular targets and six drug candidates for repositioning in AD. Our study provides further support for the use of bioinformatics tools as exploratory strategies in neurodegenerative diseases research, and also provides new perspectives on molecular targets and drug therapies for future investigation and validation in AD.

Antisocial personality disorder as a predictor of criminal behaviour in a longitudinal study of a cohort of abusers of several classes of drugs: relation to type of substance and type of crime.

PubMed

Fridell, Mats; Hesse, Morten; Jaeger, Mads Meier; Kühlhorn, Eckart

2008-06-01

Mixed findings have been made with regard to the long-term predictive validity of antisocial personality disorder (ASPD) on criminal behaviour in samples of substance abusers. A longitudinal record-linkage study of a cohort of 1052 drug abusers admitted 1977-1995 was undertaken. Subjects were recruited from a detoxification and short-term rehabilitation unit in Lund, Sweden, and followed through criminal justice registers from their first treatment episode to death or to the year 2004. In a ML multinomial random effects regression, subjects diagnosed with antisocial personality disorders were 2.16 times more likely to be charged with theft only (p<0.001), and 2.44 times more likely to be charged committing multiple types of crime during an observation year (p<0.001). The findings of the current study support the predictive validity of the DSM-III-R diagnosis of ASPD. ASPD should be taken seriously in drug abusers, and be targeted in treatment to prevent crime in society.

Structures of Pseudomonas aeruginosa β-ketoacyl-(acyl-carrier-protein) synthase II (FabF) and a C164Q mutant provide templates for antibacterial drug discovery and identify a buried potassium ion and a ligand-binding site that is an artefact of the crystal form

PubMed Central

Baum, Bernhard; Lecker, Laura S. M.; Zoltner, Martin; Jaenicke, Elmar; Schnell, Robert; Hunter, William N.; Brenk, Ruth

2015-01-01

Bacterial infections remain a serious health concern, in particular causing life-threatening infections of hospitalized and immunocompromised patients. The situation is exacerbated by the rise in antibacterial drug resistance, and new treatments are urgently sought. In this endeavour, accurate structures of molecular targets can support early-stage drug discovery. Here, crystal structures, in three distinct forms, of recombinant Pseudomonas aeruginosa β-ketoacyl-(acyl-carrier-protein) synthase II (FabF) are presented. This enzyme, which is involved in fatty-acid biosynthesis, has been validated by genetic and chemical means as an antibiotic target in Gram-positive bacteria and represents a potential target in Gram-negative bacteria. The structures of apo FabF, of a C164Q mutant in which the binding site is altered to resemble the substrate-bound state and of a complex with 3-(benzoylamino)-2-hydroxybenzoic acid are reported. This compound mimics aspects of a known natural product inhibitor, platensimycin, and surprisingly was observed binding outside the active site, interacting with a symmetry-related molecule. An unusual feature is a completely buried potassium-binding site that was identified in all three structures. Comparisons suggest that this may represent a conserved structural feature of FabF relevant to fold stability. The new structures provide templates for structure-based ligand design and, together with the protocols and reagents, may underpin a target-based drug-discovery project for urgently needed antibacterials. PMID:26249693

Ligand-based receptor tyrosine kinase partial agonists: New paradigm for cancer drug discovery?

PubMed

Riese, David J

2011-02-01

INTRODUCTION: Receptor tyrosine kinases (RTKs) are validated targets for oncology drug discovery and several RTK antagonists have been approved for the treatment of human malignancies. Nonetheless, the discovery and development of RTK antagonists has lagged behind the discovery and development of agents that target G-protein coupled receptors. In part, this is because it has been difficult to discover analogs of naturally-occurring RTK agonists that function as antagonists. AREAS COVERED: Here we describe ligands of ErbB receptors that function as partial agonists for these receptors, thereby enabling these ligands to antagonize the activity of full agonists for these receptors. We provide insights into the mechanisms by which these ligands function as antagonists. We discuss how information concerning these mechanisms can be translated into screens for novel small molecule- and antibody-based antagonists of ErbB receptors and how such antagonists hold great potential as targeted cancer chemotherapeutics. EXPERT OPINION: While there have been a number of important key findings into this field, the identification of the structural basis of ligand functional specificity is still of the greatest importance. While it is true that, with some notable exceptions, peptide hormones and growth factors have not proven to be good platforms for oncology drug discovery; addressing the fundamental issues of antagonistic partial agonists for receptor tyrosine kinases has the potential to steer oncology drug discovery in new directions. Mechanism based approaches are now emerging to enable the discovery of RTK partial agonists that may antagonize both agonist-dependent and -independent RTK signaling and may hold tremendous promise as targeted cancer chemotherapeutics.

Targeting RAS Membrane Association: Back to the Future for Anti-RAS Drug Discovery?

PubMed Central

Cox, Adrienne D.; Der, Channing J.; Philips, Mark R.

2015-01-01

RAS proteins require membrane association for their biological activity, making this association a logical target for anti-RAS therapeutics. Lipid modification of RAS proteins by a farnesyl isoprenoid is an obligate step in that association, and is an enzymatic process. Accordingly, farnesyltransferase inhibitors (FTIs) were developed as potential anti-RAS drugs. The lack of efficacy of FTIs as anti-cancer drugs was widely seen as indicating that blocking RAS membrane association was a flawed approach to cancer treatment. However, a deeper understanding of RAS modification and trafficking has revealed that this was an erroneous conclusion. In the presence of FTIs, KRAS and NRAS, which are the RAS isoforms most frequently mutated in cancer, become substrates for alternative modification, can still associate with membranes, and can still function. Thus, FTIs failed not because blocking RAS membrane association is an ineffective approach, but because FTIs failed to accomplish that task. Recent findings regarding RAS isoform trafficking and the regulation of RAS subcellular localization have rekindled interest in efforts to target these processes. In particular, improved understanding of the palmitoylation/depalmitoylation cycle that regulates RAS interaction with the plasma membrane, endomembranes and cytosol, and of the potential importance of RAS chaperones, have led to new approaches. Efforts to validate and target other enzymatically regulated post-translational modifications are also ongoing. In this review, we revisit lessons learned, describe the current state of the art, and highlight challenging but promising directions to achieve the goal of disrupting RAS membrane association and subcellular localization for anti-RAS drug development. PMID:25878363

Single-Domain Antibodies and the Promise of Modular Targeting in Cancer Imaging and Treatment.

PubMed

Iezzi, María Elena; Policastro, Lucía; Werbajh, Santiago; Podhajcer, Osvaldo; Canziani, Gabriela Alicia

2018-01-01

Monoclonal antibodies and their fragments have significantly changed the outcome of cancer in the clinic, effectively inhibiting tumor cell proliferation, triggering antibody-dependent immune effector cell activation and complement mediated cell death. Along with a continued expansion in number, diversity, and complexity of validated tumor targets there is an increasing focus on engineering recombinant antibody fragments for lead development. Single-domain antibodies (sdAbs), in particular those engineered from the variable heavy-chain fragment (VHH gene) found in Camelidae heavy-chain antibodies (or IgG2 and IgG3), are the smallest fragments that retain the full antigen-binding capacity of the antibody with advantageous properties as drugs. For similar reasons, growing attention is being paid to the yet smaller variable heavy chain new antigen receptor (VNAR) fragments found in Squalidae. sdAbs have been selected, mostly from immune VHH libraries, to inhibit or modulate enzyme activity, bind soluble factors, internalize cell membrane receptors, or block cytoplasmic targets. This succinct review is a compilation of recent data documenting the application of engineered, recombinant sdAb in the clinic as epitope recognition "modules" to build monomeric, dimeric and multimeric ligands that target, tag and stall solid tumor growth in vivo . Size, affinity, specificity, and the development profile of sdAbs drugs are seemingly consistent with desirable clinical efficacy and safety requirements. But the hepatotoxicity of the tetrameric anti-DR5-VHH drug in patients with pre-existing anti-drug antibodies halted the phase I clinical trial and called for a thorough pre-screening of the immune and poly-specific reactivities of the sdAb leads.

Small Molecule Screen for Candidate Antimalarials Targeting Plasmodium Kinesin-5*

PubMed Central

Liu, Liqiong; Richard, Jessica; Kim, Sunyoung; Wojcik, Edward J.

2014-01-01

Plasmodium falciparum and vivax are responsible for the majority of malaria infections worldwide, resulting in over a million deaths annually. Malaria parasites now show measured resistance to all currently utilized drugs. Novel antimalarial drugs are urgently needed. The Plasmodium Kinesin-5 mechanoenzyme is a suitable “next generation” target. Discovered via small molecule screen experiments, the human Kinesin-5 has multiple allosteric sites that are “druggable.” One site in particular, unique in its sequence divergence across all homologs in the superfamily and even within the same family, exhibits exquisite drug specificity. We propose that Plasmodium Kinesin-5 shares this allosteric site and likewise can be targeted to uncover inhibitors with high specificity. To test this idea, we performed a screen for inhibitors selective for Plasmodium Kinesin-5 ATPase activity in parallel with human Kinesin-5. Our screen of nearly 2000 compounds successfully identified compounds that selectively inhibit both P. vivax and falciparum Kinesin-5 motor domains but, as anticipated, do not impact human Kinesin-5 activity. Of note is a candidate drug that did not biochemically compete with the ATP substrate for the conserved active site or disrupt the microtubule-binding site. Together, our experiments identified MMV666693 as a selective allosteric inhibitor of Plasmodium Kinesin-5; this is the first identified protein target for the Medicines of Malaria Venture validated collection of parasite proliferation inhibitors. This work demonstrates that chemical screens against human kinesins are adaptable to homologs in disease organisms and, as such, extendable to strategies to combat infectious disease. PMID:24737313

Remote magnetic targeting of iron oxide nanoparticles for cardiovascular diagnosis and therapeutic drug delivery: where are we now?

PubMed Central

Bietenbeck, Michael; Florian, Anca; Faber, Cornelius; Sechtem, Udo; Yilmaz, Ali

2016-01-01

Magnetic resonance imaging (MRI) allows for an accurate assessment of both functional and structural cardiac parameters, and thereby appropriate diagnosis and validation of cardiovascular diseases. The diagnostic yield of cardiovascular MRI examinations is often increased by the use of contrast agents that are almost exclusively based on gadolinium compounds. Another clinically approved contrast medium is composed of superparamagnetic iron oxide nanoparticles (IONs). These particles may expand the field of contrast-enhanced cardiovascular MRI as recently shown in clinical studies focusing on acute myocardial infarction (AMI) and atherosclerosis. Furthermore, IONs open up new research opportunities such as remote magnetic drug targeting (MDT). The approach of MDT relies on the coupling of bioactive molecules and magnetic nanoparticles to form an injectable complex. This complex, in turn, can be attracted to and retained at a desired target inside the body with the help of applied magnetic fields. In comparison to common systemic drug applications, MDT techniques promise both higher concentrations at the target site and lower concentrations elsewhere in the body. Moreover, concurrent or subsequent MRI can be used for noninvasive monitoring of drug distribution and successful delivery to the desired organ in vivo. This review does not only illustrate the basic conceptual and biophysical principles of IONs, but also focuses on new research activities and achievements in the cardiovascular field, mainly in the management of AMI. Based on the presentation of successful MDT applications in preclinical models of AMI, novel approaches and the translational potential of MDT are discussed. PMID:27486321

Bridging Adult Experience to Pediatrics in Oncology Drug Development.

PubMed

Leong, Ruby; Zhao, Hong; Reaman, Gregory; Liu, Qi; Wang, Yaning; Stewart, Clinton F; Burckart, Gilbert

2017-10-01

Pediatric drug development in the United States has grown under the current regulations made permanent by the Food and Drug Administration Safety and Innovation Act of 2012. Over 1200 pediatric studies have now been submitted to the US FDA, but there is still a high rate of failure to obtain pediatric labeling for the indication pursued. Pediatric oncology represents special problems in that the disease is most often dissimilar to any cancer found in the adult population. Therefore, the development of drug dosing in pediatric oncology patients represents a special challenge. Potential approaches to pediatric dosing in oncology patients include extrapolation of efficacy from adult studies in those few cases where the disease is similar, inclusion of adolescent patients in adult trials when possible, and bridging the adult dose to the pediatric dose. An analysis of the recommended phase 2 dose for 40 molecularly targeted agents in pediatric patients provides some insight into current practices. Increased knowledge of tumor biology and efforts to identify and validate molecular targets and genetic abnormalities that drive childhood cancers can lead to increased opportunities for precision medicine in the treatment of pediatric cancers. © 2017, The American College of Clinical Pharmacology.

Public-Private Partnerships in Lead Discovery: Overview and Case Studies.

PubMed

Gottwald, Matthias; Becker, Andreas; Bahr, Inke; Mueller-Fahrnow, Anke

2016-09-01

The pharmaceutical industry is faced with significant challenges in its efforts to discover new drugs that address unmet medical needs. Safety concerns and lack of efficacy are the two main technical reasons for attrition. Improved early research tools including predictive in silico, in vitro, and in vivo models, as well as a deeper understanding of the disease biology, therefore have the potential to improve success rates. The combination of internal activities with external collaborations in line with the interests and needs of all partners is a successful approach to foster innovation and to meet the challenges. Collaboration can take place in different ways, depending on the requirements of the participants. In this review, the value of public-private partnership approaches will be discussed, using examples from the Innovative Medicines Initiative (IMI). These examples describe consortia approaches to develop tools and processes for improving target identification and validation, as well as lead identification and optimization. The project "Kinetics for Drug Discovery" (K4DD), focusing on the adoption of drug-target binding kinetics analysis in the drug discovery decision-making process, is described in more detail. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Psmir: a database of potential associations between small molecules and miRNAs

PubMed Central

Meng, Fanlin; Wang, Jing; Dai, Enyu; Yang, Feng; Chen, Xiaowen; Wang, Shuyuan; Yu, Xuexin; Liu, Dianming; Jiang, Wei

2016-01-01

miRNAs are key post-transcriptional regulators of many essential biological processes, and their dysregulation has been validated in almost all human cancers. Restoring aberrantly expressed miRNAs might be a novel therapeutics. Recently, many studies have demonstrated that small molecular compounds can affect miRNA expression. Thus, prediction of associations between small molecules and miRNAs is important for investigation of miRNA-targeted drugs. Here, we analyzed 39 miRNA-perturbed gene expression profiles, and then calculated the similarity of transcription responses between miRNA perturbation and drug treatment to predict drug-miRNA associations. At the significance level of 0.05, we obtained 6501 candidate associations between 1295 small molecules and 25 miRNAs, which included 624 FDA approved drugs. Finally, we constructed the Psmir database to store all potential associations and the related materials. In a word, Psmir served as a valuable resource for dissecting the biological significance in small molecules’ effects on miRNA expression, which will facilitate developing novel potential therapeutic targets or treatments for human cancers. Psmir is supported by all major browsers, and is freely available at http://www.bio-bigdata.com/Psmir/. PMID:26759061

Psmir: a database of potential associations between small molecules and miRNAs.

PubMed

Meng, Fanlin; Wang, Jing; Dai, Enyu; Yang, Feng; Chen, Xiaowen; Wang, Shuyuan; Yu, Xuexin; Liu, Dianming; Jiang, Wei

2016-01-13

miRNAs are key post-transcriptional regulators of many essential biological processes, and their dysregulation has been validated in almost all human cancers. Restoring aberrantly expressed miRNAs might be a novel therapeutics. Recently, many studies have demonstrated that small molecular compounds can affect miRNA expression. Thus, prediction of associations between small molecules and miRNAs is important for investigation of miRNA-targeted drugs. Here, we analyzed 39 miRNA-perturbed gene expression profiles, and then calculated the similarity of transcription responses between miRNA perturbation and drug treatment to predict drug-miRNA associations. At the significance level of 0.05, we obtained 6501 candidate associations between 1295 small molecules and 25 miRNAs, which included 624 FDA approved drugs. Finally, we constructed the Psmir database to store all potential associations and the related materials. In a word, Psmir served as a valuable resource for dissecting the biological significance in small molecules' effects on miRNA expression, which will facilitate developing novel potential therapeutic targets or treatments for human cancers. Psmir is supported by all major browsers, and is freely available at http://www.bio-bigdata.com/Psmir/.

Poor drug distribution as a possible explanation for the results of the PRECISE trial.

PubMed

Sampson, John H; Archer, Gary; Pedain, Christoph; Wembacher-Schröder, Eva; Westphal, Manfred; Kunwar, Sandeep; Vogelbaum, Michael A; Coan, April; Herndon, James E; Raghavan, Raghu; Brady, Martin L; Reardon, David A; Friedman, Allan H; Friedman, Henry S; Rodríguez-Ponce, M Inmaculada; Chang, Susan M; Mittermeyer, Stephan; Croteau, David; Puri, Raj K

2010-08-01

Convection-enhanced delivery (CED) is a novel intracerebral drug delivery technique with considerable promise for delivering therapeutic agents throughout the CNS. Despite this promise, Phase III clinical trials employing CED have failed to meet clinical end points. Although this may be due to inactive agents or a failure to rigorously validate drug targets, the authors have previously demonstrated that catheter positioning plays a major role in drug distribution using this technique. The purpose of the present work was to retrospectively analyze the expected drug distribution based on catheter positioning data available from the CED arm of the PRECISE trial. Data on catheter positioning from all patients randomized to the CED arm of the PRECISE trial were available for analyses. BrainLAB iPlan Flow software was used to estimate the expected drug distribution. Only 49.8% of catheters met all positioning criteria. Still, catheter positioning score (hazard ratio 0.93, p = 0.043) and the number of optimally positioned catheters (hazard ratio 0.72, p = 0.038) had a significant effect on progression-free survival. Estimated coverage of relevant target volumes was low, however, with only 20.1% of the 2-cm penumbra surrounding the resection cavity covered on average. Although tumor location and resection cavity volume had no effect on coverage volume, estimations of drug delivery to relevant target volumes did correlate well with catheter score (p < 0.003), and optimally positioned catheters had larger coverage volumes (p < 0.002). Only overall survival (p = 0.006) was higher for investigators considered experienced after adjusting for patient age and Karnofsky Performance Scale score. The potential efficacy of drugs delivered by CED may be severely constrained by ineffective delivery in many patients. Routine use of software algorithms and alternative catheter designs and infusion parameters may improve the efficacy of drugs delivered by CED.

Toxins and drug discovery.

PubMed

Harvey, Alan L

2014-12-15

Components from venoms have stimulated many drug discovery projects, with some notable successes. These are briefly reviewed, from captopril to ziconotide. However, there have been many more disappointments on the road from toxin discovery to approval of a new medicine. Drug discovery and development is an inherently risky business, and the main causes of failure during development programmes are outlined in order to highlight steps that might be taken to increase the chances of success with toxin-based drug discovery. These include having a clear focus on unmet therapeutic needs, concentrating on targets that are well-validated in terms of their relevance to the disease in question, making use of phenotypic screening rather than molecular-based assays, and working with development partners with the resources required for the long and expensive development process. Copyright © 2014 The Author. Published by Elsevier Ltd.. All rights reserved.

Can Untargeted Metabolomics Be Utilized in Drug Discovery/Development?

PubMed

Caldwell, Gary W; Leo, Gregory C

2017-01-01

Untargeted metabolomics is a promising approach for reducing the significant attrition rate for discovering and developing drugs in the pharmaceutical industry. This review aims to highlight the practical decision-making value of untargeted metabolomics for the advancement of drug candidates in drug discovery/development including potentially identifying and validating novel therapeutic targets, creating alternative screening paradigms, facilitating the selection of specific and translational metabolite biomarkers, identifying metabolite signatures for the drug efficacy mechanism of action, and understanding potential drug-induced toxicity. The review provides an overview of the pharmaceutical process workflow to discover and develop new small molecule drugs followed by the metabolomics process workflow that is involved in conducting metabolomics studies. The pros and cons of the major components of the pharmaceutical and metabolomics workflows are reviewed and discussed. Finally, selected untargeted metabolomics literature examples, from primarily 2010 to 2016, are used to illustrate why, how, and where untargeted metabolomics can be integrated into the drug discovery/preclinical drug development process. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

In Vitro Evaluation of Molecular Tumor Targets in Nuclear Medicine: Immunohistochemistry Is One Option, but Under Which Conditions?

PubMed

Reubi, Jean Claude

2017-12-01

The identification of new molecular targets for diagnostic and therapeutic applications using in vitro methods is an important challenge in nuclear medicine. One such method is immunohistochemistry, increasingly popular because it is easy to perform. This review presents the case for conducting receptor immunohistochemistry to evaluate potential molecular targets in human tumor tissue sections. The focus is on the immunohistochemistry of G-protein-coupled receptors, one of the largest families of cell surface proteins, representing a major class of drug targets and thus playing an important role in nuclear medicine. This review identifies common pitfalls and challenges and provides guidelines on performing such immunohistochemical studies. An appropriate validation of the target is a prerequisite for developing robust and informative new molecular probes. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Choline kinase-alpha by regulating cell aggressiveness and drug sensitivity is a potential druggable target for ovarian cancer.

PubMed

Granata, A; Nicoletti, R; Tinaglia, V; De Cecco, L; Pisanu, M E; Ricci, A; Podo, F; Canevari, S; Iorio, E; Bagnoli, M; Mezzanzanica, D

2014-01-21

Aberrant choline metabolism has been proposed as a novel cancer hallmark. We recently showed that epithelial ovarian cancer (EOC) possesses an altered MRS-choline profile, characterised by increased phosphocholine (PCho) content to which mainly contribute over-expression and activation of choline kinase-alpha (ChoK-alpha). To assess its biological relevance, ChoK-alpha expression was downmodulated by transient RNA interference in EOC in vitro models. Gene expression profiling by microarray analysis and functional analysis was performed to identify the pathway/functions perturbed in ChoK-alpha-silenced cells, then validated by in vitro experiments. In silenced cells, compared with control, we observed: (I) a significant reduction of both CHKA transcript and ChoK-alpha protein expression; (II) a dramatic, proportional drop in PCho content ranging from 60 to 71%, as revealed by (1)H-magnetic spectroscopy analysis; (III) a 35-36% of cell growth inhibition, with no evidences of apoptosis or modification of the main cellular survival signalling pathways; (IV) 476 differentially expressed genes, including genes related to lipid metabolism. Ingenuity pathway analysis identified cellular functions related to cell death and cellular proliferation and movement as the most perturbed. Accordingly, CHKA-silenced cells displayed a significant delay in wound repair, a reduced migration and invasion capability were also observed. Furthermore, although CHKA silencing did not directly induce cell death, a significant increase of sensitivity to platinum, paclitaxel and doxorubicin was observed even in a drug-resistant context. We showed for the first time in EOC that CHKA downregulation significantly decreased the aggressive EOC cell behaviour also affecting cells' sensitivity to drug treatment. These observations open the way to further analysis for ChoK-alpha validation as a new EOC therapeutic target to be used alone or in combination with conventional drugs.

MicroRNA therapeutics in cardiovascular medicine

PubMed Central

Thum, Thomas

2012-01-01

Cardiovascular diseases are the most common causes of human morbidity and mortality despite significant therapeutic improvements by surgical, interventional and pharmacological approaches in the last decade. MicroRNAs (miRNAs) are important and powerful mediators in a wide range of diseases and thus emerged as interesting new drug targets. An array of animal and even human miRNA-based therapeutic studies has been performed, which validate miRNAs as being successfully targetable to treat a wide range of diseases. Here, the current knowledge about miRNAs therapeutics in cardiovascular diseases on their way to clinical use are reviewed and discussed. PMID:22162462

TINS, target immobilized NMR screening: an efficient and sensitive method for ligand discovery.

PubMed

Vanwetswinkel, Sophie; Heetebrij, Robert J; van Duynhoven, John; Hollander, Johan G; Filippov, Dmitri V; Hajduk, Philip J; Siegal, Gregg

2005-02-01

We propose a ligand screening method, called TINS (target immobilized NMR screening), which reduces the amount of target required for the fragment-based approach to drug discovery. Binding is detected by comparing 1D NMR spectra of compound mixtures in the presence of a target immobilized on a solid support to a control sample. The method has been validated by the detection of a variety of ligands for protein and nucleic acid targets (K(D) from 60 to 5000 muM). The ligand binding capacity of a protein was undiminished after 2000 different compounds had been applied, indicating the potential to apply the assay for screening typical fragment libraries. TINS can be used in competition mode, allowing rapid characterization of the ligand binding site. TINS may allow screening of targets that are difficult to produce or that are insoluble, such as membrane proteins.

Utilizing random Forest QSAR models with optimized parameters for target identification and its application to target-fishing server.

PubMed

Lee, Kyoungyeul; Lee, Minho; Kim, Dongsup

2017-12-28

The identification of target molecules is important for understanding the mechanism of "target deconvolution" in phenotypic screening and "polypharmacology" of drugs. Because conventional methods of identifying targets require time and cost, in-silico target identification has been considered an alternative solution. One of the well-known in-silico methods of identifying targets involves structure activity relationships (SARs). SARs have advantages such as low computational cost and high feasibility; however, the data dependency in the SAR approach causes imbalance of active data and ambiguity of inactive data throughout targets. We developed a ligand-based virtual screening model comprising 1121 target SAR models built using a random forest algorithm. The performance of each target model was tested by employing the ROC curve and the mean score using an internal five-fold cross validation. Moreover, recall rates for top-k targets were calculated to assess the performance of target ranking. A benchmark model using an optimized sampling method and parameters was examined via external validation set. The result shows recall rates of 67.6% and 73.9% for top-11 (1% of the total targets) and top-33, respectively. We provide a website for users to search the top-k targets for query ligands available publicly at http://rfqsar.kaist.ac.kr . The target models that we built can be used for both predicting the activity of ligands toward each target and ranking candidate targets for a query ligand using a unified scoring scheme. The scores are additionally fitted to the probability so that users can estimate how likely a ligand-target interaction is active. The user interface of our web site is user friendly and intuitive, offering useful information and cross references.

Modeling the microscopic electrical properties of thrombin binding aptamer (TBA) for label-free biosensors.

PubMed

Alfinito, Eleonora; Reggiani, Lino; Cataldo, Rosella; De Nunzio, Giorgio; Giotta, Livia; Guascito, Maria Rachele

2017-02-10

Aptamers are chemically produced oligonucleotides, able to bind a variety of targets such as drugs, proteins and pathogens with high sensitivity and selectivity. Therefore, aptamers are largely employed for producing label-free biosensors (aptasensors), with significant applications in diagnostics and drug delivery. In particular, the anti-thrombin aptamers are biomolecules of high interest for clinical use, because of their ability to recognize and bind the thrombin enzyme. Among them, the DNA 15-mer aptamer (TBA), has been widely explored around the possibility of using it in aptasensors. This paper proposes a microscopic model of the electrical properties of TBA and of the aptamer-thrombin complex, combining information from both structure and function, following the issues addressed in an emerging branch of electronics known as proteotronics. The theoretical results are compared and validated with measurements reported in the literature. Finally, the model suggests resistance measurements as a novel tool for testing aptamer-target affinity.

Modeling the microscopic electrical properties of thrombin binding aptamer (TBA) for label-free biosensors

NASA Astrophysics Data System (ADS)

Alfinito, Eleonora; Reggiani, Lino; Cataldo, Rosella; De Nunzio, Giorgio; Giotta, Livia; Guascito, Maria Rachele

2017-02-01

Aptamers are chemically produced oligonucleotides, able to bind a variety of targets such as drugs, proteins and pathogens with high sensitivity and selectivity. Therefore, aptamers are largely employed for producing label-free biosensors (aptasensors), with significant applications in diagnostics and drug delivery. In particular, the anti-thrombin aptamers are biomolecules of high interest for clinical use, because of their ability to recognize and bind the thrombin enzyme. Among them, the DNA 15-mer aptamer (TBA), has been widely explored around the possibility of using it in aptasensors. This paper proposes a microscopic model of the electrical properties of TBA and of the aptamer-thrombin complex, combining information from both structure and function, following the issues addressed in an emerging branch of electronics known as proteotronics. The theoretical results are compared and validated with measurements reported in the literature. Finally, the model suggests resistance measurements as a novel tool for testing aptamer-target affinity.

Crystal structure of the Alcanivorax borkumensis YdaH transporter reveals an unusual topology

NASA Astrophysics Data System (ADS)

Bolla, Jani Reddy; Su, Chih-Chia; Delmar, Jared A.; Radhakrishnan, Abhijith; Kumar, Nitin; Chou, Tsung-Han; Long, Feng; Rajashankar, Kanagalaghatta R.; Yu, Edward W.

2015-04-01

The potential of the folic acid biosynthesis pathway as a target for the development of antibiotics has been clinically validated. However, many pathogens have developed resistance to these antibiotics, prompting a re-evaluation of potential drug targets within the pathway. The ydaH gene of Alcanivorax borkumensis encodes an integral membrane protein of the AbgT family of transporters for which no structural information was available. Here we report the crystal structure of A. borkumensis YdaH, revealing a dimeric molecule with an architecture distinct from other families of transporters. YdaH is a bowl-shaped dimer with a solvent-filled basin extending from the cytoplasm to halfway across the membrane bilayer. Each subunit of the transporter contains nine transmembrane helices and two hairpins that suggest a plausible pathway for substrate transport. Further analyses also suggest that YdaH could act as an antibiotic efflux pump and mediate bacterial resistance to sulfonamide antimetabolite drugs.

Unique Mechanism of Action of the Thiourea Drug Isoxyl on Mycobacterium tuberculosis*

PubMed Central

Phetsuksiri, Benjawan; Jackson, Mary; Scherman, Hataichanok; McNeil, Michael; Besra, Gurdyal S.; Baulard, Alain R.; Slayden, Richard A.; DeBarber, Andrea E.; Barry, Clifton E.; Baird, Mark S.; Crick, Dean C.; Brennan, Patrick J.

2016-01-01

The thiourea isoxyl (thiocarlide; 4,4′-diisoamyloxydiphenylthiourea) is known to be an effective anti-tuberculosis drug, active against a range of multidrug-resistant strains of Mycobacterium tuberculosis and has been used clinically. Little was known of its mode of action. We now demonstrate that isoxyl results in a dose-dependent decrease in the synthesis of oleic and, consequently, tuberculostearic acid in M. tuberculosis with complete inhibition at 3 μg/ml. Synthesis of mycolic acid was also affected. The anti-bacterial effect of isoxyl was partially reversed by supplementing growth medium with oleic acid. The specificity of this inhibition pointed to a Δ9-stearoyl desaturase as the drug target. Development of a cell-free assay for Δ9-desaturase activity allowed direct demonstration of the inhibition of oleic acid synthesis by isoxyl. Interestingly, sterculic acid, a known inhibitor of Δ9-desaturases, emulated the effect of isoxyl on oleic acid synthesis but did not affect mycolic acid synthesis, demonstrating the lack of a relationship between the two effects of the drug. The three putative fatty acid desaturases in the M. tuberculosis genome, desA1, desA2, and desA3, were cloned and expressed in Mycobacterium bovis BCG. Cell-free assays and whole cell labeling demonstrated increased Δ9-desaturase activity and oleic acid synthesis only in the desA3-overexpressing strain and an increase in the minimal inhibitory concentration for isoxyl, indicating that DesA3 is the target of the drug. These results validate membrane-bound Δ9-desaturase, DesA3, as a new therapeutic target, and the thioureas as anti-tuberculosis drugs worthy of further development. PMID:14559907

Nanospheres Encapsulating Anti-Leishmanial Drugs for Their Specific Macrophage Targeting, Reduced Toxicity, and Deliberate Intracellular Release

PubMed Central

Shukla, Anil Kumar; Patra, Sanjukta

2012-01-01

Abstract The current work focuses on the study of polymeric, biodegradable nanoparticles (NPs) for the encapsulation of doxorubicin and mitomycin C (anti-leishmanial drugs), and their efficient delivery to macrophages, the parasite's home. The biodegradable polymer methoxypoly-(ethylene glycol)-b-poly (lactic acid) (MPEG-PLA) was used to prepare polymeric NPs encapsulating doxorubicin and mitomycin C. The morphology, mean diameter, and surface area of spherical NPs were determined by transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM), and BET surface area analysis. X-ray diffraction was performed to validate drug encapsulation. An in vitro release profile of the drugs suggested a fairly slow release. These polymeric NPs were efficiently capable of releasing drug inside macrophages at a slower pace than the free drug, which was monitored by epi-fluorescence microscopy. Encapsulation of doxorubicin and mitomycin C into NPs also decreases cellular toxicity in mouse macrophages (J774.1A). PMID:22925019

Single-cell and subcellular pharmacokinetic imaging allows insight into drug action in vivo.

PubMed

Thurber, Greg M; Yang, Katy S; Reiner, Thomas; Kohler, Rainer H; Sorger, Peter; Mitchison, Tim; Weissleder, Ralph

2013-01-01

Pharmacokinetic analysis at the organ level provides insight into how drugs distribute throughout the body, but cannot explain how drugs work at the cellular level. Here we demonstrate in vivo single-cell pharmacokinetic imaging of PARP-1 inhibitors and model drug behaviour under varying conditions. We visualize intracellular kinetics of the PARP-1 inhibitor distribution in real time, showing that PARP-1 inhibitors reach their cellular target compartment, the nucleus, within minutes in vivo both in cancer and normal cells in various cancer models. We also use these data to validate predictive finite element modelling. Our theoretical and experimental data indicate that tumour cells are exposed to sufficiently high PARP-1 inhibitor concentrations in vivo and suggest that drug inefficiency is likely related to proteomic heterogeneity or insensitivity of cancer cells to DNA-repair inhibition. This suggests that single-cell pharmacokinetic imaging and derived modelling improve our understanding of drug action at single-cell resolution in vivo.

An integrated molecular analysis of lung adenocarcinomas identifies potential therapeutic targets among TTF1-negative tumors including DNA repair proteins and Nrf2

PubMed Central

Cardnell, Robert J.G.; Behrens, Carmen; Diao, Lixia; Fan, YouHong; Tang, Ximing; Tong, Pan; John D., Minna; Mills, Gordon B.; Heymach, John V.; Wistuba, Ignacio I.; Wang, Jing; Byers., Lauren A.

2015-01-01

Purpose Thyroid transcription factor-1 (TTF1) immunohistochemistry (IHC) is used clinically to differentiate primary lung adenocarcinomas (LUAD) from squamous lung cancers and metastatic adenocarcinomas from other primary sites. However, a subset of LUAD (15-20%) does not express TTF1 and TTF1-negative patients have worse clinical outcomes. As there are no established targeted agents with activity in TTF1-negative LUAD, we performed an integrated molecular analysis to identify potential therapeutic targets. Experimental Design Using two clinical LUAD cohorts (274 tumors), one from our institution (PROSPECT) and the TCGA, we interrogated proteomic profiles (by reverse-phase protein array (RPPA)), gene expression, and mutational data. Drug response data from 74 cell lines were used to validate potential therapeutic agents. Results Strong correlations were observed between TTF1 IHC and TTF1 measurements by RPPA (Rho=0.57, p<0.001) and gene expression (NKX2-1, Rho=0.61, p<0.001). Established driver mutations (e.g. BRAF and EGFR) were associated with high TTF1 expression. In contrast, TTF1-negative LUAD had a higher frequency of inactivating KEAP1 mutations (p=0.001). Proteomic profiling identified increased expression of DNA repair proteins (e.g., Chk1 and the DNA repair score) and suppressed PI3K/MAPK signaling among TTF1-negative tumors, with differences in total proteins confirmed at the mRNA level. Cell line analysis showed drugs targeting DNA repair to be more active in TTF1-low cell lines. Conclusions Combined genomic and proteomic analyses demonstrated infrequent alteration of validated lung cancer targets (including the absence of BRAF mutations in TTF1-negative LUAD), but identified novel potential targets for TTF1-negative LUAD includingKEAP1/Nrf2 and DNA repair pathways. PMID:25878335

The Human Kinome Targeted by FDA Approved Multi-Target Drugs and Combination Products: A Comparative Study from the Drug-Target Interaction Network Perspective.

PubMed

Li, Ying Hong; Wang, Pan Pan; Li, Xiao Xu; Yu, Chun Yan; Yang, Hong; Zhou, Jin; Xue, Wei Wei; Tan, Jun; Zhu, Feng

2016-01-01

The human kinome is one of the most productive classes of drug target, and there is emerging necessity for treating complex diseases by means of polypharmacology (multi-target drugs and combination products). However, the advantages of the multi-target drugs and the combination products are still under debate. A comparative analysis between FDA approved multi-target drugs and combination products, targeting the human kinome, was conducted by mapping targets onto the phylogenetic tree of the human kinome. The approach of network medicine illustrating the drug-target interactions was applied to identify popular targets of multi-target drugs and combination products. As identified, the multi-target drugs tended to inhibit target pairs in the human kinome, especially the receptor tyrosine kinase family, while the combination products were able to against targets of distant homology relationship. This finding asked for choosing the combination products as a better solution for designing drugs aiming at targets of distant homology relationship. Moreover, sub-networks of drug-target interactions in specific disease were generated, and mechanisms shared by multi-target drugs and combination products were identified. In conclusion, this study performed an analysis between approved multi-target drugs and combination products against the human kinome, which could assist the discovery of next generation polypharmacology.

Biochemical and structural investigations on phosphoribosylpyrophosphate synthetase from Mycobacterium smegmatis

PubMed Central

Donini, Stefano; Garavaglia, Silvia; Ferraris, Davide M.; Miggiano, Riccardo; Mori, Shigetarou; Shibayama, Keigo

2017-01-01

Mycobacterium smegmatis represents one model for studying the biology of its pathogenic relative Mycobacterium tuberculosis. The structural characterization of a M. tuberculosis ortholog protein can serve as a valid tool for the development of molecules active against the M. tuberculosis target. In this context, we report the biochemical and structural characterization of M. smegmatis phosphoribosylpyrophosphate synthetase (PrsA), the ortholog of M. tuberculosis PrsA, the unique enzyme responsible for the synthesis of phosphoribosylpyrophosphate (PRPP). PRPP is a key metabolite involved in several biosynthetic pathways including those for histidine, tryptophan, nucleotides and decaprenylphosphoryl-arabinose, an essential precursor for the mycobacterial cell wall biosynthesis. Since M. tuberculosis PrsA has been validated as a drug target for the development of antitubercular agents, the data presented here will add to the knowledge of the mycobacterial enzyme and could contribute to the development of M. tuberculosis PrsA inhibitors of potential pharmacological interest. PMID:28419153

Biochemical and structural investigations on phosphoribosylpyrophosphate synthetase from Mycobacterium smegmatis.

PubMed

Donini, Stefano; Garavaglia, Silvia; Ferraris, Davide M; Miggiano, Riccardo; Mori, Shigetarou; Shibayama, Keigo; Rizzi, Menico

2017-01-01

Mycobacterium smegmatis represents one model for studying the biology of its pathogenic relative Mycobacterium tuberculosis. The structural characterization of a M. tuberculosis ortholog protein can serve as a valid tool for the development of molecules active against the M. tuberculosis target. In this context, we report the biochemical and structural characterization of M. smegmatis phosphoribosylpyrophosphate synthetase (PrsA), the ortholog of M. tuberculosis PrsA, the unique enzyme responsible for the synthesis of phosphoribosylpyrophosphate (PRPP). PRPP is a key metabolite involved in several biosynthetic pathways including those for histidine, tryptophan, nucleotides and decaprenylphosphoryl-arabinose, an essential precursor for the mycobacterial cell wall biosynthesis. Since M. tuberculosis PrsA has been validated as a drug target for the development of antitubercular agents, the data presented here will add to the knowledge of the mycobacterial enzyme and could contribute to the development of M. tuberculosis PrsA inhibitors of potential pharmacological interest.

Development and Evaluation of Amphotericin B Loaded Iron Oxide Nanoparticles for Targeted Drug Delivery to Systemic Fungal Infections

NASA Astrophysics Data System (ADS)

Balabathula, Pavan

A targeted nanotheronostic drug delivery system to diagnose and treat life threatening invasive fungal infections (IFIs) such as cryptococcal meningitis was designed, developed, characterized, and evaluated. To address the development processes, first, iron oxide nanoparticles (IONP) (34-40 nm) coated with bovine serum albumin (BSA), loaded and targeted with amphotericin B (AMB) (AMB-IONP) was formulated by applying a layer by layer approach. Several designs (A, B, C, D, & E) of AMB-IONP were developed and their physicochemical properties such as drug loading with HPLC method, particle size, poly dispersity index (PDI), and zeta-potential using dynamic light scattering (DLS) technique, morphology with transmission electronic microscopy (TEM), and in vitro drug release profile with dialysis method were evaluated. Second, uptake (with fluorescence microscopy and flow cytometry) and killing efficacy (with susceptibility testing) of AMB-IONP in fungal clinical isolates of Candida species were evaluated and compared with standard drug AMB deoxycholate (AMB-D) data. Third, the cellular uptake mechanisms with endocytosis inhibitors and intracellular trafficking using TEM for design D were evaluated in selected isolates. Fourth, a stable lyophilized AMB-IONP formulation was developed and was suitable for clinical trials. A validated isocratic HPLC method was developed and validated for the quantitative determination of AMB. Design D was determined to be the lead formulation with drug loading of 13.6+/-6.9 of AMB/mg of IONP. The size, zeta-potential, and PDI for all formulation designs were found to be in an optimum range for a nanomedicine with ≤36 nm, ˜ -20 mV, and ≤0.2, respectively. The TEM images confirmed that the nanoparticles were monodispersed and spherical in shape. The drug release profile indicated a burst release up to 3 hours for designs A and B, followed by a sustained drug release profile up to 72 hours. Designs C and D (with and without glutaraldehyde) also had a sustained drug release profile up to 72 hours. The major mechanisms of drug release from these formulations were determined to be Fickian and non-Fickian diffusion with first order and Higuchi kinetic models as best fit. The cellular uptake profile for design D exhibited a time dependent uptake with maximum uptake at 0.5 and 4 hours for C. albicans and C. glabrata, respectively. All designs exhibited improved efficacy over AMB-D in the susceptibility testing conducted on clinical isolates of Candida. Design D was found to have an enhanced killing ability and was 16-25 fold more efficacious than AMB-D. An in vitro cellular association study found the uptake mechanism was energy dependent. An endocytosis inhibitor evaluation determined the major particle uptake pathway for C. albicans was lipid-raft mediated endocytosis, whereas for C. glabrata, it was clathrin-, caveolar-, and lipid-raft-mediated endocytosis. TEM and confocal images provided evidence the AMB-IONP were localized at or near the cell wall and membrane wall and inside the cytoplasm, nucleus and endolysosomal vesicles for tested isolates. The lyophilized formulation of AMB-IONP was successfully prepared using an appropriate amount (1:16 to the weight of IONP) of the lyoprotectant, sucrose. A short term stability study of both formulations (lyophilized and aqueous dispersion) at 5°C and 25°C for up to two months showed the lyophilized form was stable. In conclusion, a targeted nanotheronostic drug delivery system (AMB-IONP) was successfully designed, developed, characterized and evaluated as a potential drug product for IFIs treatment.

The target invites a foe: antibody-drug conjugates in gynecologic oncology.

PubMed

Campos, Maira P; Konecny, Gottfried E

2018-02-01

Antibody-drug conjugates (ADCs) represent a promising new class of cancer therapeutics. Currently more than 60 ADCs are in clinical development, however, only very few trials focus on gynecologic malignancies. In this review, we summarize the most recent advances in ADC drug development with an emphasis on how this progress relates to patients diagnosed with gynecologic malignancies and breast cancer. The cytotoxic payloads of the majority of the ADCs that are currently in clinical trials for gynecologic malignancies or breast cancer are auristatins (MMAE, MMAF), maytansinoids (DM1, DM4), calicheamicin, pyrrolobenzodiazepines and SN-38. Both cleavable and noncleavable linkers are currently being investigated in clinical trials. A number of novel target antigens are currently being validated in ongoing clinical trials including folate receptor alpha, mesothelin, CA-125, NaPi2b, NOTCH3, protein tyrosine kinase-like 7, ephrin-A4, TROP2, CEACAM5, and LAMP1. For most ADCs currently in clinical development, dose-limiting toxicities appear to be unrelated to the targeted antigen but more tightly associated with the payload. Rational drug design involving optimization of the antibody, the linker and the conjugation chemistry is aimed at improving the therapeutic index of new ADCs. Antibody-drug conjugates can increase the efficacy and decrease the toxicity of their payloads in comparison with traditional cyctotoxic agents. A better and quicker translation of recent scientific advances in the field of ADCs into rational clinical trials for patients diagnosed with ovarian, endometrial or cervical cancer could create real improvements in tumor response, survival and quality of life for our patients.

Similarity-based prediction for Anatomical Therapeutic Chemical classification of drugs by integrating multiple data sources.

PubMed

Liu, Zhongyang; Guo, Feifei; Gu, Jiangyong; Wang, Yong; Li, Yang; Wang, Dan; Lu, Liang; Li, Dong; He, Fuchu

2015-06-01

Anatomical Therapeutic Chemical (ATC) classification system, widely applied in almost all drug utilization studies, is currently the most widely recognized classification system for drugs. Currently, new drug entries are added into the system only on users' requests, which leads to seriously incomplete drug coverage of the system, and bioinformatics prediction is helpful during this process. Here we propose a novel prediction model of drug-ATC code associations, using logistic regression to integrate multiple heterogeneous data sources including chemical structures, target proteins, gene expression, side-effects and chemical-chemical associations. The model obtains good performance for the prediction not only on ATC codes of unclassified drugs but also on new ATC codes of classified drugs assessed by cross-validation and independent test sets, and its efficacy exceeds previous methods. Further to facilitate the use, the model is developed into a user-friendly web service SPACE ( S: imilarity-based P: redictor of A: TC C: od E: ), which for each submitted compound, will give candidate ATC codes (ranked according to the decreasing probability_score predicted by the model) together with corresponding supporting evidence. This work not only contributes to knowing drugs' therapeutic, pharmacological and chemical properties, but also provides clues for drug repositioning and side-effect discovery. In addition, the construction of the prediction model also provides a general framework for similarity-based data integration which is suitable for other drug-related studies such as target, side-effect prediction etc. The web service SPACE is available at http://www.bprc.ac.cn/space. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Combining NMR and X-ray crystallography in fragment-based drug discovery: discovery of highly potent and selective BACE-1 inhibitors.

PubMed

Wyss, Daniel F; Wang, Yu-Sen; Eaton, Hugh L; Strickland, Corey; Voigt, Johannes H; Zhu, Zhaoning; Stamford, Andrew W

2012-01-01

Fragment-based drug discovery (FBDD) has become increasingly popular over the last decade. We review here how we have used highly structure-driven fragment-based approaches to complement more traditional lead discovery to tackle high priority targets and those struggling for leads. Combining biomolecular nuclear magnetic resonance (NMR), X-ray crystallography, and molecular modeling with structure-assisted chemistry and innovative biology as an integrated approach for FBDD can solve very difficult problems, as illustrated in this chapter. Here, a successful FBDD campaign is described that has allowed the development of a clinical candidate for BACE-1, a challenging CNS drug target. Crucial to this achievement were the initial identification of a ligand-efficient isothiourea fragment through target-based NMR screening and the determination of its X-ray crystal structure in complex with BACE-1, which revealed an extensive H-bond network with the two active site aspartate residues. This detailed 3D structural information then enabled the design and validation of novel, chemically stable and accessible heterocyclic acylguanidines as aspartic acid protease inhibitor cores. Structure-assisted fragment hit-to-lead optimization yielded iminoheterocyclic BACE-1 inhibitors that possess desirable molecular properties as potential therapeutic agents to test the amyloid hypothesis of Alzheimer's disease in a clinical setting.

Exploiting nature's rich source of proteasome inhibitors as starting points in drug development.

PubMed

Gräwert, Melissa Ann; Groll, Michael

2012-02-01

Cancer is the No. 2 cause of death in the Western world and one of the most expensive diseases to treat. Thus, it is not surprising, that every major pharmaceutical and biotechnology company has a blockbuster oncology product. In 2003, Millennium Pharmaceuticals entered the race with Velcade®, a first-in-class proteasome inhibitor that has been approved by the FDA for treatment of multiple myeloma and its sales have passed the billion dollar mark. Velcade®'s extremely toxic boronic acid pharmacophore, however, contributes to a number of severe side effects. Nevertheless, the launching of this product has validated the proteasome as a target in fighting cancer and further proteasome inhibitors have entered the market as anti-cancer drugs. Additionally, proteasome inhibitors have found application as crop protection agents, anti-parasitics, immunosuppressives, as well as in new therapies for muscular dystrophies and inflammation. Many of these compounds are based on microbial metabolites. In this review, we emphasize the important role of the structural elucidation of the various unique binding mechanisms of these compounds that have been optimized throughout evolution to target the proteasome. Based on this knowledge, medicinal chemists have further optimized these natural products, resulting in potential drugs with reduced off-target activities. This journal is © The Royal Society of Chemistry 2012

Effects of the above the influence brand on adolescent drug use prevention normative beliefs.

PubMed

Evans, W Douglas; Holtz, Kristen; White, Tanya; Snider, Jeremy

2014-01-01

Health brands are based on the relations between individuals and health behaviors and lifestyles. Brands can be measured by the brand equity construct validated in previous studies. The National Youth Anti-Drug Media Campaign brands alternative, non-drug use behaviors as a behavior change strategy. This study goes beyond previous campaign evaluations, which did not include specific brand equity measurements. Using data from a nationally representative media tracking, this study examined the relation between antidrug campaign brand equity and adoption of targeted attitudes, beliefs, and behaviors. Data were gathered before the relaunch of the campaign, and follow-up data collected 3 months later. On the basis of factor analysis, the authors developed a higher order antidrug brand equity factor and regressed campaign outcomes on that factor in multivariable models. The authors observed significant effects of higher brand equity on higher levels of targeted antidrug attitudes and normative beliefs at follow-up. The authors also observed some counterintuitive relations (i.e., less positive attitudes at follow-up). They interpreted these results in light of the changing messages and campaign strategy. The authors conclude that antidrug brand equity is an important construct for understanding campaign effectiveness. The present campaign shows signs of changing targeted antidrug attitudes and beliefs among youth with brand equity.

Open innovation for phenotypic drug discovery: The PD2 assay panel.

PubMed

Lee, Jonathan A; Chu, Shaoyou; Willard, Francis S; Cox, Karen L; Sells Galvin, Rachelle J; Peery, Robert B; Oliver, Sarah E; Oler, Jennifer; Meredith, Tamika D; Heidler, Steven A; Gough, Wendy H; Husain, Saba; Palkowitz, Alan D; Moxham, Christopher M

2011-07-01

Phenotypic lead generation strategies seek to identify compounds that modulate complex, physiologically relevant systems, an approach that is complementary to traditional, target-directed strategies. Unlike gene-specific assays, phenotypic assays interrogate multiple molecular targets and signaling pathways in a target "agnostic" fashion, which may reveal novel functions for well-studied proteins and discover new pathways of therapeutic value. Significantly, existing compound libraries may not have sufficient chemical diversity to fully leverage a phenotypic strategy. To address this issue, Eli Lilly and Company launched the Phenotypic Drug Discovery Initiative (PD(2)), a model of open innovation whereby external research groups can submit compounds for testing in a panel of Lilly phenotypic assays. This communication describes the statistical validation, operations, and initial screening results from the first PD(2) assay panel. Analysis of PD(2) submissions indicates that chemical diversity from open source collaborations complements internal sources. Screening results for the first 4691 compounds submitted to PD(2) have confirmed hit rates from 1.6% to 10%, with the majority of active compounds exhibiting acceptable potency and selectivity. Phenotypic lead generation strategies, in conjunction with novel chemical diversity obtained via open-source initiatives such as PD(2), may provide a means to identify compounds that modulate biology by novel mechanisms and expand the innovation potential of drug discovery.

Plasmodium falciparum Calcium-Dependent Protein Kinase 2 Is Critical for Male Gametocyte Exflagellation but Not Essential for Asexual Proliferation

PubMed Central

Molina-Cruz, Alvaro; Brzostowski, Joseph; Mu, Jianbing

2017-01-01

ABSTRACT Drug development efforts have focused mostly on the asexual blood stages of the malaria parasite Plasmodium falciparum. Except for primaquine, which has its own limitations, there are no available drugs that target the transmission of the parasite to mosquitoes. Therefore, there is a need to validate new parasite proteins that can be targeted for blocking transmission. P. falciparum calcium-dependent protein kinases (PfCDPKs) play critical roles at various stages of the parasite life cycle and, importantly, are absent in the human host. These features mark them as attractive drug targets. In this study, using CRISPR/Cas9 we successfully knocked out PfCDPK2 from blood-stage parasites, which was previously thought to be an indispensable protein. The growth rate of the PfCDPK2 knockout (KO) parasites was similar to that of wild-type parasites, confirming that PfCDPK2 function is not essential for the asexual proliferation of the parasite in vitro. The mature male and female gametocytes of PfCDPK2 KO parasites become round after induction. However, they fail to infect female Anopheles stephensi mosquitoes due to a defect(s) in male gametocyte exflagellation and possibly in female gametes. PMID:29042501

Improved BM212 MmpL3 Inhibitor Analogue Shows Efficacy in Acute Murine Model of Tuberculosis Infection

PubMed Central

Alfonso, Salvatore; Cocozza, Martina; Porretta, Giulio Cesare; Ballell, Lluís; Rullas, Joaquin; Ortega, Fátima; De Logu, Alessandro; Agus, Emanuela; La Rosa, Valentina; Pasca, Maria Rosalia; De Rossi, Edda; Wae, Baojie; Franzblau, Scott G.; Manetti, Fabrizio; Botta, Maurizio; Biava, Mariangela

2013-01-01

1,5-Diphenyl pyrroles were previously identified as a class of compounds endowed with high in vitro efficacy against M. tuberculosis. To improve the physical chemical properties and drug-like parameters of this class of compounds, a medicinal chemistry effort was undertaken. By selecting the optimal substitution patterns for the phenyl rings at N1 and C5 and by replacing the thiomorpholine moiety with a morpholine one, a new series of compounds was produced. The replacement of the sulfur with oxygen gave compounds with lower lipophilicity and improved in vitro microsomal stability. Moreover, since the parent compound of this family has been shown to target MmpL3, mycobacterial mutants resistant to two compounds have been isolated and characterized by sequencing the mmpL3 gene; all the mutants showed point mutations in this gene. The best compound identified to date was progressed to dose-response studies in an acute murine TB infection model. The resulting ED99 of 49 mg/Kg is within the range of commonly employed tuberculosis drugs, demonstrating the potential of this chemical series. The in vitro and in vivo target validation evidence presented here adds further weight to MmpL3 as a druggable target of interest for anti-tubercular drug discovery. PMID:23437287

Evaluation of HDL-modulating interventions for cardiovascular risk reduction using a systems pharmacology approach[S

PubMed Central

Gadkar, Kapil; Lu, James; Sahasranaman, Srikumar; Davis, John; Mazer, Norman A.; Ramanujan, Saroja

2016-01-01

The recent failures of cholesteryl ester transport protein inhibitor drugs to decrease CVD risk, despite raising HDL cholesterol (HDL-C) levels, suggest that pharmacologic increases in HDL-C may not always reflect elevations in reverse cholesterol transport (RCT), the process by which HDL is believed to exert its beneficial effects. HDL-modulating therapies can affect HDL properties beyond total HDL-C, including particle numbers, size, and composition, and may contribute differently to RCT and CVD risk. The lack of validated easily measurable pharmacodynamic markers to link drug effects to RCT, and ultimately to CVD risk, complicates target and compound selection and evaluation. In this work, we use a systems pharmacology model to contextualize the roles of different HDL targets in cholesterol metabolism and provide quantitative links between HDL-related measurements and the associated changes in RCT rate to support target and compound evaluation in drug development. By quantifying the amount of cholesterol removed from the periphery over the short-term, our simulations show the potential for infused HDL to treat acute CVD. For the primary prevention of CVD, our analysis suggests that the induction of ApoA-I synthesis may be a more viable approach, due to the long-term increase in RCT rate. PMID:26522778

Evaluation of HDL-modulating interventions for cardiovascular risk reduction using a systems pharmacology approach.

PubMed

Gadkar, Kapil; Lu, James; Sahasranaman, Srikumar; Davis, John; Mazer, Norman A; Ramanujan, Saroja

2016-01-01

The recent failures of cholesteryl ester transport protein inhibitor drugs to decrease CVD risk, despite raising HDL cholesterol (HDL-C) levels, suggest that pharmacologic increases in HDL-C may not always reflect elevations in reverse cholesterol transport (RCT), the process by which HDL is believed to exert its beneficial effects. HDL-modulating therapies can affect HDL properties beyond total HDL-C, including particle numbers, size, and composition, and may contribute differently to RCT and CVD risk. The lack of validated easily measurable pharmacodynamic markers to link drug effects to RCT, and ultimately to CVD risk, complicates target and compound selection and evaluation. In this work, we use a systems pharmacology model to contextualize the roles of different HDL targets in cholesterol metabolism and provide quantitative links between HDL-related measurements and the associated changes in RCT rate to support target and compound evaluation in drug development. By quantifying the amount of cholesterol removed from the periphery over the short-term, our simulations show the potential for infused HDL to treat acute CVD. For the primary prevention of CVD, our analysis suggests that the induction of ApoA-I synthesis may be a more viable approach, due to the long-term increase in RCT rate. Copyright © 2016 by the American Society for Biochemistry and Molecular Biology, Inc.

Identification of a new binding site in E. coli FabH using Molecular dynamics simulations: validation by computational alanine mutagenesis and docking studies.

PubMed

Ramamoorthy, Divya; Turos, Edward; Guida, Wayne C

2013-05-24

FabH (Fatty acid biosynthesis, enzyme H, also referred to as β-ketoacyl-ACP-synthase III) is a key condensing enzyme in the type II fatty acid synthesis (FAS) system. The FAS pathway in bacteria is essential for growth and survival and vastly differs from the human FAS pathway. Enzymes involved in this pathway have arisen as promising biomolecular targets for discovery of new antibacterial drugs. However, currently there are no clinical drugs that selectively target FabH, and known inhibitors of FabH all act within the active site. FabH exerts its catalytic function as a dimer, which could potentially be exploited in developing new strategies for inhibitor design. The aim of this study was to elucidate structural details of the dimer interface region by means of computational modeling, including molecular dynamics (MD) simulations, in order to derive information for the structure-based design of new FabH inhibitors. The dimer interface region was analyzed by MD simulations, trajectory snapshots were collected for further analyses, and docking studies were performed with potential small molecule disruptors. Alanine mutation and docking studies strongly suggest that the dimer interface could be a potential target for anti-infection drug discovery.

In-vitro photo-translocation of antiretroviral drug delivery into TZMbl cells

NASA Astrophysics Data System (ADS)

Malabi, Rudzani; Manoto, Sello; Ombinda-Lemboumba, Saturnin; Maaza, Malik; Mthunzi-Kufa, Patience

2017-02-01

The current human immunodeficiency virus (HIV) treatment regime possesses the ability to diminish the viral capacity to unnoticeable levels; however complete eradication of the virus cannot be achieved while latent HIV-1 reservoirs go unchallenged. Therapeutic targeting of HIV therefore requires further investigation and current therapies need modification in order to address HIV eradication. This deflects research towards investigating potential novel antiretroviral drug delivery systems. The use of femtosecond (fs) laser pulses in promoting targeted optical drug delivery of antiretroviral drugs (ARVs) into TZMbl cells revolves around using ultrafast laser pulses that have high peak powers, which precisely disrupt the cell plasma membrane in order to allow immediate transportation and expression of exogenous material into the live mammalian cells. A photo-translocation optical setup was built and validated by characterisation of the accurate parameters such as wavelength (800 nm) and pulse duration (115 fs). Optimisation of drug translocation parameters were done by performing trypan blue translocation studies. Cellular responses were determined via cell viability (Adenosine Triphosphate activity) and cell cytotoxicity (Lactate Dehydrogenase) assays which were done to study the influence of the drugs and laser exposure on the cells. After laser irradiation, high cell viability was observed and low toxicity levels were observed after exposure of the cells to both the ARVs and the laser. Our results confirmed that, with minimal damage and high therapeutic levels of ARVs, the fs laser assisted drug delivery system is efficient with benefits of non-invasive and non-toxic treatment to the cells.

Systems Perturbation Analysis of a Large-Scale Signal Transduction Model Reveals Potentially Influential Candidates for Cancer Therapeutics

PubMed Central

Puniya, Bhanwar Lal; Allen, Laura; Hochfelder, Colleen; Majumder, Mahbubul; Helikar, Tomáš

2016-01-01

Dysregulation in signal transduction pathways can lead to a variety of complex disorders, including cancer. Computational approaches such as network analysis are important tools to understand system dynamics as well as to identify critical components that could be further explored as therapeutic targets. Here, we performed perturbation analysis of a large-scale signal transduction model in extracellular environments that stimulate cell death, growth, motility, and quiescence. Each of the model’s components was perturbed under both loss-of-function and gain-of-function mutations. Using 1,300 simulations under both types of perturbations across various extracellular conditions, we identified the most and least influential components based on the magnitude of their influence on the rest of the system. Based on the premise that the most influential components might serve as better drug targets, we characterized them for biological functions, housekeeping genes, essential genes, and druggable proteins. The most influential components under all environmental conditions were enriched with several biological processes. The inositol pathway was found as most influential under inactivating perturbations, whereas the kinase and small lung cancer pathways were identified as the most influential under activating perturbations. The most influential components were enriched with essential genes and druggable proteins. Moreover, known cancer drug targets were also classified in influential components based on the affected components in the network. Additionally, the systemic perturbation analysis of the model revealed a network motif of most influential components which affect each other. Furthermore, our analysis predicted novel combinations of cancer drug targets with various effects on other most influential components. We found that the combinatorial perturbation consisting of PI3K inactivation and overactivation of IP3R1 can lead to increased activity levels of apoptosis-related components and tumor-suppressor genes, suggesting that this combinatorial perturbation may lead to a better target for decreasing cell proliferation and inducing apoptosis. Finally, our approach shows a potential to identify and prioritize therapeutic targets through systemic perturbation analysis of large-scale computational models of signal transduction. Although some components of the presented computational results have been validated against independent gene expression data sets, more laboratory experiments are warranted to more comprehensively validate the presented results. PMID:26904540

Drug-Associated Risk Tool: development and validation of a self-assessment questionnaire to screen for hospitalised patients at risk for drug-related problems.

PubMed

Kaufmann, Carole P; Stämpfli, Dominik; Mory, Nadine; Hersberger, Kurt E; Lampert, Markus L

2018-03-09

Identifying patients with a high risk for drug-related problems (DRPs) might optimise the allocation of targeted pharmaceutical care during the hospital stay and on discharge. To develop a self-assessment screening tool to identify patients at risk for DRPs and validate the tool regarding feasibility, acceptability and the reliability of the patients' answers. Prospective validation study. Two mid-sized hospitals (300-400 beds). 195 patients, exclusion criteria: under 18 years old, patients with a health status not allowing a meaningful communication (eg, delirium, acute psychosis, advanced dementia, aphasia, clouded consciousness state), palliative or terminally ill patients. Twenty-seven risk factors for the development of DRPs, identified in a previous study, provided the basis of the self-assessment questionnaire, the Drug-Associated Risk Tool (DART). Consenting patients filled in DART, and we compared their answers with objective patient data from medical records and laboratory data. One hundred and sixty-four patients filled in DART V.1.0 in an average time of 7 min. After a first validation, we identified statements with a low sensitivity and revised the wording of the questions related to heart insufficiency, renal impairment or liver impairment. The revised DART (V.2.0) was validated in 31 patients presenting heart insufficiency, renal impairment or liver impairment as comorbidity and reached an average specificity of 88% (range 27-100) and an average sensitivity of 67% (range 21-100). DART showed a satisfying feasibility and reliability. The specificity of the statements was mostly high. The sensitivity varied and was higher in statements concerning diseases that require regular disease control and attention to self-care and drug management. Asking patients about their conditions, medications and related problems can facilitate getting a first, broad picture of the risk for DRPs and possible pharmaceutical needs. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

The Chemical Validation and Standardization Platform (CVSP): large-scale automated validation of chemical structure datasets.

PubMed

Karapetyan, Karen; Batchelor, Colin; Sharpe, David; Tkachenko, Valery; Williams, Antony J

2015-01-01

There are presently hundreds of online databases hosting millions of chemical compounds and associated data. As a result of the number of cheminformatics software tools that can be used to produce the data, subtle differences between the various cheminformatics platforms, as well as the naivety of the software users, there are a myriad of issues that can exist with chemical structure representations online. In order to help facilitate validation and standardization of chemical structure datasets from various sources we have delivered a freely available internet-based platform to the community for the processing of chemical compound datasets. The chemical validation and standardization platform (CVSP) both validates and standardizes chemical structure representations according to sets of systematic rules. The chemical validation algorithms detect issues with submitted molecular representations using pre-defined or user-defined dictionary-based molecular patterns that are chemically suspicious or potentially requiring manual review. Each identified issue is assigned one of three levels of severity - Information, Warning, and Error - in order to conveniently inform the user of the need to browse and review subsets of their data. The validation process includes validation of atoms and bonds (e.g., making aware of query atoms and bonds), valences, and stereo. The standard form of submission of collections of data, the SDF file, allows the user to map the data fields to predefined CVSP fields for the purpose of cross-validating associated SMILES and InChIs with the connection tables contained within the SDF file. This platform has been applied to the analysis of a large number of data sets prepared for deposition to our ChemSpider database and in preparation of data for the Open PHACTS project. In this work we review the results of the automated validation of the DrugBank dataset, a popular drug and drug target database utilized by the community, and ChEMBL 17 data set. CVSP web site is located at http://cvsp.chemspider.com/. A platform for the validation and standardization of chemical structure representations of various formats has been developed and made available to the community to assist and encourage the processing of chemical structure files to produce more homogeneous compound representations for exchange and interchange between online databases. While the CVSP platform is designed with flexibility inherent to the rules that can be used for processing the data we have produced a recommended rule set based on our own experiences with the large data sets such as DrugBank, ChEMBL, and data sets from ChemSpider.

Discovery of novel STAT3 small molecule inhibitors via in silico site-directed fragment-based drug design.

PubMed

Yu, Wenying; Xiao, Hui; Lin, Jiayuh; Li, Chenglong

2013-06-13

Constitutive activation of signal transducer and activator of transcription 3 (STAT3) has been validated as an attractive therapeutic target for cancer therapy. To stop both STAT3 activation and dimerization, a viable strategy is to design inhibitors blocking its SH2 domain phosphotyrosine binding site that is responsible for both actions. A new fragment-based drug design (FBDD) strategy, in silico site-directed FBDD, was applied in this study. A designed novel compound, 5,8-dioxo-6-(pyridin-3-ylamino)-5,8-dihydronaphthalene-1-sulfonamide (LY5), was confirmed to bind to STAT3 SH2 by fluorescence polarization assay. In addition, four out of the five chosen compounds have IC50 values lower than 5 μM for the U2OS cancer cells. 8 (LY5) has an IC50 range in 0.5-1.4 μM in various cancer cell lines. 8 also suppresses tumor growth in an in vivo mouse model. This study has demonstrated the utility of this approach and could be used to other drug targets in general.

Chemophototherapy: An Emerging Treatment Option for Solid Tumors

PubMed Central

Luo, Dandan; Carter, Kevin A.; Miranda, Dyego

2016-01-01

Near infrared (NIR) light penetrates human tissues with limited depth, thereby providing a method to safely deliver non‐ionizing radiation to well‐defined target tissue volumes. Light‐based therapies including photodynamic therapy (PDT) and laser‐induced thermal therapy have been validated clinically for curative and palliative treatment of solid tumors. However, these monotherapies can suffer from incomplete tumor killing and have not displaced existing ablative modalities. The combination of phototherapy and chemotherapy (chemophototherapy, CPT), when carefully planned, has been shown to be an effective tumor treatment option preclinically and clinically. Chemotherapy can enhance the efficacy of PDT by targeting surviving cancer cells or by inhibiting regrowth of damaged tumor blood vessels. Alternatively, PDT‐mediated vascular permeabilization has been shown to enhance the deposition of nanoparticulate drugs into tumors for enhanced accumulation and efficacy. Integrated nanoparticles have been reported that combine photosensitizers and drugs into a single agent. More recently, light‐activated nanoparticles have been developed that release their payload in response to light irradiation to achieve improved drug bioavailability with superior efficacy. CPT can potently eradicate tumors with precise spatial control, and further clinical testing is warranted. PMID:28105389

NPACT: Naturally Occurring Plant-based Anti-cancer Compound-Activity-Target database

PubMed Central

Mangal, Manu; Sagar, Parul; Singh, Harinder; Raghava, Gajendra P. S.; Agarwal, Subhash M.

2013-01-01

Plant-derived molecules have been highly valued by biomedical researchers and pharmaceutical companies for developing drugs, as they are thought to be optimized during evolution. Therefore, we have collected and compiled a central resource Naturally Occurring Plant-based Anti-cancer Compound-Activity-Target database (NPACT, http://crdd.osdd.net/raghava/npact/) that gathers the information related to experimentally validated plant-derived natural compounds exhibiting anti-cancerous activity (in vitro and in vivo), to complement the other databases. It currently contains 1574 compound entries, and each record provides information on their structure, manually curated published data on in vitro and in vivo experiments along with reference for users referral, inhibitory values (IC50/ED50/EC50/GI50), properties (physical, elemental and topological), cancer types, cell lines, protein targets, commercial suppliers and drug likeness of compounds. NPACT can easily be browsed or queried using various options, and an online similarity tool has also been made available. Further, to facilitate retrieval of existing data, each record is hyperlinked to similar databases like SuperNatural, Herbal Ingredients’ Targets, Comparative Toxicogenomics Database, PubChem and NCI-60 GI50 data. PMID:23203877

Integrated in vivo genetic and pharmacologic screening identifies co-inhibition of EGRF and ROCK as a potential treatment regimen for triple-negative breast cancer.

PubMed

Iskit, Sedef; Lieftink, Cor; Halonen, Pasi; Shahrabi, Aida; Possik, Patricia A; Beijersbergen, Roderick L; Peeper, Daniel S

2016-07-12

Breast cancer is the second most common cause of cancer-related deaths worldwide among women. Despite several therapeutic options, 15% of breast cancer patients succumb to the disease owing to tumor relapse and acquired therapy resistance. Particularly in triple-negative breast cancer (TNBC), developing effective treatments remains challenging owing to the lack of a common vulnerability that can be exploited by targeted approaches. We have previously shown that tumor cells have different requirements for growth in vivo than in vitro. Therefore, to discover novel drug targets for TNBC, we performed parallel in vivo and in vitro genetic shRNA dropout screens. We identified several potential drug targets that were required for tumor growth in vivo to a greater extent than in vitro. By combining pharmacologic inhibitors acting on a subset of these candidates, we identified a synergistic interaction between EGFR and ROCK inhibitors. This combination effectively reduced TNBC cell growth by inducing cell cycle arrest. These results illustrate the power of in vivo genetic screens and warrant further validation of EGFR and ROCK as combined pharmacologic targets for breast cancer.

Magnetically assisted intraperitoneal drug delivery for cancer chemotherapy.

PubMed

Shamsi, Milad; Sedaghatkish, Amir; Dejam, Morteza; Saghafian, Mohsen; Mohammadi, Mehdi; Sanati-Nezhad, Amir

2018-11-01

Intraperitoneal (IP) chemotherapy has revived hopes during the past few years for the management of peritoneal disseminations of digestive and gynecological cancers. Nevertheless, a poor drug penetration is one key drawback of IP chemotherapy since peritoneal neoplasms are notoriously resistant to drug penetration. Recent preclinical studies have focused on targeting the aberrant tumor microenvironment to improve intratumoral drug transport. However, tumor stroma targeting therapies have limited therapeutic windows and show variable outcomes across different cohort of patients. Therefore, the development of new strategies for improving the efficacy of IP chemotherapy is a certain need. In this work, we propose a new magnetically assisted strategy to elevate drug penetration into peritoneal tumor nodules and improve IP chemotherapy. A computational model was developed to assess the feasibility and predictability of the proposed active drug delivery method. The key tumor pathophysiology, including a spatially heterogeneous construct of leaky vasculature, nonfunctional lymphatics, and dense extracellular matrix (ECM), was reconstructed in silico. The transport of intraperitoneally injected magnetic nanoparticles (MNPs) inside tumors was simulated and compared with the transport of free cytotoxic agents. Our results on magnetically assisted delivery showed an order of magnitude increase in the final intratumoral concentration of drug-coated MNPs with respect to free cytotoxic agents. The intermediate MNPs with the radius range of 200-300 nm yield optimal magnetic drug targeting (MDT) performance in 5-10 mm tumors while the MDT performance remains essentially the same over a large particle radius range of 100-500 nm for a 1 mm radius small tumor. The success of MDT in larger tumors (5-10 mm in radius) was found to be markedly dependent on the choice of magnet strength and tumor-magnet distance while these two parameters were less of a concern in small tumors. We also validated in silico results against experimental results related to tumor interstitial hypertension, conventional IP chemoperfusion, and magnetically actuated movement of MNPs in excised tissue.

In vivo targeting and imaging of tumor vasculature with radiolabeled, antibody-conjugated nanographene.

PubMed

Hong, Hao; Yang, Kai; Zhang, Yin; Engle, Jonathan W; Feng, Liangzhu; Yang, Yunan; Nayak, Tapas R; Goel, Shreya; Bean, Jero; Theuer, Charles P; Barnhart, Todd E; Liu, Zhuang; Cai, Weibo

2012-03-27

Herein we demonstrate that nanographene can be specifically directed to the tumor neovasculature in vivo through targeting of CD105 (i.e., endoglin), a vascular marker for tumor angiogenesis. The covalently functionalized nanographene oxide (GO) exhibited excellent stability and target specificity. Pharmacokinetics and tumor targeting efficacy of the GO conjugates were investigated with serial noninvasive positron emission tomography imaging and biodistribution studies, which were validated by in vitro, in vivo, and ex vivo experiments. The incorporation of an active targeting ligand (TRC105, a monoclonal antibody that binds to CD105) led to significantly improved tumor uptake of functionalized GO, which was specific for the neovasculature with little extravasation, warranting future investigation of these GO conjugates for cancer-targeted drug delivery and/or photothermal therapy to enhance therapeutic efficacy. Since poor extravasation is a major hurdle for nanomaterial-based tumor targeting in vivo, this study also establishes CD105 as a promising vascular target for future cancer nanomedicine. © 2012 American Chemical Society

cDNA Clones with Rare and Recurrent Mutations Found in Cancers | Office of Cancer Genomics

Cancer.gov

The CTD2 Center at UT- MD Anderson Cancer Center has developed High-Throughput Mutagenesis and Molecular Barcoding (HiTMMoB)1,2 pipeline to construct mutant alleles open reading frame expression clones that are either recurrent or rare in cancers. These barcoded genes can be used for context-specific functional validation, detection of novel biomarkers (pathway activation) and targets (drug sensitivity).

Rational design and validation of a Tip60 histone acetyltransferase inhibitor

NASA Astrophysics Data System (ADS)

Gao, Chunxia; Bourke, Emer; Scobie, Martin; Famme, Melina Arcos; Koolmeister, Tobias; Helleday, Thomas; Eriksson, Leif A.; Lowndes, Noel F.; Brown, James A. L.

2014-06-01

Histone acetylation is required for many aspects of gene regulation, genome maintenance and metabolism and dysfunctional acetylation is implicated in numerous diseases, including cancer. Acetylation is regulated by histone acetyltransferases (HATs) and histone deacetylases and currently, few general HAT inhibitors have been described. We identified the HAT Tip60 as an excellent candidate for targeted drug development, as Tip60 is a key mediator of the DNA damage response and transcriptional co-activator. Our modeling of Tip60 indicated that the active binding pocket possesses opposite charges at each end, with the positive charges attributed to two specific side chains. We used structure based drug design to develop a novel Tip60 inhibitor, TH1834, to fit this specific pocket. We demonstrate that TH1834 significantly inhibits Tip60 activity in vitro and treating cells with TH1834 results in apoptosis and increased unrepaired DNA damage (following ionizing radiation treatment) in breast cancer but not control cell lines. Furthermore, TH1834 did not affect the activity of related HAT MOF, as indicated by H4K16Ac, demonstrating specificity. The modeling and validation of the small molecule inhibitor TH1834 represents a first step towards developing additional specific, targeted inhibitors of Tip60 that may lead to further improvements in the treatment of breast cancer.

Personalized RNA Medicine for Pancreatic Cancer.

PubMed

Gilles, Maud-Emmanuelle; Hao, Liangliang; Huang, Ling; Rupaimoole, Rajesha; Lopez-Casas, Pedro P; Pulver, Emilia; Jeong, Jong Cheol; Muthuswamy, Senthil K; Hidalgo, Manuel; Bhatia, Sangeeta N; Slack, Frank J

2018-04-01

Purpose: Since drug responses vary between patients, it is crucial to develop pre-clinical or co-clinical strategies that forecast patient response. In this study, we tested whether RNA-based therapeutics were suitable for personalized medicine by using patient-derived-organoid (PDO) and patient-derived-xenograft (PDX) models. Experimental Design: We performed microRNA (miRNA) profiling of PDX samples to determine the status of miRNA deregulation in individual pancreatic ductal adenocarcinoma (PDAC) patients. To deliver personalized RNA-based-therapy targeting oncogenic miRNAs that form part of this common PDAC miRNA over-expression signature, we packaged antimiR oligonucleotides against one of these miRNAs in tumor-penetrating nanocomplexes (TPN) targeting cell surface proteins on PDAC tumors. Results: As a validation for our pre-clinical strategy, the therapeutic potential of one of our nano-drugs, TPN-21, was first shown to decrease tumor cell growth and survival in PDO avatars for individual patients, then in their PDX avatars. Conclusions: This general approach appears suitable for co-clinical validation of personalized RNA medicine and paves the way to prospectively identify patients with eligible miRNA profiles for personalized RNA-based therapy. Clin Cancer Res; 24(7); 1734-47. ©2018 AACR . ©2018 American Association for Cancer Research.

Novel phage display-derived neuroblastoma-targeting peptides potentiate the effect of drug nanocarriers in preclinical settings.

PubMed

Loi, Monica; Di Paolo, Daniela; Soster, Marco; Brignole, Chiara; Bartolini, Alice; Emionite, Laura; Sun, Jessica; Becherini, Pamela; Curnis, Flavio; Petretto, Andrea; Sani, Monica; Gori, Alessandro; Milanese, Marco; Gambini, Claudio; Longhi, Renato; Cilli, Michele; Allen, Theresa M; Bussolino, Federico; Arap, Wadih; Pasqualini, Renata; Corti, Angelo; Ponzoni, Mirco; Marchiò, Serena; Pastorino, Fabio

2013-09-10

Molecular targeting of drug delivery nanocarriers is expected to improve their therapeutic index while decreasing their toxicity. Here we report the identification and characterization of novel peptide ligands specific for cells present in high-risk neuroblastoma (NB), a childhood tumor mostly refractory to current therapies. To isolate such targeting moieties, we performed combined in vitro/ex-vivo phage display screenings on NB cell lines and on tumors derived from orthotopic mouse models of human NB. By designing proper subtractive protocols, we identified phage clones specific either for the primary tumor, its metastases, or for their respective stromal components. Globally, we isolated 121 phage-displayed NB-binding peptides: 26 bound the primary tumor, 15 the metastatic mass, 57 and 23 their respective microenvironments. Of these, five phage clones were further validated for their specific binding ex-vivo to biopsies from stage IV NB patients and to NB tumors derived from mice. All five clones also targeted tumor cells and vasculature in vivo when injected into NB-bearing mice. Coupling of the corresponding targeting peptides with doxorubicin-loaded liposomes led to a significant inhibition in tumor volume and enhanced survival in preclinical NB models, thereby paving the way to their clinical development. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Targeting Cullin–RING E3 ubiquitin ligases for drug discovery: structure, assembly and small-molecule modulation

PubMed Central

Bulatov, Emil; Ciulli, Alessio

2015-01-01

In the last decade, the ubiquitin–proteasome system has emerged as a valid target for the development of novel therapeutics. E3 ubiquitin ligases are particularly attractive targets because they confer substrate specificity on the ubiquitin system. CRLs [Cullin–RING (really interesting new gene) E3 ubiquitin ligases] draw particular attention, being the largest family of E3s. The CRLs assemble into functional multisubunit complexes using a repertoire of substrate receptors, adaptors, Cullin scaffolds and RING-box proteins. Drug discovery targeting CRLs is growing in importance due to mounting evidence pointing to significant roles of these enzymes in diverse biological processes and human diseases, including cancer, where CRLs and their substrates often function as tumour suppressors or oncogenes. In the present review, we provide an account of the assembly and structure of CRL complexes, and outline the current state of the field in terms of available knowledge of small-molecule inhibitors and modulators of CRL activity. A comprehensive overview of the reported crystal structures of CRL subunits, components and full-size complexes, alone or with bound small molecules and substrate peptides, is included. This information is providing increasing opportunities to aid the rational structure-based design of chemical probes and potential small-molecule therapeutics targeting CRLs. PMID:25886174

Urine Multi-drug Screening with GC-MS or LC-MS-MS Using SALLE-hybrid PPT/SPE.

PubMed

Lee, Junhui; Park, Jiwon; Go, Ahra; Moon, Heesung; Kim, Sujin; Jung, Sohee; Jeong, Wonjoon; Chung, Heesun

2018-05-14

To intoxicated patients in the emergency room, toxicological analysis can be considerably helpful for identifying the involved toxicants. In order to develop a urine multi-drug screening (UmDS) method, gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-tandem mass spectrometry (LC-MS-MS) were used to determine targeted and unknown toxicants in urine. A GC-MS method in scan mode was validated for selectivity, limit of detection (LOD) and recovery. An LC-MS-MS multiple reaction monitoring (MRM) method was validated for lower LOD, recovery and matrix effect. The results of the screening analysis were compared with patient medical records to check the reliability of the screen. Urine samples collected from an emergency room were extracted through a combination of salting-out assisted liquid-liquid extraction (SALLE) and hybrid protein precipitation/solid phase extraction (hybrid PPT/SPE) plates and examined by GC-MS and LC-MS-MS. GC-MS analysis was performed as unknown drug screen and LC-MS-MS analysis was conducted as targeted drug screen. After analysis by GC-MS, a library search was conducted using an in-house library established with the automated mass spectral deconvolution and identification system (AMDISTM). LC-MS-MS used Cliquid®2.0 software for data processing and acquisition in MRM mode. An UmDS method by GC-MS and LC-MS-MS was developed by using a SALLE-hybrid PPT/SPE and in-house library. The results of UmDS by GC-MS and LC-MS-MS showed that toxicants could be identified from 185 emergency room patient samples containing unknown toxicants. Zolpidem, acetaminophen and citalopram were the most frequently encountered drugs in emergency room patients. The UmDS analysis developed in this study can be used effectively to detect toxic substances in a short time. Hence, it could be utilized in clinical and forensic toxicology practices.

Integrative relational machine-learning for understanding drug side-effect profiles

PubMed Central

2013-01-01

Background Drug side effects represent a common reason for stopping drug development during clinical trials. Improving our ability to understand drug side effects is necessary to reduce attrition rates during drug development as well as the risk of discovering novel side effects in available drugs. Today, most investigations deal with isolated side effects and overlook possible redundancy and their frequent co-occurrence. Results In this work, drug annotations are collected from SIDER and DrugBank databases. Terms describing individual side effects reported in SIDER are clustered with a semantic similarity measure into term clusters (TCs). Maximal frequent itemsets are extracted from the resulting drug x TC binary table, leading to the identification of what we call side-effect profiles (SEPs). A SEP is defined as the longest combination of TCs which are shared by a significant number of drugs. Frequent SEPs are explored on the basis of integrated drug and target descriptors using two machine learning methods: decision-trees and inductive-logic programming. Although both methods yield explicit models, inductive-logic programming method performs relational learning and is able to exploit not only drug properties but also background knowledge. Learning efficiency is evaluated by cross-validation and direct testing with new molecules. Comparison of the two machine-learning methods shows that the inductive-logic-programming method displays a greater sensitivity than decision trees and successfully exploit background knowledge such as functional annotations and pathways of drug targets, thereby producing rich and expressive rules. All models and theories are available on a dedicated web site. Conclusions Side effect profiles covering significant number of drugs have been extracted from a drug ×side-effect association table. Integration of background knowledge concerning both chemical and biological spaces has been combined with a relational learning method for discovering rules which explicitly characterize drug-SEP associations. These rules are successfully used for predicting SEPs associated with new drugs. PMID:23802887

Integrative relational machine-learning for understanding drug side-effect profiles.

PubMed

Bresso, Emmanuel; Grisoni, Renaud; Marchetti, Gino; Karaboga, Arnaud Sinan; Souchet, Michel; Devignes, Marie-Dominique; Smaïl-Tabbone, Malika

2013-06-26

Drug side effects represent a common reason for stopping drug development during clinical trials. Improving our ability to understand drug side effects is necessary to reduce attrition rates during drug development as well as the risk of discovering novel side effects in available drugs. Today, most investigations deal with isolated side effects and overlook possible redundancy and their frequent co-occurrence. In this work, drug annotations are collected from SIDER and DrugBank databases. Terms describing individual side effects reported in SIDER are clustered with a semantic similarity measure into term clusters (TCs). Maximal frequent itemsets are extracted from the resulting drug x TC binary table, leading to the identification of what we call side-effect profiles (SEPs). A SEP is defined as the longest combination of TCs which are shared by a significant number of drugs. Frequent SEPs are explored on the basis of integrated drug and target descriptors using two machine learning methods: decision-trees and inductive-logic programming. Although both methods yield explicit models, inductive-logic programming method performs relational learning and is able to exploit not only drug properties but also background knowledge. Learning efficiency is evaluated by cross-validation and direct testing with new molecules. Comparison of the two machine-learning methods shows that the inductive-logic-programming method displays a greater sensitivity than decision trees and successfully exploit background knowledge such as functional annotations and pathways of drug targets, thereby producing rich and expressive rules. All models and theories are available on a dedicated web site. Side effect profiles covering significant number of drugs have been extracted from a drug ×side-effect association table. Integration of background knowledge concerning both chemical and biological spaces has been combined with a relational learning method for discovering rules which explicitly characterize drug-SEP associations. These rules are successfully used for predicting SEPs associated with new drugs.

Leveraging 3D chemical similarity, target and phenotypic data in the identification of drug-protein and drug-adverse effect associations.

PubMed

Vilar, Santiago; Hripcsak, George

2016-01-01

Drug-target identification is crucial to discover novel applications for existing drugs and provide more insights about mechanisms of biological actions, such as adverse drug effects (ADEs). Computational methods along with the integration of current big data sources provide a useful framework for drug-target and drug-adverse effect discovery. In this article, we propose a method based on the integration of 3D chemical similarity, target and adverse effect data to generate a drug-target-adverse effect predictor along with a simple leveraging system to improve identification of drug-targets and drug-adverse effects. In the first step, we generated a system for multiple drug-target identification based on the application of 3D drug similarity into a large target dataset extracted from the ChEMBL. Next, we developed a target-adverse effect predictor combining targets from ChEMBL with phenotypic information provided by SIDER data source. Both modules were linked to generate a final predictor that establishes hypothesis about new drug-target-adverse effect candidates. Additionally, we showed that leveraging drug-target candidates with phenotypic data is very useful to improve the identification of drug-targets. The integration of phenotypic data into drug-target candidates yielded up to twofold precision improvement. In the opposite direction, leveraging drug-phenotype candidates with target data also yielded a significant enhancement in the performance. The modeling described in the current study is simple and efficient and has applications at large scale in drug repurposing and drug safety through the identification of mechanism of action of biological effects.