Exploring astrobiology using in silico molecular structure generation.

PubMed

Meringer, Markus; Cleaves, H James

2017-12-28

The origin of life is typically understood as a transition from inanimate or disorganized matter to self-organized, 'animate' matter. This transition probably took place largely in the context of organic compounds, and most approaches, to date, have focused on using the organic chemical composition of modern organisms as the main guide for understanding this process. However, it has gradually come to be appreciated that biochemistry, as we know it, occupies a minute volume of the possible organic 'chemical space'. As the majority of abiotic syntheses appear to make a large set of compounds not found in biochemistry, as well as an incomplete subset of those that are, it is possible that life began with a significantly different set of components. Chemical graph-based structure generation methods allow for exhaustive in silico enumeration of different compound types and different types of 'chemical spaces' beyond those used by biochemistry, which can be explored to help understand the types of compounds biology uses, as well as to understand the nature of abiotic synthesis, and potentially design novel types of living systems.This article is part of the themed issue 'Reconceptualizing the origins of life'. © 2017 The Authors.

Exploring astrobiology using in silico molecular structure generation

NASA Astrophysics Data System (ADS)

Meringer, Markus; Cleaves, H. James

2017-11-01

The origin of life is typically understood as a transition from inanimate or disorganized matter to self-organized, `animate' matter. This transition probably took place largely in the context of organic compounds, and most approaches, to date, have focused on using the organic chemical composition of modern organisms as the main guide for understanding this process. However, it has gradually come to be appreciated that biochemistry, as we know it, occupies a minute volume of the possible organic `chemical space'. As the majority of abiotic syntheses appear to make a large set of compounds not found in biochemistry, as well as an incomplete subset of those that are, it is possible that life began with a significantly different set of components. Chemical graph-based structure generation methods allow for exhaustive in silico enumeration of different compound types and different types of `chemical spaces' beyond those used by biochemistry, which can be explored to help understand the types of compounds biology uses, as well as to understand the nature of abiotic synthesis, and potentially design novel types of living systems. This article is part of the themed issue 'Reconceptualizing the origins of life'.

Effect of missing data on multitask prediction methods.

PubMed

de la Vega de León, Antonio; Chen, Beining; Gillet, Valerie J

2018-05-22

There has been a growing interest in multitask prediction in chemoinformatics, helped by the increasing use of deep neural networks in this field. This technique is applied to multitarget data sets, where compounds have been tested against different targets, with the aim of developing models to predict a profile of biological activities for a given compound. However, multitarget data sets tend to be sparse; i.e., not all compound-target combinations have experimental values. There has been little research on the effect of missing data on the performance of multitask methods. We have used two complete data sets to simulate sparseness by removing data from the training set. Different models to remove the data were compared. These sparse sets were used to train two different multitask methods, deep neural networks and Macau, which is a Bayesian probabilistic matrix factorization technique. Results from both methods were remarkably similar and showed that the performance decrease because of missing data is at first small before accelerating after large amounts of data are removed. This work provides a first approximation to assess how much data is required to produce good performance in multitask prediction exercises.

QSAR Modeling Using Large-Scale Databases: Case Study for HIV-1 Reverse Transcriptase Inhibitors.

PubMed

Tarasova, Olga A; Urusova, Aleksandra F; Filimonov, Dmitry A; Nicklaus, Marc C; Zakharov, Alexey V; Poroikov, Vladimir V

2015-07-27

Large-scale databases are important sources of training sets for various QSAR modeling approaches. Generally, these databases contain information extracted from different sources. This variety of sources can produce inconsistency in the data, defined as sometimes widely diverging activity results for the same compound against the same target. Because such inconsistency can reduce the accuracy of predictive models built from these data, we are addressing the question of how best to use data from publicly and commercially accessible databases to create accurate and predictive QSAR models. We investigate the suitability of commercially and publicly available databases to QSAR modeling of antiviral activity (HIV-1 reverse transcriptase (RT) inhibition). We present several methods for the creation of modeling (i.e., training and test) sets from two, either commercially or freely available, databases: Thomson Reuters Integrity and ChEMBL. We found that the typical predictivities of QSAR models obtained using these different modeling set compilation methods differ significantly from each other. The best results were obtained using training sets compiled for compounds tested using only one method and material (i.e., a specific type of biological assay). Compound sets aggregated by target only typically yielded poorly predictive models. We discuss the possibility of "mix-and-matching" assay data across aggregating databases such as ChEMBL and Integrity and their current severe limitations for this purpose. One of them is the general lack of complete and semantic/computer-parsable descriptions of assay methodology carried by these databases that would allow one to determine mix-and-matchability of result sets at the assay level.

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

Consensus QSAR model for identifying novel H5N1 inhibitors.

PubMed

Sharma, Nitin; Yap, Chun Wei

2012-08-01

Due to the importance of neuraminidase in the pathogenesis of influenza virus infection, it has been regarded as the most important drug target for the treatment of influenza. Resistance to currently available drugs and new findings related to structure of the protein requires novel neuraminidase 1 (N1) inhibitors. In this study, a consensus QSAR model with defined applicability domain (AD) was developed using published N1 inhibitors. The consensus model was validated using an external validation set. The model achieved high sensitivity, specificity, and overall accuracy along with low false positive rate (FPR) and false discovery rate (FDR). The performance of model on the external validation set and training set were comparable, thus it was unlikely to be overfitted. The low FPR and low FDR will increase its accuracy in screening large chemical libraries. Screening of ZINC library resulted in 64,772 compounds as probable N1 inhibitors, while 173,674 compounds were defined to be outside the AD of the consensus model. The advantage of the current model is that it was developed using a large and diverse dataset and has a defined AD which prevents its use on compounds that it is not capable of predicting. The consensus model developed in this study is made available via the free software, PaDEL-DDPredictor.

SureChEMBL: a large-scale, chemically annotated patent document database.

PubMed

Papadatos, George; Davies, Mark; Dedman, Nathan; Chambers, Jon; Gaulton, Anna; Siddle, James; Koks, Richard; Irvine, Sean A; Pettersson, Joe; Goncharoff, Nicko; Hersey, Anne; Overington, John P

2016-01-04

SureChEMBL is a publicly available large-scale resource containing compounds extracted from the full text, images and attachments of patent documents. The data are extracted from the patent literature according to an automated text and image-mining pipeline on a daily basis. SureChEMBL provides access to a previously unavailable, open and timely set of annotated compound-patent associations, complemented with sophisticated combined structure and keyword-based search capabilities against the compound repository and patent document corpus; given the wealth of knowledge hidden in patent documents, analysis of SureChEMBL data has immediate applications in drug discovery, medicinal chemistry and other commercial areas of chemical science. Currently, the database contains 17 million compounds extracted from 14 million patent documents. Access is available through a dedicated web-based interface and data downloads at: https://www.surechembl.org/. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

NEW FRONTIERS IN DRUGGABILITY

PubMed Central

Kozakov, Dima; Hall, David R.; Napoleon, Raeanne L.; Yueh, Christine; Whitty, Adrian; Vajda, Sandor

2016-01-01

A powerful early approach to evaluating the druggability of proteins involved determining the hit rate in NMR-based screening of a library of small compounds. Here we show that a computational analog of this method, based on mapping proteins using small molecules as probes, can reliably reproduce druggability results from NMR-based screening, and can provide a more meaningful assessment in cases where the two approaches disagree. We apply the method to a large set of proteins. The results show that, because the method is based on the biophysics of binding rather than on empirical parameterization, meaningful information can be gained about classes of proteins and classes of compounds beyond those resembling validated targets and conventionally druglike ligands. In particular, the method identifies targets that, while not druggable by druglike compounds, may become druggable using compound classes such as macrocycles or other large molecules beyond the rule-of-five limit. PMID:26230724

SureChEMBL: a large-scale, chemically annotated patent document database

PubMed Central

Papadatos, George; Davies, Mark; Dedman, Nathan; Chambers, Jon; Gaulton, Anna; Siddle, James; Koks, Richard; Irvine, Sean A.; Pettersson, Joe; Goncharoff, Nicko; Hersey, Anne; Overington, John P.

2016-01-01

SureChEMBL is a publicly available large-scale resource containing compounds extracted from the full text, images and attachments of patent documents. The data are extracted from the patent literature according to an automated text and image-mining pipeline on a daily basis. SureChEMBL provides access to a previously unavailable, open and timely set of annotated compound-patent associations, complemented with sophisticated combined structure and keyword-based search capabilities against the compound repository and patent document corpus; given the wealth of knowledge hidden in patent documents, analysis of SureChEMBL data has immediate applications in drug discovery, medicinal chemistry and other commercial areas of chemical science. Currently, the database contains 17 million compounds extracted from 14 million patent documents. Access is available through a dedicated web-based interface and data downloads at: https://www.surechembl.org/. PMID:26582922

Characterization of a biosurfactant produced by Pseudomonas cepacia CCT6659 in the presence of industrial wastes and its application in the biodegradation of hydrophobic compounds in soil.

PubMed

Silva, Elias J; Rocha e Silva, Nathália Maria P; Rufino, Raquel D; Luna, Juliana M; Silva, Ricardo O; Sarubbo, Leonie A

2014-05-01

The bacterium Pseudomonas cepacia CCT6659 cultivated with 2% soybean waste frying oil and 2% corn steep liquor as substrates produced a biosurfactant with potential application in the bioremediation of soils. The biosurfactant was classified as an anionic biomolecule composed of 75% lipids and 25% carbohydrates. Characterization by proton nuclear magnetic resonance ((1)H and (13)C NMR) revealed the presence of carbonyl, olefinic and aliphatic groups, with typical spectra of lipids. Four sets of biodegradation experiments were carried out with soil contaminated by hydrophobic organic compounds amended with molasses in the presence of an indigenous consortium, as follows: Set 1-soil+bacterial cells; Set 2-soil+biosurfactant; Set 3-soil+bacterial cells+biosurfactant; and Set 4-soil without bacterial cells or biosurfactant (control). Significant oil biodegradation activity (83%) occurred in the first 10 days of the experiments when the biosurfactant and bacterial cells were used together (Set 3), while maximum degradation of the organic compounds (above 95%) was found in Sets 1-3 between 35 and 60 days. It is evident from the results that the biosurfactant alone and its producer species are both capable of promoting biodegradation to a large extent. Copyright © 2014 Elsevier B.V. All rights reserved.

Development and experimental test of support vector machines virtual screening method for searching Src inhibitors from large compound libraries.

PubMed

Han, Bucong; Ma, Xiaohua; Zhao, Ruiying; Zhang, Jingxian; Wei, Xiaona; Liu, Xianghui; Liu, Xin; Zhang, Cunlong; Tan, Chunyan; Jiang, Yuyang; Chen, Yuzong

2012-11-23

Src plays various roles in tumour progression, invasion, metastasis, angiogenesis and survival. It is one of the multiple targets of multi-target kinase inhibitors in clinical uses and trials for the treatment of leukemia and other cancers. These successes and appearances of drug resistance in some patients have raised significant interest and efforts in discovering new Src inhibitors. Various in-silico methods have been used in some of these efforts. It is desirable to explore additional in-silico methods, particularly those capable of searching large compound libraries at high yields and reduced false-hit rates. We evaluated support vector machines (SVM) as virtual screening tools for searching Src inhibitors from large compound libraries. SVM trained and tested by 1,703 inhibitors and 63,318 putative non-inhibitors correctly identified 93.53%~ 95.01% inhibitors and 99.81%~ 99.90% non-inhibitors in 5-fold cross validation studies. SVM trained by 1,703 inhibitors reported before 2011 and 63,318 putative non-inhibitors correctly identified 70.45% of the 44 inhibitors reported since 2011, and predicted as inhibitors 44,843 (0.33%) of 13.56M PubChem, 1,496 (0.89%) of 168 K MDDR, and 719 (7.73%) of 9,305 MDDR compounds similar to the known inhibitors. SVM showed comparable yield and reduced false hit rates in searching large compound libraries compared to the similarity-based and other machine-learning VS methods developed from the same set of training compounds and molecular descriptors. We tested three virtual hits of the same novel scaffold from in-house chemical libraries not reported as Src inhibitor, one of which showed moderate activity. SVM may be potentially explored for searching Src inhibitors from large compound libraries at low false-hit rates.

Chemical data visualization and analysis with incremental generative topographic mapping: big data challenge.

PubMed

Gaspar, Héléna A; Baskin, Igor I; Marcou, Gilles; Horvath, Dragos; Varnek, Alexandre

2015-01-26

This paper is devoted to the analysis and visualization in 2-dimensional space of large data sets of millions of compounds using the incremental version of generative topographic mapping (iGTM). The iGTM algorithm implemented in the in-house ISIDA-GTM program was applied to a database of more than 2 million compounds combining data sets of 36 chemicals suppliers and the NCI collection, encoded either by MOE descriptors or by MACCS keys. Taking advantage of the probabilistic nature of GTM, several approaches to data analysis were proposed. The chemical space coverage was evaluated using the normalized Shannon entropy. Different views of the data (property landscapes) were obtained by mapping various physical and chemical properties (molecular weight, aqueous solubility, LogP, etc.) onto the iGTM map. The superposition of these views helped to identify the regions in the chemical space populated by compounds with desirable physicochemical profiles and the suppliers providing them. The data sets similarity in the latent space was assessed by applying several metrics (Euclidean distance, Tanimoto and Bhattacharyya coefficients) to data probability distributions based on cumulated responsibility vectors. As a complementary approach, data sets were compared by considering them as individual objects on a meta-GTM map, built on cumulated responsibility vectors or property landscapes produced with iGTM. We believe that the iGTM methodology described in this article represents a fast and reliable way to analyze and visualize large chemical databases.

Two-temperature synthesis of non-linear optical compound CdGeAs2

NASA Astrophysics Data System (ADS)

Zhu, Chongqiang; Verozubova, G. A.; Mironov, Yuri P.; Lei, Zuotao; Song, Liangcheng; Ma, Tianhui; Okunev, A. O.; Yang, Chunhui

2016-12-01

In this work, we report on a new approach to synthesize large-scale nonlinear optical chalcopyrite compound CdGeAs2 (cadmium germanium arsenide), in which the arsenic (As) precursor and the mixture of the cadmium (Cd) and the germanium (Ge) were separated in two distinct temperature-defined zones of a furnace. Through probing the intermediate product prepared at pre-set temperature points of hot-zone area, it was revealed that the ternary compound CdGeAs2 was formed through chemical reactions among Cd3As2, CdAs2, GeAs, GeAs2 and Ge. A new intermediate crystalline compound, with determined crystal parameter c=0.9139 nm and unknown a parameter, was identified when the temperature of the mixture of Cd and Ge was set to 680 °C, which, however, disappeared when the temperature was set to 770 °C, yielding pure CdGeAs2 product. Most likely, the identified new intermediate compound has layered graphite-like structure. Moreover, we show that the described two-temperature synthesis method allows us to produce near 250 g CdGeAs2 product during one run in a horizontal furnace and 500 g in a tilted horizontal furnace with rotated reactor.

Automatic analysis of quantitative NMR data of pharmaceutical compound libraries.

PubMed

Liu, Xuejun; Kolpak, Michael X; Wu, Jiejun; Leo, Gregory C

2012-08-07

In drug discovery, chemical library compounds are usually dissolved in DMSO at a certain concentration and then distributed to biologists for target screening. Quantitative (1)H NMR (qNMR) is the preferred method for the determination of the actual concentrations of compounds because the relative single proton peak areas of two chemical species represent the relative molar concentrations of the two compounds, that is, the compound of interest and a calibrant. Thus, an analyte concentration can be determined using a calibration compound at a known concentration. One particularly time-consuming step in the qNMR analysis of compound libraries is the manual integration of peaks. In this report is presented an automated method for performing this task without prior knowledge of compound structures and by using an external calibration spectrum. The script for automated integration is fast and adaptable to large-scale data sets, eliminating the need for manual integration in ~80% of the cases.

Designing a diverse high-quality library for crystallography-based FBDD screening.

PubMed

Tounge, Brett A; Parker, Michael H

2011-01-01

A well-chosen set of fragments is able to cover a large chemical space using a small number of compounds. The actual size and makeup of the fragment set is dependent on the screening method since each technique has its own practical limits in terms of the number of compounds that can be screened and requirements for compound solubility. In this chapter, an overview of the general requirements for a fragment library is presented for different screening platforms. In the case of the FBDD work at Johnson & Johnson Pharmaceutical Research and Development, L.L.C., our main screening technology is X-ray crystallography. Since every soaked protein crystal needs to be diffracted and a protein structure determined to delineate if a fragment binds, the size of our initial screening library cannot be a rate-limiting factor. For this reason, we have chosen 900 as the appropriate primary fragment library size. To choose the best set, we have developed our own mix of simple property ("Rule of 3") and "bad" substructure filtering. While this gets one a long way in terms of limiting the fragment pool, there are still tens of thousands of compounds to choose from after this initial step. Many of the choices left at this stage are not drug-like, so we have developed an FBDD Score to help select a 900-compound set. The details of this score and the filtering are presented. Copyright © 2011 Elsevier Inc. All rights reserved.

Regioselectivity of enzymatic and photochemical single electron transfer promoted carbon-carbon bond fragmentation reactions of tetrameric lignin model compounds.

PubMed

Cho, Dae Won; Latham, John A; Park, Hea Jung; Yoon, Ung Chan; Langan, Paul; Dunaway-Mariano, Debra; Mariano, Patrick S

2011-04-15

New types of tetrameric lignin model compounds, which contain the common β-O-4 and β-1 structural subunits found in natural lignins, have been prepared and carbon-carbon bond fragmentation reactions of their cation radicals, formed by photochemical (9,10-dicyanoanthracene) and enzymatic (lignin peroxidase) SET-promoted methods, have been explored. The results show that cation radical intermediates generated from the tetrameric model compounds undergo highly regioselective C-C bond cleavage in their β-1 subunits. The outcomes of these processes suggest that, independent of positive charge and odd-electron distributions, cation radicals of lignins formed by SET to excited states of sensitizers or heme-iron centers in enzymes degrade selectively through bond cleavage reactions in β-1 vs β-O-4 moieties. In addition, the findings made in the enzymatic studies demonstrate that the sterically large tetrameric lignin model compounds undergo lignin peroxidase-catalyzed cleavage via a mechanism involving preliminary formation of an enzyme-substrate complex.

Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure.

PubMed

Zhu, Hao; Martin, Todd M; Ye, Lin; Sedykh, Alexander; Young, Douglas M; Tropsha, Alexander

2009-12-01

Few quantitative structure-activity relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity end points. In this study, a comprehensive data set of 7385 compounds with their most conservative lethal dose (LD(50)) values has been compiled. A combinatorial QSAR approach has been employed to develop robust and predictive models of acute toxicity in rats caused by oral exposure to chemicals. To enable fair comparison between the predictive power of models generated in this study versus a commercial toxicity predictor, TOPKAT (Toxicity Prediction by Komputer Assisted Technology), a modeling subset of the entire data set was selected that included all 3472 compounds used in TOPKAT's training set. The remaining 3913 compounds, which were not present in the TOPKAT training set, were used as the external validation set. QSAR models of five different types were developed for the modeling set. The prediction accuracy for the external validation set was estimated by determination coefficient R(2) of linear regression between actual and predicted LD(50) values. The use of the applicability domain threshold implemented in most models generally improved the external prediction accuracy but expectedly led to the decrease in chemical space coverage; depending on the applicability domain threshold, R(2) ranged from 0.24 to 0.70. Ultimately, several consensus models were developed by averaging the predicted LD(50) for every compound using all five models. The consensus models afforded higher prediction accuracy for the external validation data set with the higher coverage as compared to individual constituent models. The validated consensus LD(50) models developed in this study can be used as reliable computational predictors of in vivo acute toxicity.

Bayesian screening for active compounds in high-dimensional chemical spaces combining property descriptors and molecular fingerprints.

PubMed

Vogt, Martin; Bajorath, Jürgen

2008-01-01

Bayesian classifiers are increasingly being used to distinguish active from inactive compounds and search large databases for novel active molecules. We introduce an approach to directly combine the contributions of property descriptors and molecular fingerprints in the search for active compounds that is based on a Bayesian framework. Conventionally, property descriptors and fingerprints are used as alternative features for virtual screening methods. Following the approach introduced here, probability distributions of descriptor values and fingerprint bit settings are calculated for active and database molecules and the divergence between the resulting combined distributions is determined as a measure of biological activity. In test calculations on a large number of compound activity classes, this methodology was found to consistently perform better than similarity searching using fingerprints and multiple reference compounds or Bayesian screening calculations using probability distributions calculated only from property descriptors. These findings demonstrate that there is considerable synergy between different types of property descriptors and fingerprints in recognizing diverse structure-activity relationships, at least in the context of Bayesian modeling.

History of sterile compounding in U.S. hospitals: learning from the tragic lessons of the past.

PubMed

Myers, Charles E

2013-08-15

The evolution of sterile compounding in the context of hospital patient care, the evolution of related technology, past incidents of morbidity and mortality associated with preparations compounded in various settings, and efforts over the years to improve compounding practices are reviewed. Tightened United States Pharmacopeial Convention standards (since 2004) for sterile compounding made it difficult for hospitals to achieve all of the sterile compounding necessary for patient care. Shortages of manufactured injections added to the need for compounding. Non-hospital-based compounding pharmacies increased sterile compounding to meet the needs. Gaps in federal and state laws and regulations about compounding pharmacies led to deficiencies in their regulation. Lapses in sterility led to injuries and deaths. Perspectives offered include potential actions, including changes in practitioner education, better surveillance of sterile compounding, regulatory reforms, reexamination of the causes of drug shortages, and the development of new technologies. Over the years, there have been numerous exhortations for voluntary better performance in sterile compounding. In addition, professional leadership has been vigorous and extensive in the form of guidance, publications, education, enforceable standards, and development of various associations and organizations dealing with safe compounding practices. Yet problems continue to occur. We must engage in diligent learning from the injuries and tragedies that have occurred. Assuming that we are already doing all we can to avoid problems would be an abdication of the professional mission of pharmacists. It would be wrong thinking to assume that the recent problems in large-scale compounding pharmacies are the only problems that warrant attention. It is time for a systematic assessment of the nature and the dimensions of the problems in every type of setting where sterile compounding occurs. It also is time for some innovative thinking about ensuring safety in sterile compounding.

Development and experimental test of support vector machines virtual screening method for searching Src inhibitors from large compound libraries

PubMed Central

2012-01-01

Background Src plays various roles in tumour progression, invasion, metastasis, angiogenesis and survival. It is one of the multiple targets of multi-target kinase inhibitors in clinical uses and trials for the treatment of leukemia and other cancers. These successes and appearances of drug resistance in some patients have raised significant interest and efforts in discovering new Src inhibitors. Various in-silico methods have been used in some of these efforts. It is desirable to explore additional in-silico methods, particularly those capable of searching large compound libraries at high yields and reduced false-hit rates. Results We evaluated support vector machines (SVM) as virtual screening tools for searching Src inhibitors from large compound libraries. SVM trained and tested by 1,703 inhibitors and 63,318 putative non-inhibitors correctly identified 93.53%~ 95.01% inhibitors and 99.81%~ 99.90% non-inhibitors in 5-fold cross validation studies. SVM trained by 1,703 inhibitors reported before 2011 and 63,318 putative non-inhibitors correctly identified 70.45% of the 44 inhibitors reported since 2011, and predicted as inhibitors 44,843 (0.33%) of 13.56M PubChem, 1,496 (0.89%) of 168 K MDDR, and 719 (7.73%) of 9,305 MDDR compounds similar to the known inhibitors. Conclusions SVM showed comparable yield and reduced false hit rates in searching large compound libraries compared to the similarity-based and other machine-learning VS methods developed from the same set of training compounds and molecular descriptors. We tested three virtual hits of the same novel scaffold from in-house chemical libraries not reported as Src inhibitor, one of which showed moderate activity. SVM may be potentially explored for searching Src inhibitors from large compound libraries at low false-hit rates. PMID:23173901

Systematic assessment of scaffold hopping versus activity cliff formation across bioactive compound classes following a molecular hierarchy.

PubMed

Stumpfe, Dagmar; Dimova, Dilyana; Bajorath, Jürgen

2015-07-01

Scaffold hopping and activity cliff formation define opposite ends of the activity landscape feature spectrum. To rationalize these events at the level of scaffolds, active compounds involved in scaffold hopping were required to contain topologically distinct scaffolds but have only limited differences in potency, whereas compounds involved in activity cliffs were required to share the same scaffold but have large differences in potency. A systematic search was carried out for compounds involved in scaffold hopping and/or activity cliff formation. Results obtained for compound data sets covering more than 300 human targets revealed clear trends. If scaffolds represented multiple but fewer than 10 active compounds, nearly 90% of all scaffolds were exclusively involved in hopping events. With increasing compound coverage, the fraction of scaffolds involved in both scaffold hopping and activity cliff formation significantly increased to more than 50%. However, ∼40% of the scaffolds representing large numbers of active compounds continued to be exclusively involved in scaffold hopping. More than 200 scaffolds with broad target coverage were identified that consistently represented potent compounds and yielded an abundance of scaffold hops in the low-nanomolar range. These and other subsets of scaffolds we characterized are of prime interest for structure-activity relationship (SAR) exploration and compound design. Therefore, the complete scaffold classification generated in the course of our analysis is made freely available. Copyright © 2015 Elsevier Ltd. All rights reserved.

In Silico Mining for Antimalarial Structure-Activity Knowledge and Discovery of Novel Antimalarial Curcuminoids.

PubMed

Viira, Birgit; Gendron, Thibault; Lanfranchi, Don Antoine; Cojean, Sandrine; Horvath, Dragos; Marcou, Gilles; Varnek, Alexandre; Maes, Louis; Maran, Uko; Loiseau, Philippe M; Davioud-Charvet, Elisabeth

2016-06-29

Malaria is a parasitic tropical disease that kills around 600,000 patients every year. The emergence of resistant Plasmodium falciparum parasites to artemisinin-based combination therapies (ACTs) represents a significant public health threat, indicating the urgent need for new effective compounds to reverse ACT resistance and cure the disease. For this, extensive curation and homogenization of experimental anti-Plasmodium screening data from both in-house and ChEMBL sources were conducted. As a result, a coherent strategy was established that allowed compiling coherent training sets that associate compound structures to the respective antimalarial activity measurements. Seventeen of these training sets led to the successful generation of classification models discriminating whether a compound has a significant probability to be active under the specific conditions of the antimalarial test associated with each set. These models were used in consensus prediction of the most likely active from a series of curcuminoids available in-house. Positive predictions together with a few predicted as inactive were then submitted to experimental in vitro antimalarial testing. A large majority from predicted compounds showed antimalarial activity, but not those predicted as inactive, thus experimentally validating the in silico screening approach. The herein proposed consensus machine learning approach showed its potential to reduce the cost and duration of antimalarial drug discovery.

Action recognition using mined hierarchical compound features.

PubMed

Gilbert, Andrew; Illingworth, John; Bowden, Richard

2011-05-01

The field of Action Recognition has seen a large increase in activity in recent years. Much of the progress has been through incorporating ideas from single-frame object recognition and adapting them for temporal-based action recognition. Inspired by the success of interest points in the 2D spatial domain, their 3D (space-time) counterparts typically form the basic components used to describe actions, and in action recognition the features used are often engineered to fire sparsely. This is to ensure that the problem is tractable; however, this can sacrifice recognition accuracy as it cannot be assumed that the optimum features in terms of class discrimination are obtained from this approach. In contrast, we propose to initially use an overcomplete set of simple 2D corners in both space and time. These are grouped spatially and temporally using a hierarchical process, with an increasing search area. At each stage of the hierarchy, the most distinctive and descriptive features are learned efficiently through data mining. This allows large amounts of data to be searched for frequently reoccurring patterns of features. At each level of the hierarchy, the mined compound features become more complex, discriminative, and sparse. This results in fast, accurate recognition with real-time performance on high-resolution video. As the compound features are constructed and selected based upon their ability to discriminate, their speed and accuracy increase at each level of the hierarchy. The approach is tested on four state-of-the-art data sets, the popular KTH data set to provide a comparison with other state-of-the-art approaches, the Multi-KTH data set to illustrate performance at simultaneous multiaction classification, despite no explicit localization information provided during training. Finally, the recent Hollywood and Hollywood2 data sets provide challenging complex actions taken from commercial movie sequences. For all four data sets, the proposed hierarchical approach outperforms all other methods reported thus far in the literature and can achieve real-time operation.

Pharmaceutical Raw Material Identification Using Miniature Near-Infrared (MicroNIR) Spectroscopy and Supervised Pattern Recognition Using Support Vector Machine

PubMed Central

Hsiung, Chang; Pederson, Christopher G.; Zou, Peng; Smith, Valton; von Gunten, Marc; O’Brien, Nada A.

2016-01-01

Near-infrared spectroscopy as a rapid and non-destructive analytical technique offers great advantages for pharmaceutical raw material identification (RMID) to fulfill the quality and safety requirements in pharmaceutical industry. In this study, we demonstrated the use of portable miniature near-infrared (MicroNIR) spectrometers for NIR-based pharmaceutical RMID and solved two challenges in this area, model transferability and large-scale classification, with the aid of support vector machine (SVM) modeling. We used a set of 19 pharmaceutical compounds including various active pharmaceutical ingredients (APIs) and excipients and six MicroNIR spectrometers to test model transferability. For the test of large-scale classification, we used another set of 253 pharmaceutical compounds comprised of both chemically and physically different APIs and excipients. We compared SVM with conventional chemometric modeling techniques, including soft independent modeling of class analogy, partial least squares discriminant analysis, linear discriminant analysis, and quadratic discriminant analysis. Support vector machine modeling using a linear kernel, especially when combined with a hierarchical scheme, exhibited excellent performance in both model transferability and large-scale classification. Hence, ultra-compact, portable and robust MicroNIR spectrometers coupled with SVM modeling can make on-site and in situ pharmaceutical RMID for large-volume applications highly achievable. PMID:27029624

Use and engineering of efflux pumps for the export of olefins in microbes

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

Mukhopadhyay, Aindrila

2016-07-14

The scope of the project is to investigate efflux pump systems in engineered host microorganisms, such as E. coli, and develop a pump engineered to export a target compound. To initiate the project in coordination with other TOTAL driven projects, the first target compound to be studied was 1-hexene. However, we were investigating other chemicals as Styrene. The main goal of the project was to generate a set of optimized efflux pump systems for microorganisms (E. coli and Streptomyces or other host) engineered to contain biosynthetic pathways to export large titers of target compounds that are toxic (or accumulate andmore » push back biosynthesis) to the host cell. An optimized microbial host will utilize specific and efficient cell wall located pumps to extrude harmful target compounds and enable greater production of these compounds.« less

Compounding pharmacy conundrum: "we cannot live without them but we cannot live with them" according to the present paradigm.

PubMed

Guharoy, Roy; Noviasky, John; Haydar, Ziad; Fakih, Mohamad G; Hartman, Christian

2013-04-01

Compounding pharmacies serve a critical role in modern health care to meet special patient care needs. Although the US Food and Drug Administration (FDA) has clearly delineated jurisdiction over drug companies and products manufactured under Good Manufacturing Practice (GMP) regulations to ensure quality, potency, and purity, compounding pharmacies are regulated by the State Boards and are not registered by the FDA. In recent years, some compounding pharmacies acted like a manufacturer, preparing large amounts of injectable drugs with interstate activities. Multiple outbreaks have been linked to compounding pharmacies, including a recent outbreak of fungal meningitis related to contaminated methylprednisolone, exposing > 14,000 patients in multiple states. This tragedy underscores the urgency of addressing safety related to compounding pharmacies. There is a call for action at the federal and state levels to set minimum production standards, impose new labeling conditions on compounded drugs, and require large-scale compounders be regulated by the FDA. "Industrial" compounding must come under FDA oversight, require those pharmacies to meet GMP standards, and ensure quality and safe products for patient use. Moreover, compliance with the Institute for Safe Medication Practices 2011 recommendations that any type of sterile compounding must be in compliance with the United States Pharmacopoeia chapter 797 guidelines will reduce the risk of patient harm from microbial contamination. Finally, other critical factors that require close attention include addressing injectable products compounded in hospitals and other outpatient health-care centers. The FDA and State Boards of Pharmacy must be adequately funded to exercise the oversight effectively.

Modeling the gas-phase thermochemistry of organosulfur compounds.

PubMed

Vandeputte, Aäron G; Sabbe, Maarten K; Reyniers, Marie-Françoise; Marin, Guy B

2011-06-27

Key to understanding the involvement of organosulfur compounds in a variety of radical chemistries, such as atmospheric chemistry, polymerization, pyrolysis, and so forth, is knowledge of their thermochemical properties. For organosulfur compounds and radicals, thermochemical data are, however, much less well documented than for hydrocarbons. The traditional recourse to the Benson group additivity method offers no solace since only a very limited number of group additivity values (GAVs) is available. In this work, CBS-QB3 calculations augmented with 1D hindered rotor corrections for 122 organosulfur compounds and 45 organosulfur radicals were used to derive 93 Benson group additivity values, 18 ring-strain corrections, 2 non-nearest-neighbor interactions, and 3 resonance corrections for standard enthalpies of formation, standard molar entropies, and heat capacities for organosulfur compounds and organosulfur radicals. The reported GAVs are consistent with previously reported GAVs for hydrocarbons and hydrocarbon radicals and include 77 contributions, among which 26 radical contributions, which, to the best of our knowledge, have not been reported before. The GAVs allow one to estimate the standard enthalpies of formation at 298 K, the standard entropies at 298 K, and standard heat capacities in the temperature range 300-1500 K for a large set of organosulfur compounds, that is, thiols, thioketons, polysulfides, alkylsulfides, thials, dithioates, and cyclic sulfur compounds. For a validation set of 26 organosulfur compounds, the mean absolute deviation between experimental and group additively modeled enthalpies of formation amounts to 1.9  kJ  mol(-1). For an additional set of 14 organosulfur compounds, it was shown that the mean absolute deviations between calculated and group additively modeled standard entropies and heat capacities are restricted to 4 and 2 J  mol(-1)  K(-1), respectively. As an alternative to Benson GAVs, 26 new hydrogen-bond increments are reported, which can also be useful for the prediction of radical thermochemistry. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Comparison of the applicability domain of a quantitative structure-activity relationship for estrogenicity with a large chemical inventory.

PubMed

Netzeva, Tatiana I; Gallegos Saliner, Ana; Worth, Andrew P

2006-05-01

The aim of the present study was to illustrate that it is possible and relatively straightforward to compare the domain of applicability of a quantitative structure-activity relationship (QSAR) model in terms of its physicochemical descriptors with a large inventory of chemicals. A training set of 105 chemicals with data for relative estrogenic gene activation, obtained in a recombinant yeast assay, was used to develop the QSAR. A binary classification model for predicting active versus inactive chemicals was developed using classification tree analysis and two descriptors with a clear physicochemical meaning (octanol-water partition coefficient, or log Kow, and the number of hydrogen bond donors, or n(Hdon)). The model demonstrated a high overall accuracy (90.5%), with a sensitivity of 95.9% and a specificity of 78.1%. The robustness of the model was evaluated using the leave-many-out cross-validation technique, whereas the predictivity was assessed using an artificial external test set composed of 12 compounds. The domain of the QSAR training set was compared with the chemical space covered by the European Inventory of Existing Commercial Chemical Substances (EINECS), as incorporated in the CDB-EC software, in the log Kow / n(Hdon) plane. The results showed that the training set and, therefore, the applicability domain of the QSAR model covers a small part of the physicochemical domain of the inventory, even though a simple method for defining the applicability domain (ranges in the descriptor space) was used. However, a large number of compounds are located within the narrow descriptor window.

Compound Passport Service: supporting corporate collection owners in open innovation.

PubMed

Andrews, David M; Degorce, Sébastien L; Drake, David J; Gustafsson, Magnus; Higgins, Kevin M; Winter, Jon J

2015-10-01

A growing number of early discovery collaborative agreements are being put in place between large pharma companies and partners in which the rights for assets can reside with a partner, exclusively or jointly. Our corporate screening collection, like many others, was built on the premise that compounds generated in-house and not the subject of paper or patent disclosure were proprietary to the company. Collaborative screening arrangements and medicinal chemistry now make the origin, ownership rights and usage of compounds difficult to determine and manage. The Compound Passport Service is a dynamic database, managed and accessed through a set of reusable services that borrows from social media concepts to allow sample owners to take control of their samples in a much more active way. Copyright © 2015 Elsevier Ltd. All rights reserved.

Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics.

PubMed

Ferreira da Costa, Joana; Silva, David; Caamaño, Olga; Brea, José M; Loza, Maria Isabel; Munteanu, Cristian R; Pazos, Alejandro; García-Mera, Xerardo; González-Díaz, Humbert

2018-06-25

Predicting drug-protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein. Unfortunately, these models fail to account for large and complex big data sets of preclinical assays reported in public databases. This includes multiple conditions of assays, such as different experimental parameters, biological assays, target proteins, cell lines, organism of the target, or organism of assay. On the other hand, perturbation theory (PT) models allow us to predict the properties of a query compound or molecular system in experimental assays with multiple boundary conditions based on a previously known case of reference. In this work, we report the first PTML (PT + ML) study of a large ChEMBL data set of preclinical assays of compounds targeting dopamine pathway proteins. The best PTML model found predicts 50000 cases with accuracy of 70-91% in training and external validation series. We also compared the linear PTML model with alternative PTML models trained with multiple nonlinear methods (artificial neural network (ANN), Random Forest, Deep Learning, etc.). Some of the nonlinear methods outperform the linear model but at the cost of a notable increment of the complexity of the model. We illustrated the practical use of the new model with a proof-of-concept theoretical-experimental study. We reported for the first time the organic synthesis, chemical characterization, and pharmacological assay of a new series of l-prolyl-l-leucyl-glycinamide (PLG) peptidomimetic compounds. In addition, we performed a molecular docking study for some of these compounds with the software Vina AutoDock. The work ends with a PTML model predictive study of the outcomes of the new compounds in a large number of assays. Therefore, this study offers a new computational methodology for predicting the outcome for any compound in new assays. This PTML method focuses on the prediction with a simple linear model of multiple pharmacological parameters (IC 50 , EC 50 , K i , etc.) for compounds in assays involving different cell lines used, organisms of the protein target, or organism of assay for proteins in the dopamine pathway.

Assessing Hypervalency in Iodanes.

PubMed

Stirling, András

2018-02-01

The so-called hypervalent iodane compounds are very useful and versatile reactants and oxidizing agents in modern organic chemistry. The hypercoordinated central iodine in these compounds hints at a hypervalent state, which is often stressed to justify their reactivity. In this study a theoretical analysis of the electronic structure of a large, representative set of hypercoordinated iodane compounds has been carried out. We observed that the iodonium is not hypervalent in these compounds. In contrast, the analysis reveals a variation of the iodine valence state from a normal octet state to hypovalent depending on the ligands, but irrespective of the coordination number. On the basis of the calculations the reactivity of these compounds can be ascribed to the strong unquenched charge separation present in these molecules which represents a compromise between Coulomb interaction and the resistance of iodonium toward hypervalency. In extreme cases this leads to hypovalency and enhanced reactivity. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

A national-scale assessment of micro-organic contaminants in groundwater of England and Wales.

PubMed

Manamsa, Katya; Crane, Emily; Stuart, Marianne; Talbot, John; Lapworth, Dan; Hart, Alwyn

2016-10-15

A large variety of micro-organic (MO) compounds is used in huge quantities for a range of purposes (e.g. manufacturing, food production, healthcare) and is now being frequently detected in the aquatic environment. Interest in the occurrence of MO contaminants in the terrestrial and aquatic environments continues to grow, as well as in their environmental fate and potential toxicity. However, the contamination of groundwater resources by MOs has a limited evidence base compared to other freshwater resources. Of particular concern are newly 'emerging contaminants' such as pharmaceuticals and lifestyle compounds, particularly those with potential endocrine disrupting properties. While groundwater often has a high degree of protection from pollution due to physical, chemical and biological attenuation processes in the subsurface compared to surface aquatic environments, trace concentrations of a large range of compounds are still detected in groundwater and in some cases may persist for decades due to the long residence times of groundwater systems. This study provides the first national-scale assessment of micro-organic compounds in groundwater in England and Wales. A large set of monitoring data was analysed to determine the relative occurrence and detected concentrations of different groups of compounds and to determine relationships with land-use, aquifer type and groundwater vulnerability. MOs detected including emerging compounds such as caffeine, DEET, bisphenol A, anti-microbial agents and pharmaceuticals as well as a range of legacy contaminants including chlorinated solvents and THMs, petroleum hydrocarbons, pesticides and other industrial compounds. There are clear differences in MOs between land-use types, particularly for urban-industrial and natural land-use. Temporal trends of MO occurrence are assessed but establishing long-term trends is not yet possible. Copyright © 2016 British Geological Survey, NERC. Published by Elsevier B.V. All rights reserved.

Predicting human liver microsomal stability with machine learning techniques.

PubMed

Sakiyama, Yojiro; Yuki, Hitomi; Moriya, Takashi; Hattori, Kazunari; Suzuki, Misaki; Shimada, Kaoru; Honma, Teruki

2008-02-01

To ensure a continuing pipeline in pharmaceutical research, lead candidates must possess appropriate metabolic stability in the drug discovery process. In vitro ADMET (absorption, distribution, metabolism, elimination, and toxicity) screening provides us with useful information regarding the metabolic stability of compounds. However, before the synthesis stage, an efficient process is required in order to deal with the vast quantity of data from large compound libraries and high-throughput screening. Here we have derived a relationship between the chemical structure and its metabolic stability for a data set of in-house compounds by means of various in silico machine learning such as random forest, support vector machine (SVM), logistic regression, and recursive partitioning. For model building, 1952 proprietary compounds comprising two classes (stable/unstable) were used with 193 descriptors calculated by Molecular Operating Environment. The results using test compounds have demonstrated that all classifiers yielded satisfactory results (accuracy > 0.8, sensitivity > 0.9, specificity > 0.6, and precision > 0.8). Above all, classification by random forest as well as SVM yielded kappa values of approximately 0.7 in an independent validation set, slightly higher than other classification tools. These results suggest that nonlinear/ensemble-based classification methods might prove useful in the area of in silico ADME modeling.

QSAR Modeling of Rat Acute Toxicity by Oral Exposure

PubMed Central

Zhu, Hao; Martin, Todd M.; Ye, Lin; Sedykh, Alexander; Young, Douglas M.; Tropsha, Alexander

2009-01-01

Few Quantitative Structure-Activity Relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity endpoints. In this study, a comprehensive dataset of 7,385 compounds with their most conservative lethal dose (LD50) values has been compiled. A combinatorial QSAR approach has been employed to develop robust and predictive models of acute toxicity in rats caused by oral exposure to chemicals. To enable fair comparison between the predictive power of models generated in this study versus a commercial toxicity predictor, TOPKAT (Toxicity Prediction by Komputer Assisted Technology), a modeling subset of the entire dataset was selected that included all 3,472 compounds used in the TOPKAT’s training set. The remaining 3,913 compounds, which were not present in the TOPKAT training set, were used as the external validation set. QSAR models of five different types were developed for the modeling set. The prediction accuracy for the external validation set was estimated by determination coefficient R2 of linear regression between actual and predicted LD50 values. The use of the applicability domain threshold implemented in most models generally improved the external prediction accuracy but expectedly led to the decrease in chemical space coverage; depending on the applicability domain threshold, R2 ranged from 0.24 to 0.70. Ultimately, several consensus models were developed by averaging the predicted LD50 for every compound using all 5 models. The consensus models afforded higher prediction accuracy for the external validation dataset with the higher coverage as compared to individual constituent models. The validated consensus LD50 models developed in this study can be used as reliable computational predictors of in vivo acute toxicity. PMID:19845371

Interpreting linear support vector machine models with heat map molecule coloring

PubMed Central

2011-01-01

Background Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity. Results We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor. Conclusions In combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor. PMID:21439031

Bayesian models trained with HTS data for predicting β-haematin inhibition and in vitro antimalarial activity.

PubMed

Wicht, Kathryn J; Combrinck, Jill M; Smith, Peter J; Egan, Timothy J

2015-08-15

A large quantity of high throughput screening (HTS) data for antimalarial activity has become available in recent years. This includes both phenotypic and target-based activity. Realising the maximum value of these data remains a challenge. In this respect, methods that allow such data to be used for virtual screening maximise efficiency and reduce costs. In this study both in vitro antimalarial activity and inhibitory data for β-haematin formation, largely obtained from publically available sources, has been used to develop Bayesian models for inhibitors of β-haematin formation and in vitro antimalarial activity. These models were used to screen two in silico compound libraries. In the first, the 1510 U.S. Food and Drug Administration approved drugs available on PubChem were ranked from highest to lowest Bayesian score based on a training set of β-haematin inhibiting compounds active against Plasmodium falciparum that did not include any of the clinical antimalarials or close analogues. The six known clinical antimalarials that inhibit β-haematin formation were ranked in the top 2.1% of compounds. Furthermore, the in vitro antimalarial hit-rate for this prioritised set of compounds was found to be 81% in the case of the subset where activity data are available in PubChem. In the second, a library of about 5000 commercially available compounds (Aldrich(CPR)) was virtually screened for ability to inhibit β-haematin formation and then for in vitro antimalarial activity. A selection of 34 compounds was purchased and tested, of which 24 were predicted to be β-haematin inhibitors. The hit rate for inhibition of β-haematin formation was found to be 25% and a third of these were active against P. falciparum, corresponding to enrichments estimated at about 25- and 140-fold relative to random screening, respectively. Copyright © 2014 Elsevier Ltd. All rights reserved.

Stability and compatibility assessment techniques for total parenteral nutrition admixtures: setting the bar according to pharmacopeial standards.

PubMed

Driscoll, David F

2005-05-01

The stability and compatibility of total parenteral nutrition mixtures compounded for patients requiring nutritional support is paramount to their safety on intravenous infusion. The most significant pharmaceutical issues associated with mixing total parenteral nutrition formulations affecting their safety involve the stability of lipid-injectable emulsions and the compatibility of calcium and phosphate salts. Methods of analysis for stability and compatibility have varied, and the assessments have mostly been largely qualitative. Although pharmacopeial standards have been primarily applicable to pharmaceutical manufacturers, recent efforts by the United States Pharmacopeia have been directed at standardizing pharmacy practices involved in the safe mixing of compounded sterile preparations. The adoption of chapter 797 entitled 'Pharmaceutical compounding - sterile preparations' on 1 January 2004 has had a dramatic impact on pharmacy practice in the United States. More recently, the United States Pharmacopeia has also proposed a new chapter 729 entitled 'Globule size distribution in lipid-injectable emulsions', setting specific limits on the sizes and concentrations of lipid droplets in the formulation, which may have implications for all-in-one mixtures. Finally, new efforts are under way to establish limits on the level of acceptable amounts of particulates intrinsically introduced by the manufacturer, and thus may have ramifications for particulates extrinsically introduced or initiated during compounding by the pharmacist. With careful monitoring and the development of appropriate pharmacopeial-based specifications that limit the size and concentration of large-diameter fat globules and eliminate the possibility of dibasic calcium phosphate precipitates, improved patient outcomes may be achieved.

Developing an Efficient and General Strategy for Immobilization of Small Molecules onto Microarrays Using Isocyanate Chemistry.

PubMed

Zhu, Chenggang; Zhu, Xiangdong; Landry, James P; Cui, Zhaomeng; Li, Quanfu; Dang, Yongjun; Mi, Lan; Zheng, Fengyun; Fei, Yiyan

2016-03-16

Small-molecule microarray (SMM) is an effective platform for identifying lead compounds from large collections of small molecules in drug discovery, and efficient immobilization of molecular compounds is a pre-requisite for the success of such a platform. On an isocyanate functionalized surface, we studied the dependence of immobilization efficiency on chemical residues on molecular compounds, terminal residues on isocyanate functionalized surface, lengths of spacer molecules, and post-printing treatment conditions, and we identified a set of optimized conditions that enable us to immobilize small molecules with significantly improved efficiencies, particularly for those molecules with carboxylic acid residues that are known to have low isocyanate reactivity. We fabricated microarrays of 3375 bioactive compounds on isocyanate functionalized glass slides under these optimized conditions and confirmed that immobilization percentage is over 73%.

Development of a Sigma-2 Receptor affinity filter through a Monte Carlo based QSAR analysis.

PubMed

Rescifina, Antonio; Floresta, Giuseppe; Marrazzo, Agostino; Parenti, Carmela; Prezzavento, Orazio; Nastasi, Giovanni; Dichiara, Maria; Amata, Emanuele

2017-08-30

For the first time in sigma-2 (σ 2 ) receptor field, a quantitative structure-activity relationship (QSAR) model has been built using pK i values of the whole set of known selective σ 2 receptor ligands (548 compounds), taken from the Sigma-2 Receptor Selective Ligands Database (S2RSLDB) (http://www.researchdsf.unict.it/S2RSLDB/), through the Monte Carlo technique and employing the software CORAL. The model has been developed by using a large and structurally diverse set of compounds, allowing for a prediction of different populations of chemical compounds endpoint (σ 2 receptor pK i ). The statistical quality reached, suggested that model for pK i determination is robust and possesses a satisfactory predictive potential. The statistical quality is high for both visible and invisible sets. The screening of the FDA approved drugs, external to our dataset, suggested that sixteen compounds might be repositioned as σ 2 receptor ligands (predicted pK i ≥8). A literature check showed that six of these compounds have already been tested for affinity at σ 2 receptor and, of these, two (Flunarizine and Terbinafine) have shown an experimental σ 2 receptor pK i >7. This suggests that this QSAR model may be used as focusing screening filter in order to prospectively find or repurpose new drugs with high affinity for the σ 2 receptor, and overall allowing for an enhanced hit rate respect to a random screening. Copyright © 2017 Elsevier B.V. All rights reserved.

Benchmark data sets for structure-based computational target prediction.

PubMed

Schomburg, Karen T; Rarey, Matthias

2014-08-25

Structure-based computational target prediction methods identify potential targets for a bioactive compound. Methods based on protein-ligand docking so far face many challenges, where the greatest probably is the ranking of true targets in a large data set of protein structures. Currently, no standard data sets for evaluation exist, rendering comparison and demonstration of improvements of methods cumbersome. Therefore, we propose two data sets and evaluation strategies for a meaningful evaluation of new target prediction methods, i.e., a small data set consisting of three target classes for detailed proof-of-concept and selectivity studies and a large data set consisting of 7992 protein structures and 72 drug-like ligands allowing statistical evaluation with performance metrics on a drug-like chemical space. Both data sets are built from openly available resources, and any information needed to perform the described experiments is reported. We describe the composition of the data sets, the setup of screening experiments, and the evaluation strategy. Performance metrics capable to measure the early recognition of enrichments like AUC, BEDROC, and NSLR are proposed. We apply a sequence-based target prediction method to the large data set to analyze its content of nontrivial evaluation cases. The proposed data sets are used for method evaluation of our new inverse screening method iRAISE. The small data set reveals the method's capability and limitations to selectively distinguish between rather similar protein structures. The large data set simulates real target identification scenarios. iRAISE achieves in 55% excellent or good enrichment a median AUC of 0.67 and RMSDs below 2.0 Å for 74% and was able to predict the first true target in 59 out of 72 cases in the top 2% of the protein data set of about 8000 structures.

BioCompoundML: A General Biofuel Property Screening Tool for Biological Molecules Using Random Forest Classifiers

DOE PAGES

Whitmore, Leanne S.; Davis, Ryan W.; McCormick, Robert L.; ...

2016-09-15

Screening a large number of biologically derived molecules for potential fuel compounds without recourse to experimental testing is important in identifying understudied yet valuable molecules. Experimental testing, although a valuable standard for measuring fuel properties, has several major limitations, including the requirement of testably high quantities, considerable expense, and a large amount of time. This paper discusses the development of a general-purpose fuel property tool, using machine learning, whose outcome is to screen molecules for desirable fuel properties. BioCompoundML adopts a general methodology, requiring as input only a list of training compounds (with identifiers and measured values) and a listmore » of testing compounds (with identifiers). For the training data, BioCompoundML collects open data from the National Center for Biotechnology Information, incorporates user-provided features, imputes missing values, performs feature reduction, builds a classifier, and clusters compounds. BioCompoundML then collects data for the testing compounds, predicts class membership, and determines whether compounds are found in the range of variability of the training data set. We demonstrate this tool using three different fuel properties: research octane number (RON), threshold soot index (TSI), and melting point (MP). Here we provide measures of its success with these properties using randomized train/test measurements: average accuracy is 88% in RON, 85% in TSI, and 94% in MP; average precision is 88% in RON, 88% in TSI, and 95% in MP; and average recall is 88% in RON, 82% in TSI, and 97% in MP. The receiver operator characteristics (area under the curve) were estimated at 0.88 in RON, 0.86 in TSI, and 0.87 in MP. We also measured the success of BioCompoundML by sending 16 compounds for direct RON determination. Finally, we provide a screen of 1977 hydrocarbons/oxygenates within the 8696 compounds in MetaCyc, identifying compounds with high predictive strength for high or low RON.« less

Large-scale high-throughput computer-aided discovery of advanced materials using cloud computing

NASA Astrophysics Data System (ADS)

Bazhirov, Timur; Mohammadi, Mohammad; Ding, Kevin; Barabash, Sergey

Recent advances in cloud computing made it possible to access large-scale computational resources completely on-demand in a rapid and efficient manner. When combined with high fidelity simulations, they serve as an alternative pathway to enable computational discovery and design of new materials through large-scale high-throughput screening. Here, we present a case study for a cloud platform implemented at Exabyte Inc. We perform calculations to screen lightweight ternary alloys for thermodynamic stability. Due to the lack of experimental data for most such systems, we rely on theoretical approaches based on first-principle pseudopotential density functional theory. We calculate the formation energies for a set of ternary compounds approximated by special quasirandom structures. During an example run we were able to scale to 10,656 CPUs within 7 minutes from the start, and obtain results for 296 compounds within 38 hours. The results indicate that the ultimate formation enthalpy of ternary systems can be negative for some of lightweight alloys, including Li and Mg compounds. We conclude that compared to traditional capital-intensive approach that requires in on-premises hardware resources, cloud computing is agile and cost-effective, yet scalable and delivers similar performance.

Discrimination Enhancement with Transient Feature Analysis of a Graphene Chemical Sensor.

PubMed

Nallon, Eric C; Schnee, Vincent P; Bright, Collin J; Polcha, Michael P; Li, Qiliang

2016-01-19

A graphene chemical sensor is subjected to a set of structurally and chemically similar hydrocarbon compounds consisting of toluene, o-xylene, p-xylene, and mesitylene. The fractional change in resistance of the sensor upon exposure to these compounds exhibits a similar response magnitude among compounds, whereas large variation is observed within repetitions for each compound, causing a response overlap. Therefore, traditional features depending on maximum response change will cause confusion during further discrimination and classification analysis. More robust features that are less sensitive to concentration, sampling, and drift variability would provide higher quality information. In this work, we have explored the advantage of using transient-based exponential fitting coefficients to enhance the discrimination of similar compounds. The advantages of such feature analysis to discriminate each compound is evaluated using principle component analysis (PCA). In addition, machine learning-based classification algorithms were used to compare the prediction accuracies when using fitting coefficients as features. The additional features greatly enhanced the discrimination between compounds while performing PCA and also improved the prediction accuracy by 34% when using linear discrimination analysis.

Automated large-scale file preparation, docking, and scoring: evaluation of ITScore and STScore using the 2012 Community Structure-Activity Resource benchmark.

PubMed

Grinter, Sam Z; Yan, Chengfei; Huang, Sheng-You; Jiang, Lin; Zou, Xiaoqin

2013-08-26

In this study, we use the recently released 2012 Community Structure-Activity Resource (CSAR) data set to evaluate two knowledge-based scoring functions, ITScore and STScore, and a simple force-field-based potential (VDWScore). The CSAR data set contains 757 compounds, most with known affinities, and 57 crystal structures. With the help of the script files for docking preparation, we use the full CSAR data set to evaluate the performances of the scoring functions on binding affinity prediction and active/inactive compound discrimination. The CSAR subset that includes crystal structures is used as well, to evaluate the performances of the scoring functions on binding mode and affinity predictions. Within this structure subset, we investigate the importance of accurate ligand and protein conformational sampling and find that the binding affinity predictions are less sensitive to non-native ligand and protein conformations than the binding mode predictions. We also find the full CSAR data set to be more challenging in making binding mode predictions than the subset with structures. The script files used for preparing the CSAR data set for docking, including scripts for canonicalization of the ligand atoms, are offered freely to the academic community.

Structure-activity models of oral clearance, cytotoxicity, and LD50: a screen for promising anticancer compounds

PubMed Central

Boik, John C; Newman, Robert A

2008-01-01

Background Quantitative structure-activity relationship (QSAR) models have become popular tools to help identify promising lead compounds in anticancer drug development. Few QSAR studies have investigated multitask learning, however. Multitask learning is an approach that allows distinct but related data sets to be used in training. In this paper, a suite of three QSAR models is developed to identify compounds that are likely to (a) exhibit cytotoxic behavior against cancer cells, (b) exhibit high rat LD50 values (low systemic toxicity), and (c) exhibit low to modest human oral clearance (favorable pharmacokinetic characteristics). Models were constructed using Kernel Multitask Latent Analysis (KMLA), an approach that can effectively handle a large number of correlated data features, nonlinear relationships between features and responses, and multitask learning. Multitask learning is particularly useful when the number of available training records is small relative to the number of features, as was the case with the oral clearance data. Results Multitask learning modestly but significantly improved the classification precision for the oral clearance model. For the cytotoxicity model, which was constructed using a large number of records, multitask learning did not affect precision but did reduce computation time. The models developed here were used to predict activities for 115,000 natural compounds. Hundreds of natural compounds, particularly in the anthraquinone and flavonoids groups, were predicted to be cytotoxic, have high LD50 values, and have low to moderate oral clearance. Conclusion Multitask learning can be useful in some QSAR models. A suite of QSAR models was constructed and used to screen a large drug library for compounds likely to be cytotoxic to multiple cancer cell lines in vitro, have low systemic toxicity in rats, and have favorable pharmacokinetic properties in humans. PMID:18554402

Structure-activity models of oral clearance, cytotoxicity, and LD50: a screen for promising anticancer compounds.

PubMed

Boik, John C; Newman, Robert A

2008-06-13

Quantitative structure-activity relationship (QSAR) models have become popular tools to help identify promising lead compounds in anticancer drug development. Few QSAR studies have investigated multitask learning, however. Multitask learning is an approach that allows distinct but related data sets to be used in training. In this paper, a suite of three QSAR models is developed to identify compounds that are likely to (a) exhibit cytotoxic behavior against cancer cells, (b) exhibit high rat LD50 values (low systemic toxicity), and (c) exhibit low to modest human oral clearance (favorable pharmacokinetic characteristics). Models were constructed using Kernel Multitask Latent Analysis (KMLA), an approach that can effectively handle a large number of correlated data features, nonlinear relationships between features and responses, and multitask learning. Multitask learning is particularly useful when the number of available training records is small relative to the number of features, as was the case with the oral clearance data. Multitask learning modestly but significantly improved the classification precision for the oral clearance model. For the cytotoxicity model, which was constructed using a large number of records, multitask learning did not affect precision but did reduce computation time. The models developed here were used to predict activities for 115,000 natural compounds. Hundreds of natural compounds, particularly in the anthraquinone and flavonoids groups, were predicted to be cytotoxic, have high LD50 values, and have low to moderate oral clearance. Multitask learning can be useful in some QSAR models. A suite of QSAR models was constructed and used to screen a large drug library for compounds likely to be cytotoxic to multiple cancer cell lines in vitro, have low systemic toxicity in rats, and have favorable pharmacokinetic properties in humans.

New Linear Partitioning Models Based on Experimental Water: Supercritical CO2 Partitioning Data of Selected Organic Compounds.

PubMed

Burant, Aniela; Thompson, Christopher; Lowry, Gregory V; Karamalidis, Athanasios K

2016-05-17

Partitioning coefficients of organic compounds between water and supercritical CO2 (sc-CO2) are necessary to assess the risk of migration of these chemicals from subsurface CO2 storage sites. Despite the large number of potential organic contaminants, the current data set of published water-sc-CO2 partitioning coefficients is very limited. Here, the partitioning coefficients of thiophene, pyrrole, and anisole were measured in situ over a range of temperatures and pressures using a novel pressurized batch-reactor system with dual spectroscopic detectors: a near-infrared spectrometer for measuring the organic analyte in the CO2 phase and a UV detector for quantifying the analyte in the aqueous phase. Our measured partitioning coefficients followed expected trends based on volatility and aqueous solubility. The partitioning coefficients and literature data were then used to update a published poly parameter linear free-energy relationship and to develop five new linear free-energy relationships for predicting water-sc-CO2 partitioning coefficients. A total of four of the models targeted a single class of organic compounds. Unlike models that utilize Abraham solvation parameters, the new relationships use vapor pressure and aqueous solubility of the organic compound at 25 °C and CO2 density to predict partitioning coefficients over a range of temperature and pressure conditions. The compound class models provide better estimates of partitioning behavior for compounds in that class than does the model built for the entire data set.

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.

De Novo Design of Bioactive Small Molecules by Artificial Intelligence

PubMed Central

Merk, Daniel; Friedrich, Lukas; Grisoni, Francesca

2018-01-01

Abstract Generative artificial intelligence offers a fresh view on molecular design. We present the first‐time prospective application of a deep learning model for designing new druglike compounds with desired activities. For this purpose, we trained a recurrent neural network to capture the constitution of a large set of known bioactive compounds represented as SMILES strings. By transfer learning, this general model was fine‐tuned on recognizing retinoid X and peroxisome proliferator‐activated receptor agonists. We synthesized five top‐ranking compounds designed by the generative model. Four of the compounds revealed nanomolar to low‐micromolar receptor modulatory activity in cell‐based assays. Apparently, the computational model intrinsically captured relevant chemical and biological knowledge without the need for explicit rules. The results of this study advocate generative artificial intelligence for prospective de novo molecular design, and demonstrate the potential of these methods for future medicinal chemistry. PMID:29319225

Natural products as modulator of autophagy with potential clinical prospects.

PubMed

Wang, Peiqi; Zhu, Lingjuan; Sun, Dejuan; Gan, Feihong; Gao, Suyu; Yin, Yuanyuan; Chen, Lixia

2017-03-01

Natural compounds derived from living organisms are well defined for their remarkable biological and pharmacological properties likely to be translated into clinical use. Therefore, delving into the mechanisms by which natural compounds protect against diverse diseases may be of great therapeutic benefits for medical practice. Autophagy, an intricate lysosome-dependent digestion process, with implications in a wide variety of pathophysiological settings, has attracted extensive attention over the past few decades. Hitherto, accumulating evidence has revealed that a large number of natural products are involved in autophagy modulation, either inducing or inhibiting autophagy, through multiple signaling pathways and transcriptional regulators. In this review, we summarize natural compounds regulating autophagy in multifarious diseases including cancer, neurodegenerative diseases, cardiovascular diseases, metabolic diseases, and immune diseases, hoping to inspire further investigation of the underlying mechanisms of natural compounds and to facilitate their clinical use for multiple human diseases.

Plant Enhanced Bioremediation of Dissolved Toluene in Large Scale Column Setup

NASA Astrophysics Data System (ADS)

Basu, S.; Yadav, B. K.; Mathur, S.

2016-12-01

Hydrocarbons like BTEX compounds entering the soil-water system through anthropogenic activities can be long lasting sources of pollution, and thus, it is essential to look for remediation options that are environmentally benign. Bioremediation is a promising cost effective technique causing no harm to the contaminated ecosystem as compared to the traditional physicochemical methods. Natural microbes degrade contaminants from polluted soil water resources in bioremediation; however this process of natural bioremediation is quite slow under prevailing environmental conditions of a typical polluted site. Research has also proven that plants play an important role when it comes to accelerate the degradation rate cost-effectively in enhanced bioremediation technique. Thus in this study, fate and transport of dissolved toluene from a source zone to down-gradient receptors in a continuous soil-water plant system was investigated. For this, two sets of large scale column experiments were performed by connecting them with a treatment wetland having canna plants in first set and unplanted gravel bed in the second set. A continuous source of toluene contaminated water was supplied at the top of the column setups. A constant groundwater flow velocity of 0.625 cm/hr was maintained in the vertical direction. Free drainage was allowed at the bottom and a constant hydraulic head of 2.0 cm was maintained at the top boundary throughout the period of the experiments in both the cases. The observed microbial colonies using the plate counting method along with measured dissolved oxygen (DO) proved that the BTEX compound degraded aerobically at a faster rate in the first set. Plants played a positive role in enhancing biodegradation rate of the BTEX compound during its transport through the porous media. Finally the observed data of the column experiments were compared with the breakthrough curves obtained numerically solving the advection dispersion equation. The results of this research can be used to obtain vital information on framing the engineered bioremediation planning for contaminated sites.

Characterization of ToxCast Phase II compounds disruption of ...

EPA Pesticide Factsheets

The development of multi-well microelectrode array (mwMEA) systems has increased in vitro screening throughput making them an effective method to screen and prioritize large sets of compounds for potential neurotoxicity. In the present experiments, a multiplexed approach was used to determine compound effects on both neural function and cell health in primary cortical networks grown on mwMEA plates following exposure to ~1100 compounds from EPA’s Phase II ToxCast libraries. On DIV 13, baseline activity (40 min) was recorded prior to exposure to each compound at 40 µM. DMSO and the GABAA antagonist bicuculline (BIC) were included as controls on each mwMEA plate. Changes in spontaneous network activity (mean firing rate; MFR) and cell viability (lactate dehydrogenase; LDH and CellTiter Blue; CTB) were assessed within the same well following compound exposure. Activity calls (“hits”) were established using the 90th and 20th percentiles of the compound-induced change in MFR (medians of triplicates) across all tested compounds; compounds above (top 10% of compounds increasing MFR), and below (bottom 20% of compounds decreasing MFR) these thresholds, respectively were considered hits. MFR was altered beyond one of these thresholds by 322 compounds. Four compound categories accounted for 66% of the hits, including: insecticides (e.g. abamectin, lindane, prallethrin), pharmaceuticals (e.g. haloperidol, reserpine), fungicides (e.g. hexaconazole, fenamidone), and h

New inhibitor targeting human transcription factor HSF1: effects on the heat shock response and tumor cell survival

PubMed Central

Vilaboa, Nuria; Boré, Alba; Martin-Saavedra, Francisco; Bayford, Melanie; Winfield, Natalie; Firth-Clark, Stuart; Kirton, Stewart B.

2017-01-01

Abstract Comparative modeling of the DNA-binding domain of human HSF1 facilitated the prediction of possible binding pockets for small molecules and definition of corresponding pharmacophores. In silico screening of a large library of lead-like compounds identified a set of compounds that satisfied the pharmacophoric criteria, a selection of which compounds was purchased to populate a biased sublibrary. A discriminating cell-based screening assay identified compound 001, which was subjected to systematic analysis of structure–activity relationships, resulting in the development of compound 115 (IHSF115). IHSF115 bound to an isolated HSF1 DNA-binding domain fragment. The compound did not affect heat-induced oligomerization, nuclear localization and specific DNA binding but inhibited the transcriptional activity of human HSF1, interfering with the assembly of ATF1-containing transcription complexes. IHSF115 was employed to probe the human heat shock response at the transcriptome level. In contrast to earlier studies of differential regulation in HSF1-naïve and -depleted cells, our results suggest that a large majority of heat-induced genes is positively regulated by HSF1. That IHSF115 effectively countermanded repression in a significant fraction of heat-repressed genes suggests that repression of these genes is mediated by transcriptionally active HSF1. IHSF115 is cytotoxic for a variety of human cancer cell lines, multiple myeloma lines consistently exhibiting high sensitivity. PMID:28369544

Modeling of adipose/blood partition coefficient for environmental chemicals.

PubMed

Papadaki, K C; Karakitsios, S P; Sarigiannis, D A

2017-12-01

A Quantitative Structure Activity Relationship (QSAR) model was developed in order to predict the adipose/blood partition coefficient of environmental chemical compounds. The first step of QSAR modeling was the collection of inputs. Input data included the experimental values of adipose/blood partition coefficient and two sets of molecular descriptors for 67 organic chemical compounds; a) the descriptors from Linear Free Energy Relationship (LFER) and b) the PaDEL descriptors. The datasets were split to training and prediction set and were analysed using two statistical methods; Genetic Algorithm based Multiple Linear Regression (GA-MLR) and Artificial Neural Networks (ANN). The models with LFER and PaDEL descriptors, coupled with ANN, produced satisfying performance results. The fitting performance (R 2 ) of the models, using LFER and PaDEL descriptors, was 0.94 and 0.96, respectively. The Applicability Domain (AD) of the models was assessed and then the models were applied to a large number of chemical compounds with unknown values of adipose/blood partition coefficient. In conclusion, the proposed models were checked for fitting, validity and applicability. It was demonstrated that they are stable, reliable and capable to predict the values of adipose/blood partition coefficient of "data poor" chemical compounds that fall within the applicability domain. Copyright © 2017. Published by Elsevier Ltd.

Thermodynamic equilibrium solubility measurements in simulated fluids by 96-well plate method in early drug discovery.

PubMed

Bharate, Sonali S; Vishwakarma, Ram A

2015-04-01

An early prediction of solubility in physiological media (PBS, SGF and SIF) is useful to predict qualitatively bioavailability and absorption of lead candidates. Despite of the availability of multiple solubility estimation methods, none of the reported method involves simplified fixed protocol for diverse set of compounds. Therefore, a simple and medium-throughput solubility estimation protocol is highly desirable during lead optimization stage. The present work introduces a rapid method for assessment of thermodynamic equilibrium solubility of compounds in aqueous media using 96-well microplate. The developed protocol is straightforward to set up and takes advantage of the sensitivity of UV spectroscopy. The compound, in stock solution in methanol, is introduced in microgram quantities into microplate wells followed by drying at an ambient temperature. Microplates were shaken upon addition of test media and the supernatant was analyzed by UV method. A plot of absorbance versus concentration of a sample provides saturation point, which is thermodynamic equilibrium solubility of a sample. The established protocol was validated using a large panel of commercially available drugs and with conventional miniaturized shake flask method (r(2)>0.84). Additionally, the statistically significant QSPR models were established using experimental solubility values of 52 compounds. Copyright © 2015 Elsevier Ltd. All rights reserved.

Searching for substructures in fragment spaces.

PubMed

Ehrlich, Hans-Christian; Volkamer, Andrea; Rarey, Matthias

2012-12-21

A common task in drug development is the selection of compounds fulfilling specific structural features from a large data pool. While several methods that iteratively search through such data sets exist, their application is limited compared to the infinite character of molecular space. The introduction of the concept of fragment spaces (FSs), which are composed of molecular fragments and their connection rules, made the representation of large combinatorial data sets feasible. At the same time, search algorithms face the problem of structural features spanning over multiple fragments. Due to the combinatorial nature of FSs, an enumeration of all products is impossible. In order to overcome these time and storage issues, we present a method that is able to find substructures in FSs without explicit product enumeration. This is accomplished by splitting substructures into subsubstructures and mapping them onto fragments with respect to fragment connectivity rules. The method has been evaluated on three different drug discovery scenarios considering the exploration of a molecule class, the elaboration of decoration patterns for a molecular core, and the exhaustive query for peptides in FSs. FSs can be searched in seconds, and found products contain novel compounds not present in the PubChem database which may serve as hints for new lead structures.

Structural similarity based kriging for quantitative structure activity and property relationship modeling.

PubMed

Teixeira, Ana L; Falcao, Andre O

2014-07-28

Structurally similar molecules tend to have similar properties, i.e. closer molecules in the molecular space are more likely to yield similar property values while distant molecules are more likely to yield different values. Based on this principle, we propose the use of a new method that takes into account the high dimensionality of the molecular space, predicting chemical, physical, or biological properties based on the most similar compounds with measured properties. This methodology uses ordinary kriging coupled with three different molecular similarity approaches (based on molecular descriptors, fingerprints, and atom matching) which creates an interpolation map over the molecular space that is capable of predicting properties/activities for diverse chemical data sets. The proposed method was tested in two data sets of diverse chemical compounds collected from the literature and preprocessed. One of the data sets contained dihydrofolate reductase inhibition activity data, and the second molecules for which aqueous solubility was known. The overall predictive results using kriging for both data sets comply with the results obtained in the literature using typical QSPR/QSAR approaches. However, the procedure did not involve any type of descriptor selection or even minimal information about each problem, suggesting that this approach is directly applicable to a large spectrum of problems in QSAR/QSPR. Furthermore, the predictive results improve significantly with the similarity threshold between the training and testing compounds, allowing the definition of a confidence threshold of similarity and error estimation for each case inferred. The use of kriging for interpolation over the molecular metric space is independent of the training data set size, and no reparametrizations are necessary when more compounds are added or removed from the set, and increasing the size of the database will consequentially improve the quality of the estimations. Finally it is shown that this model can be used for checking the consistency of measured data and for guiding an extension of the training set by determining the regions of the molecular space for which new experimental measurements could be used to maximize the model's predictive performance.

ADME evaluation in drug discovery. 1. Applications of genetic algorithms to the prediction of blood-brain partitioning of a large set of drugs.

PubMed

Hou, Tingjun; Xu, Xiaojie

2002-12-01

In this study, the relationships between the brain-blood concentration ratio of 96 structurally diverse compounds with a large number of structurally derived descriptors were investigated. The linear models were based on molecular descriptors that can be calculated for any compound simply from a knowledge of its molecular structure. The linear correlation coefficients of the models were optimized by genetic algorithms (GAs), and the descriptors used in the linear models were automatically selected from 27 structurally derived descriptors. The GA optimizations resulted in a group of linear models with three or four molecular descriptors with good statistical significance. The change of descriptor use as the evolution proceeds demonstrates that the octane/water partition coefficient and the partial negative solvent-accessible surface area multiplied by the negative charge are crucial to brain-blood barrier permeability. Moreover, we found that the predictions using multiple QSPR models from GA optimization gave quite good results in spite of the diversity of structures, which was better than the predictions using the best single model. The predictions for the two external sets with 37 diverse compounds using multiple QSPR models indicate that the best linear models with four descriptors are sufficiently effective for predictive use. Considering the ease of computation of the descriptors, the linear models may be used as general utilities to screen the blood-brain barrier partitioning of drugs in a high-throughput fashion.

Porous extraction paddle: a solid phase extraction technique for studying the urine metabolome

PubMed Central

Shao, Gang; MacNeil, Michael; Yao, Yuanyuan; Giese, Roger W.

2016-01-01

RATIONALE A method was needed to accomplish solid phase extraction of a large urine volume in a convenient way where resources are limited, towards a goal of metabolome and xenobiotic exposome analysis at another, distant location. METHODS A porous extraction paddle (PEP) was set up, comprising a porous nylon bag containing extraction particles that is flattened and immobilized between two stainless steel meshes. Stirring the PEP after attachment to a shaft of a motor mounted on the lid of the jar containing the urine accomplishes extraction. The bag contained a mixture of nonpolar and partly nonpolar particles to extract a diversity of corresponding compounds. RESULTS Elution of a urine-exposed, water-washed PEP with aqueous methanol containing triethylammonium acetate (conditions intended to give a complete elution), followed by MALDI-TOF/TOF-MS, demonstrated that a diversity of compounds had been extracted ranging from uric acid to peptides. CONCLUSION The PEP allows the user to extract a large liquid sample in a jar simply by turning on a motor. The technique will be helpful in conducting metabolomics and xenobiotic exposome studies of urine, encouraging the extraction of large volumes to set up a convenient repository sample (e.g. 2 g of exposed adsorbent in a cryovial) for shipment and re-analysis in various ways in the future, including scaled-up isolation of unknown chemicals for identification. PMID:27624170

Porous extraction paddle: a solid phase extraction technique for studying the urine metabolome.

PubMed

Shao, Gang; MacNeil, Michael; Yao, Yuanyuan; Giese, Roger W

2016-09-14

A method was needed to accomplish solid phase extraction of a large urine volume in a convenient way where resources are limited, towards a goal of metabolome and xenobiotic exposome analysis at another, distant location. A porous extraction paddle (PEP) was set up, comprising a porous nylon bag containing extraction particles that is flattened and immobilized between two stainless steel meshes. Stirring the PEP after attachment to a shaft of a motor mounted on the lid of the jar containing the urine accomplishes extraction. The bag contained a mixture of nonpolar and partly nonpolar particles to extract a diversity of corresponding compounds. Elution of a urine-exposed, water-washed PEP with aqueous methanol containing triethylammonium acetate (conditions intended to give a complete elution), followed by MALDI-TOF/TOF-MS, demonstrated that a diversity of compounds had been extracted ranging from uric acid to peptides. The PEP allows the user to extract a large liquid sample in a jar simply by turning on a motor. The technique will be helpful in conducting metabolomics and xenobiotic exposome studies of urine, encouraging the extraction of large volumes to set up a convenient repository sample (e.g. 2 g of exposed adsorbent in a cryovial) for shipment and re-analysis in various ways in the future, including scaled-up isolation of unknown chemicals for identification. This article is protected by copyright. All rights reserved.

Reverse bifurcation and fractal of the compound logistic map

NASA Astrophysics Data System (ADS)

Wang, Xingyuan; Liang, Qingyong

2008-07-01

The nature of the fixed points of the compound logistic map is researched and the boundary equation of the first bifurcation of the map in the parameter space is given out. Using the quantitative criterion and rule of chaotic system, the paper reveal the general features of the compound logistic map transforming from regularity to chaos, the following conclusions are shown: (1) chaotic patterns of the map may emerge out of double-periodic bifurcation and (2) the chaotic crisis phenomena and the reverse bifurcation are found. At the same time, we analyze the orbit of critical point of the compound logistic map and put forward the definition of Mandelbrot-Julia set of compound logistic map. We generalize the Welstead and Cromer's periodic scanning technology and using this technology construct a series of Mandelbrot-Julia sets of compound logistic map. We investigate the symmetry of Mandelbrot-Julia set and study the topological inflexibility of distributing of period region in the Mandelbrot set, and finds that Mandelbrot set contain abundant information of structure of Julia sets by founding the whole portray of Julia sets based on Mandelbrot set qualitatively.

Multi-view 3D echocardiography compounding based on feature consistency

NASA Astrophysics Data System (ADS)

Yao, Cheng; Simpson, John M.; Schaeffter, Tobias; Penney, Graeme P.

2011-09-01

Echocardiography (echo) is a widely available method to obtain images of the heart; however, echo can suffer due to the presence of artefacts, high noise and a restricted field of view. One method to overcome these limitations is to use multiple images, using the 'best' parts from each image to produce a higher quality 'compounded' image. This paper describes our compounding algorithm which specifically aims to reduce the effect of echo artefacts as well as improving the signal-to-noise ratio, contrast and extending the field of view. Our method weights image information based on a local feature coherence/consistency between all the overlapping images. Validation has been carried out using phantom, volunteer and patient datasets consisting of up to ten multi-view 3D images. Multiple sets of phantom images were acquired, some directly from the phantom surface, and others by imaging through hard and soft tissue mimicking material to degrade the image quality. Our compounding method is compared to the original, uncompounded echocardiography images, and to two basic statistical compounding methods (mean and maximum). Results show that our method is able to take a set of ten images, degraded by soft and hard tissue artefacts, and produce a compounded image of equivalent quality to images acquired directly from the phantom. Our method on phantom, volunteer and patient data achieves almost the same signal-to-noise improvement as the mean method, while simultaneously almost achieving the same contrast improvement as the maximum method. We show a statistically significant improvement in image quality by using an increased number of images (ten compared to five), and visual inspection studies by three clinicians showed very strong preference for our compounded volumes in terms of overall high image quality, large field of view, high endocardial border definition and low cavity noise.

Applying Mondrian Cross-Conformal Prediction To Estimate Prediction Confidence on Large Imbalanced Bioactivity Data Sets.

PubMed

Sun, Jiangming; Carlsson, Lars; Ahlberg, Ernst; Norinder, Ulf; Engkvist, Ola; Chen, Hongming

2017-07-24

Conformal prediction has been proposed as a more rigorous way to define prediction confidence compared to other application domain concepts that have earlier been used for QSAR modeling. One main advantage of such a method is that it provides a prediction region potentially with multiple predicted labels, which contrasts to the single valued (regression) or single label (classification) output predictions by standard QSAR modeling algorithms. Standard conformal prediction might not be suitable for imbalanced data sets. Therefore, Mondrian cross-conformal prediction (MCCP) which combines the Mondrian inductive conformal prediction with cross-fold calibration sets has been introduced. In this study, the MCCP method was applied to 18 publicly available data sets that have various imbalance levels varying from 1:10 to 1:1000 (ratio of active/inactive compounds). Our results show that MCCP in general performed well on bioactivity data sets with various imbalance levels. More importantly, the method not only provides confidence of prediction and prediction regions compared to standard machine learning methods but also produces valid predictions for the minority class. In addition, a compound similarity based nonconformity measure was investigated. Our results demonstrate that although it gives valid predictions, its efficiency is much worse than that of model dependent metrics.

Multiple machine learning based descriptive and predictive workflow for the identification of potential PTP1B inhibitors.

PubMed

Chandra, Sharat; Pandey, Jyotsana; Tamrakar, Akhilesh Kumar; Siddiqi, Mohammad Imran

2017-01-01

In insulin and leptin signaling pathway, Protein-Tyrosine Phosphatase 1B (PTP1B) plays a crucial controlling role as a negative regulator, which makes it an attractive therapeutic target for both Type-2 Diabetes (T2D) and obesity. In this work, we have generated classification models by using the inhibition data set of known PTP1B inhibitors to identify new inhibitors of PTP1B utilizing multiple machine learning techniques like naïve Bayesian, random forest, support vector machine and k-nearest neighbors, along with structural fingerprints and selected molecular descriptors. Several models from each algorithm have been constructed and optimized, with the different combination of molecular descriptors and structural fingerprints. For the training and test sets, most of the predictive models showed more than 90% of overall prediction accuracies. The best model was obtained with support vector machine approach and has Matthews Correlation Coefficient of 0.82 for the external test set, which was further employed for the virtual screening of Maybridge small compound database. Five compounds were subsequently selected for experimental assay. Out of these two compounds were found to inhibit PTP1B with significant inhibitory activity in in-vitro inhibition assay. The structural fragments which are important for PTP1B inhibition were identified by naïve Bayesian method and can be further exploited to design new molecules around the identified scaffolds. The descriptive and predictive modeling strategy applied in this study is capable of identifying PTP1B inhibitors from the large compound libraries. Copyright © 2016 Elsevier Inc. All rights reserved.

Rapid Scanning Structure-Activity Relationships in Combinatorial Data Sets: Identification of Activity Switches

PubMed Central

Medina-Franco, José L.; Edwards, Bruce S.; Pinilla, Clemencia; Appel, Jon R.; Giulianotti, Marc A.; Santos, Radleigh G.; Yongye, Austin B.; Sklar, Larry A.; Houghten, Richard A.

2013-01-01

We present a general approach to describe the structure-activity relationships (SAR) of combinatorial data sets with activity for two biological endpoints with emphasis on the rapid identification of substitutions that have a large impact on activity and selectivity. The approach uses Dual-Activity Difference (DAD) maps that represent a visual and quantitative analysis of all pairwise comparisons of one, two, or more substitutions around a molecular template. Scanning the SAR of data sets using DAD maps allows the visual and quantitative identification of activity switches defined as specific substitutions that have an opposite effect on the activity of the compounds against two targets. The approach also rapidly identifies single- and double-target R-cliffs, i.e., compounds where a single or double substitution around the central scaffold dramatically modifies the activity for one or two targets, respectively. The approach introduced in this report can be applied to any analogue series with two biological activity endpoints. To illustrate the approach, we discuss the SAR of 106 pyrrolidine bis-diketopiperazines tested against two formylpeptide receptors obtained from positional scanning deconvolution methods of mixture-based libraries. PMID:23705689

Metabolome searcher: a high throughput tool for metabolite identification and metabolic pathway mapping directly from mass spectrometry and using genome restriction.

PubMed

Dhanasekaran, A Ranjitha; Pearson, Jon L; Ganesan, Balasubramanian; Weimer, Bart C

2015-02-25

Mass spectrometric analysis of microbial metabolism provides a long list of possible compounds. Restricting the identification of the possible compounds to those produced by the specific organism would benefit the identification process. Currently, identification of mass spectrometry (MS) data is commonly done using empirically derived compound databases. Unfortunately, most databases contain relatively few compounds, leaving long lists of unidentified molecules. Incorporating genome-encoded metabolism enables MS output identification that may not be included in databases. Using an organism's genome as a database restricts metabolite identification to only those compounds that the organism can produce. To address the challenge of metabolomic analysis from MS data, a web-based application to directly search genome-constructed metabolic databases was developed. The user query returns a genome-restricted list of possible compound identifications along with the putative metabolic pathways based on the name, formula, SMILES structure, and the compound mass as defined by the user. Multiple queries can be done simultaneously by submitting a text file created by the user or obtained from the MS analysis software. The user can also provide parameters specific to the experiment's MS analysis conditions, such as mass deviation, adducts, and detection mode during the query so as to provide additional levels of evidence to produce the tentative identification. The query results are provided as an HTML page and downloadable text file of possible compounds that are restricted to a specific genome. Hyperlinks provided in the HTML file connect the user to the curated metabolic databases housed in ProCyc, a Pathway Tools platform, as well as the KEGG Pathway database for visualization and metabolic pathway analysis. Metabolome Searcher, a web-based tool, facilitates putative compound identification of MS output based on genome-restricted metabolic capability. This enables researchers to rapidly extend the possible identifications of large data sets for metabolites that are not in compound databases. Putative compound names with their associated metabolic pathways from metabolomics data sets are returned to the user for additional biological interpretation and visualization. This novel approach enables compound identification by restricting the possible masses to those encoded in the genome.

DNA-encoded chemical libraries: advancing beyond conventional small-molecule libraries.

PubMed

Franzini, Raphael M; Neri, Dario; Scheuermann, Jörg

2014-04-15

DNA-encoded chemical libraries (DECLs) represent a promising tool in drug discovery. DECL technology allows the synthesis and screening of chemical libraries of unprecedented size at moderate costs. In analogy to phage-display technology, where large antibody libraries are displayed on the surface of filamentous phage and are genetically encoded in the phage genome, DECLs feature the display of individual small organic chemical moieties on DNA fragments serving as amplifiable identification barcodes. The DNA-tag facilitates the synthesis and allows the simultaneous screening of very large sets of compounds (up to billions of molecules), because the hit compounds can easily be identified and quantified by PCR-amplification of the DNA-barcode followed by high-throughput DNA sequencing. Several approaches have been used to generate DECLs, differing both in the methods used for library encoding and for the combinatorial assembly of chemical moieties. For example, DECLs can be used for fragment-based drug discovery, displaying a single molecule on DNA or two chemical moieties at the extremities of complementary DNA strands. DECLs can vary substantially in the chemical structures and the library size. While ultralarge libraries containing billions of compounds have been reported containing four or more sets of building blocks, also smaller libraries have been shown to be efficient for ligand discovery. In general, it has been found that the overall library size is a poor predictor for library performance and that the number and diversity of the building blocks are rather important indicators. Smaller libraries consisting of two to three sets of building blocks better fulfill the criteria of drug-likeness and often have higher quality. In this Account, we present advances in the DECL field from proof-of-principle studies to practical applications for drug discovery, both in industry and in academia. DECL technology can yield specific binders to a variety of target proteins and is likely to become a standard tool for pharmaceutical hit discovery, lead expansion, and Chemical Biology research. The introduction of new methodologies for library encoding and for compound synthesis in the presence of DNA is an exciting research field and will crucially contribute to the performance and the propagation of the technology.

Designing a multiroute synthesis scheme in combinatorial chemistry.

PubMed

Akavia, Adi; Senderowitz, Hanoch; Lerner, Alon; Shamir, Ron

2004-01-01

Solid-phase mix-and-split combinatorial synthesis is often used to produce large arrays of compounds to be tested during the various stages of the drug development process. This method can be represented by a synthesis graph in which nodes correspond to grow operations and arcs to beads transferred among the different reaction vessels. In this work, we address the problem of designing such a graph which maximizes the number of produced target compounds (namely, compounds out of an input library of desired molecules), given constraints on the number of beads used for library synthesis and on the number of reaction vessels available for concurrent grow steps. We present a heuristic based on a discrete search for solving this problem, test our solution on several data sets, explore its behavior, and show that it achieves good performance.

High-throughput high-volume nuclear imaging for preclinical in vivo compound screening§.

PubMed

Macholl, Sven; Finucane, Ciara M; Hesterman, Jacob; Mather, Stephen J; Pauplis, Rachel; Scully, Deirdre; Sosabowski, Jane K; Jouannot, Erwan

2017-12-01

Preclinical single-photon emission computed tomography (SPECT)/CT imaging studies are hampered by low throughput, hence are found typically within small volume feasibility studies. Here, imaging and image analysis procedures are presented that allow profiling of a large volume of radiolabelled compounds within a reasonably short total study time. Particular emphasis was put on quality control (QC) and on fast and unbiased image analysis. 2-3 His-tagged proteins were simultaneously radiolabelled by 99m Tc-tricarbonyl methodology and injected intravenously (20 nmol/kg; 100 MBq; n = 3) into patient-derived xenograft (PDX) mouse models. Whole-body SPECT/CT images of 3 mice simultaneously were acquired 1, 4, and 24 h post-injection, extended to 48 h and/or by 0-2 h dynamic SPECT for pre-selected compounds. Organ uptake was quantified by automated multi-atlas and manual segmentations. Data were plotted automatically, quality controlled and stored on a collaborative image management platform. Ex vivo uptake data were collected semi-automatically and analysis performed as for imaging data. >500 single animal SPECT images were acquired for 25 proteins over 5 weeks, eventually generating >3500 ROI and >1000 items of tissue data. SPECT/CT images clearly visualized uptake in tumour and other tissues even at 48 h post-injection. Intersubject uptake variability was typically 13% (coefficient of variation, COV). Imaging results correlated well with ex vivo data. The large data set of tumour, background and systemic uptake/clearance data from 75 mice for 25 compounds allows identification of compounds of interest. The number of animals required was reduced considerably by longitudinal imaging compared to dissection experiments. All experimental work and analyses were accomplished within 3 months expected to be compatible with drug development programmes. QC along all workflow steps, blinding of the imaging contract research organization to compound properties and automation provide confidence in the data set. Additional ex vivo data were useful as a control but could be omitted from future studies in the same centre. For even larger compound libraries, radiolabelling could be expedited and the number of imaging time points adapted to increase weekly throughput. Multi-atlas segmentation could be expanded via SPECT/MRI; however, this would require an MRI-compatible mouse hotel. Finally, analysis of nuclear images of radiopharmaceuticals in clinical trials may benefit from the automated analysis procedures developed.

De Novo Design of Bioactive Small Molecules by Artificial Intelligence.

PubMed

Merk, Daniel; Friedrich, Lukas; Grisoni, Francesca; Schneider, Gisbert

2018-01-01

Generative artificial intelligence offers a fresh view on molecular design. We present the first-time prospective application of a deep learning model for designing new druglike compounds with desired activities. For this purpose, we trained a recurrent neural network to capture the constitution of a large set of known bioactive compounds represented as SMILES strings. By transfer learning, this general model was fine-tuned on recognizing retinoid X and peroxisome proliferator-activated receptor agonists. We synthesized five top-ranking compounds designed by the generative model. Four of the compounds revealed nanomolar to low-micromolar receptor modulatory activity in cell-based assays. Apparently, the computational model intrinsically captured relevant chemical and biological knowledge without the need for explicit rules. The results of this study advocate generative artificial intelligence for prospective de novo molecular design, and demonstrate the potential of these methods for future medicinal chemistry. © 2018 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

Biconnectivity of the cellular metabolism: A cross-species study and its implication for human diseases

PubMed Central

Kim, P.; Lee, D.-S.; Kahng, B.

2015-01-01

The maintenance of stability during perturbations is essential for living organisms, and cellular networks organize multiple pathways to enable elements to remain connected and communicate, even when some pathways are broken. Here, we evaluated the biconnectivity of the metabolic networks of 506 species in terms of the clustering coefficients and the largest biconnected components (LBCs), wherein a biconnected component (BC) indicates a set of nodes in which every pair is connected by more than one path. Via comparison with the rewired networks, we illustrated how biconnectivity in cellular metabolism is achieved on small and large scales. Defining the biconnectivity of individual metabolic compounds by counting the number of species in which the compound belonged to the LBC, we demonstrated that biconnectivity is significantly correlated with the evolutionary age and functional importance of a compound. The prevalence of diseases associated with each metabolic compound quantifies the compounds vulnerability, i.e., the likelihood that it will cause a metabolic disorder. Moreover, the vulnerability depends on both the biconnectivity and the lethality of the compound. This fact can be used in drug discovery and medical treatments. PMID:26490723

Compilation and physicochemical classification analysis of a diverse hERG inhibition database

NASA Astrophysics Data System (ADS)

Didziapetris, Remigijus; Lanevskij, Kiril

2016-12-01

A large and chemically diverse hERG inhibition data set comprised of 6690 compounds was constructed on the basis of ChEMBL bioactivity database and original publications dealing with experimental determination of hERG activities using patch-clamp and competitive displacement assays. The collected data were converted to binary format at 10 µM activity threshold and subjected to gradient boosting machine classification analysis using a minimal set of physicochemical and topological descriptors. The tested parameters involved lipophilicity (log P), ionization (p K a ), polar surface area, aromaticity, molecular size and flexibility. The employed approach allowed classifying the compounds with an overall 75-80 % accuracy, even though it only accounted for non-specific interactions between hERG and ligand molecules. The observed descriptor-response profiles were consistent with common knowledge about hERG ligand binding site, but also revealed several important quantitative trends, as well as slight inter-assay variability in hERG inhibition data. The results suggest that even weakly basic groups (p K a < 6) might substantially contribute to hERG inhibition potential, whereas the role of lipophilicity depends on the compound's ionization state, and the influence of log P decreases in the order of bases > zwitterions > neutrals > acids. Given its robust performance and clear physicochemical interpretation, the proposed model may provide valuable information to direct drug discovery efforts towards compounds with reduced risk of hERG-related cardiotoxicity.

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

Gilchrist, Kristin H., E-mail: kgilchrist@rti.org; Lewis, Gregory F.; Gay, Elaine A.

Microelectrode arrays (MEAs) recording extracellular field potentials of human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CM) provide a rich data set for functional assessment of drug response. The aim of this work is the development of a method for a systematic analysis of arrhythmia using MEAs, with emphasis on the development of six parameters accounting for different types of cardiomyocyte signal irregularities. We describe a software approach to carry out such analysis automatically including generation of a heat map that enables quick visualization of arrhythmic liability of compounds. We also implemented signal processing techniques for reliable extraction of the repolarization peak formore » field potential duration (FPD) measurement even from recordings with low signal to noise ratios. We measured hiPS-CM's on a 48 well MEA system with 5 minute recordings at multiple time points (0.5, 1, 2 and 4 h) after drug exposure. We evaluated concentration responses for seven compounds with a combination of hERG, QT and clinical proarrhythmia properties: Verapamil, Ranolazine, Flecainide, Amiodarone, Ouabain, Cisapride, and Terfenadine. The predictive utility of MEA parameters as surrogates of these clinical effects were examined. The beat rate and FPD results exhibited good correlations with previous MEA studies in stem cell derived cardiomyocytes and clinical data. The six-parameter arrhythmia assessment exhibited excellent predictive agreement with the known arrhythmogenic potential of the tested compounds, and holds promise as a new method to predict arrhythmic liability. - Highlights: • Six parameters describing arrhythmia were defined and measured for known compounds. • Software for efficient parameter extraction from large MEA data sets was developed. • The proposed cellular parameter set is predictive of clinical drug proarrhythmia.« less

Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine

PubMed Central

Yuan, Hua; Huang, Jianping; Cao, Chenzhong

2009-01-01

Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT) and murine local lymph node assay (LLNA) are the two most important in vivo models for identification of skin sensitizers. In order to reduce the number of animal tests, quantitative structure-activity relationships (QSARs) are strongly encouraged in the assessment of skin sensitization of chemicals. This paper has investigated the skin sensitization potential of 162 compounds with LLNA results and 92 compounds with GPMT results using a support vector machine. A particle swarm optimization algorithm was implemented for feature selection from a large number of molecular descriptors calculated by Dragon. For the LLNA data set, the classification accuracies are 95.37% and 88.89% for the training and the test sets, respectively. For the GPMT data set, the classification accuracies are 91.80% and 90.32% for the training and the test sets, respectively. The classification performances were greatly improved compared to those reported in the literature, indicating that the support vector machine optimized by particle swarm in this paper is competent for the identification of skin sensitizers. PMID:19742136

DESCRIPTIVE ANALYSIS OF DIVALENT SALTS

PubMed Central

YANG, HEIDI HAI-LING; LAWLESS, HARRY T.

2005-01-01

Many divalent salts (e.g., calcium, iron, zinc), have important nutritional value and are used to fortify food or as dietary supplements. Sensory characterization of some divalent salts in aqueous solutions by untrained judges has been reported in the psychophysical literature, but formal sensory evaluation by trained panels is lacking. To provide this information, a trained descriptive panel evaluated the sensory characteristics of 10 divalent salts including ferrous sulfate, chloride and gluconate; calcium chloride, lactate and glycerophosphate; zinc sulfate and chloride; and magnesium sulfate and chloride. Among the compounds tested, iron compounds were highest in metallic taste; zinc compounds had higher astringency and a glutamate-like sensation; and bitterness was pronounced for magnesium and calcium salts. Bitterness was affected by the anion in ferrous and calcium salts. Results from the trained panelists were largely consistent with the psychophysical literature using untrained judges, but provided a more comprehensive set of oral sensory attributes. PMID:16614749

Rational Design of Glycomimetic Compounds Targeting the Saccharomyces cerevisiae Transglycosylase Gas2.

PubMed

Delso, Ignacio; Valero-González, Jessika; Marca, Eduardo; Tejero, Tomás; Hurtado-Guerrero, Ramón; Merino, Pedro

2016-02-01

The transglycosylase Saccharomyces cerevisiae Gas2 (ScGas2) belongs to a large family of enzymes that are key players in yeast cell wall remodeling. Despite its biologic importance, no studies on the synthesis of substrate-based compounds as potential inhibitors have been reported. We have synthesized a series of docking-guided glycomimetics that were evaluated by fluorescence spectroscopy and saturation-transfer difference (STD) NMR experiments, revealing that a minimum of three glucose units linked via a β-(1,3) linkage are required for achieving molecular recognition at the binding donor site. The binding mode of our compounds is further supported by STD-NMR experiments using the active site-mutants Y107Q and Y244Q. Our results are important for both understanding of ScGas2-substrate interactions and setting up the basis for future design of glycomimetics as new antifungal agents. © 2015 John Wiley & Sons A/S.

Organics, Isotopes, and Volatiles in Gale Crater Sedimentary Rocks

NASA Astrophysics Data System (ADS)

Mahaffy, P. R.

2016-12-01

Solid samples analyzed by the Curiosity rover on the long traverse from the Gale crater floor to the flanks of Mt. Sharp spread a range of environments from fluvial to lacustrine to eolian, and span 100 m of stratigraphic thickness. The diverse chemical and isotopic composition of organic compounds and inorganic volatiles revealed in these samples analyzed over a period of more than 2 Mars years is described with an emphasis on the search for organics, the chemical environments and physical-chemical processes that respectively preserve or destroy organics, and unexpectedly large variations in H, S, and Cl isotopes. In addition to a set of aromatic and aliphatic chorine containing organic compounds thermally released from the Cumberland mudstone drilled early in the mission compounds [Freissinet et al., 2015], additional S-containing organics have been identified in the Mojave drill sample in the Pahrump Hills section that was characterized in detail over a 5 month period. This set of S and Cl containing compounds is definitively identified by gas chromatograph mass spectrometer (GCMS) analyses. In addition, fragments of other organic compounds are evident in the evolved gas analysis (EGA) experiments implemented by the Sample Analysis at Mars (SAM) instrument and utilization of SAM's derivatization agent has revealed the presence of high molecular weight compounds. Two factors complicate the search for organic compounds preserved from ancient Mars. First the nearly ubiquitous oxychlorine compounds such as perchlorates decompose on heating in the SAM ovens in the EGA experiments and there is evidence that the hot O2 released combusts organic compounds to produce CO2. Secondly, the cosmic radiation that penetrates through the thin Mars atmosphere meters into the surface transforms near surface organic compounds over time. Fortunately, the SAM mass spectrometer can measure spallogenic (3He and 21Ne) and neutron-capture (36Ar) noble gases to secure an estimate of the duration of radiation exposure. Measurement protocols developed to work around both of these limitations will be discussed. C. Freissinet et al, JGR (2015) 120(3), 495-514.

Combining Computational Methods for Hit to Lead Optimization in Mycobacterium tuberculosis Drug Discovery

PubMed Central

Ekins, Sean; Freundlich, Joel S.; Hobrath, Judith V.; White, E. Lucile; Reynolds, Robert C

2013-01-01

Purpose Tuberculosis treatments need to be shorter and overcome drug resistance. Our previous large scale phenotypic high-throughput screening against Mycobacterium tuberculosis (Mtb) has identified 737 active compounds and thousands that are inactive. We have used this data for building computational models as an approach to minimize the number of compounds tested. Methods A cheminformatics clustering approach followed by Bayesian machine learning models (based on publicly available Mtb screening data) was used to illustrate that application of these models for screening set selections can enrich the hit rate. Results In order to explore chemical diversity around active cluster scaffolds of the dose-response hits obtained from our previous Mtb screens a set of 1924 commercially available molecules have been selected and evaluated for antitubercular activity and cytotoxicity using Vero, THP-1 and HepG2 cell lines with 4.3%, 4.2% and 2.7% hit rates, respectively. We demonstrate that models incorporating antitubercular and cytotoxicity data in Vero cells can significantly enrich the selection of non-toxic actives compared to random selection. Across all cell lines, the Molecular Libraries Small Molecule Repository (MLSMR) and cytotoxicity model identified ~10% of the hits in the top 1% screened (>10 fold enrichment). We also showed that seven out of nine Mtb active compounds from different academic published studies and eight out of eleven Mtb active compounds from a pharmaceutical screen (GSK) would have been identified by these Bayesian models. Conclusion Combining clustering and Bayesian models represents a useful strategy for compound prioritization and hit-to lead optimization of antitubercular agents. PMID:24132686

Natural product-like virtual libraries: recursive atom-based enumeration.

PubMed

Yu, Melvin J

2011-03-28

A new molecular enumerator is described that allows chemically and architecturally diverse sets of natural product-like and drug-like structures to be generated from a core structure as simple as a single carbon atom or as complex as a polycyclic ring system. Integrated with a rudimentary machine-learning algorithm, the enumerator has the ability to assemble biased virtual libraries enriched in compounds predicted to meet target criteria. The ability to dynamically generate relatively small focused libraries in a recursive manner could reduce the computational time and infrastructure necessary to construct and manage extremely large static libraries. Depending on enumeration conditions, natural product-like structures can be produced with a wide range of heterocyclic and alicyclic ring assemblies. Because natural products represent a proven source of validated structures for identifying and designing new drug candidates, mimicking the structural and topological diversity found in nature with a dynamic set of virtual natural product-like compounds may facilitate the creation of new ideas for novel, biologically relevant lead structures in areas of uncharted chemical space.

Thermal degradation products formed from carotenoids during a heat-induced degradation process of paprika oleoresins (Capsicum annuum L.).

PubMed

Pérez-Gálvez, Antonio; Rios, José J; Mínguez-Mosquera, María Isabel

2005-06-15

The high-temperature treatment of paprika oleoresins (Capsicum annuum L.) modified the carotenoid profile, yielding several degradation products, which were analyzed by HPLC-APCI-MS. From the initial MS data, compounds were grouped in two sets. Set 1 grouped compounds with m/z 495, and set 2 included compounds with m/z 479, in both cases for the protonated molecular mass. Two compounds of the first set were tentatively identified as 9,10,11,12,13,14,19,20-octanor-capsorubin (compound II) and 9,10,11,12,13,14,19,20-octanor-5,6-epoxide-capsanthin (compound IV), after isolation by semipreparative HPLC and analysis by EI-MS. Compounds VII, VIII, and IX from set 2 were assigned as 9,10,11,12,13,14,19,20-octanor-capsanthin and isomers, respectively. As these compounds were the major products formed in the thermal process, it was possible to apply derivatization techniques (hydrogenation and silylation) to analyze them by EI-MS, before and after chemical derivatization. Taking into account structures of the degradation products, the cyclization of polyolefins could be considered as the general reaction pathway in thermally induced reactions, yielding in the present study xylene as byproduct and the corresponding nor-carotenoids.

Predicting novel substrates for enzymes with minimal experimental effort with active learning

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

Pertusi, Dante A.; Moura, Matthew E.; Jeffryes, James G.

Enzymatic substrate promiscuity is more ubiquitous than previously thought, with significant consequences for understanding metabolism and its application to biocatalysis. This realization has given rise to the need for efficient characterization of enzyme promiscuity. Enzyme promiscuity is currently characterized with a limited number of human-selected compounds that may not be representative of the enzyme's versatility. While testing large numbers of compounds may be impractical, computational approaches can exploit existing data to determine the most informative substrates to test next, thereby more thoroughly exploring an enzyme's versatility. To demonstrate this, we used existing studies and tested compounds for four different enzymes,more » developed support vector machine (SVM) models using these datasets, and selected additional compounds for experiments using an active learning approach. SVMs trained on a chemically diverse set of compounds were discovered to achieve maximum accuracies of similar to 80% using similar to 33% fewer compounds than datasets based on all compounds tested in existing studies. Active learning-selected compounds for testing resolved apparent conflicts in the existing training data, while adding diversity to the dataset. The application of these algorithms to wide arrays of metabolic enzymes would result in a library of SVMs that can predict high-probability promiscuous enzymatic reactions and could prove a valuable resource for the design of novel metabolic pathways.« less

Predicting novel substrates for enzymes with minimal experimental effort with active learning.

PubMed

Pertusi, Dante A; Moura, Matthew E; Jeffryes, James G; Prabhu, Siddhant; Walters Biggs, Bradley; Tyo, Keith E J

2017-11-01

Enzymatic substrate promiscuity is more ubiquitous than previously thought, with significant consequences for understanding metabolism and its application to biocatalysis. This realization has given rise to the need for efficient characterization of enzyme promiscuity. Enzyme promiscuity is currently characterized with a limited number of human-selected compounds that may not be representative of the enzyme's versatility. While testing large numbers of compounds may be impractical, computational approaches can exploit existing data to determine the most informative substrates to test next, thereby more thoroughly exploring an enzyme's versatility. To demonstrate this, we used existing studies and tested compounds for four different enzymes, developed support vector machine (SVM) models using these datasets, and selected additional compounds for experiments using an active learning approach. SVMs trained on a chemically diverse set of compounds were discovered to achieve maximum accuracies of ~80% using ~33% fewer compounds than datasets based on all compounds tested in existing studies. Active learning-selected compounds for testing resolved apparent conflicts in the existing training data, while adding diversity to the dataset. The application of these algorithms to wide arrays of metabolic enzymes would result in a library of SVMs that can predict high-probability promiscuous enzymatic reactions and could prove a valuable resource for the design of novel metabolic pathways. Copyright © 2017 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis.

PubMed

Ekins, Sean; Kaneko, Takushi; Lipinski, Christopher A; Bradford, Justin; Dole, Krishna; Spektor, Anna; Gregory, Kellan; Blondeau, David; Ernst, Sylvia; Yang, Jeremy; Goncharoff, Nicko; Hohman, Moses M; Bunin, Barry A

2010-11-01

There is an urgent need for new drugs against tuberculosis which annually claims 1.7-1.8 million lives. One approach to identify potential leads is to screen in vitro small molecules against Mycobacterium tuberculosis (Mtb). Until recently there was no central repository to collect information on compounds screened. Consequently, it has been difficult to analyze molecular properties of compounds that inhibit the growth of Mtb in vitro. We have collected data from publically available sources on over 300 000 small molecules deposited in the Collaborative Drug Discovery TB Database. A cheminformatics analysis on these compounds indicates that inhibitors of the growth of Mtb have statistically higher mean logP, rule of 5 alerts, while also having lower HBD count, atom count and lower PSA (ChemAxon descriptors), compared to compounds that are classed as inactive. Additionally, Bayesian models for selecting Mtb active compounds were evaluated with over 100 000 compounds and, they demonstrated 10 fold enrichment over random for the top ranked 600 compounds. This represents a promising approach for finding compounds active against Mtb in whole cells screened under the same in vitro conditions. Various sets of Mtb hit molecules were also examined by various filtering rules used widely in the pharmaceutical industry to identify compounds with potentially reactive moieties. We found differences between the number of compounds flagged by these rules in Mtb datasets, malaria hits, FDA approved drugs and antibiotics. Combining these approaches may enable selection of compounds with increased probability of inhibition of whole cell Mtb activity.

Preclinical dose number and its application in understanding drug absorption risk and formulation design for preclinical species.

PubMed

Wuelfing, W Peter; Daublain, Pierre; Kesisoglou, Filippos; Templeton, Allen; McGregor, Caroline

2015-04-06

In the drug discovery setting, the ability to rapidly identify drug absorption risk in preclinical species at high doses from easily measured physical properties is desired. This is due to the large number of molecules being evaluated and their high attrition rate, which make resource-intensive in vitro and in silico evaluation unattractive. High-dose in vivo data from rat, dog, and monkey are analyzed here, using a preclinical dose number (PDo) concept based on the dose number described by Amidon and other authors (Pharm. Res., 1993, 10, 264-270). PDo, as described in this article, is simply calculated as dose (mg/kg) divided by compound solubility in FaSSIF (mg/mL) and approximates the volume of biorelevant media per kilogram of animal that would be needed to fully dissolve the dose. High PDo values were found to be predictive of difficulty in achieving drug exposure (AUC)-dose proportionality in in vivo studies, as could be expected; however, this work analyzes a large data set (>900 data points) and provides quantitative guidance to identify drug absorption risk in preclinical species based on a single solubility measurement commonly carried out in drug discovery. Above the PDo values defined, >50% of all in vivo studies exhibited poor AUC-dose proportionality in rat, dog, and monkey, and these values can be utilized as general guidelines in discovery and early development to rapidly assess risk of solubility-limited absorption for a given compound. A preclinical dose number generated by biorelevant dilutions of formulated compounds (formulated PDo) was also evaluated and defines solubility targets predictive of suitable AUC-dose proportionality in formulation development efforts. Application of these guidelines can serve to efficiently identify compounds in discovery that are likely to present extreme challenges with respect to solubility-limited absorption in preclinical species as well as reduce the testing of poor formulations in vivo, which is a key ethical and resource matter.

Combinatorial support vector machines approach for virtual screening of selective multi-target serotonin reuptake inhibitors from large compound libraries.

PubMed

Shi, Z; Ma, X H; Qin, C; Jia, J; Jiang, Y Y; Tan, C Y; Chen, Y Z

2012-02-01

Selective multi-target serotonin reuptake inhibitors enhance antidepressant efficacy. Their discovery can be facilitated by multiple methods, including in silico ones. In this study, we developed and tested an in silico method, combinatorial support vector machines (COMBI-SVMs), for virtual screening (VS) multi-target serotonin reuptake inhibitors of seven target pairs (serotonin transporter paired with noradrenaline transporter, H(3) receptor, 5-HT(1A) receptor, 5-HT(1B) receptor, 5-HT(2C) receptor, melanocortin 4 receptor and neurokinin 1 receptor respectively) from large compound libraries. COMBI-SVMs trained with 917-1951 individual target inhibitors correctly identified 22-83.3% (majority >31.1%) of the 6-216 dual inhibitors collected from literature as independent testing sets. COMBI-SVMs showed moderate to good target selectivity in misclassifying as dual inhibitors 2.2-29.8% (majority <15.4%) of the individual target inhibitors of the same target pair and 0.58-7.1% of the other 6 targets outside the target pair. COMBI-SVMs showed low dual inhibitor false hit rates (0.006-0.056%, 0.042-0.21%, 0.2-4%) in screening 17 million PubChem compounds, 168,000 MDDR compounds, and 7-8181 MDDR compounds similar to the dual inhibitors. Compared with similarity searching, k-NN and PNN methods, COMBI-SVM produced comparable dual inhibitor yields, similar target selectivity, and lower false hit rate in screening 168,000 MDDR compounds. The annotated classes of many COMBI-SVMs identified MDDR virtual hits correlate with the reported effects of their predicted targets. COMBI-SVM is potentially useful for searching selective multi-target agents without explicit knowledge of these agents. Copyright © 2011 Elsevier Inc. All rights reserved.

Prioritization of anti-malarial hits from nature: chemo-informatic profiling of natural products with in vitro antiplasmodial activities and currently registered anti-malarial drugs.

PubMed

Egieyeh, Samuel Ayodele; Syce, James; Malan, Sarel F; Christoffels, Alan

2016-01-29

A large number of natural products have shown in vitro antiplasmodial activities. Early identification and prioritization of these natural products with potential for novel mechanism of action, desirable pharmacokinetics and likelihood for development into drugs is advantageous. Chemo-informatic profiling of these natural products were conducted and compared to currently registered anti-malarial drugs (CRAD). Natural products with in vitro antiplasmodial activities (NAA) were compiled from various sources. These natural products were sub-divided into four groups based on inhibitory concentration (IC50). Key molecular descriptors and physicochemical properties were computed for these compounds and analysis of variance used to assess statistical significance amongst the sets of compounds. Molecular similarity analysis, estimation of drug-likeness, in silico pharmacokinetic profiling, and exploration of structure-activity landscape were also carried out on these sets of compounds. A total of 1040 natural products were selected and a total of 13 molecular descriptors were analysed. Significant differences were observed among the sub-groups of NAA and CRAD for at least 11 of the molecular descriptors, including number of hydrogen bond donors and acceptors, molecular weight, polar and hydrophobic surface areas, chiral centres, oxygen and nitrogen atoms, and shape index. The remaining molecular descriptors, including clogP, number of rotatable bonds and number of aromatic rings, did not show any significant difference when comparing the two compound sets. Molecular similarity and chemical space analysis identified natural products that were structurally diverse from CRAD. Prediction of the pharmacokinetic properties and drug-likeness of these natural products identified over 50% with desirable drug-like properties. Nearly 70% of all natural products were identified as potentially promiscuous compounds. Structure-activity landscape analysis highlighted compound pairs that form 'activity cliffs'. In all, prioritization strategies for the NAA were proposed. Chemo-informatic profiling of NAA and CRAD have produced a wealth of information that may guide decisions and facilitate anti-malarial drug development from natural products. Articulation of the information provided within an interactive data-mining environment led to a prioritized list of NAA.

Materials prediction via classification learning

DOE PAGES

Balachandran, Prasanna V.; Theiler, James; Rondinelli, James M.; ...

2015-08-25

In the paradigm of materials informatics for accelerated materials discovery, the choice of feature set (i.e. attributes that capture aspects of structure, chemistry and/or bonding) is critical. Ideally, the feature sets should provide a simple physical basis for extracting major structural and chemical trends and furthermore, enable rapid predictions of new material chemistries. Orbital radii calculated from model pseudopotential fits to spectroscopic data are potential candidates to satisfy these conditions. Although these radii (and their linear combinations) have been utilized in the past, their functional forms are largely justified with heuristic arguments. Here we show that machine learning methods naturallymore » uncover the functional forms that mimic most frequently used features in the literature, thereby providing a mathematical basis for feature set construction without a priori assumptions. We apply these principles to study two broad materials classes: (i) wide band gap AB compounds and (ii) rare earth-main group RM intermetallics. The AB compounds serve as a prototypical example to demonstrate our approach, whereas the RM intermetallics show how these concepts can be used to rapidly design new ductile materials. In conclusion, our predictive models indicate that ScCo, ScIr, and YCd should be ductile, whereas each was previously proposed to be brittle.« less

Materials Prediction via Classification Learning

PubMed Central

Balachandran, Prasanna V.; Theiler, James; Rondinelli, James M.; Lookman, Turab

2015-01-01

In the paradigm of materials informatics for accelerated materials discovery, the choice of feature set (i.e. attributes that capture aspects of structure, chemistry and/or bonding) is critical. Ideally, the feature sets should provide a simple physical basis for extracting major structural and chemical trends and furthermore, enable rapid predictions of new material chemistries. Orbital radii calculated from model pseudopotential fits to spectroscopic data are potential candidates to satisfy these conditions. Although these radii (and their linear combinations) have been utilized in the past, their functional forms are largely justified with heuristic arguments. Here we show that machine learning methods naturally uncover the functional forms that mimic most frequently used features in the literature, thereby providing a mathematical basis for feature set construction without a priori assumptions. We apply these principles to study two broad materials classes: (i) wide band gap AB compounds and (ii) rare earth-main group RM intermetallics. The AB compounds serve as a prototypical example to demonstrate our approach, whereas the RM intermetallics show how these concepts can be used to rapidly design new ductile materials. Our predictive models indicate that ScCo, ScIr, and YCd should be ductile, whereas each was previously proposed to be brittle. PMID:26304800

Electrothermal adsorption and desorption of volatile organic compounds on activated carbon fiber cloth.

PubMed

Son, H K; Sivakumar, S; Rood, M J; Kim, B J

2016-01-15

Adsorption is an effective means to selectively remove volatile organic compounds (VOCs) from industrial gas streams and is particularly of use for gas streams that exhibit highly variable daily concentrations of VOCs. Adsorption of such gas streams by activated carbon fiber cloths (ACFCs) and subsequent controlled desorption can provide gas streams of well-defined concentration that can then be more efficiently treated by biofiltration than streams exhibiting large variability in concentration. In this study, we passed VOC-containing gas through an ACFC vessel for adsorption and then desorption in a concentration-controlled manner via electrothermal heating. Set-point concentrations (40-900 ppm(v)) and superficial gas velocity (6.3-9.9 m/s) were controlled by a data acquisition and control system. The results of the average VOC desorption, desorption factor and VOC in-and-out ratio were calculated and compared for various gas set-point concentrations and superficial gas velocities. Our results reveal that desorption is strongly dependent on the set-point concentration and that the VOC desorption rate can be successfully equalized and controlled via an electrothermal adsorption system. Copyright © 2015 Elsevier B.V. All rights reserved.

hERG blocking potential of acids and zwitterions characterized by three thresholds for acidity, size and reactivity.

PubMed

Nikolov, Nikolai G; Dybdahl, Marianne; Jónsdóttir, Svava Ó; Wedebye, Eva B

2014-11-01

Ionization is a key factor in hERG K(+) channel blocking, and acids and zwitterions are known to be less probable hERG blockers than bases and neutral compounds. However, a considerable number of acidic compounds block hERG, and the physico-chemical attributes which discriminate acidic blockers from acidic non-blockers have not been fully elucidated. We propose a rule for prediction of hERG blocking by acids and zwitterionic ampholytes based on thresholds for only three descriptors related to acidity, size and reactivity. The training set of 153 acids and zwitterionic ampholytes was predicted with a concordance of 91% by a decision tree based on the rule. Two external validations were performed with sets of 35 and 48 observations, respectively, both showing concordances of 91%. In addition, a global QSAR model of hERG blocking was constructed based on a large diverse training set of 1374 chemicals covering all ionization classes, externally validated showing high predictivity and compared to the decision tree. The decision tree was found to be superior for the acids and zwitterionic ampholytes classes. Copyright © 2014 Elsevier Ltd. All rights reserved.

Functional Analysis of Metabolomics Data.

PubMed

Chagoyen, Mónica; López-Ibáñez, Javier; Pazos, Florencio

2016-01-01

Metabolomics aims at characterizing the repertory of small chemical compounds in a biological sample. As it becomes more massive and larger sets of compounds are detected, a functional analysis is required to convert these raw lists of compounds into biological knowledge. The most common way of performing such analysis is "annotation enrichment analysis," also used in transcriptomics and proteomics. This approach extracts the annotations overrepresented in the set of chemical compounds arisen in a given experiment. Here, we describe the protocols for performing such analysis as well as for visualizing a set of compounds in different representations of the metabolic networks, in both cases using free accessible web tools.

How to benchmark methods for structure-based virtual screening of large compound libraries.

PubMed

Christofferson, Andrew J; Huang, Niu

2012-01-01

Structure-based virtual screening is a useful computational technique for ligand discovery. To systematically evaluate different docking approaches, it is important to have a consistent benchmarking protocol that is both relevant and unbiased. Here, we describe the designing of a benchmarking data set for docking screen assessment, a standard docking screening process, and the analysis and presentation of the enrichment of annotated ligands among a background decoy database.

Large positive magnetoresistance in intermetallic compound NdCo2Si2

NASA Astrophysics Data System (ADS)

Roy Chowdhury, R.; Dhara, S.; Das, I.; Bandyopadhyay, B.; Rawat, R.

2018-04-01

The magnetic, magneto-transport and magnetocaloric properties of antiferromagnetic intermetallic compound NdCo2Si2 (TN = 32K) have been studied. The compound yields a positive magnetoresistance (MR) of about ∼ 123 % at ∼ 5K in 8 T magnetic field. The MR value is significantly large vis - a - vis earlier reports of large MR in intermetallic compounds, and possibly associated with the changes in magnetic structure of the compound. The large MR value can be explained in terms of field induced pseudo-gaps on Fermi surface.

Enabling Large-Scale Design, Synthesis and Validation of Small Molecule Protein-Protein Antagonists

PubMed Central

Koes, David; Khoury, Kareem; Huang, Yijun; Wang, Wei; Bista, Michal; Popowicz, Grzegorz M.; Wolf, Siglinde; Holak, Tad A.; Dömling, Alexander; Camacho, Carlos J.

2012-01-01

Although there is no shortage of potential drug targets, there are only a handful known low-molecular-weight inhibitors of protein-protein interactions (PPIs). One problem is that current efforts are dominated by low-yield high-throughput screening, whose rigid framework is not suitable for the diverse chemotypes present in PPIs. Here, we developed a novel pharmacophore-based interactive screening technology that builds on the role anchor residues, or deeply buried hot spots, have in PPIs, and redesigns these entry points with anchor-biased virtual multicomponent reactions, delivering tens of millions of readily synthesizable novel compounds. Application of this approach to the MDM2/p53 cancer target led to high hit rates, resulting in a large and diverse set of confirmed inhibitors, and co-crystal structures validate the designed compounds. Our unique open-access technology promises to expand chemical space and the exploration of the human interactome by leveraging in-house small-scale assays and user-friendly chemistry to rationally design ligands for PPIs with known structure. PMID:22427896

Prediction of new bioactive molecules using a Bayesian belief network.

PubMed

Abdo, Ammar; Leclère, Valérie; Jacques, Philippe; Salim, Naomie; Pupin, Maude

2014-01-27

Natural products and synthetic compounds are a valuable source of new small molecules leading to novel drugs to cure diseases. However identifying new biologically active small molecules is still a challenge. In this paper, we introduce a new activity prediction approach using Bayesian belief network for classification (BBNC). The roots of the network are the fragments composing a compound. The leaves are, on one side, the activities to predict and, on another side, the unknown compound. The activities are represented by sets of known compounds, and sets of inactive compounds are also used. We calculated a similarity between an unknown compound and each activity class. The more similar activity is assigned to the unknown compound. We applied this new approach on eight well-known data sets extracted from the literature and compared its performance to three classical machine learning algorithms. Experiments showed that BBNC provides interesting prediction rates (from 79% accuracy for high diverse data sets to 99% for low diverse ones) with a short time calculation. Experiments also showed that BBNC is particularly effective for homogeneous data sets but has been found to perform less well with structurally heterogeneous sets. However, it is important to stress that we believe that using several approaches whenever possible for activity prediction can often give a broader understanding of the data than using only one approach alone. Thus, BBNC is a useful addition to the computational chemist's toolbox.

Analyzing large data sets acquired through telemetry from rats exposed to organophosphorous compounds: an EEG study.

PubMed

de Araujo Furtado, Marcio; Zheng, Andy; Sedigh-Sarvestani, Madineh; Lumley, Lucille; Lichtenstein, Spencer; Yourick, Debra

2009-10-30

The organophosphorous compound soman is an acetylcholinesterase inhibitor that causes damage to the brain. Exposure to soman causes neuropathology as a result of prolonged and recurrent seizures. In the present study, long-term recordings of cortical EEG were used to develop an unbiased means to quantify measures of seizure activity in a large data set while excluding other signal types. Rats were implanted with telemetry transmitters and exposed to soman followed by treatment with therapeutics similar to those administered in the field after nerve agent exposure. EEG, activity and temperature were recorded continuously for a minimum of 2 days pre-exposure and 15 days post-exposure. A set of automatic MATLAB algorithms have been developed to remove artifacts and measure the characteristics of long-term EEG recordings. The algorithms use short-time Fourier transforms to compute the power spectrum of the signal for 2-s intervals. The spectrum is then divided into the delta, theta, alpha, and beta frequency bands. A linear fit to the power spectrum is used to distinguish normal EEG activity from artifacts and high amplitude spike wave activity. Changes in time spent in seizure over a prolonged period are a powerful indicator of the effects of novel therapeutics against seizures. A graphical user interface has been created that simultaneously plots the raw EEG in the time domain, the power spectrum, and the wavelet transform. Motor activity and temperature are associated with EEG changes. The accuracy of this algorithm is also verified against visual inspection of video recordings up to 3 days after exposure.

The novel antibacterial compound walrycin A induces human PXR transcriptional activity

PubMed Central

Berthier, Alexandre; Oger, Frédérik; Gheeraert, Céline; Boulahtouf, Abdel; Le Guével, Rémy; Balaguer, Patrick; Staels, Bart; Salbert, Gilles; Lefebvre, Philippe

2012-01-01

The human pregnane X receptor (PXR) is a ligand-regulated transcription factor belonging to the nuclear receptor superfamily. PXR is activated by a large, structurally diverse, set of endogenous and xenobiotic compounds, and coordinates the expression of genes central to metabolism and excretion of potentially harmful chemicals and therapeutic drugs in humans. Walrycin A is a novel antibacterial compound targeting the WalK/WalR two-component signal transduction system of Gram (+) bacteria. Here we report that, in hepatoma cells, walrycin A potently activates a gene set known to be regulated by the xenobiotic sensor PXR. Walrycin A was as efficient as the reference PXR agonist rifampicin to activate PXR in a transactivation assay at non cytoxic concentrations. Using a limited proteolysis assay, we show that walrycin A induces conformational changes at a concentration which correlates with walrycin A ability to enhance the expression of prototypic target genes, suggesting that walrycin A interacts with PXR. The activation of the canonical human PXR target gene CYP3A4 by walrycin A is dose- and PXR-dependent. Finally, in silico docking experiments suggest that the walrycin A oxidation product Russig’s blue is the actual a ligand for PXR. Taken together, these results identify walrycin A as novel human PXR activator. PMID:22314385

Fundamental relation between molecular geometry and real-space topology. Combined AIM, ELI-D, and ASF analysis of hapticities and intramolecular hydrogen-hydrogen bonds in zincocene-related compounds.

PubMed

Mebs, Stefan; Chilleck, Maren Annika; Meindl, Kathrin; Hübschle, Christian Bertram

2014-06-19

Despite numerous advanced and widely distributed bonding theories such as MO, VB, NBO, AIM, and ELF/ELI-D, complex modes of bonding such as M-Cp*((R)) interactions (hapticities) in asymmetrical metallocenes or weak intramolecular interactions (e.g., hydrogen-hydrogen (H···H) bonds) still remain a challenge for these theories in terms of defining whether or not an atom-atom interaction line (a "chemical bond") should be drawn. In this work the intramolecular Zn-C(Cp*(R)) (R = Me, -(CH2)2NMe2, and -(CH2)3NMe2) and H···H connectivity of a systematic set of 12 zincocene-related compounds is analyzed in terms of AIM and ELI-D topology combined with the recently introduced aspherical stockholder fragment (ASF) surfaces. This computational analysis unravels a distinct dependency of the AIM and ELI-D topology against the molecular geometry for both types of interactions, which confirms and extends earlier findings on smaller sets of compounds. According to these results the complete real-space topology including strong, medium, and weak interactions of very large compounds such as proteins may be reliably predicted by sole inspection of accurately determined molecular geometries, which would on the one hand afford new applications (e.g., accurate estimation of numbers, types, and strengths of intra- and intermolecular interactions) and on the other hand have deep implications on the significance of the method.

Determination of the n-octanol/water partition coefficients of weakly ionizable basic compounds by reversed-phase high-performance liquid chromatography with neutral model compounds.

PubMed

Liang, Chao; Han, Shu-ying; Qiao, Jun-qin; Lian, Hong-zhen; Ge, Xin

2014-11-01

A strategy to utilize neutral model compounds for lipophilicity measurement of ionizable basic compounds by reversed-phase high-performance liquid chromatography is proposed in this paper. The applicability of the novel protocol was justified by theoretical derivation. Meanwhile, the linear relationships between logarithm of apparent n-octanol/water partition coefficients (logKow '') and logarithm of retention factors corresponding to the 100% aqueous fraction of mobile phase (logkw ) were established for a basic training set, a neutral training set and a mixed training set of these two. As proved in theory, the good linearity and external validation results indicated that the logKow ''-logkw relationships obtained from a neutral model training set were always reliable regardless of mobile phase pH. Afterwards, the above relationships were adopted to determine the logKow of harmaline, a weakly dissociable alkaloid. As far as we know, this is the first report on experimental logKow data for harmaline (logKow = 2.28 ± 0.08). Introducing neutral compounds into a basic model training set or using neutral model compounds alone is recommended to measure the lipophilicity of weakly ionizable basic compounds especially those with high hydrophobicity for the advantages of more suitable model compound choices and convenient mobile phase pH control. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Consensus Modeling of Oral Rat Acute Toxicity

EPA Science Inventory

An acute toxicity dataset (oral rat LD50) with about 7400 compounds was compiled from the ChemIDplus database. This dataset was divided into a modeling set and a prediction set. The compounds in the prediction set were selected so that they were present in the modeling set used...

Investigation of common fluorophores for the detection of nitrated explosives by fluorescence quenching.

PubMed

Meaney, Melissa S; McGuffin, Victoria L

2008-03-03

Previous studies have indicated that nitrated explosives may be detected by fluorescence quenching of pyrene and related compounds. The use of pyrene, however, invokes numerous health and waste disposal hazards. In the present study, ten safer fluorophores are identified for quenching detection of target nitrated compounds. Initially, Stern-Volmer constants are measured for each fluorophore with nitrobenzene and 4-nitrotoluene to determine the sensitivity of the quenching interaction. For quenching constants greater than 50 M(-1), sensitivity and selectivity are investigated further using an extended set of target quenchers. Nitromethane, nitrobenzene, 4-nitrotoluene, and 2,6-dinitrotoluene are chosen to represent nitrated explosives and their degradation products; aniline, benzoic acid, and phenol are chosen to represent potential interfering compounds. Among the fluorophores investigated, purpurin, malachite green, and phenol red demonstrate the greatest sensitivity and selectivity for nitrated compounds. Correlation of the quenching rate constants for these fluorophores to Rehm-Weller theory suggests an electron-transfer quenching mechanism. As a result of the large quenching constants, purpurin, malachite green, and phenol red are the most promising for future detection of nitrated explosives via fluorescence quenching.

Blocked inverted indices for exact clustering of large chemical spaces.

PubMed

Thiel, Philipp; Sach-Peltason, Lisa; Ottmann, Christian; Kohlbacher, Oliver

2014-09-22

The calculation of pairwise compound similarities based on fingerprints is one of the fundamental tasks in chemoinformatics. Methods for efficient calculation of compound similarities are of the utmost importance for various applications like similarity searching or library clustering. With the increasing size of public compound databases, exact clustering of these databases is desirable, but often computationally prohibitively expensive. We present an optimized inverted index algorithm for the calculation of all pairwise similarities on 2D fingerprints of a given data set. In contrast to other algorithms, it neither requires GPU computing nor yields a stochastic approximation of the clustering. The algorithm has been designed to work well with multicore architectures and shows excellent parallel speedup. As an application example of this algorithm, we implemented a deterministic clustering application, which has been designed to decompose virtual libraries comprising tens of millions of compounds in a short time on current hardware. Our results show that our implementation achieves more than 400 million Tanimoto similarity calculations per second on a common desktop CPU. Deterministic clustering of the available chemical space thus can be done on modern multicore machines within a few days.

Oxygen speciation in upgraded fast pyrolysis bio-oils by comprehensive two-dimensional gas chromatography.

PubMed

Omais, Badaoui; Crepier, Julien; Charon, Nadège; Courtiade, Marion; Quignard, Alain; Thiébaut, Didier

2013-04-21

Biomass fast pyrolysis is considered as a promising route to produce liquid for the transportation field from a renewable resource. However, the derived bio-oils are mainly oxygenated (45-50%w/w O on a wet basis) and contain almost no hydrocarbons. Therefore, upgrading is necessary to obtain a liquid with lower oxygen content and characterization of oxygenated compounds in these products is essential to assist conversion reactions. For this purpose, comprehensive two-dimensional gas chromatography (GC × GC) can be investigated. Oxygen speciation in such matrices is hampered by the large diversity of oxygenated families and the complexity of the hydrocarbon matrix. Moreover, response factors must be taken into account for oxygenate quantification as the Flame Ionisation Detector (FID) response varies when a molecule contains heteroatoms. To conclude, no distillation cuts were accessible and the analysis had to cover a large range of boiling points (30-630 °C). To take up this analytical challenge, a thorough optimization approach was developed. In fact, four GC × GC column sets were investigated to separate oxygenated compounds from the hydrocarbon matrix. Both model mixtures and the upgraded biomass flash pyrolysis oil were injected using GC × GC-FID to reach a suitable chromatographic separation. The advantages and drawbacks of each column combination for oxygen speciation in upgraded bio-oils are highlighted in this study. Among the four sets, an original polar × semi-polar column combination was selected and enabled the identification by GC × GC-ToF/MS of more than 40 compounds belonging to eight chemical families: ketones, furans, alcohols, phenols, carboxylic acids, guaiacols, anisols, and esters. For quantification purpose, the GC × GC-FID chromatogram was divided into more than 60 blobs corresponding to the previously identified analyte and hydrocarbon zones. A database associating each blob to a molecule and its specific response factor (determined by standards injection at different concentrations) was created. A detailed molecular quantification by GC × GC-FID was therefore accessible after integration of the corrected normalized areas. This paper aims to present a detail level in terms of characterization of oxygenated compounds in upgraded bio-oils which to our knowledge has never been reached so far. It is based on an original column set selection and an extremely accurate quantification procedure.

Expanding the analyte set of the JPL Electronic Nose to include inorganic compounds

NASA Technical Reports Server (NTRS)

Ryan, M. A.; Homer, M. L.; Zhou, H.; Mannat, K.; Manfreda, A.; Kisor, A.; Shevade, A.; Yen, S. P. S.

2005-01-01

An array-based sensing system based on 32 polymer/carbon composite conductometric sensors is under development at JPL. Until the present phase of development, the analyte set has focuses on organic compounds and a few selected inorganic compounds, notably ammonia and hydrazine.

Stability of total nutrient admixtures with lipid injectable emulsions in glass versus plastic packaging.

PubMed

Driscoll, David F; Silvestri, Anthony P; Bistrian, Bruce R; Mikrut, Bernard A

2007-02-15

The physical stability of two emulsions compounded as part of a total nutrient admixture (TNA) was studied in lipids packaged in either glass or plastic containers. Five weight-based adult TNA formulations that were designed to meet the full nutritional needs of adults with body weights between 40 and 80 kg were studied. Triplicate preparations of each TNA were assessed over 30 hours at room temperature by applying currently proposed United States Pharmacopeia (USP) criteria for mean droplet diameter, large-diameter tail, and globule-size distribution (GSD) for lipid injectable emulsions. In accordance with conditions set forth in USP chapter 729, the higher levels of volume-weighted percent of fat exceeding 5 microm (PFAT(5)) should not exceed 0.05% of the total lipid concentration. Significant differences were noted among TNA admixtures based on whether the lipid emulsion product was manufactured in glass or plastic. The plastic-contained TNAs failed the proposed USP methods for large-diameter fat globules in all formulations from the outset, and 60% had significant growth in large-diameter fat globules over time. In contrast, glass-contained TNAs were stable throughout and in all cases would have passed proposed USP limits. Certain lipid injectable emulsions packaged in plastic containers have baseline abnormal GSD profiles compared with those packaged in glass containers. When used to compound TNAs, the abnormal profile worsens and produces less stable TNAs than those compounded with lipid injectable emulsions packaged in glass containers.

Charting, navigating, and populating natural product chemical space for drug discovery.

PubMed

Lachance, Hugo; Wetzel, Stefan; Kumar, Kamal; Waldmann, Herbert

2012-07-12

Natural products are a heterogeneous group of compounds with diverse, yet particular molecular properties compared to synthetic compounds and drugs. All relevant analyses show that natural products indeed occupy parts of chemical space not explored by available screening collections while at the same time largely adhering to the rule-of-five. This renders them a valuable, unique, and necessary component of screening libraries used in drug discovery. With ChemGPS-NP on the Web and Scaffold Hunter two tools are available to the scientific community to guide exploration of biologically relevant NP chemical space in a focused and targeted fashion with a view to guide novel synthesis approaches. Several of the examples given illustrate the possibility of bridging the gap between computational methods and compound library synthesis and the possibility of integrating cheminformatics and chemical space analyses with synthetic chemistry and biochemistry to successfully explore chemical space for the identification of novel small molecule modulators of protein function.The examples also illustrate the synergistic potential of the chemical space concept and modern chemical synthesis for biomedical research and drug discovery. Chemical space analysis can map under explored biologically relevant parts of chemical space and identify the structure types occupying these parts. Modern synthetic methodology can then be applied to efficiently fill this “virtual space” with real compounds.From a cheminformatics perspective, there is a clear demand for open-source and easy to use tools that can be readily applied by educated nonspecialist chemists and biologists in their daily research. This will include further development of Scaffold Hunter, ChemGPS-NP, and related approaches on the Web. Such a “cheminformatics toolbox” would enable chemists and biologists to mine their own data in an intuitive and highly interactive process and without the need for specialized computer science and cheminformatics expertise. We anticipate that it may be a viable, if not necessary, step for research initiatives based on large high-throughput screening campaigns,in particular in the pharmaceutical industry, to make the most out of the recent advances in computational tools in order to leverage and take full advantage of the large data sets generated and available in house. There are “holes” in these data sets that can and should be identified and explored by chemistry and biology.

Genome analysis of Desulfotomaculum gibsoniae strain GrollT a highly versatile Gram-positive sulfate-reducing bacterium

PubMed Central

Kuever, Jan; Visser, Michael; Loeffler, Claudia; Boll, Matthias; Worm, Petra; Sousa, Diana Z.; Plugge, Caroline M.; Schaap, Peter J.; Muyzer, Gerard; Pereira, Ines A.C.; Parshina, Sofiya N.; Goodwin, Lynne A.; Kyrpides, Nikos C.; Detter, Janine; Woyke, Tanja; Chain, Patrick; Davenport, Karen W.; Rohde, Manfred; Spring, Stefan; Klenk, Hans-Peter; Stams, Alfons J.M.

2014-01-01

Desulfotomaculum gibsoniae is a mesophilic member of the polyphyletic spore-forming genus Desulfotomaculum within the family Peptococcaceae. This bacterium was isolated from a freshwater ditch and is of interest because it can grow with a large variety of organic substrates, in particular several aromatic compounds, short-chain and medium-chain fatty acids, which are degraded completely to carbon dioxide coupled to the reduction of sulfate. It can grow autotrophically with H2 + CO2 and sulfate and slowly acetogenically with H2 + CO2, formate or methoxylated aromatic compounds in the absence of sulfate. It does not require any vitamins for growth. Here, we describe the features of D. gibsoniae strain GrollT together with the genome sequence and annotation. The chromosome has 4,855,529 bp organized in one circular contig and is the largest genome of all sequenced Desulfotomaculum spp. to date. A total of 4,666 candidate protein-encoding genes and 96 RNA genes were identified. Genes of the acetyl-CoA pathway, possibly involved in heterotrophic growth and in CO2 fixation during autotrophic growth, are present. The genome contains a large set of genes for the anaerobic transformation and degradation of aromatic compounds, which are lacking in the other sequenced Desulfotomaculum genomes. PMID:25197466

Using Weighted Entropy to Rank Chemicals in Quantitative High Throughput Screening Experiments

PubMed Central

Shockley, Keith R.

2014-01-01

Quantitative high throughput screening (qHTS) experiments can simultaneously produce concentration-response profiles for thousands of chemicals. In a typical qHTS study, a large chemical library is subjected to a primary screen in order to identify candidate hits for secondary screening, validation studies or prediction modeling. Different algorithms, usually based on the Hill equation logistic model, have been used to classify compounds as active or inactive (or inconclusive). However, observed concentration-response activity relationships may not adequately fit a sigmoidal curve. Furthermore, it is unclear how to prioritize chemicals for follow-up studies given the large uncertainties that often accompany parameter estimates from nonlinear models. Weighted Shannon entropy can address these concerns by ranking compounds according to profile-specific statistics derived from estimates of the probability mass distribution of response at the tested concentration levels. This strategy can be used to rank all tested chemicals in the absence of a pre-specified model structure or the approach can complement existing activity call algorithms by ranking the returned candidate hits. The weighted entropy approach was evaluated here using data simulated from the Hill equation model. The procedure was then applied to a chemical genomics profiling data set interrogating compounds for androgen receptor agonist activity. PMID:24056003

Atom-type-based AI topological descriptors: application in structure-boiling point correlations of oxo organic compounds.

PubMed

Ren, Biye

2003-01-01

Structure-boiling point relationships are studied for a series of oxo organic compounds by means of multiple linear regression (MLR) analysis. Excellent MLR models based on the recently introduced Xu index and the atom-type-based AI indices are obtained for the two subsets containing respectively 77 ethers and 107 carbonyl compounds and a combined set of 184 oxo compounds. The best models are tested using the leave-one-out cross-validation and an external test set, respectively. The MLR model produces a correlation coefficient of r = 0.9977 and a standard error of s = 3.99 degrees C for the training set of 184 compounds, and r(cv) = 0.9974 and s(cv) = 4.16 degrees C for the cross-validation set, and r(pred) = 0.9949 and s(pred) = 4.38 degrees C for the prediction set of 21 compounds. For the two subsets containing respectively 77 ethers and 107 carbonyl compounds, the quality of the models is further improved. The standard errors are reduced to 3.30 and 3.02 degrees C, respectively. Furthermore, the results obtained from this study indicate that the boiling points of the studied oxo compound dominantly depend on molecular size and also depend on individual atom types, especially oxygen heteroatoms in molecules due to strong polar interactions between molecules. These excellent structure-boiling point models not only provide profound insights into the role of structural features in a molecule but also illustrate the usefulness of these indices in QSPR/QSAR modeling of complex compounds.

Profiling Animal Toxicants by Automatically Mining Public Bioassay Data: A Big Data Approach for Computational Toxicology

PubMed Central

Zhang, Jun; Hsieh, Jui-Hua; Zhu, Hao

2014-01-01

In vitro bioassays have been developed and are currently being evaluated as potential alternatives to traditional animal toxicity models. Already, the progress of high throughput screening techniques has resulted in an enormous amount of publicly available bioassay data having been generated for a large collection of compounds. When a compound is tested using a collection of various bioassays, all the testing results can be considered as providing a unique bio-profile for this compound, which records the responses induced when the compound interacts with different cellular systems or biological targets. Profiling compounds of environmental or pharmaceutical interest using useful toxicity bioassay data is a promising method to study complex animal toxicity. In this study, we developed an automatic virtual profiling tool to evaluate potential animal toxicants. First, we automatically acquired all PubChem bioassay data for a set of 4,841 compounds with publicly available rat acute toxicity results. Next, we developed a scoring system to evaluate the relevance between these extracted bioassays and animal acute toxicity. Finally, the top ranked bioassays were selected to profile the compounds of interest. The resulting response profiles proved to be useful to prioritize untested compounds for their animal toxicity potentials and form a potential in vitro toxicity testing panel. The protocol developed in this study could be combined with structure-activity approaches and used to explore additional publicly available bioassay datasets for modeling a broader range of animal toxicities. PMID:24950175

Profiling animal toxicants by automatically mining public bioassay data: a big data approach for computational toxicology.

PubMed

Zhang, Jun; Hsieh, Jui-Hua; Zhu, Hao

2014-01-01

In vitro bioassays have been developed and are currently being evaluated as potential alternatives to traditional animal toxicity models. Already, the progress of high throughput screening techniques has resulted in an enormous amount of publicly available bioassay data having been generated for a large collection of compounds. When a compound is tested using a collection of various bioassays, all the testing results can be considered as providing a unique bio-profile for this compound, which records the responses induced when the compound interacts with different cellular systems or biological targets. Profiling compounds of environmental or pharmaceutical interest using useful toxicity bioassay data is a promising method to study complex animal toxicity. In this study, we developed an automatic virtual profiling tool to evaluate potential animal toxicants. First, we automatically acquired all PubChem bioassay data for a set of 4,841 compounds with publicly available rat acute toxicity results. Next, we developed a scoring system to evaluate the relevance between these extracted bioassays and animal acute toxicity. Finally, the top ranked bioassays were selected to profile the compounds of interest. The resulting response profiles proved to be useful to prioritize untested compounds for their animal toxicity potentials and form a potential in vitro toxicity testing panel. The protocol developed in this study could be combined with structure-activity approaches and used to explore additional publicly available bioassay datasets for modeling a broader range of animal toxicities.

Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.

PubMed

Lane, Thomas; Russo, Daniel P; Zorn, Kimberley M; Clark, Alex M; Korotcov, Alexandru; Tkachenko, Valery; Reynolds, Robert C; Perryman, Alexander L; Freundlich, Joel S; Ekins, Sean

2018-04-26

Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences and 1.7 million deaths. The need to develop new treatments for those infected with Mycobacterium tuberculosis ( Mtb) has led to many large-scale phenotypic screens and many thousands of new active compounds identified in vitro. However, with limited funding, efforts to discover new active molecules against Mtb needs to be more efficient. Several computational machine learning approaches have been shown to have good enrichment and hit rates. We have curated small molecule Mtb data and developed new models with a total of 18,886 molecules with activity cutoffs of 10 μM, 1 μM, and 100 nM. These data sets were used to evaluate different machine learning methods (including deep learning) and metrics and to generate predictions for additional molecules published in 2017. One Mtb model, a combined in vitro and in vivo data Bayesian model at a 100 nM activity yielded the following metrics for 5-fold cross validation: accuracy = 0.88, precision = 0.22, recall = 0.91, specificity = 0.88, kappa = 0.31, and MCC = 0.41. We have also curated an evaluation set ( n = 153 compounds) published in 2017, and when used to test our model, it showed the comparable statistics (accuracy = 0.83, precision = 0.27, recall = 1.00, specificity = 0.81, kappa = 0.36, and MCC = 0.47). We have also compared these models with additional machine learning algorithms showing Bayesian machine learning models constructed with literature Mtb data generated by different laboratories generally were equivalent to or outperformed deep neural networks with external test sets. Finally, we have also compared our training and test sets to show they were suitably diverse and different in order to represent useful evaluation sets. Such Mtb machine learning models could help prioritize compounds for testing in vitro and in vivo.

jCompoundMapper: An open source Java library and command-line tool for chemical fingerprints

PubMed Central

2011-01-01

Background The decomposition of a chemical graph is a convenient approach to encode information of the corresponding organic compound. While several commercial toolkits exist to encode molecules as so-called fingerprints, only a few open source implementations are available. The aim of this work is to introduce a library for exactly defined molecular decompositions, with a strong focus on the application of these features in machine learning and data mining. It provides several options such as search depth, distance cut-offs, atom- and pharmacophore typing. Furthermore, it provides the functionality to combine, to compare, or to export the fingerprints into several formats. Results We provide a Java 1.6 library for the decomposition of chemical graphs based on the open source Chemistry Development Kit toolkit. We reimplemented popular fingerprinting algorithms such as depth-first search fingerprints, extended connectivity fingerprints, autocorrelation fingerprints (e.g. CATS2D), radial fingerprints (e.g. Molprint2D), geometrical Molprint, atom pairs, and pharmacophore fingerprints. We also implemented custom fingerprints such as the all-shortest path fingerprint that only includes the subset of shortest paths from the full set of paths of the depth-first search fingerprint. As an application of jCompoundMapper, we provide a command-line executable binary. We measured the conversion speed and number of features for each encoding and described the composition of the features in detail. The quality of the encodings was tested using the default parametrizations in combination with a support vector machine on the Sutherland QSAR data sets. Additionally, we benchmarked the fingerprint encodings on the large-scale Ames toxicity benchmark using a large-scale linear support vector machine. The results were promising and could often compete with literature results. On the large Ames benchmark, for example, we obtained an AUC ROC performance of 0.87 with a reimplementation of the extended connectivity fingerprint. This result is comparable to the performance achieved by a non-linear support vector machine using state-of-the-art descriptors. On the Sutherland QSAR data set, the best fingerprint encodings showed a comparable or better performance on 5 of the 8 benchmarks when compared against the results of the best descriptors published in the paper of Sutherland et al. Conclusions jCompoundMapper is a library for chemical graph fingerprints with several tweaking possibilities and exporting options for open source data mining toolkits. The quality of the data mining results, the conversion speed, the LPGL software license, the command-line interface, and the exporters should be useful for many applications in cheminformatics like benchmarks against literature methods, comparison of data mining algorithms, similarity searching, and similarity-based data mining. PMID:21219648

Extraordinarily Adaptive Properties of the Genetically Encoded Amino Acids

PubMed Central

Ilardo, Melissa; Meringer, Markus; Freeland, Stephen; Rasulev, Bakhtiyor; Cleaves II, H. James

2015-01-01

Using novel advances in computational chemistry, we demonstrate that the set of 20 genetically encoded amino acids, used nearly universally to construct all coded terrestrial proteins, has been highly influenced by natural selection. We defined an adaptive set of amino acids as one whose members thoroughly cover relevant physico-chemical properties, or “chemistry space.” Using this metric, we compared the encoded amino acid alphabet to random sets of amino acids. These random sets were drawn from a computationally generated compound library containing 1913 alternative amino acids that lie within the molecular weight range of the encoded amino acids. Sets that cover chemistry space better than the genetically encoded alphabet are extremely rare and energetically costly. Further analysis of more adaptive sets reveals common features and anomalies, and we explore their implications for synthetic biology. We present these computations as evidence that the set of 20 amino acids found within the standard genetic code is the result of considerable natural selection. The amino acids used for constructing coded proteins may represent a largely global optimum, such that any aqueous biochemistry would use a very similar set. PMID:25802223

Quality evaluation of tandem mass spectral libraries.

PubMed

Oberacher, Herbert; Weinmann, Wolfgang; Dresen, Sebastian

2011-06-01

Tandem mass spectral libraries are gaining more and more importance for the identification of unknowns in different fields of research, including metabolomics, forensics, toxicology, and environmental analysis. Particularly, the recent invention of reliable, robust, and transferable libraries has increased the general acceptance of these tools. Herein, we report on results obtained from thorough evaluation of the match reliabilities of two tandem mass spectral libraries: the MSforID library established by the Oberacher group in Innsbruck and the Weinmann library established by the Weinmann group in Freiburg. Three different experiments were performed: (1) Spectra of the libraries were searched against their corresponding library after excluding either this single compound-specific spectrum or all compound-specific spectra prior to searching; (2) the libraries were searched against each other using either library as reference set or sample set; (3) spectra acquired on different mass spectrometric instruments were matched to both libraries. Almost 13,000 tandem mass spectra were included in this study. The MSforID search algorithm was used for spectral matching. Statistical evaluation of the library search results revealed that principally both libraries enable the sensitive and specific identification of compounds. Due to higher mass accuracy of the QqTOF compared with the QTrap instrument, matches to the MSforID library were more reliable when comparing spectra with both libraries. Furthermore, only the MSforID library was shown to be efficiently transferable to different kinds of tandem mass spectrometers, including "tandem-in-time" instruments; this is due to the coverage of a large range of different collision energy settings-including the very low range-which is an outstanding characteristics of the MSforID library.

Computing smallest intervention strategies for multiple metabolic networks in a boolean model.

PubMed

Lu, Wei; Tamura, Takeyuki; Song, Jiangning; Akutsu, Tatsuya

2015-02-01

This article considers the problem whereby, given two metabolic networks N1 and N2, a set of source compounds, and a set of target compounds, we must find the minimum set of reactions whose removal (knockout) ensures that the target compounds are not producible in N1 but are producible in N2. Similar studies exist for the problem of finding the minimum knockout with the smallest side effect for a single network. However, if technologies of external perturbations are advanced in the near future, it may be important to develop methods of computing the minimum knockout for multiple networks (MKMN). Flux balance analysis (FBA) is efficient if a well-polished model is available. However, that is not always the case. Therefore, in this article, we study MKMN in Boolean models and an elementary mode (EM)-based model. Integer linear programming (ILP)-based methods are developed for these models, since MKMN is NP-complete for both the Boolean model and the EM-based model. Computer experiments are conducted with metabolic networks of clostridium perfringens SM101 and bifidobacterium longum DJO10A, respectively known as bad bacteria and good bacteria for the human intestine. The results show that larger networks are more likely to have MKMN solutions. However, solving for these larger networks takes a very long time, and often the computation cannot be completed. This is reasonable, because small networks do not have many alternative pathways, making it difficult to satisfy the MKMN condition, whereas in large networks the number of candidate solutions explodes. Our developed software minFvskO is available online.

New target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0

PubMed Central

2014-01-01

Background We recently developed a freely available mobile app (TB Mobile) for both iOS and Android platforms that displays Mycobacterium tuberculosis (Mtb) active molecule structures and their targets with links to associated data. The app was developed to make target information available to as large an audience as possible. Results We now report a major update of the iOS version of the app. This includes enhancements that use an implementation of ECFP_6 fingerprints that we have made open source. Using these fingerprints, the user can propose compounds with possible anti-TB activity, and view the compounds within a cluster landscape. Proposed compounds can also be compared to existing target data, using a näive Bayesian scoring system to rank probable targets. We have curated an additional 60 new compounds and their targets for Mtb and added these to the original set of 745 compounds. We have also curated 20 further compounds (many without targets in TB Mobile) to evaluate this version of the app with 805 compounds and associated targets. Conclusions TB Mobile can now manage a small collection of compounds that can be imported from external sources, or exported by various means such as email or app-to-app inter-process communication. This means that TB Mobile can be used as a node within a growing ecosystem of mobile apps for cheminformatics. It can also cluster compounds and use internal algorithms to help identify potential targets based on molecular similarity. TB Mobile represents a valuable dataset, data-visualization aid and target prediction tool. PMID:25302078

New target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0.

PubMed

Clark, Alex M; Sarker, Malabika; Ekins, Sean

2014-01-01

We recently developed a freely available mobile app (TB Mobile) for both iOS and Android platforms that displays Mycobacterium tuberculosis (Mtb) active molecule structures and their targets with links to associated data. The app was developed to make target information available to as large an audience as possible. We now report a major update of the iOS version of the app. This includes enhancements that use an implementation of ECFP_6 fingerprints that we have made open source. Using these fingerprints, the user can propose compounds with possible anti-TB activity, and view the compounds within a cluster landscape. Proposed compounds can also be compared to existing target data, using a näive Bayesian scoring system to rank probable targets. We have curated an additional 60 new compounds and their targets for Mtb and added these to the original set of 745 compounds. We have also curated 20 further compounds (many without targets in TB Mobile) to evaluate this version of the app with 805 compounds and associated targets. TB Mobile can now manage a small collection of compounds that can be imported from external sources, or exported by various means such as email or app-to-app inter-process communication. This means that TB Mobile can be used as a node within a growing ecosystem of mobile apps for cheminformatics. It can also cluster compounds and use internal algorithms to help identify potential targets based on molecular similarity. TB Mobile represents a valuable dataset, data-visualization aid and target prediction tool.

A QSAR study of integrase strand transfer inhibitors based on a large set of pyrimidine, pyrimidone, and pyridopyrazine carboxamide derivatives

NASA Astrophysics Data System (ADS)

de Campos, Luana Janaína; de Melo, Eduardo Borges

2017-08-01

In the present study, 199 compounds derived from pyrimidine, pyrimidone and pyridopyrazine carboxamides with inhibitory activity against HIV-1 integrase were modeled. Subsequently, a multivariate QSAR study was conducted with 54 molecules employed by Ordered Predictors Selection (OPS) and Partial Least Squares (PLS) for the selection of variables and model construction, respectively. Topological, electrotopological, geometric, and molecular descriptors were used. The selected real model was robust and free from chance correlation; in addition, it demonstrated favorable internal and external statistical quality. Once statistically validated, the training model was used to predict the activity of a second data set (n = 145). The root mean square deviation (RMSD) between observed and predicted values was 0.698. Although it is a value outside of the standards, only 15 (10.34%) of the samples exhibited higher residual values than 1 log unit, a result considered acceptable. Results of Williams and Euclidean applicability domains relative to the prediction showed that the predictions did not occur by extrapolation and that the model is representative of the chemical space of test compounds.

New large solar photocatalytic plant: set-up and preliminary results.

PubMed

Malato, S; Blanco, J; Vidal, A; Fernández, P; Cáceres, J; Trincado, P; Oliveira, J C; Vincent, M

2002-04-01

A European industrial consortium called SOLARDETOX has been created as the result of an EC-DGXII BRITE-EURAM-III-financed project on solar photocatalytic detoxification of water. The project objective was to develop a simple, efficient and commercially competitive water-treatment technology, based on compound parabolic collectors (CPCs) solar collectors and TiO2 photocatalysis, to make possible easy design and installation. The design, set-up and preliminary results of the main project deliverable, the first European industrial solar detoxification treatment plant, is presented. This plant has been designed for the batch treatment of 2 m3 of water with a 100 m2 collector-aperture area and aqueous aerated suspensions of polycrystalline TiO2 irradiated by sunlight. Fully automatic control reduces operation and maintenance manpower. Plant behaviour has been compared (using dichloroacetic acid and cyanide at 50 mg l(-1) initial concentration as model compounds) with the small CPC pilot plants installed at the Plataforma Solar de Almería several years ago. The first results with high-content cyanide (1 g l(-1)) waste water are presented and plant treatment capacity is calculated.

Literature-based compound profiling: application to toxicogenomics.

PubMed

Frijters, Raoul; Verhoeven, Stefan; Alkema, Wynand; van Schaik, René; Polman, Jan

2007-11-01

To reduce continuously increasing costs in drug development, adverse effects of drugs need to be detected as early as possible in the process. In recent years, compound-induced gene expression profiling methodologies have been developed to assess compound toxicity, including Gene Ontology term and pathway over-representation analyses. The objective of this study was to introduce an additional approach, in which literature information is used for compound profiling to evaluate compound toxicity and mode of toxicity. Gene annotations were built by text mining in Medline abstracts for retrieval of co-publications between genes, pathology terms, biological processes and pathways. This literature information was used to generate compound-specific keyword fingerprints, representing over-represented keywords calculated in a set of regulated genes after compound administration. To see whether keyword fingerprints can be used for assessment of compound toxicity, we analyzed microarray data sets of rat liver treated with 11 hepatotoxicants. Analysis of keyword fingerprints of two genotoxic carcinogens, two nongenotoxic carcinogens, two peroxisome proliferators and two randomly generated gene sets, showed that each compound produced a specific keyword fingerprint that correlated with the experimentally observed histopathological events induced by the individual compounds. By contrast, the random sets produced a flat aspecific keyword profile, indicating that the fingerprints induced by the compounds reflect biological events rather than random noise. A more detailed analysis of the keyword profiles of diethylhexylphthalate, dimethylnitrosamine and methapyrilene (MPy) showed that the differences in the keyword fingerprints of these three compounds are based upon known distinct modes of action. Visualization of MPy-linked keywords and MPy-induced genes in a literature network enabled us to construct a mode of toxicity proposal for MPy, which is in agreement with known effects of MPy in literature. Compound keyword fingerprinting based on information retrieved from literature is a powerful approach for compound profiling, allowing evaluation of compound toxicity and analysis of the mode of action.

ToxAlerts: a Web server of structural alerts for toxic chemicals and compounds with potential adverse reactions.

PubMed

Sushko, Iurii; Salmina, Elena; Potemkin, Vladimir A; Poda, Gennadiy; Tetko, Igor V

2012-08-27

The article presents a Web-based platform for collecting and storing toxicological structural alerts from literature and for virtual screening of chemical libraries to flag potentially toxic chemicals and compounds that can cause adverse side effects. An alert is uniquely identified by a SMARTS template, a toxicological endpoint, and a publication where the alert was described. Additionally, the system allows storing complementary information such as name, comments, and mechanism of action, as well as other data. Most importantly, the platform can be easily used for fast virtual screening of large chemical datasets, focused libraries, or newly designed compounds against the toxicological alerts, providing a detailed profile of the chemicals grouped by structural alerts and endpoints. Such a facility can be used for decision making regarding whether a compound should be tested experimentally, validated with available QSAR models, or eliminated from consideration altogether. The alert-based screening can also be helpful for an easier interpretation of more complex QSAR models. The system is publicly accessible and tightly integrated with the Online Chemical Modeling Environment (OCHEM, http://ochem.eu). The system is open and expandable: any registered OCHEM user can introduce new alerts, browse, edit alerts introduced by other users, and virtually screen his/her data sets against all or selected alerts. The user sets being passed through the structural alerts can be used at OCHEM for other typical tasks: exporting in a wide variety of formats, development of QSAR models, additional filtering by other criteria, etc. The database already contains almost 600 structural alerts for such endpoints as mutagenicity, carcinogenicity, skin sensitization, compounds that undergo metabolic activation, and compounds that form reactive metabolites and, thus, can cause adverse reactions. The ToxAlerts platform is accessible on the Web at http://ochem.eu/alerts, and it is constantly growing.

Discovery of the glycogen phosphorylase-modulating activity of a resveratrol glucoside by using a virtual screening protocol optimized for solvation effects.

PubMed

Mavrokefalos, Nikolaos; Myrianthopoulos, Vassilios; Chajistamatiou, Aikaterini S; Chrysina, Evangelia D; Mikros, Emmanuel

2015-04-01

The identification of natural products that can modulate blood glucose levels is of great interest as it can possibly facilitate the utilization of mild interventions such as herbal medicine or functional foods in the treatment of chronic diseases like diabetes. One of the established drug targets for antihyperglycemic therapy is glycogen phosphorylase. To evaluate the glycogen phosphorylase inhibitory properties of an in-house compound collection consisting to a large extent of natural products, a stepwise virtual and experimental screening protocol was devised and implemented. The fact that the active site of glycogen phosphorylase is highly hydrated emphasized that a methodological aspect needed to be efficiently addressed prior to an in silico evaluation of the compound collection. The effect of water molecules on docking calculations was regarded as a key parameter in terms of virtual screening protocol optimization. Statistical analysis of 125 structures of glycogen phosphorylase and solvent mapping focusing on the active site hydration motif in combination with a retrospective screening revealed the importance of a set of 29 crystallographic water molecules for achieving high enrichment as to the discrimination between active compounds and inactive decoys. The scaling of Van der Waals radii of system atoms had an additional effect on screening performance. Having optimized the in silico protocol, a prospective evaluation of the in-house compound collection derived a set of 18 top-ranked natural products that were subsequently evaluated in vitro for their activity as glycogen phosphorylase inhibitors. Two phenolic glucosides with glycogen phosphorylase-modulating activity were identified, whereas the most potent compound affording mid-micromolar inhibition was a glucosidic derivative of resveratrol, a stilbene well-known for its wide range of biological activities. Results show the possible phytotherapeutic and nutraceutical potential of products common in the Mediterranean countries, such as red wine and Vitis products in general or green raw salads and herbal preparations, where such compounds are abundant. Georg Thieme Verlag KG Stuttgart · New York.

ToxAlerts: A Web Server of Structural Alerts for Toxic Chemicals and Compounds with Potential Adverse Reactions

PubMed Central

2012-01-01

The article presents a Web-based platform for collecting and storing toxicological structural alerts from literature and for virtual screening of chemical libraries to flag potentially toxic chemicals and compounds that can cause adverse side effects. An alert is uniquely identified by a SMARTS template, a toxicological endpoint, and a publication where the alert was described. Additionally, the system allows storing complementary information such as name, comments, and mechanism of action, as well as other data. Most importantly, the platform can be easily used for fast virtual screening of large chemical datasets, focused libraries, or newly designed compounds against the toxicological alerts, providing a detailed profile of the chemicals grouped by structural alerts and endpoints. Such a facility can be used for decision making regarding whether a compound should be tested experimentally, validated with available QSAR models, or eliminated from consideration altogether. The alert-based screening can also be helpful for an easier interpretation of more complex QSAR models. The system is publicly accessible and tightly integrated with the Online Chemical Modeling Environment (OCHEM, http://ochem.eu). The system is open and expandable: any registered OCHEM user can introduce new alerts, browse, edit alerts introduced by other users, and virtually screen his/her data sets against all or selected alerts. The user sets being passed through the structural alerts can be used at OCHEM for other typical tasks: exporting in a wide variety of formats, development of QSAR models, additional filtering by other criteria, etc. The database already contains almost 600 structural alerts for such endpoints as mutagenicity, carcinogenicity, skin sensitization, compounds that undergo metabolic activation, and compounds that form reactive metabolites and, thus, can cause adverse reactions. The ToxAlerts platform is accessible on the Web at http://ochem.eu/alerts, and it is constantly growing. PMID:22876798

A graph-based approach to construct target-focused libraries for virtual screening.

PubMed

Naderi, Misagh; Alvin, Chris; Ding, Yun; Mukhopadhyay, Supratik; Brylinski, Michal

2016-01-01

Due to exorbitant costs of high-throughput screening, many drug discovery projects commonly employ inexpensive virtual screening to support experimental efforts. However, the vast majority of compounds in widely used screening libraries, such as the ZINC database, will have a very low probability to exhibit the desired bioactivity for a given protein. Although combinatorial chemistry methods can be used to augment existing compound libraries with novel drug-like compounds, the broad chemical space is often too large to be explored. Consequently, the trend in library design has shifted to produce screening collections specifically tailored to modulate the function of a particular target or a protein family. Assuming that organic compounds are composed of sets of rigid fragments connected by flexible linkers, a molecule can be decomposed into its building blocks tracking their atomic connectivity. On this account, we developed eSynth, an exhaustive graph-based search algorithm to computationally synthesize new compounds by reconnecting these building blocks following their connectivity patterns. We conducted a series of benchmarking calculations against the Directory of Useful Decoys, Enhanced database. First, in a self-benchmarking test, the correctness of the algorithm is validated with the objective to recover a molecule from its building blocks. Encouragingly, eSynth can efficiently rebuild more than 80 % of active molecules from their fragment components. Next, the capability to discover novel scaffolds is assessed in a cross-benchmarking test, where eSynth successfully reconstructed 40 % of the target molecules using fragments extracted from chemically distinct compounds. Despite an enormous chemical space to be explored, eSynth is computationally efficient; half of the molecules are rebuilt in less than a second, whereas 90 % take only about a minute to be generated. eSynth can successfully reconstruct chemically feasible molecules from molecular fragments. Furthermore, in a procedure mimicking the real application, where one expects to discover novel compounds based on a small set of already developed bioactives, eSynth is capable of generating diverse collections of molecules with the desired activity profiles. Thus, we are very optimistic that our effort will contribute to targeted drug discovery. eSynth is freely available to the academic community at www.brylinski.org/content/molecular-synthesis.Graphical abstractAssuming that organic compounds are composed of sets of rigid fragments connected by flexible linkers, a molecule can be decomposed into its building blocks tracking their atomic connectivity. Here, we developed eSynth, an automated method to synthesize new compounds by reconnecting these building blocks following the connectivity patterns via an exhaustive graph-based search algorithm. eSynth opens up a possibility to rapidly construct virtual screening libraries for targeted drug discovery.

Comparison of methods for the prediction of human clearance from hepatocyte intrinsic clearance for a set of reference compounds and an external evaluation set.

PubMed

Yamagata, Tetsuo; Zanelli, Ugo; Gallemann, Dieter; Perrin, Dominique; Dolgos, Hugues; Petersson, Carl

2017-09-01

1. We compared direct scaling, regression model equation and the so-called "Poulin et al." methods to scale clearance (CL) from in vitro intrinsic clearance (CL int ) measured in human hepatocytes using two sets of compounds. One reference set comprised of 20 compounds with known elimination pathways and one external evaluation set based on 17 compounds development in Merck (MS). 2. A 90% prospective confidence interval was calculated using the reference set. This interval was found relevant for the regression equation method. The three outliers identified were justified on the basis of their elimination mechanism. 3. The direct scaling method showed a systematic underestimation of clearance in both the reference and evaluation sets. The "Poulin et al." and the regression equation methods showed no obvious bias in either the reference or evaluation sets. 4. The regression model equation was slightly superior to the "Poulin et al." method in the reference set and showed a better absolute average fold error (AAFE) of value 1.3 compared to 1.6. A larger difference was observed in the evaluation set were the regression method and "Poulin et al." resulted in an AAFE of 1.7 and 2.6, respectively (removing the three compounds with known issues mentioned above). A similar pattern was observed for the correlation coefficient. Based on these data we suggest the regression equation method combined with a prospective confidence interval as the first choice for the extrapolation of human in vivo hepatic metabolic clearance from in vitro systems.

Emission from international sea transportation and environmental impact

NASA Astrophysics Data System (ADS)

Endresen, Øyvind; Sørgârd, Eirik; Sundet, Jostein K.; Dalsøren, Stig B.; Isaksen, Ivar S. A.; Berglen, Tore F.; Gravir, Gjermund

2003-09-01

Emission generated by the international merchant fleet has been suggested to represent a significant contribution to the global anthropogenic emissions. To analyze the impacts of these emissions, we present detailed model studies of the changes in atmospheric composition of pollutants and greenhouse compounds due to emissions from cargo and passenger ships in international trade. Global emission inventories of NOx, SO2, CO, CO2, and volatile organic compounds (VOC) are developed by a bottom-up approach combining ship-type specific engine emission modeling, oil cargo VOC vapor modeling, alternative global distribution methods, and ship operation data. Calculated bunker fuel consumption is found in agreement with international sales statistics. The Automated Mutual-assistance Vessel Rescue system (AMVER) data set is found to best reflect the distributions of cargo ships in international trade. A method based on the relative reporting frequency weighted by the ship size for each vessel type is recommended. We have exploited this modeled ship emissions inventory to estimate perturbations of the global distribution of ozone, methane, sulfate, and nitrogen compounds using a global 3-D chemical transport model with interactive ozone and sulfate chemistry. Ozone perturbations are highly nonlinear, being most efficient in regions of low background pollution. Different data sets (e.g., AMVER, The Comprehensive Ocean-Atmosphere Data Set (COADS)) lead to highly different regional perturbations. A maximum ozone perturbation of approximately 12 ppbv is obtained in the North Atlantic and in the North Pacific during summer months. Global average sulfate loading increases with 2.9%, while the increase is significantly larger over parts of western Europe (up to 8%). In contrast to the AMVER data, the COADS data give particularly large enhancements over the North Atlantic. Ship emissions reduce methane lifetime by approximately 5%. CO2 and O3 give positive radiative forcing (RF), and CH4 and sulfate give negative forcing. The total RF is small (0.01-0.02 W/m2) and connected with large uncertainties. Increase in acidification is 3-10% in certain coastal areas. The approach presented here is clearly useful for characterizing the present impact of ship emission and will be valuable for assessing the potential effect of various emission-control options.

Investigating Aspergillus nidulans secretome during colonisation of cork cell walls.

PubMed

Martins, Isabel; Garcia, Helga; Varela, Adélia; Núñez, Oscar; Planchon, Sébastien; Galceran, Maria Teresa; Renaut, Jenny; Rebelo, Luís P N; Silva Pereira, Cristina

2014-02-26

Cork, the outer bark of Quercus suber, shows a unique compositional structure, a set of remarkable properties, including high recalcitrance. Cork colonisation by Ascomycota remains largely overlooked. Herein, Aspergillus nidulans secretome on cork was analysed (2DE). Proteomic data were further complemented by microscopic (SEM) and spectroscopic (ATR-FTIR) evaluation of the colonised substrate and by targeted analysis of lignin degradation compounds (UPLC-HRMS). Data showed that the fungus formed an intricate network of hyphae around the cork cell walls, which enabled polysaccharides and lignin superficial degradation, but probably not of suberin. The degradation of polysaccharides was suggested by the identification of few polysaccharide degrading enzymes (β-glucosidases and endo-1,5-α-l-arabinosidase). Lignin degradation, which likely evolved throughout a Fenton-like mechanism relying on the activity of alcohol oxidases, was supported by the identification of small aromatic compounds (e.g. cinnamic acid and veratrylaldehyde) and of several putative high molecular weight lignin degradation products. In addition, cork recalcitrance was corroborated by the identification of several protein species which are associated with autolysis. Finally, stringent comparative proteomics revealed that A. nidulans colonisation of cork and wood share a common set of enzymatic mechanisms. However the higher polysaccharide accessibility in cork might explain the increase of β-glucosidase in cork secretome. Cork degradation by fungi remains largely overlook. Herein we aimed at understanding how A. nidulans colonise cork cell walls and how this relates to wood colonisation. To address this, the protein species consistently present in the secretome were analysed, as well as major alterations occurring in the substrate, including lignin degradation compounds being released. The obtained data demonstrate that this fungus has superficially attacked the cork cell walls apparently by using both enzymatic and Fenton-like reactions. Only a few polysaccharide degrading enzymes could be detected in the secretome which was dominated by protein species associated with autolysis. Lignin degradation was corroborated by the identification of some degradation products, but the suberin barrier in the cell wall remained virtually intact. Comparative proteomics revealed that cork and wood colonisation share a common set of enzymatic mechanisms. Copyright © 2013 Elsevier B.V. All rights reserved.

Quantitative structure-permeability relationships at various pH values for acidic and basic drugs and drug-like compounds.

PubMed

Oja, M; Maran, U

2015-01-01

Absorption in gastrointestinal tract compartments varies and is largely influenced by pH. Therefore, considering pH in studies and analyses of membrane permeability provides an opportunity to gain a better understanding of the behaviour of compounds and to obtain good permeability estimates for prediction purposes. This study concentrates on relationships between the chemical structure and membrane permeability of acidic and basic drugs and drug-like compounds. The membrane permeability of 36 acidic and 61 basic compounds was measured using the parallel artificial membrane permeability assay (PAMPA) at pH 3, 5, 7.4 and 9. Descriptive and/or predictive single-parameter quantitative structure-permeability relationships were derived for all pH values. For acidic compounds, membrane permeability is mainly influenced by hydrogen bond donor properties, as revealed by models with r(2) > 0.8 for pH 3 and pH 5. For basic compounds, the best (r(2) > 0.7) structure-permeability relationships are obtained with the octanol-water distribution coefficient for pH 7.4 and pH 9, indicating the importance of partition properties. In addition to the validation set, the prediction quality of the developed models was tested with folic acid and astemizole, showing good matches between experimental and calculated membrane permeabilities at key pHs. Selected QSAR models are available at the QsarDB repository ( http://dx.doi.org/10.15152/QDB.166 ).

Are there physicochemical differences between allosteric and competitive ligands?

PubMed Central

Lu, Jing; Carlson, Heather A.

2017-01-01

Previous studies have compared the physicochemical properties of allosteric compounds to non-allosteric compounds. Those studies have found that allosteric compounds tend to be smaller, more rigid, more hydrophobic, and more drug-like than non-allosteric compounds. However, previous studies have not properly corrected for the fact that some protein targets have much more data than other systems. This generates concern regarding the possible skew that can be introduced by the inherent bias in the available data. Hence, this study aims to determine how robust the previous findings are to the addition of newer data. This study utilizes the Allosteric Database (ASD v3.0) and ChEMBL v20 to systematically obtain large datasets of both allosteric and competitive ligands. This dataset contains 70,219 and 9,511 unique ligands for the allosteric and competitive sets, respectively. Physically relevant compound descriptors were computed to examine the differences in their chemical properties. Particular attention was given to removing redundancy in the data and normalizing across ligand diversity and varied protein targets. The resulting distributions only show that allosteric ligands tend to be more aromatic and rigid and do not confirm the increase in hydrophobicity or difference in drug-likeness. These results are robust across different normalization schemes. PMID:29125840

Classifying compound mechanism of action for linking whole cell phenotypes to molecular targets

PubMed Central

Bourne, Christina R.; Wakeham, Nancy; Bunce, Richard A.; Berlin, K. Darrell; Barrow, William W.

2013-01-01

Drug development programs have proven successful when performed at a whole cell level, thus incorporating solubility and permeability into the primary screen. However, linking those results to the target within the cell has been a major set-back. The Phenotype Microarray system, marketed and sold by Biolog, seeks to address this need by assessing the phenotype in combination with a variety of chemicals with known mechanism of action (MOA). We have evaluated this system for usefulness in deducing the MOA for three test compounds. To achieve this, we constructed a database with 21 known antimicrobials, which served as a comparison for grouping our unknown MOA compounds. Pearson correlation and Ward linkage calculations were used to generate a dendrogram that produced clustering largely by known MOA, although there were exceptions. Of the three unknown compounds, one was definitively placed as an anti-folate. The second and third compounds’ MOA were not clearly identified, likely due to unique MOA not represented within the commercial database. The availability of the database generated in this report for S. aureus ATCC 29213 will increase the accessibility of this technique to other investigators. From our analysis, the Phenotype Microarray system can group compounds with clear MOA, but distinction of unique or broadly acting MOA at this time is less clear. PMID:22434711

Mechanistic systems modeling to guide drug discovery and development

PubMed Central

Schmidt, Brian J.; Papin, Jason A.; Musante, Cynthia J.

2013-01-01

A crucial question that must be addressed in the drug development process is whether the proposed therapeutic target will yield the desired effect in the clinical population. Pharmaceutical and biotechnology companies place a large investment on research and development, long before confirmatory data are available from human trials. Basic science has greatly expanded the computable knowledge of disease processes, both through the generation of large omics data sets and a compendium of studies assessing cellular and systemic responses to physiologic and pathophysiologic stimuli. Given inherent uncertainties in drug development, mechanistic systems models can better inform target selection and the decision process for advancing compounds through preclinical and clinical research. PMID:22999913

Mechanistic systems modeling to guide drug discovery and development.

PubMed

Schmidt, Brian J; Papin, Jason A; Musante, Cynthia J

2013-02-01

A crucial question that must be addressed in the drug development process is whether the proposed therapeutic target will yield the desired effect in the clinical population. Pharmaceutical and biotechnology companies place a large investment on research and development, long before confirmatory data are available from human trials. Basic science has greatly expanded the computable knowledge of disease processes, both through the generation of large omics data sets and a compendium of studies assessing cellular and systemic responses to physiologic and pathophysiologic stimuli. Given inherent uncertainties in drug development, mechanistic systems models can better inform target selection and the decision process for advancing compounds through preclinical and clinical research. Copyright © 2012 Elsevier Ltd. All rights reserved.

Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals.

PubMed

Trisciuzzi, Daniela; Alberga, Domenico; Mansouri, Kamel; Judson, Richard; Novellino, Ettore; Mangiatordi, Giuseppe Felice; Nicolotti, Orazio

2017-11-27

We present a practical and easy-to-run in silico workflow exploiting a structure-based strategy making use of docking simulations to derive highly predictive classification models of the androgenic potential of chemicals. Models were trained on a high-quality chemical collection comprising 1689 curated compounds made available within the CoMPARA consortium from the US Environmental Protection Agency and were integrated with a two-step applicability domain whose implementation had the effect of improving both the confidence in prediction and statistics by reducing the number of false negatives. Among the nine androgen receptor X-ray solved structures, the crystal 2PNU (entry code from the Protein Data Bank) was associated with the best performing structure-based classification model. Three validation sets comprising each 2590 compounds extracted by the DUD-E collection were used to challenge model performance and the effectiveness of Applicability Domain implementation. Next, the 2PNU model was applied to screen and prioritize two collections of chemicals. The first is a small pool of 12 representative androgenic compounds that were accurately classified based on outstanding rationale at the molecular level. The second is a large external blind set of 55450 chemicals with potential for human exposure. We show how the use of molecular docking provides highly interpretable models and can represent a real-life option as an alternative nontesting method for predictive toxicology.

New clues on carcinogenicity-related substructures derived from mining two large datasets of chemical compounds.

PubMed

Golbamaki, Azadi; Benfenati, Emilio; Golbamaki, Nazanin; Manganaro, Alberto; Merdivan, Erinc; Roncaglioni, Alessandra; Gini, Giuseppina

2016-04-02

In this study, new molecular fragments associated with genotoxic and nongenotoxic carcinogens are introduced to estimate the carcinogenic potential of compounds. Two rule-based carcinogenesis models were developed with the aid of SARpy: model R (from rodents' experimental data) and model E (from human carcinogenicity data). Structural alert extraction method of SARpy uses a completely automated and unbiased manner with statistical significance. The carcinogenicity models developed in this study are collections of carcinogenic potential fragments that were extracted from two carcinogenicity databases: the ANTARES carcinogenicity dataset with information from bioassay on rats and the combination of ISSCAN and CGX datasets, which take into accounts human-based assessment. The performance of these two models was evaluated in terms of cross-validation and external validation using a 258 compound case study dataset. Combining R and H predictions and scoring a positive or negative result when both models are concordant on a prediction, increased accuracy to 72% and specificity to 79% on the external test set. The carcinogenic fragments present in the two models were compared and analyzed from the point of view of chemical class. The results of this study show that the developed rule sets will be a useful tool to identify some new structural alerts of carcinogenicity and provide effective information on the molecular structures of carcinogenic chemicals.

New antitrichomonal drug-like chemicals selected by bond (edge)-based TOMOCOMD-CARDD descriptors.

PubMed

Meneses-Marcel, Alfredo; Rivera-Borroto, Oscar M; Marrero-Ponce, Yovani; Montero, Alina; Machado Tugores, Yanetsy; Escario, José Antonio; Gómez Barrio, Alicia; Montero Pereira, David; Nogal, Juan José; Kouznetsov, Vladimir V; Ochoa Puentes, Cristian; Bohórquez, Arnold R; Grau, Ricardo; Torrens, Francisco; Ibarra-Velarde, Froylán; Arán, Vicente J

2008-09-01

Bond-based quadratic indices, new TOMOCOMD-CARDD molecular descriptors, and linear discriminant analysis (LDA) were used to discover novel lead trichomonacidals. The obtained LDA-based quantitative structure-activity relationships (QSAR) models, using nonstochastic and stochastic indices, were able to classify correctly 87.91% (87.50%) and 89.01% (84.38%) of the chemicals in training (test) sets, respectively. They showed large Matthews correlation coefficients of 0.75 (0.71) and 0.78 (0.65) for the training (test) sets, correspondingly. Later, both models were applied to the virtual screening of 21 chemicals to find new lead antitrichomonal agents. Predictions agreed with experimental results to a great extent because a correct classification for both models of 95.24% (20 of 21) of the chemicals was obtained. Of the 21 compounds that were screened and synthesized, 2 molecules (chemicals G-1, UC-245) showed high to moderate cytocidal activity at the concentration of 10 microg/ml, another 2 compounds (G-0 and CRIS-148) showed high cytocidal activity only at the concentration of 100 microg/ml, and the remaining chemicals (from CRIS-105 to CRIS-153, except CRIS-148) were inactive at these assayed concentrations. Finally, the best candidate, G-1 (cytocidal activity of 100% at 10 microg/ml) was in vivo assayed in ovariectomized Wistar rats achieving promising results as a trichomonacidal drug-like compound.

Bond-based bilinear indices for computational discovery of novel trypanosomicidal drug-like compounds through virtual screening.

PubMed

Castillo-Garit, Juan Alberto; del Toro-Cortés, Oremia; Vega, Maria C; Rolón, Miriam; Rojas de Arias, Antonieta; Casañola-Martin, Gerardo M; Escario, José A; Gómez-Barrio, Alicia; Marrero-Ponce, Yovani; Torrens, Francisco; Abad, Concepción

2015-01-01

Two-dimensional bond-based bilinear indices and linear discriminant analysis are used in this report to perform a quantitative structure-activity relationship study to identify new trypanosomicidal compounds. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop the theoretical models. Two discriminant models, computed using bond-based bilinear indices, are developed and both show accuracies higher than 86% for training and test sets. The stochastic model correctly indentifies nine out of eleven compounds of a set of organic chemicals obtained from our synthetic collaborators. The in vitro antitrypanosomal activity of this set against epimastigote forms of Trypanosoma cruzi is assayed. Both models show a good agreement between theoretical predictions and experimental results. Three compounds showed IC50 values for epimastigote elimination (AE) lower than 50 μM, while for the benznidazole the IC50 = 54.7 μM which was used as reference compound. The value of IC50 for cytotoxicity of these compounds is at least 5 times greater than their value of IC50 for AE. Finally, we can say that, the present algorithm constitutes a step forward in the search for efficient ways of discovering new antitrypanosomal compounds. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Estimation of lower flammability limits of C-H compounds in air at atmospheric pressure, evaluation of temperature dependence and diluent effect.

PubMed

Mendiburu, Andrés Z; de Carvalho, João A; Coronado, Christian R

2015-03-21

Estimation of the lower flammability limits of C-H compounds at 25 °C and 1 atm; at moderate temperatures and in presence of diluent was the objective of this study. A set of 120 C-H compounds was divided into a correlation set and a prediction set of 60 compounds each. The absolute average relative error for the total set was 7.89%; for the correlation set, it was 6.09%; and for the prediction set it was 9.68%. However, it was shown that by considering different sources of experimental data the values were reduced to 6.5% for the prediction set and to 6.29% for the total set. The method showed consistency with Le Chatelier's law for binary mixtures of C-H compounds. When tested for a temperature range from 5 °C to 100 °C, the absolute average relative errors were 2.41% for methane; 4.78% for propane; 0.29% for iso-butane and 3.86% for propylene. When nitrogen was added, the absolute average relative errors were 2.48% for methane; 5.13% for propane; 0.11% for iso-butane and 0.15% for propylene. When carbon dioxide was added, the absolute relative errors were 1.80% for methane; 5.38% for propane; 0.86% for iso-butane and 1.06% for propylene. Copyright © 2014 Elsevier B.V. All rights reserved.

Atmospheric Chemistry of Micrometeoritic Organic Compounds

NASA Technical Reports Server (NTRS)

Kress, M. E.; Belle, C. L.; Pevyhouse, A. R.; Iraci, L. T.

2011-01-01

Micrometeorites approx.100 m in diameter deliver most of the Earth s annual accumulation of extraterrestrial material. These small particles are so strongly heated upon atmospheric entry that most of their volatile content is vaporized. Here we present preliminary results from two sets of experiments to investigate the fate of the organic fraction of micrometeorites. In the first set of experiments, 300 m particles of a CM carbonaceous chondrite were subject to flash pyrolysis, simulating atmospheric entry. In addition to CO and CO2, many organic compounds were released, including functionalized benzenes, hydrocarbons, and small polycyclic aromatic hydrocarbons. In the second set of experiments, we subjected two of these compounds to conditions that simulate the heterogeneous chemistry of Earth s upper atmosphere. We find evidence that meteor-derived compounds can follow reaction pathways leading to the formation of more complex organic compounds.

Discovery of New Compounds Active against Plasmodium falciparum by High Throughput Screening of Microbial Natural Products.

PubMed

Pérez-Moreno, Guiomar; Cantizani, Juan; Sánchez-Carrasco, Paula; Ruiz-Pérez, Luis Miguel; Martín, Jesús; El Aouad, Noureddine; Pérez-Victoria, Ignacio; Tormo, José Rubén; González-Menendez, Víctor; González, Ignacio; de Pedro, Nuria; Reyes, Fernando; Genilloud, Olga; Vicente, Francisca; González-Pacanowska, Dolores

2016-01-01

Due to the low structural diversity within the set of antimalarial drugs currently available in the clinic and the increasing number of cases of resistance, there is an urgent need to find new compounds with novel modes of action to treat the disease. Microbial natural products are characterized by their large diversity provided in terms of the chemical complexity of the compounds and the novelty of structures. Microbial natural products extracts have been underexplored in the search for new antiparasitic drugs and even more so in the discovery of new antimalarials. Our objective was to find new druggable natural products with antimalarial properties from the MEDINA natural products collection, one of the largest natural product libraries harboring more than 130,000 microbial extracts. In this work, we describe the optimization process and the results of a phenotypic high throughput screen (HTS) based on measurements of Plasmodium lactate dehydrogenase. A subset of more than 20,000 extracts from the MEDINA microbial products collection has been explored, leading to the discovery of 3 new compounds with antimalarial activity. In addition, we report on the novel antiplasmodial activity of 4 previously described natural products.

Design strategies to minimize the radiative efficiency of global warming molecules

PubMed Central

Bera, Partha P.; Francisco, Joseph S.; Lee, Timothy J.

2010-01-01

A strategy is devised to screen molecules based on their radiative efficiency. The methodology should be useful as one additional constraint when determining the best molecule to use for an industrial application. The strategy is based on the results of a recent study where we examined molecular properties of global warming molecules using ab initio electronic structure methods to determine which fundamental molecular properties are important in assessing the radiative efficiency of a molecule. Six classes of perfluorinated compounds are investigated. For similar numbers of fluorine atoms, their absorption of radiation in the IR window decreases according to perfluoroethers > perfluorothioethers ≈ sulfur/carbon compounds > perfluorocarbons > perfluoroolefins > carbon/nitrogen compounds. Perfluoroethers and hydrofluorethers are shown to possess a large absorption in the IR window due to (i) the C─O bonds are very polar, (ii) the C-O stretches fall within the IR window and have large IR intensity due to their polarity, and (iii) the IR intensity for C-F stretches in which the fluorine atom is bonded to the carbon that is bonded to the oxygen atom is enhanced due to a larger C─F bond polarity. Lengthening the carbon chain leads to a larger overall absorption in the IR window, though the IR intensity per bond is smaller. Finally, for a class of partially fluorinated compounds with a set number of electronegative atoms, the overall absorption in the IR window can vary significantly, as much as a factor of 2, depending on how the fluorine atoms are distributed within the molecule. PMID:20439762

[Indoor air pollution by volatile organic compounds in large buildings: pollution levels and remaining issues after revision of the Act on Maintenance of Sanitation in Buildings in 2002].

PubMed

Sakai, Kiyoshi; Kamijima, Michihiro; Shibata, Eiji; Ohno, Hiroyuki; Nakajima, Tamie

2010-09-01

This study aimed to clarify indoor air pollution levels of volatile organic compounds (VOCs), especially 2-ethyl-1-hexanol (2E1H) in large buildings after revising of the Act on Maintenance of Sanitation in Buildings in 2002. We measured indoor air VOC concentrations in 57 (97%) out of a total of 61 large buildings completed within one year in half of the area of Nagoya, Japan, from 2003 through 2007. Airborne concentrations of 13 carbonyl compounds were determined with diffusion samplers and high-performance liquid chromatography, and of the other 32 VOCs with diffusion samplers and gas chromatography with a mass spectrometer. Formaldehyde was detected in all samples of indoor air but the concentrations were lower than the indoor air quality standard value set in Japan (100 microg/m3). Geometric mean concentrations of the other major VOCs, namely toluene, xylene, ethylbenzene, styrene, p-dichlorobenzene and acetaldehyde were also low. 2E1H was found to be one of the predominating VOCs in indoor air of large buildings. A few rooms in a small number of buildings surveyed showed high concentrations of 2E1H, while low concentrations were observed in most rooms of those buildings as well as in other buildings. It was estimated that about 310 buildings had high indoor air pollution levels of 2E1H, with increase during the 5 years from 2003 in Japan. Indoor air pollution levels of VOCs in new large buildings are generally good, although a few rooms in a small number of buildings showed high concentrations in 2E1H, a possible causative chemical in sick building symptoms. Therefore, 2E1H needs particular attention as an important indoor air pollutant.

Computational dissection of human episodic memory reveals mental process-specific genetic profiles

PubMed Central

Luksys, Gediminas; Fastenrath, Matthias; Coynel, David; Freytag, Virginie; Gschwind, Leo; Heck, Angela; Jessen, Frank; Maier, Wolfgang; Milnik, Annette; Riedel-Heller, Steffi G.; Scherer, Martin; Spalek, Klara; Vogler, Christian; Wagner, Michael; Wolfsgruber, Steffen; Papassotiropoulos, Andreas; de Quervain, Dominique J.-F.

2015-01-01

Episodic memory performance is the result of distinct mental processes, such as learning, memory maintenance, and emotional modulation of memory strength. Such processes can be effectively dissociated using computational models. Here we performed gene set enrichment analyses of model parameters estimated from the episodic memory performance of 1,765 healthy young adults. We report robust and replicated associations of the amine compound SLC (solute-carrier) transporters gene set with the learning rate, of the collagen formation and transmembrane receptor protein tyrosine kinase activity gene sets with the modulation of memory strength by negative emotional arousal, and of the L1 cell adhesion molecule (L1CAM) interactions gene set with the repetition-based memory improvement. Furthermore, in a large functional MRI sample of 795 subjects we found that the association between L1CAM interactions and memory maintenance revealed large clusters of differences in brain activity in frontal cortical areas. Our findings provide converging evidence that distinct genetic profiles underlie specific mental processes of human episodic memory. They also provide empirical support to previous theoretical and neurobiological studies linking specific neuromodulators to the learning rate and linking neural cell adhesion molecules to memory maintenance. Furthermore, our study suggests additional memory-related genetic pathways, which may contribute to a better understanding of the neurobiology of human memory. PMID:26261317

Computational dissection of human episodic memory reveals mental process-specific genetic profiles.

PubMed

Luksys, Gediminas; Fastenrath, Matthias; Coynel, David; Freytag, Virginie; Gschwind, Leo; Heck, Angela; Jessen, Frank; Maier, Wolfgang; Milnik, Annette; Riedel-Heller, Steffi G; Scherer, Martin; Spalek, Klara; Vogler, Christian; Wagner, Michael; Wolfsgruber, Steffen; Papassotiropoulos, Andreas; de Quervain, Dominique J-F

2015-09-01

Episodic memory performance is the result of distinct mental processes, such as learning, memory maintenance, and emotional modulation of memory strength. Such processes can be effectively dissociated using computational models. Here we performed gene set enrichment analyses of model parameters estimated from the episodic memory performance of 1,765 healthy young adults. We report robust and replicated associations of the amine compound SLC (solute-carrier) transporters gene set with the learning rate, of the collagen formation and transmembrane receptor protein tyrosine kinase activity gene sets with the modulation of memory strength by negative emotional arousal, and of the L1 cell adhesion molecule (L1CAM) interactions gene set with the repetition-based memory improvement. Furthermore, in a large functional MRI sample of 795 subjects we found that the association between L1CAM interactions and memory maintenance revealed large clusters of differences in brain activity in frontal cortical areas. Our findings provide converging evidence that distinct genetic profiles underlie specific mental processes of human episodic memory. They also provide empirical support to previous theoretical and neurobiological studies linking specific neuromodulators to the learning rate and linking neural cell adhesion molecules to memory maintenance. Furthermore, our study suggests additional memory-related genetic pathways, which may contribute to a better understanding of the neurobiology of human memory.

Predicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data

PubMed Central

Perryman, Alexander L.; Stratton, Thomas P.; Ekins, Sean; Freundlich, Joel S.

2015-01-01

Purpose Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability. Methods Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism). Results “Pruning” out the moderately unstable/moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 hour. Conclusions Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources. PMID:26415647

Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.

PubMed

Perryman, Alexander L; Stratton, Thomas P; Ekins, Sean; Freundlich, Joel S

2016-02-01

Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability. Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism). "Pruning" out the moderately unstable / moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 h. Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources.

Effect of hydrothermal liquefaction aqueous phase recycling on bio-crude yields and composition.

PubMed

Biller, Patrick; Madsen, René B; Klemmer, Maika; Becker, Jacob; Iversen, Bo B; Glasius, Marianne

2016-11-01

Hydrothermal liquefaction (HTL) is a promising thermo-chemical processing technology for the production of biofuels but produces large amounts of process water. Therefore recirculation of process water from HTL of dried distillers grains with solubles (DDGS) is investigated. Two sets of recirculation on a continuous reactor system using K2CO3 as catalyst were carried out. Following this, the process water was recirculated in batch experiments for a total of 10 rounds. To assess the effect of alkali catalyst, non-catalytic HTL process water recycling was performed with 9 recycle rounds. Both sets of experiments showed a large increase in bio-crude yields from approximately 35 to 55wt%. The water phase and bio-crude samples from all experiments were analysed via quantitative gas chromatography-mass spectrometry (GC-MS) to investigate their composition and build-up of organic compounds. Overall the results show an increase in HTL conversion efficiency and a lower volume, more concentrated aqueous by-product following recycling. Copyright © 2016 Elsevier Ltd. All rights reserved.

Assessment of the Accuracy of the Bethe-Salpeter (BSE/GW) Oscillator Strengths.

PubMed

Jacquemin, Denis; Duchemin, Ivan; Blondel, Aymeric; Blase, Xavier

2016-08-09

Aiming to assess the accuracy of the oscillator strengths determined at the BSE/GW level, we performed benchmark calculations using three complementary sets of molecules. In the first, we considered ∼80 states in Thiel's set of compounds and compared the BSE/GW oscillator strengths to recently determined ADC(3/2) and CC3 reference values. The second set includes the oscillator strengths of the low-lying states of 80 medium to large dyes for which we have determined CC2/aug-cc-pVTZ values. The third set contains 30 anthraquinones for which experimental oscillator strengths are available. We find that BSE/GW accurately reproduces the trends for all series with excellent correlation coefficients to the benchmark data and generally very small errors. Indeed, for Thiel's sets, the BSE/GW values are more accurate (using CC3 references) than both CC2 and ADC(3/2) values on both absolute and relative scales. For all three sets, BSE/GW errors also tend to be nicely spread with almost equal numbers of positive and negative deviations as compared to reference values.

Large Dataset of Acute Oral Toxicity Data Created for Testing ...

EPA Pesticide Factsheets

Acute toxicity data is a common requirement for substance registration in the US. Currently only data derived from animal tests are accepted by regulatory agencies, and the standard in vivo tests use lethality as the endpoint. Non-animal alternatives such as in silico models are being developed due to animal welfare and resource considerations. We compiled a large dataset of oral rat LD50 values to assess the predictive performance currently available in silico models. Our dataset combines LD50 values from five different sources: literature data provided by The Dow Chemical Company, REACH data from eChemportal, HSDB (Hazardous Substances Data Bank), RTECS data from Leadscope, and the training set underpinning TEST (Toxicity Estimation Software Tool). Combined these data sources yield 33848 chemical-LD50 pairs (data points), with 23475 unique data points covering 16439 compounds. The entire dataset was loaded into a chemical properties database. All of the compounds were registered in DSSTox and 59.5% have publically available structures. Compounds without a structure in DSSTox are currently having their structures registered. The structural data will be used to evaluate the predictive performance and applicable chemical domains of three QSAR models (TIMES, PROTOX, and TEST). Future work will combine the dataset with information from ToxCast assays, and using random forest modeling, assess whether ToxCast assays are useful in predicting acute oral toxicity. Pre

Distributed chemical computing using ChemStar: an open source java remote method invocation architecture applied to large scale molecular data from PubChem.

PubMed

Karthikeyan, M; Krishnan, S; Pandey, Anil Kumar; Bender, Andreas; Tropsha, Alexander

2008-04-01

We present the application of a Java remote method invocation (RMI) based open source architecture to distributed chemical computing. This architecture was previously employed for distributed data harvesting of chemical information from the Internet via the Google application programming interface (API; ChemXtreme). Due to its open source character and its flexibility, the underlying server/client framework can be quickly adopted to virtually every computational task that can be parallelized. Here, we present the server/client communication framework as well as an application to distributed computing of chemical properties on a large scale (currently the size of PubChem; about 18 million compounds), using both the Marvin toolkit as well as the open source JOELib package. As an application, for this set of compounds, the agreement of log P and TPSA between the packages was compared. Outliers were found to be mostly non-druglike compounds and differences could usually be explained by differences in the underlying algorithms. ChemStar is the first open source distributed chemical computing environment built on Java RMI, which is also easily adaptable to user demands due to its "plug-in architecture". The complete source codes as well as calculated properties along with links to PubChem resources are available on the Internet via a graphical user interface at http://moltable.ncl.res.in/chemstar/.

A cluster-based strategy for assessing the overlap between large chemical libraries and its application to a recent acquisition.

PubMed

Engels, Michael F M; Gibbs, Alan C; Jaeger, Edward P; Verbinnen, Danny; Lobanov, Victor S; Agrafiotis, Dimitris K

2006-01-01

We report on the structural comparison of the corporate collections of Johnson & Johnson Pharmaceutical Research & Development (JNJPRD) and 3-Dimensional Pharmaceuticals (3DP), performed in the context of the recent acquisition of 3DP by JNJPRD. The main objective of the study was to assess the druglikeness of the 3DP library and the extent to which it enriched the chemical diversity of the JNJPRD corporate collection. The two databases, at the time of acquisition, collectively contained more than 1.1 million compounds with a clearly defined structural description. The analysis was based on a clustering approach and aimed at providing an intuitive quantitative estimate and visual representation of this enrichment. A novel hierarchical clustering algorithm called divisive k-means was employed in combination with Kelley's cluster-level selection method to partition the combined data set into clusters, and the diversity contribution of each library was evaluated as a function of the relative occupancy of these clusters. Typical 3DP chemotypes enriching the diversity of the JNJPRD collection were catalogued and visualized using a modified maximum common substructure algorithm. The joint collection of JNJPRD and 3DP compounds was also compared to other databases of known medicinally active or druglike compounds. The potential of the methodology for the analysis of very large chemical databases is discussed.

Comparison of Multiple Linear Regressions and Neural Networks based QSAR models for the design of new antitubercular compounds.

PubMed

Ventura, Cristina; Latino, Diogo A R S; Martins, Filomena

2013-01-01

The performance of two QSAR methodologies, namely Multiple Linear Regressions (MLR) and Neural Networks (NN), towards the modeling and prediction of antitubercular activity was evaluated and compared. A data set of 173 potentially active compounds belonging to the hydrazide family and represented by 96 descriptors was analyzed. Models were built with Multiple Linear Regressions (MLR), single Feed-Forward Neural Networks (FFNNs), ensembles of FFNNs and Associative Neural Networks (AsNNs) using four different data sets and different types of descriptors. The predictive ability of the different techniques used were assessed and discussed on the basis of different validation criteria and results show in general a better performance of AsNNs in terms of learning ability and prediction of antitubercular behaviors when compared with all other methods. MLR have, however, the advantage of pinpointing the most relevant molecular characteristics responsible for the behavior of these compounds against Mycobacterium tuberculosis. The best results for the larger data set (94 compounds in training set and 18 in test set) were obtained with AsNNs using seven descriptors (R(2) of 0.874 and RMSE of 0.437 against R(2) of 0.845 and RMSE of 0.472 in MLRs, for test set). Counter-Propagation Neural Networks (CPNNs) were trained with the same data sets and descriptors. From the scrutiny of the weight levels in each CPNN and the information retrieved from MLRs, a rational design of potentially active compounds was attempted. Two new compounds were synthesized and tested against M. tuberculosis showing an activity close to that predicted by the majority of the models. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Computing Smallest Intervention Strategies for Multiple Metabolic Networks in a Boolean Model

PubMed Central

Lu, Wei; Song, Jiangning; Akutsu, Tatsuya

2015-01-01

Abstract This article considers the problem whereby, given two metabolic networks N1 and N2, a set of source compounds, and a set of target compounds, we must find the minimum set of reactions whose removal (knockout) ensures that the target compounds are not producible in N1 but are producible in N2. Similar studies exist for the problem of finding the minimum knockout with the smallest side effect for a single network. However, if technologies of external perturbations are advanced in the near future, it may be important to develop methods of computing the minimum knockout for multiple networks (MKMN). Flux balance analysis (FBA) is efficient if a well-polished model is available. However, that is not always the case. Therefore, in this article, we study MKMN in Boolean models and an elementary mode (EM)-based model. Integer linear programming (ILP)-based methods are developed for these models, since MKMN is NP-complete for both the Boolean model and the EM-based model. Computer experiments are conducted with metabolic networks of clostridium perfringens SM101 and bifidobacterium longum DJO10A, respectively known as bad bacteria and good bacteria for the human intestine. The results show that larger networks are more likely to have MKMN solutions. However, solving for these larger networks takes a very long time, and often the computation cannot be completed. This is reasonable, because small networks do not have many alternative pathways, making it difficult to satisfy the MKMN condition, whereas in large networks the number of candidate solutions explodes. Our developed software minFvskO is available online. PMID:25684199

Organochlorine compounds in bed sediment and fish tissue in the South Platte River Basin, USA, 1992-1993

USGS Publications Warehouse

Tate, C.M.; Heiny, J.S.

1996-01-01

Bed-sediment and fish-tissue samples were collected in the South Platte River Basin to determine the occurrence and distribution of organochlorine compounds in the basin. During August-November 1992 and August 1993, bed sediment (23 sites) and fish tissue (subset of 19 sites) were sampled and analyzed for 32 organochlorine compounds in bed sediment and 27 compounds in fish tissue. More types of organochlorine compounds were detected in fish tissue than in bed sediment. Total DDT, p,p???-DDE, o,p???-DDE, p,p???-DDD, total PCS, Dacthal??, dieldrin, cis-chlordane, cis-nonachlor, trans-nonachlor, and p,p???-DDT were detected in fish tissue at >25% of the sites; p,p???-DDE, total DDT, cis-chlordane, and trans-chlordane were detected in bed sediment at >25% of the sites. Organochlorine concentrations in bed sediment and fish tissue were related to land-use settings. Few organochlorine compounds were detected at minimally impacted sites located in rangeland, forest, and built-up land-use settings. Chlordane-related compounds and p,p???-methoxychlor in bed sediment and fish tissue, endrin in fish tissue, and endosulfan I in bed sediment were associated with urban and mixed (urban and agricultural) sites. Dacthal?? in bed sediment and fish tissue was associated with agricultural sites. The compounds HCB, ??-HCH, PCA, and toxaphene were detected only at mixed land-use sites. Although DDT and DDT-metabolites, dieldrin, and total PCB were detected in urban, mixed, and agricultural land-use settings, highest mean concentrations were detected at mixed land-use sites. Mixed land-use sites had the greatest number of organochlorine compounds detected in fish tissue, whereas urban and mixed sites had the greatest number of organochlorine compounds detected in bed sediment. Measuring concentrations of organochlorine compounds in bed sediment and fish tissue at the same site offers a more complete picture of the persistence of organochlorine compounds in the environment and their relation to land-use settings.

Origin-based polyphenolic fingerprinting of Theobroma cacao in unfermented and fermented beans.

PubMed

D'Souza, Roy N; Grimbs, Sergio; Behrends, Britta; Bernaert, Herwig; Ullrich, Matthias S; Kuhnert, Nikolai

2017-09-01

A comprehensive analysis of cocoa polyphenols from unfermented and fermented cocoa beans from a wide range of geographic origins was carried out to catalogue systematic differences based on their origin as well as fermentation status. This study identifies previously unknown compounds with the goal to ascertain, which of these are responsible for the largest differences between bean types. UHPLC coupled with ultra-high resolution time-of-flight mass spectrometry was employed to identify and relatively quantify various oligomeric proanthocyanidins and their glycosides amongst several other unreported compounds. A series of biomarkers allowing a clear distinction between unfermented and fermented cocoa beans and for beans of different origins were identified. The large sample set employed allowed comparison of statistically significant variations of key cocoa constituents. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automated compound classification using a chemical ontology.

PubMed

Bobach, Claudia; Böhme, Timo; Laube, Ulf; Püschel, Anett; Weber, Lutz

2012-12-29

Classification of chemical compounds into compound classes by using structure derived descriptors is a well-established method to aid the evaluation and abstraction of compound properties in chemical compound databases. MeSH and recently ChEBI are examples of chemical ontologies that provide a hierarchical classification of compounds into general compound classes of biological interest based on their structural as well as property or use features. In these ontologies, compounds have been assigned manually to their respective classes. However, with the ever increasing possibilities to extract new compounds from text documents using name-to-structure tools and considering the large number of compounds deposited in databases, automated and comprehensive chemical classification methods are needed to avoid the error prone and time consuming manual classification of compounds. In the present work we implement principles and methods to construct a chemical ontology of classes that shall support the automated, high-quality compound classification in chemical databases or text documents. While SMARTS expressions have already been used to define chemical structure class concepts, in the present work we have extended the expressive power of such class definitions by expanding their structure-based reasoning logic. Thus, to achieve the required precision and granularity of chemical class definitions, sets of SMARTS class definitions are connected by OR and NOT logical operators. In addition, AND logic has been implemented to allow the concomitant use of flexible atom lists and stereochemistry definitions. The resulting chemical ontology is a multi-hierarchical taxonomy of concept nodes connected by directed, transitive relationships. A proposal for a rule based definition of chemical classes has been made that allows to define chemical compound classes more precisely than before. The proposed structure-based reasoning logic allows to translate chemistry expert knowledge into a computer interpretable form, preventing erroneous compound assignments and allowing automatic compound classification. The automated assignment of compounds in databases, compound structure files or text documents to their related ontology classes is possible through the integration with a chemical structure search engine. As an application example, the annotation of chemical structure files with a prototypic ontology is demonstrated.

Automated compound classification using a chemical ontology

PubMed Central

2012-01-01

Background Classification of chemical compounds into compound classes by using structure derived descriptors is a well-established method to aid the evaluation and abstraction of compound properties in chemical compound databases. MeSH and recently ChEBI are examples of chemical ontologies that provide a hierarchical classification of compounds into general compound classes of biological interest based on their structural as well as property or use features. In these ontologies, compounds have been assigned manually to their respective classes. However, with the ever increasing possibilities to extract new compounds from text documents using name-to-structure tools and considering the large number of compounds deposited in databases, automated and comprehensive chemical classification methods are needed to avoid the error prone and time consuming manual classification of compounds. Results In the present work we implement principles and methods to construct a chemical ontology of classes that shall support the automated, high-quality compound classification in chemical databases or text documents. While SMARTS expressions have already been used to define chemical structure class concepts, in the present work we have extended the expressive power of such class definitions by expanding their structure-based reasoning logic. Thus, to achieve the required precision and granularity of chemical class definitions, sets of SMARTS class definitions are connected by OR and NOT logical operators. In addition, AND logic has been implemented to allow the concomitant use of flexible atom lists and stereochemistry definitions. The resulting chemical ontology is a multi-hierarchical taxonomy of concept nodes connected by directed, transitive relationships. Conclusions A proposal for a rule based definition of chemical classes has been made that allows to define chemical compound classes more precisely than before. The proposed structure-based reasoning logic allows to translate chemistry expert knowledge into a computer interpretable form, preventing erroneous compound assignments and allowing automatic compound classification. The automated assignment of compounds in databases, compound structure files or text documents to their related ontology classes is possible through the integration with a chemical structure search engine. As an application example, the annotation of chemical structure files with a prototypic ontology is demonstrated. PMID:23273256

Straightforward hit identification approach in fragment-based discovery of bromodomain-containing protein 4 (BRD4) inhibitors.

PubMed

Borysko, Petro; Moroz, Yurii S; Vasylchenko, Oleksandr V; Hurmach, Vasyl V; Starodubtseva, Anastasia; Stefanishena, Natalia; Nesteruk, Kateryna; Zozulya, Sergey; Kondratov, Ivan S; Grygorenko, Oleksandr O

2018-05-09

A combination approach of a fragment screening and "SAR by catalog" was used for the discovery of bromodomain-containing protein 4 (BRD4) inhibitors. Initial screening of 3695-fragment library against bromodomain 1 of BRD4 using thermal shift assay (TSA), followed by initial hit validation, resulted in 73 fragment hits, which were used to construct a follow-up library selected from available screening collection. Additionally, analogs of inactive fragments, as well as a set of randomly selected compounds were also prepared (3 × 3200 compounds in total). Screening of the resulting sets using TSA, followed by re-testing at several concentrations, counter-screen, and TR-FRET assay resulted in 18 confirmed hits. Compounds derived from the initial fragment set showed better hit rate as compared to the other two sets. Finally, building dose-response curves revealed three compounds with IC 50  = 1.9-7.4 μM. For these compounds, binding sites and conformations in the BRD4 (4UYD) have been determined by docking. Copyright © 2018 Elsevier Ltd. All rights reserved.

WND-CHARM: Multi-purpose image classification using compound image transforms

PubMed Central

Orlov, Nikita; Shamir, Lior; Macura, Tomasz; Johnston, Josiah; Eckley, D. Mark; Goldberg, Ilya G.

2008-01-01

We describe a multi-purpose image classifier that can be applied to a wide variety of image classification tasks without modifications or fine-tuning, and yet provide classification accuracy comparable to state-of-the-art task-specific image classifiers. The proposed image classifier first extracts a large set of 1025 image features including polynomial decompositions, high contrast features, pixel statistics, and textures. These features are computed on the raw image, transforms of the image, and transforms of transforms of the image. The feature values are then used to classify test images into a set of pre-defined image classes. This classifier was tested on several different problems including biological image classification and face recognition. Although we cannot make a claim of universality, our experimental results show that this classifier performs as well or better than classifiers developed specifically for these image classification tasks. Our classifier’s high performance on a variety of classification problems is attributed to (i) a large set of features extracted from images; and (ii) an effective feature selection and weighting algorithm sensitive to specific image classification problems. The algorithms are available for free download from openmicroscopy.org. PMID:18958301

Complex versus simple models: ion-channel cardiac toxicity prediction.

PubMed

Mistry, Hitesh B

2018-01-01

There is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established large-scale biophysical cardiac models and a simple linear model B net was conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category using two different classification schemes based on information within CredibleMeds. The predictive performance of each model within each data-set for each classification scheme was assessed via a leave-one-out cross validation. Overall the B net model performed equally as well as the leading cardiac models in two of the data-sets and outperformed both cardiac models on the latest. These results highlight the importance of benchmarking complex versus simple models but also encourage the development of simple models.

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?"

Sponge-associated microbial Antarctic communities exhibiting antimicrobial activity against Burkholderia cepacia complex bacteria.

PubMed

Papaleo, Maria Cristiana; Fondi, Marco; Maida, Isabel; Perrin, Elena; Lo Giudice, Angelina; Michaud, Luigi; Mangano, Santina; Bartolucci, Gianluca; Romoli, Riccardo; Fani, Renato

2012-01-01

The aerobic heterotrophic bacterial communities isolated from three different Antarctic sponge species were analyzed for their ability to produce antimicrobial compounds active toward Cystic Fibrosis opportunistic pathogens belonging to the Burkholderia cepacia complex (Bcc). The phylogenetic analysis performed on the 16S rRNA genes affiliated the 140 bacterial strains analyzed to 15 genera. Just three of them (Psychrobacter, Pseudoalteromonas and Arthrobacter) were shared by the three sponges. The further Random Amplified Polymorphic DNA analysis allowed to demonstrate that microbial communities are highly sponge-specific and a very low degree of genus/species/strain sharing was detected. Data obtained revealed that most of these sponge-associated Antarctic bacteria and belonging to different genera were able to completely inhibit the growth of bacteria belonging to the Bcc. On the other hand, the same Antarctic strains did not have any effect on the growth of other pathogenic bacteria, strongly suggesting that the inhibition is specific for Bcc bacteria. Moreover, the antimicrobial compounds synthesized by the most active Antarctic bacteria are very likely Volatile Organic Compounds (VOCs), a finding that was confirmed by the SPME-GC-MS technique, which revealed the production of a large set of VOCs by a representative set of Antarctic bacteria. The synthesis of these VOCs appeared to be related neither to the presence of pks genes nor the presence of plasmid molecules. The whole body of data obtained in this work indicates that sponge-associated bacteria represent an untapped source for the identification of new antimicrobial compounds and are paving the way for the discovery of new drugs that can be efficiently and successfully used for the treatment of CF infections. Copyright © 2011 Elsevier Inc. All rights reserved.

An Analysis of Quality in the Modular Housing Industry.

DTIC Science & Technology

1991-12-01

finishing, Station 5, installs rough plumbing and applies the first coat of drywall joint compound . The unit continues to ceiling/roof setting, Station...with I joint compound and drywall or plywood plates. 3 14. Rigid waferboard, oriented strand board, or plywood is used for exterior wall sheathing to...completed and tested, the second coat of joint compound is placed, and windows and doors are set. Insulation, exterior sheathing, roof sheathing

New Linear Partitioning Models Based on Experimental Water: Supercritical CO 2 Partitioning Data of Selected Organic Compounds

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

Burant, Aniela; Thompson, Christopher; Lowry, Gregory V.

2016-05-17

Partitioning coefficients of organic compounds between water and supercritical CO2 (sc-CO2) are necessary to assess the risk of migration of these chemicals from subsurface CO2 storage sites. Despite the large number of potential organic contaminants, the current data set of published water-sc-CO2 partitioning coefficients is very limited. Here, the partitioning coefficients of thiophene, pyrrole, and anisole were measured in situ over a range of temperatures and pressures using a novel pressurized batch reactor system with dual spectroscopic detectors: a near infrared spectrometer for measuring the organic analyte in the CO2 phase, and a UV detector for quantifying the analyte inmore » the aqueous phase. Our measured partitioning coefficients followed expected trends based on volatility and aqueous solubility. The partitioning coefficients and literature data were then used to update a published poly-parameter linear free energy relationship and to develop five new linear free energy relationships for predicting water-sc-CO2 partitioning coefficients. Four of the models targeted a single class of organic compounds. Unlike models that utilize Abraham solvation parameters, the new relationships use vapor pressure and aqueous solubility of the organic compound at 25 °C and CO2 density to predict partitioning coefficients over a range of temperature and pressure conditions. The compound class models provide better estimates of partitioning behavior for compounds in that class than the model built for the entire dataset.« less

P. falciparum in vitro killing rates allow to discriminate between different antimalarial mode-of-action.

PubMed

Sanz, Laura M; Crespo, Benigno; De-Cózar, Cristina; Ding, Xavier C; Llergo, Jose L; Burrows, Jeremy N; García-Bustos, Jose F; Gamo, Francisco-Javier

2012-01-01

Chemotherapy is still the cornerstone for malaria control. Developing drugs against Plasmodium parasites and monitoring their efficacy requires methods to accurately determine the parasite killing rate in response to treatment. Commonly used techniques essentially measure metabolic activity as a proxy for parasite viability. However, these approaches are susceptible to artefacts, as viability and metabolism are two parameters that are coupled during the parasite life cycle but can be differentially affected in response to drug actions. Moreover, traditional techniques do not allow to measure the speed-of-action of compounds on parasite viability, which is an essential efficacy determinant. We present here a comprehensive methodology to measure in vitro the direct effect of antimalarial compounds over the parasite viability, which is based on limiting serial dilution of treated parasites and re-growth monitoring. This methodology allows to precisely determine the killing rate of antimalarial compounds, which can be quantified by the parasite reduction ratio and parasite clearance time, which are key mode-of-action parameters. Importantly, we demonstrate that this technique readily permits to determine compound killing activities that might be otherwise missed by traditional, metabolism-based techniques. The analysis of a large set of antimalarial drugs reveals that this viability-based assay allows to discriminate compounds based on their antimalarial mode-of-action. This approach has been adapted to perform medium throughput screening, facilitating the identification of fast-acting antimalarial compounds, which are crucially needed for the control and possibly the eradication of malaria.

P. falciparum In Vitro Killing Rates Allow to Discriminate between Different Antimalarial Mode-of-Action

PubMed Central

Sanz, Laura M.; Crespo, Benigno; De-Cózar, Cristina; Ding, Xavier C.; Llergo, Jose L.; Burrows, Jeremy N.; García-Bustos, Jose F.; Gamo, Francisco-Javier

2012-01-01

Chemotherapy is still the cornerstone for malaria control. Developing drugs against Plasmodium parasites and monitoring their efficacy requires methods to accurately determine the parasite killing rate in response to treatment. Commonly used techniques essentially measure metabolic activity as a proxy for parasite viability. However, these approaches are susceptible to artefacts, as viability and metabolism are two parameters that are coupled during the parasite life cycle but can be differentially affected in response to drug actions. Moreover, traditional techniques do not allow to measure the speed-of-action of compounds on parasite viability, which is an essential efficacy determinant. We present here a comprehensive methodology to measure in vitro the direct effect of antimalarial compounds over the parasite viability, which is based on limiting serial dilution of treated parasites and re-growth monitoring. This methodology allows to precisely determine the killing rate of antimalarial compounds, which can be quantified by the parasite reduction ratio and parasite clearance time, which are key mode-of-action parameters. Importantly, we demonstrate that this technique readily permits to determine compound killing activities that might be otherwise missed by traditional, metabolism-based techniques. The analysis of a large set of antimalarial drugs reveals that this viability-based assay allows to discriminate compounds based on their antimalarial mode-of-action. This approach has been adapted to perform medium throughput screening, facilitating the identification of fast-acting antimalarial compounds, which are crucially needed for the control and possibly the eradication of malaria. PMID:22383983

A high throughput drug screening assay to identify compounds that promote oligodendrocyte differentiation using acutely dissociated and purified oligodendrocyte precursor cells.

PubMed

Lariosa-Willingham, Karen D; Rosler, Elen S; Tung, Jay S; Dugas, Jason C; Collins, Tassie L; Leonoudakis, Dmitri

2016-09-05

Multiple sclerosis is caused by an autoimmune response resulting in demyelination and neural degeneration. The adult central nervous system has the capacity to remyelinate axons in part through the generation of new oligodendrocytes (OLs). To identify clinical candidate compounds that may promote remyelination, we have developed a high throughput screening (HTS) assay to identify compounds that promote the differentiation of oligodendrocyte precursor cells (OPCs) into OLs. Using acutely dissociated and purified rat OPCs coupled with immunofluorescent image quantification, we have developed an OL differentiation assay. We have validated this assay with a known promoter of differentiation, thyroid hormone, and subsequently used the assay to screen the NIH clinical collection library. We have identified twenty-seven hit compounds which were validated by dose response analysis and the generation of half maximal effective concentration (EC50) values allowed for the ranking of efficacy. The assay identified novel promoters of OL differentiation which we attribute to (1) the incorporation of an OL toxicity pre-screen to allow lowering the concentrations of toxic compounds and (2) the utilization of freshly purified, non-passaged OPCs. These features set our assay apart from other OL differentiation assays used for drug discovery efforts. This acute primary OL-based differentiation assay should be of use to those interested in screening large compound libraries for the identification of drugs for the treatment of MS and other demyelinating diseases.

Large reversible magnetostrictive effect of MnCoSi-based compounds prepared by high-magnetic-field solidification

NASA Astrophysics Data System (ADS)

Hu, Q. B.; Hu, Y.; Zhang, S.; Tang, W.; He, X. J.; Li, Z.; Cao, Q. Q.; Wang, D. H.; Du, Y. W.

2018-01-01

The MnCoSi compound is a potential magnetostriction material since the magnetic field can drive a metamagnetic transition from an antiferromagnetic phase to a high magnetization phase in it, which accompanies a large lattice distortion. However, a large driving magnetic field, magnetic hysteresis, and poor mechanical properties seriously hinder its application for magnetostriction. By substituting Fe for Mn and introducing vacancies of the Mn element, textured and dense Mn0.97Fe0.03CoSi and Mn0.88CoSi compounds are prepared through a high-magnetic-field solidification approach. As a result, large room-temperature and reversible magnetostriction effects are observed in these compounds at a low magnetic field. The origin of this large magnetostriction effect and potential applications are discussed.

Illicit and pharmaceutical drug consumption estimated via wastewater analysis. Part A: chemical analysis and drug use estimates.

PubMed

Baker, David R; Barron, Leon; Kasprzyk-Hordern, Barbara

2014-07-15

This paper presents, for the first time, community-wide estimation of drug and pharmaceuticals consumption in England using wastewater analysis and a large number of compounds. Among groups of compounds studied were: stimulants, hallucinogens and their metabolites, opioids, morphine derivatives, benzodiazepines, antidepressants and others. Obtained results showed the usefulness of wastewater analysis in order to provide estimates of local community drug consumption. It is noticeable that where target compounds could be compared to NHS prescription statistics, good comparisons were apparent between the two sets of data. These compounds include oxycodone, dihydrocodeine, methadone, tramadol, temazepam and diazepam. Whereas, discrepancies were observed for propoxyphene, codeine, dosulepin and venlafaxine (over-estimations in each case except codeine). Potential reasons for discrepancies include: sales of drugs sold without prescription and not included within NHS data, abuse of a drug with the compound trafficked through illegal sources, different consumption patterns in different areas, direct disposal leading to over estimations when using parent compound as the drug target residue and excretion factors not being representative of the local community. It is noticeable that using a metabolite (and not a parent drug) as a biomarker leads to higher certainty of obtained estimates. With regard to illicit drugs, consistent and logical results were reported. Monitoring of these compounds over a one week period highlighted the expected recreational use of many of these drugs (e.g. cocaine and MDMA) and the more consistent use of others (e.g. methadone). Copyright © 2014 Elsevier B.V. All rights reserved.

Concentrations and co-occurrence correlations of 88 volatile organic compounds (VOCs) in the ambient air of 13 semi-rural to urban locations in the United States

USGS Publications Warehouse

Pankow, J.F.; Luo, W.; Bender, D.A.; Isabelle, L.M.; Hollingsworth, J.S.; Chen, C.; Asher, W.E.; Zogorski, J.S.

2003-01-01

The ambient air concentrations of 88 volatile organic compounds were determined in samples taken at 13 semi-rural to urban locations in Maine, Massachusetts, New Jersey, Pennsylvania, Ohio, Illinois, Louisiana, and California. The sampling periods ranged from 7 to 29 months, yielding a large data set with a total of 23,191 individual air concentration values, some of which were designated "ND" (not detected). For each compound at each sampling site, the air concentrations (ca, ppbV) are reported in terms of means, medians, and means of the detected values. The analytical method utilized adsorption/thermal desorption with air-sampling cartridges. The analytes included numerous halogenated alkanes, halogenated alkenes, ethers, alcohols, nitriles, esters, ketones, aromatics, a disulfide, and a furan. At some sites, the air concentrations of the gasoline-related aromatic compounds and the gasoline additive methyl tert-butyl ether were seasonally dependent, with concentrations that maximized in the winter. For each site studied here, the concentrations of some compounds were highly correlated one with another (e.g., the BTEX group (benzene, toluene, ethylbenzene, and the xylenes). Other aromatic compounds were also all generally correlated with one another, while the concentrations of other compound pairs were not correlated (e.g., benzene was not correlated with CFC-12). The concentrations found for the BTEX group were generally lower than the values that have been previously reported for urbanized and industrialized areas of other nations. ?? 2003 Elsevier Ltd. All rights reserved.

Binding Free Energy Calculations for Lead Optimization: Assessment of Their Accuracy in an Industrial Drug Design Context.

PubMed

Homeyer, Nadine; Stoll, Friederike; Hillisch, Alexander; Gohlke, Holger

2014-08-12

Correctly ranking compounds according to their computed relative binding affinities will be of great value for decision making in the lead optimization phase of industrial drug discovery. However, the performance of existing computationally demanding binding free energy calculation methods in this context is largely unknown. We analyzed the performance of the molecular mechanics continuum solvent, the linear interaction energy (LIE), and the thermodynamic integration (TI) approach for three sets of compounds from industrial lead optimization projects. The data sets pose challenges typical for this early stage of drug discovery. None of the methods was sufficiently predictive when applied out of the box without considering these challenges. Detailed investigations of failures revealed critical points that are essential for good binding free energy predictions. When data set-specific features were considered accordingly, predictions valuable for lead optimization could be obtained for all approaches but LIE. Our findings lead to clear recommendations for when to use which of the above approaches. Our findings also stress the important role of expert knowledge in this process, not least for estimating the accuracy of prediction results by TI, using indicators such as the size and chemical structure of exchanged groups and the statistical error in the predictions. Such knowledge will be invaluable when it comes to the question which of the TI results can be trusted for decision making.

Estimation of Radiative Efficiency of Chemicals with Potentially Significant Global Warming Potential

EPA Pesticide Factsheets

The set of commercially available chemical substances in commerce that may have significant global warming potential (GWP) is not well defined. Although there are currently over 200 chemicals with high GWP reported by the Intergovernmental Panel on Climate Change, World Meteorological Organization, or Environmental Protection Agency, there may be hundreds of additional chemicals that may also have significant GWP. Evaluation of various approaches to estimate radiative efficiency (RE) and atmospheric lifetime will help to refine GWP estimates for compounds where no measured IR spectrum is available. This study compares values of RE calculated using computational chemistry techniques for 235 chemical compounds against the best available values. It is important to assess the reliability of the underlying computational methods for computing RE to understand the sources of deviations from the best available values. Computed vibrational frequency data is used to estimate RE values using several Pinnock-type models. The values derived using these models are found to be in reasonable agreement with reported RE values (though significant improvement is obtained through scaling). The effect of varying the computational method and basis set used to calculate the frequency data is also discussed. It is found that the vibrational intensities have a strong dependence on basis set and are largely responsible for differences in computed values of RE in this study. Deviations of

Concentrations of hormones, pharmaceuticals and other micropollutants in groundwater affected by septic systems in New England and New York.

PubMed

Phillips, P J; Schubert, C; Argue, D; Fisher, I; Furlong, E T; Foreman, W; Gray, J; Chalmers, A

2015-04-15

Septic-system discharges can be an important source of micropollutants (including pharmaceuticals and endocrine active compounds) to adjacent groundwater and surface water systems. Groundwater samples were collected from well networks tapping glacial till in New England (NE) and sandy surficial aquifer New York (NY) during one sampling round in 2011. The NE network assesses the effect of a single large septic system that receives discharge from an extended health care facility for the elderly. The NY network assesses the effect of many small septic systems used seasonally on a densely populated portion of Fire Island. The data collected from these two networks indicate that hydrogeologic and demographic factors affect micropollutant concentrations in these systems. The highest micropollutant concentrations from the NE network were present in samples collected from below the leach beds and in a well downgradient of the leach beds. Total concentrations for personal care/domestic use compounds, pharmaceutical compounds and plasticizer compounds generally ranged from 1 to over 20 μg/L in the NE network samples. High tris(2-butoxyethyl phosphate) plasticizer concentrations in wells beneath and downgradient of the leach beds (>20 μg/L) may reflect the presence of this compound in cleaning agents at the extended health-care facility. The highest micropollutant concentrations for the NY network were present in the shoreline wells and reflect groundwater that is most affected by septic system discharges. One of the shoreline wells had personal care/domestic use, pharmaceutical, and plasticizer concentrations ranging from 0.4 to 5.7 μg/L. Estradiol equivalency quotient concentrations were also highest in a shoreline well sample (3.1 ng/L). Most micropollutant concentrations increase with increasing specific conductance and total nitrogen concentrations for shoreline well samples. These findings suggest that septic systems serving institutional settings and densely populated areas in coastal settings may be locally important sources of micropollutants to adjacent aquifer and marine systems. Published by Elsevier B.V.

Generation of standard gas mixtures of halogenated, aliphatic, and aromatic compounds and prediction of the individual output rates based on molecular formula and boiling point.

PubMed

Thorenz, Ute R; Kundel, Michael; Müller, Lars; Hoffmann, Thorsten

2012-11-01

In this work, we describe a simple diffusion capillary device for the generation of various organic test gases. Using a set of basic equations the output rate of the test gas devices can easily be predicted only based on the molecular formula and the boiling point of the compounds of interest. Since these parameters are easily accessible for a large number of potential analytes, even for those compounds which are typically not listed in physico-chemical handbooks or internet databases, the adjustment of the test gas source to the concentration range required for the individual analytical application is straightforward. The agreement of the predicted and measured values is shown to be valid for different groups of chemicals, such as halocarbons, alkanes, alkenes, and aromatic compounds and for different dimensions of the diffusion capillaries. The limits of the predictability of the output rates are explored and observed to result in an underprediction of the output rates when very thin capillaries are used. It is demonstrated that pressure variations are responsible for the observed deviation of the output rates. To overcome the influence of pressure variations and at the same time to establish a suitable test gas source for highly volatile compounds, also the usability of permeation sources is explored, for example for the generation of molecular bromine test gases.

Identification of Potential Pharmacoperones Capable of Rescuing the Functionality of Misfolded Vasopressin 2 Receptor Involved in Nephrogenic Diabetes Insipidus.

PubMed

Smith, Emery; Janovick, Jo Ann; Bannister, Thomas D; Shumate, Justin; Scampavia, Louis; Conn, P Michael; Spicer, Timothy P

2016-09-01

Pharmacoperones correct the folding of otherwise misfolded protein mutants, restoring function (i.e., providing "rescue") by correcting their trafficking. Currently, most pharmacoperones possess intrinsic antagonist activity because they were identified using methods initially aimed at discovering such functions. Here, we describe an ultra-high-throughput homogeneous cell-based assay with a cAMP detection system, a method specifically designed to identify pharmacoperones of the vasopressin type 2 receptor (V2R), a GPCR that, when mutated, is associated with nephrogenic diabetes insipidus. Previously developed methods to identify compounds capable of altering cellular trafficking of V2R were modified and used to screen a 645,000 compound collection by measuring the ability of library compounds to rescue a mutant hV2R [L83Q], using a cell-based luminescent detection system. The campaign initially identified 3734 positive modulators of cAMP. The confirmation and counterscreen identified only 147 of the active compounds with an EC50 of ≤5 µM. Of these, 83 were reconfirmed as active through independently obtained pure samples and were also inactive in a relevant counterscreen. Active and tractable compounds within this set can be categorized into three predominant structural clusters, described here, in the first report detailing the results of a large-scale pharmacoperone high-throughput screening campaign. © 2016 Society for Laboratory Automation and Screening.

Characterization of sulfur and nitrogen compounds in Brazilian petroleum derivatives using ionic liquid capillary columns in comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometric detection.

PubMed

Cappelli Fontanive, Fernando; Souza-Silva, Érica Aparecida; Macedo da Silva, Juliana; Bastos Caramão, Elina; Alcaraz Zini, Claudia

2016-08-26

Diesel and naphtha samples were analyzed using ionic liquid (IL) columns to evaluate the best column set for the investigation of organic sulfur compounds (OSC) and nitrogen(N)-containing compounds analyses with comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry detector (GC×GC/TOFMS). Employing a series of stationary phase sets, namely DB-5MS/DB-17, DB-17/DB-5MS, DB-5MS/IL-59, and IL-59/DB-5MS, the following parameters were systematically evaluated: number of tentatively identified OSC, 2D chromatographic space occupation, number of polyaromatic hydrocarbons (PAH) and OSC co-elutions, and percentage of asymmetric peaks. DB-5MS/IL-59 was chosen for OSC analysis, while IL59/DB-5MS was chosen for nitrogen compounds, as each stationary phase set provided the best chromatographic efficiency for these two classes of compounds, respectively. Most compounds were tentatively identified by Lee and Van den Dool and Kratz retention indexes, and spectra-matching to library. Whenever available, compounds were also positively identified via injection of authentic standards. Copyright © 2016 Elsevier B.V. All rights reserved.

QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes.

PubMed

Basant, Nikita; Gupta, Shikha

2017-06-01

The safety assessment process of chemicals requires information on their mutagenic potential. The experimental determination of mutagenicity of a large number of chemicals is tedious and time and cost intensive, thus compelling for alternative methods. We have established local and global QSAR models for discriminating low and high mutagenic compounds and predicting their mutagenic activity in a quantitative manner in Salmonella typhimurium (TA) bacterial strains (TA98 and TA100). The decision treeboost (DTB)-based classification QSAR models discriminated among two categories with accuracies of >96% and the regression QSAR models precisely predicted the mutagenic activity of diverse chemicals yielding high correlations (R 2 ) between the experimental and model-predicted values in the respective training (>0.96) and test (>0.94) sets. The test set root mean squared error (RMSE) and mean absolute error (MAE) values emphasized the usefulness of the developed models for predicting new compounds. Relevant structural features of diverse chemicals that were responsible and influence the mutagenic activity were identified. The applicability domains of the developed models were defined. The developed models can be used as tools for screening new chemicals for their mutagenicity assessment for regulatory purpose.

Inside the Mind of a Medicinal Chemist: The Role of Human Bias in Compound Prioritization during Drug Discovery

PubMed Central

Kutchukian, Peter S.; Vasilyeva, Nadya Y.; Xu, Jordan; Lindvall, Mika K.; Dillon, Michael P.; Glick, Meir; Coley, John D.; Brooijmans, Natasja

2012-01-01

Medicinal chemists’ “intuition” is critical for success in modern drug discovery. Early in the discovery process, chemists select a subset of compounds for further research, often from many viable candidates. These decisions determine the success of a discovery campaign, and ultimately what kind of drugs are developed and marketed to the public. Surprisingly little is known about the cognitive aspects of chemists’ decision-making when they prioritize compounds. We investigate 1) how and to what extent chemists simplify the problem of identifying promising compounds, 2) whether chemists agree with each other about the criteria used for such decisions, and 3) how accurately chemists report the criteria they use for these decisions. Chemists were surveyed and asked to select chemical fragments that they would be willing to develop into a lead compound from a set of ∼4,000 available fragments. Based on each chemist’s selections, computational classifiers were built to model each chemist’s selection strategy. Results suggest that chemists greatly simplified the problem, typically using only 1–2 of many possible parameters when making their selections. Although chemists tended to use the same parameters to select compounds, differing value preferences for these parameters led to an overall lack of consensus in compound selections. Moreover, what little agreement there was among the chemists was largely in what fragments were undesirable. Furthermore, chemists were often unaware of the parameters (such as compound size) which were statistically significant in their selections, and overestimated the number of parameters they employed. A critical evaluation of the problem space faced by medicinal chemists and cognitive models of categorization were especially useful in understanding the low consensus between chemists. PMID:23185259

Generation of ligand-based pharmacophore model and virtual screening for identification of novel tubulin inhibitors with potent anticancer activity.

PubMed

Chiang, Yi-Kun; Kuo, Ching-Chuan; Wu, Yu-Shan; Chen, Chung-Tong; Coumar, Mohane Selvaraj; Wu, Jian-Sung; Hsieh, Hsing-Pang; Chang, Chi-Yen; Jseng, Huan-Yi; Wu, Ming-Hsine; Leou, Jiun-Shyang; Song, Jen-Shin; Chang, Jang-Yang; Lyu, Ping-Chiang; Chao, Yu-Sheng; Wu, Su-Ying

2009-07-23

A pharmacophore model, Hypo1, was built on the basis of 21 training-set indole compounds with varying levels of antiproliferative activity. Hypo1 possessed important chemical features required for the inhibitors and demonstrated good predictive ability for biological activity, with high correlation coefficients of 0.96 and 0.89 for the training-set and test-set compounds, respectively. Further utilization of the Hypo1 pharmacophore model to screen chemical database in silico led to the identification of four compounds with antiproliferative activity. Among these four compounds, 43 showed potent antiproliferative activity against various cancer cell lines with the strongest inhibition on the proliferation of KB cells (IC(50) = 187 nM). Further biological characterization revealed that 43 effectively inhibited tubulin polymerization and significantly induced cell cycle arrest in G(2)-M phase. In addition, 43 also showed the in vivo-like anticancer effects. To our knowledge, 43 is the most potent antiproliferative compound with antitubulin activity discovered by computer-aided drug design. The chemical novelty of 43 and its anticancer activities make this compound worthy of further lead optimization.

Compound-Specific Isotope Analysis of Diesel Fuels in a Forensic Investigation

NASA Astrophysics Data System (ADS)

Muhammad, Syahidah; Frew, Russell; Hayman, Alan

2015-02-01

Compound-specific isotope analysis (CSIA) offers great potential as a tool to provide chemical evidence in a forensic investigation. Many attempts to trace environmental oil spills were successful where isotopic values were particularly distinct. However, difficulties arise when a large data set is analyzed and the isotopic differences between samples are subtle. In the present study, discrimination of diesel oils involved in a diesel theft case was carried out to infer the relatedness of the samples to potential source samples. This discriminatory analysis used a suite of hydrocarbon diagnostic indices, alkanes, to generate carbon and hydrogen isotopic data of the compositions of the compounds which were then processed using multivariate statistical analyses to infer the relatedness of the data set. The results from this analysis were put into context by comparing the data with the δ13C and δ2H of alkanes in commercial diesel samples obtained from various locations in the South Island of New Zealand. Based on the isotopic character of the alkanes, it is suggested that diesel fuels involved in the diesel theft case were distinguishable. This manuscript shows that CSIA when used in tandem with multivariate statistical analysis provide a defensible means to differentiate and source-apportion qualitatively similar oils at the molecular level. This approach was able to overcome confounding challenges posed by the near single-point source of origin i.e. the very subtle differences in isotopic values between the samples.

Simultaneous prediction of enzyme orthologs from chemical transformation patterns for de novo metabolic pathway reconstruction

PubMed Central

Tabei, Yasuo; Yamanishi, Yoshihiro; Kotera, Masaaki

2016-01-01

Motivation: Metabolic pathways are an important class of molecular networks consisting of compounds, enzymes and their interactions. The understanding of global metabolic pathways is extremely important for various applications in ecology and pharmacology. However, large parts of metabolic pathways remain unknown, and most organism-specific pathways contain many missing enzymes. Results: In this study we propose a novel method to predict the enzyme orthologs that catalyze the putative reactions to facilitate the de novo reconstruction of metabolic pathways from metabolome-scale compound sets. The algorithm detects the chemical transformation patterns of substrate–product pairs using chemical graph alignments, and constructs a set of enzyme-specific classifiers to simultaneously predict all the enzyme orthologs that could catalyze the putative reactions of the substrate–product pairs in the joint learning framework. The originality of the method lies in its ability to make predictions for thousands of enzyme orthologs simultaneously, as well as its extraction of enzyme-specific chemical transformation patterns of substrate–product pairs. We demonstrate the usefulness of the proposed method by applying it to some ten thousands of metabolic compounds, and analyze the extracted chemical transformation patterns that provide insights into the characteristics and specificities of enzymes. The proposed method will open the door to both primary (central) and secondary metabolism in genomics research, increasing research productivity to tackle a wide variety of environmental and public health matters. Availability and Implementation: Contact: maskot@bio.titech.ac.jp PMID:27307627

Compound-specific isotope analysis of diesel fuels in a forensic investigation

PubMed Central

Muhammad, Syahidah A.; Frew, Russell D.; Hayman, Alan R.

2015-01-01

Compound-specific isotope analysis (CSIA) offers great potential as a tool to provide chemical evidence in a forensic investigation. Many attempts to trace environmental oil spills were successful where isotopic values were particularly distinct. However, difficulties arise when a large data set is analyzed and the isotopic differences between samples are subtle. In the present study, discrimination of diesel oils involved in a diesel theft case was carried out to infer the relatedness of the samples to potential source samples. This discriminatory analysis used a suite of hydrocarbon diagnostic indices, alkanes, to generate carbon and hydrogen isotopic data of the compositions of the compounds which were then processed using multivariate statistical analyses to infer the relatedness of the data set. The results from this analysis were put into context by comparing the data with the δ13C and δ2H of alkanes in commercial diesel samples obtained from various locations in the South Island of New Zealand. Based on the isotopic character of the alkanes, it is suggested that diesel fuels involved in the diesel theft case were distinguishable. This manuscript shows that CSIA when used in tandem with multivariate statistical analysis provide a defensible means to differentiate and source-apportion qualitatively similar oils at the molecular level. This approach was able to overcome confounding challenges posed by the near single-point source of origin, i.e., the very subtle differences in isotopic values between the samples. PMID:25774366

Biased ligand quantification in drug discovery: from theory to high throughput screening to identify new biased μ opioid receptor agonists

PubMed Central

Winpenny, David; Clark, Mellissa

2016-01-01

Background and Purpose Biased GPCR ligands are able to engage with their target receptor in a manner that preferentially activates distinct downstream signalling and offers potential for next generation therapeutics. However, accurate quantification of ligand bias in vitro is complex, and current best practice is not amenable for testing large numbers of compound. We have therefore sought to apply ligand bias theory to an industrial scale screening campaign for the identification of new biased μ receptor agonists. Experimental Approach μ receptor assays with appropriate dynamic range were developed for both Gαi‐dependent signalling and β‐arrestin2 recruitment. Δlog(Emax/EC50) analysis was validated as an alternative for the operational model of agonism in calculating pathway bias towards Gαi‐dependent signalling. The analysis was applied to a high throughput screen to characterize the prevalence and nature of pathway bias among a diverse set of compounds with μ receptor agonist activity. Key Results A high throughput screening campaign yielded 440 hits with greater than 10‐fold bias relative to DAMGO. To validate these results, we quantified pathway bias of a subset of hits using the operational model of agonism. The high degree of correlation across these biased hits confirmed that Δlog(Emax/EC50) was a suitable method for identifying genuine biased ligands within a large collection of diverse compounds. Conclusions and Implications This work demonstrates that using Δlog(Emax/EC50), drug discovery can apply the concept of biased ligand quantification on a large scale and accelerate the deliberate discovery of novel therapeutics acting via this complex pharmacology. PMID:26791140

Crystal structure, spectroscopic studies and quantum mechanical calculations of 2-[((3-iodo-4-methyl)phenylimino)methyl]-5-nitrothiophene.

PubMed

Özdemir Tarı, Gonca; Gümüş, Sümeyye; Ağar, Erbil

2015-04-15

The title compound, 2-[((3-iodo-4-methyl)phenylimino)methyl]-5-nitrothiophene, C12H9O2N2I1S1, was synthesized and characterized by IR, UV-Vis and single-crystal X-ray diffraction technique. The molecular structure was optimized at the B3LYP, B3PW91 and PBEPBE levels of the density functional method (DFT) with the 6-311G+(d,p) basis set. Using the TD-DFT method, the electronic absorption spectra of the title compound was computed in both the gas phase and ethanol solvent. The harmonic vibrational frequencies of the title compound were calculated using the same methods with the 6-311G+(d,p) basis set. The calculated results were compared with the experimental determination results of the compound. The energetic behavior such as the total energy, atomic charges, dipole moment of the title compound in solvent media were examined using the B3LYP, B3PW91 and PBEPBE methods with the 6-311G+(d,p) basis set by applying the Onsager and the polarizable continuum model (PCM). The molecular orbitals (FMOs) analysis, the molecular electrostatic potential map (MEP) and the nonlinear optical properties (NLO) for the title compound were obtained with the same levels of theory. And then thermodynamic properties for the title compound were obtained using the same methods with the 6-311G(d,p) basis set. Copyright © 2015 Elsevier B.V. All rights reserved.

Tables of compound-discount interest rate multipliers for evaluating forestry investments.

Treesearch

Allen L. Lundgren

1971-01-01

Tables, prepared by computer, are presented for 10 selected compound-discount interest rate multipliers commonly used in financial analyses of forestry investments. Two set of tables are given for each of the 10 multipliers. The first set gives multipliers for each year from 1 to 40 years; the second set gives multipliers at 5-year intervals from 5 to 160 years....

Toward Understanding the Cold, Hot, and Neutral Nature of Chinese Medicines Using in Silico Mode-of-Action Analysis.

PubMed

Fu, Xianjun; Mervin, Lewis H; Li, Xuebo; Yu, Huayun; Li, Jiaoyang; Mohamad Zobir, Siti Zuraidah; Zoufir, Azedine; Zhou, Yang; Song, Yongmei; Wang, Zhenguo; Bender, Andreas

2017-03-27

One important, however, poorly understood, concept of Traditional Chinese Medicine (TCM) is that of hot, cold, and neutral nature of its bioactive principles. To advance the field, in this study, we analyzed compound-nature pairs from TCM on a large scale (>23 000 structures) via chemical space visualizations to understand its physicochemical domain and in silico target prediction to understand differences related to their modes-of-action (MoA) against proteins. We found that overall TCM natures spread into different subclusters with specific molecular patterns, as opposed to forming coherent global groups. Compounds associated with cold nature had a lower clogP and contain more aliphatic rings than the other groups and were found to control detoxification, heat-clearing, heart development processes, and have sedative function, associated with "Mental and behavioural disorders" diseases. While compounds associated with hot nature were on average of lower molecular weight, have more aromatic ring systems than other groups, frequently seemed to control body temperature, have cardio-protection function, improve fertility and sexual function, and represent excitatory or activating effects, associated with "endocrine, nutritional and metabolic diseases" and "diseases of the circulatory system". Compounds associated with neutral nature had a higher polar surface area and contain more cyclohexene moieties than other groups and seem to be related to memory function, suggesting that their nature may be a useful guide for their utility in neural degenerative diseases. We were hence able to elucidate the difference between different nature classes in TCM on the molecular level, and on a large data set, for the first time, thereby helping a better understanding of TCM nature theory and bridging the gap between traditional medicine and our current understanding of the human body.

A Comparison of Parameterizations of Secondary Organic Aerosol Production: Global Budget and Spatiotemporal Variability

NASA Astrophysics Data System (ADS)

Liu, J.; Chen, Z.; Horowitz, L. W.; Carlton, A. M. G.; Fan, S.; Cheng, Y.; Ervens, B.; Fu, T. M.; He, C.; Tao, S.

2014-12-01

Secondary organic aerosols (SOA) have a profound influence on air quality and climate, but large uncertainties exist in modeling SOA on the global scale. In this study, five SOA parameterization schemes, including a two-product model (TPM), volatility basis-set (VBS) and three cloud SOA schemes (Ervens et al. (2008, 2014), Fu et al. (2008) , and He et al. (2013)), are implemented into the global chemical transport model (MOZART-4). For each scheme, model simulations are conducted with identical boundary and initial conditions. The VBS scheme produces the highest global annual SOA production (close to 35 Tg·y-1), followed by three cloud schemes (26-30 Tg·y-1) and TPM (23 Tg·y-1). Though sharing a similar partitioning theory to the TPM scheme, the VBS approach simulates the chemical aging of multiple generations of VOCs oxidation products, resulting in a much larger SOA source, particularly from aromatic species, over Europe, the Middle East and Eastern America. The formation of SOA in VBS, which represents the net partitioning of semi-volatile organic compounds from vapor to condensed phase, is highly sensitivity to the aging and wet removal processes of vapor-phase organic compounds. The production of SOA from cloud processes (SOAcld) is constrained by the coincidence of liquid cloud water and water-soluble organic compounds. Therefore, all cloud schemes resolve a fairly similar spatial pattern over the tropical and the mid-latitude continents. The spatiotemporal diversity among SOA parameterizations is largely driven by differences in precursor inputs. Therefore, a deeper understanding of the evolution, wet removal, and phase partitioning of semi-volatile organic compounds, particularly above remote land and oceanic areas, is critical to better constrain the global-scale distribution and related climate forcing of secondary organic aerosols.

Compound simulation of fluvial floods and storm surges in a global coupled river-coast flood model: Model development and its application to 2007 Cyclone Sidr in Bangladesh

NASA Astrophysics Data System (ADS)

Ikeuchi, Hiroaki; Hirabayashi, Yukiko; Yamazaki, Dai; Muis, Sanne; Ward, Philip J.; Winsemius, Hessel C.; Verlaan, Martin; Kanae, Shinjiro

2017-08-01

Water-related disasters, such as fluvial floods and cyclonic storm surges, are a major concern in the world's mega-delta regions. Furthermore, the simultaneous occurrence of extreme discharges from rivers and storm surges could exacerbate flood risk, compared to when they occur separately. Hence, it is of great importance to assess the compound risks of fluvial and coastal floods at a large scale, including mega-deltas. However, most studies on compound fluvial and coastal flooding have been limited to relatively small scales, and global-scale or large-scale studies have not yet addressed both of them. The objectives of this study are twofold: to develop a global coupled river-coast flood model; and to conduct a simulation of compound fluvial flooding and storm surges in Asian mega-delta regions. A state-of-the-art global river routing model was modified to represent the influence of dynamic sea surface levels on river discharges and water levels. We conducted the experiments by coupling a river model with a global tide and surge reanalysis data set. Results show that water levels in deltas and estuaries are greatly affected by the interaction between river discharge, ocean tides and storm surges. The effects of storm surges on fluvial flooding are further examined from a regional perspective, focusing on the case of Cyclone Sidr in the Ganges-Brahmaputra-Meghna Delta in 2007. Modeled results demonstrate that a >3 m storm surge propagated more than 200 km inland along rivers. We show that the performance of global river routing models can be improved by including sea level dynamics.

[Assessment of the relationship of properties of chemical compounds and their toxicity to a unified hygienic standardization for chemicals].

PubMed

Trushkov, V F; Perminov, K A; Sapozhnikova, V V; Ignatova, O L

2013-01-01

The connection of thermodynamic properties and parameters of toxicity of chemical substances was determined. Obtained data are used for the evaluation of toxicity and hygienic rate setting of chemical compounds. The relationship between enthalpy and toxicity of chemical compounds has been established. Orthogonal planning of the experiment was carried out in the course of the investigations. Equation of unified hygienic rate setting in combined, complex, conjunct influence on the organism is presented. Prospects of determination of toxicity and methodology of unified hygienic rate setting in combined, complex, conjunct influence on the organism are presented

Fast Modeling of Binding Affinities by Means of Superposing Significant Interaction Rules (SSIR) Method

PubMed Central

Besalú, Emili

2016-01-01

The Superposing Significant Interaction Rules (SSIR) method is described. It is a general combinatorial and symbolic procedure able to rank compounds belonging to combinatorial analogue series. The procedure generates structure-activity relationship (SAR) models and also serves as an inverse SAR tool. The method is fast and can deal with large databases. SSIR operates from statistical significances calculated from the available library of compounds and according to the previously attached molecular labels of interest or non-interest. The required symbolic codification allows dealing with almost any combinatorial data set, even in a confidential manner, if desired. The application example categorizes molecules as binding or non-binding, and consensus ranking SAR models are generated from training and two distinct cross-validation methods: leave-one-out and balanced leave-two-out (BL2O), the latter being suited for the treatment of binary properties. PMID:27240346

Micropollutants and chemical residues in organic and conventional meat.

PubMed

Dervilly-Pinel, Gaud; Guérin, Thierry; Minvielle, Brice; Travel, Angélique; Normand, Jérôme; Bourin, Marie; Royer, Eric; Dubreil, Estelle; Mompelat, Sophie; Hommet, Frédéric; Nicolas, Marina; Hort, Vincent; Inthavong, Chanthadary; Saint-Hilaire, Mailie; Chafey, Claude; Parinet, Julien; Cariou, Ronan; Marchand, Philippe; Le Bizec, Bruno; Verdon, Eric; Engel, Erwan

2017-10-01

The chemical contamination levels of both conventional and organic meats were assessed. The objective was to provide occurrence data in a context of chronic exposure. Environmental contaminants (17 polychlorinated dibenzodioxins/dibenzofurans, 18 polychlorinated biphenyls (PCBs), 3 hexabromocyclododecane (HBCD) isomers, 6 mycotoxins, 6 inorganic compounds) together with chemical residues arising from production inputs (75 antimicrobials, 10 coccidiostats and 121 pesticides) have been selected as relevant compounds. A dedicated sampling strategy, representative of the French production allowed quantification of a large sample set (n=266) including both conventional (n=139) and organic (n=127) raw meat from three animal species (bovine, porcine, poultry). While contamination levels below regulatory limits were measured in all the samples, significant differences were observed between both species and types of farming. Several environmental contaminants (Dioxins, PCBs, HBCD, Zn, Cu, Cd, Pb, As) were measured at significantly higher levels in organic samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

Metabolomic Technologies for Improving the Quality of Food: Practice and Promise.

PubMed

Johanningsmeier, Suzanne D; Harris, G Keith; Klevorn, Claire M

2016-01-01

It is now well documented that the diet has a significant impact on human health and well-being. However, the complete set of small molecule metabolites present in foods that make up the human diet and the role of food production systems in altering this food metabolome are still largely unknown. Metabolomic platforms that rely on nuclear magnetic resonance (NMR) and mass spectrometry (MS) analytical technologies are being employed to study the impact of agricultural practices, processing, and storage on the global chemical composition of food; to identify novel bioactive compounds; and for authentication and region-of-origin classifications. This review provides an overview of the current terminology, analytical methods, and compounds associated with metabolomic studies, and provides insight into the application of metabolomics to generate new knowledge that enables us to produce, preserve, and distribute high-quality foods for health promotion.

Open Source Drug Discovery: Highly Potent Antimalarial Compounds Derived from the Tres Cantos Arylpyrroles

PubMed Central

2016-01-01

The development of new antimalarial compounds remains a pivotal part of the strategy for malaria elimination. Recent large-scale phenotypic screens have provided a wealth of potential starting points for hit-to-lead campaigns. One such public set is explored, employing an open source research mechanism in which all data and ideas were shared in real time, anyone was able to participate, and patents were not sought. One chemical subseries was found to exhibit oral activity but contained a labile ester that could not be replaced without loss of activity, and the original hit exhibited remarkable sensitivity to minor structural change. A second subseries displayed high potency, including activity within gametocyte and liver stage assays, but at the cost of low solubility. As an open source research project, unexplored avenues are clearly identified and may be explored further by the community; new findings may be cumulatively added to the present work. PMID:27800551

Ab initio study of MF2 (M=Mn, Fe, Co, Ni) rutile-type compounds using the periodic unrestricted Hartree-Fock approach

NASA Astrophysics Data System (ADS)

de P. R. Moreira, Ibério; Dovesi, Roberto; Roetti, Carla; Saunders, Victor R.; Orlando, Roberto

2000-09-01

The ab initio periodic unrestricted Hartree-Fock method has been applied in the investigation of the ground-state structural, electronic, and magnetic properties of the rutile-type compounds MF2 (M=Mn, Fe, Co, and Ni). All electron Gaussian basis sets have been used. The systems turn out to be large band-gap antiferromagnetic insulators; the optimized geometrical parameters are in good agreement with experiment. The calculated most stable electronic state shows an antiferromagnetic order in agreement with that resulting from neutron scattering experiments. The magnetic coupling constants between nearest-neighbor magnetic ions along the [001], [111], and [100] (or [010]) directions have been calculated using several supercells. The resulting ab initio magnetic coupling constants are reasonably satisfactory when compared with available experimental data. The importance of the Jahn-Teller effect in FeF2 and CoF2 is also discussed.

Multidimensional and comprehensive two-dimensional gas chromatography of dichloromethane soluble products from a high sulfur Jordanian oil shale.

PubMed

Amer, Mohammad W; Mitrevski, Blagoj; Jackson, W Roy; Chaffee, Alan L; Marriott, Philip J

2014-03-01

A high sulfur Jordanian oil shale was converted into liquid hydrocarbons by reaction at 390 °C under N2, and the dichloromethane soluble fraction of the products was isolated then analyzed by using gas chromatography (GC). Comprehensive two-dimensional GC (GC×GC) and multidimensional GC (MDGC) were applied for component separation on a polar - non-polar column set. Flame-ionization detection (FID) was used with GC×GC for general sample profiling, and mass spectrometry (MS) for component identification in MDGC. Multidimensional GC revealed a range of thiophenes (th), benzothiophenes (bth) and small amounts of dibenzothiophenes (dbth) and benzonaphthothiophenes (bnth). In addition, a range of aliphatic alkanes and cycloalkanes, ethers, polar single ring aromatic compounds and small amounts of polycyclic aromatics were also identified. Some of these compound classes were not uniquely observable by conventional 1D GC, and certainly this is true for many of their minor constituent members. The total number of distinct compounds was very large (ca.>1000). GC×GC was shown to be appropriate for general sample profiling, and MDGC-MS proved to be a powerful technique for the separation and identification of sulfur-containing components and other polar compounds. © 2013 Published by Elsevier B.V.

Heavy Metals in ToxCast: Relevance to Food Safety (SOT) ...

EPA Pesticide Factsheets

Human exposure to heavy metals occurs through food contamination due to industrial processes, vehicle emissions and farming methods. Specific toxicity endpoints have been associated with metal exposures, e.g. lead and neurotoxicity; however, numerous varieties of heavy metals have not been systematically examined for potential toxicities. We describe results from testing a large set of heavy metal-containing compounds in extensive suites of in vitro assays to suggest possible molecular initiating events in toxicity pathways. A broad definition of heavy metals that includes As, Se and organometallics or inorganic salts containing metals in Group III or higher (MW > 40) was used to identify 75 different compounds tested in the EPA’s ToxCast assays encompassing biochemical, cellular and model organism assays. These 75, plus an additional 100 metal-containing compounds, were tested in Tox21 quantitative high-throughput screening (qHTS) assays covering nuclear receptor and stress pathways. Known activities were confirmed such as activation of stress pathways and nuclear receptors (RXR, PPARg) as well as overt cytotoxicity. Specifically, organotin and organomercury were among the most potent of over 8K chemicals tested. The HTS results support known toxicities, including promiscuous GPCR activity for mercury compounds consistent with the neuropsychiatric effects seen in mercury poisoning (Mad Hatter’s Syndrome). As such, HTS approaches provide an efficient method

Exposure assessment through realistic laboratory simulation of a soccer stadium fire.

PubMed

van Belle, N J C; van Putten, E M; de Groot, A C; Meeussen, V J A; Banus, S

2010-10-01

On Sunday April 13, 2008 a fire broke out on a grandstand in the Euroborg soccer stadium in Groningen The Netherlands. The polyamide chairs on the grandstand were set on fire and supporters were exposed to the emitted smoke which induced mild health effects. The Dutch government was concerned about potential health risks that such fires could have to exposed fans. Especially the exposure to toxic fumes was considered a risk because prior research has proven that large amounts of chemical compounds are emitted during the burning of chemical substances such as polyamide. Among these emitted compounds are HCN, CO, NO(x), NH(3) and volatile organic compounds. To study if supporters were exposed to hazardous chemical compounds we designed a laboratory controlled replica of a part of the grandstand of the Euroborg stadium to perform fire-experiments. This simulation of the fire under controlled circumstances proved that a wide variety of chemicals were emitted. Especially the emission of CO and NO(x) were high, but also the emission of formaldehyde might be toxicologically relevant. The emission of HCN and NH(3) were less than expected. Exposure assessment suggests that the exposure to NO(x) is the main health risk for the supporters that were present at the Euroborg fire. Copyright © 2010 Elsevier Ltd. All rights reserved.

Development of a convenient ex vivo model for the study of the transcorneal permeation of drugs: histological and permeability evaluation.

PubMed

Pescina, Silvia; Govoni, Paolo; Potenza, Arianna; Padula, Cristina; Santi, Patrizia; Nicoli, Sara

2015-01-01

In this paper, an ex vivo model for the study of the transcorneal permeation of drugs, based on porcine tissues, was evaluated. The setup is characterized by ease of realization, absence of O₂ and CO₂ bubbling and low cost; additionally, the large availability of porcine tissue permits a high throughput. Histological images showed the comparability between porcine and human corneas and confirmed the effectiveness of the isolation procedure. A new de-epithelization procedure based on a thermal approach was also set up to simulate cornea permeability in pathological conditions. The procedure did not affect the integrity of the underlying layers and allowed the characterization of the barrier properties of epithelium and stroma. Six compounds with different physicochemical properties were tested: fluorescein, atenolol, propranolol, diclofenac, ganciclovir and lidocaine. The model highlighted the barrier function played by epithelium toward the diffusion of hydrophilic compounds and the permselectivity with regard to more lipophilic molecules. In particular, positively charged compounds showed a significantly higher transcorneal permeability than negatively charged compounds. The comparability of results with literature data supports the goodness and the robustness of the model, especially taking into account the behavior of fluorescein, which is generally considered a marker of tissue integrity. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

21 CFR 880.5440 - Intravascular administration set.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Intravascular administration set. 880.5440 Section 880.5440 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES... Compounding Systems; Final Guidance for Industry and FDA Reviewers.” Pharmacy compounding systems classified...

Color object detection using spatial-color joint probability functions.

PubMed

Luo, Jiebo; Crandall, David

2006-06-01

Object detection in unconstrained images is an important image understanding problem with many potential applications. There has been little success in creating a single algorithm that can detect arbitrary objects in unconstrained images; instead, algorithms typically must be customized for each specific object. Consequently, it typically requires a large number of exemplars (for rigid objects) or a large amount of human intuition (for nonrigid objects) to develop a robust algorithm. We present a robust algorithm designed to detect a class of compound color objects given a single model image. A compound color object is defined as having a set of multiple, particular colors arranged spatially in a particular way, including flags, logos, cartoon characters, people in uniforms, etc. Our approach is based on a particular type of spatial-color joint probability function called the color edge co-occurrence histogram. In addition, our algorithm employs perceptual color naming to handle color variation, and prescreening to limit the search scope (i.e., size and location) for the object. Experimental results demonstrated that the proposed algorithm is insensitive to object rotation, scaling, partial occlusion, and folding, outperforming a closely related algorithm based on color co-occurrence histograms by a decisive margin.

Combining density functional theory calculations, supercomputing, and data-driven methods to design new materials (Conference Presentation)

NASA Astrophysics Data System (ADS)

Jain, Anubhav

2017-04-01

Density functional theory (DFT) simulations solve for the electronic structure of materials starting from the Schrödinger equation. Many case studies have now demonstrated that researchers can often use DFT to design new compounds in the computer (e.g., for batteries, catalysts, and hydrogen storage) before synthesis and characterization in the lab. In this talk, I will focus on how DFT calculations can be executed on large supercomputing resources in order to generate very large data sets on new materials for functional applications. First, I will briefly describe the Materials Project, an effort at LBNL that has virtually characterized over 60,000 materials using DFT and has shared the results with over 17,000 registered users. Next, I will talk about how such data can help discover new materials, describing how preliminary computational screening led to the identification and confirmation of a new family of bulk AMX2 thermoelectric compounds with measured zT reaching 0.8. I will outline future plans for how such data-driven methods can be used to better understand the factors that control thermoelectric behavior, e.g., for the rational design of electronic band structures, in ways that are different from conventional approaches.

Organic electronic devices using phthalimide compounds

DOEpatents

Hassan, Azad M.; Thompson, Mark E.

2010-09-07

Organic electronic devices comprising a phthalimide compound. The phthalimide compounds disclosed herein are electron transporters with large HOMO-LUMO gaps, high triplet energies, large reduction potentials, and/or thermal and chemical stability. As such, these phthalimide compounds are suitable for use in any of various organic electronic devices, such as OLEDs and solar cells. In an OLED, the phthalimide compounds may serve various functions, such as a host in the emissive layer, as a hole blocking material, or as an electron transport material. In a solar cell, the phthalimide compounds may serve various functions, such as an exciton blocking material. Various examples of phthalimide compounds which may be suitable for use in the present invention are disclosed.

Organic electronic devices using phthalimide compounds

DOEpatents

Hassan, Azad M.; Thompson, Mark E.

2012-10-23

Organic electronic devices comprising a phthalimide compound. The phthalimide compounds disclosed herein are electron transporters with large HOMO-LUMO gaps, high triplet energies, large reduction potentials, and/or thermal and chemical stability. As such, these phthalimide compounds are suitable for use in any of various organic electronic devices, such as OLEDs and solar cells. In an OLED, the phthalimide compounds may serve various functions, such as a host in the emissive layer, as a hole blocking material, or as an electron transport material. In a solar cell, the phthalimide compounds may serve various functions, such as an exciton blocking material. Various examples of phthalimide compounds which may be suitable for use in the present invention are disclosed.

Organic electronic devices using phthalimide compounds

DOEpatents

Hassan, Azad M.; Thompson, Mark E.

2013-03-19

Organic electronic devices comprising a phthalimide compound. The phthalimide compounds disclosed herein are electron transporters with large HOMO-LUMO gaps, high triplet energies, large reduction potentials, and/or thermal and chemical stability. As such, these phthalimide compounds are suitable for use in any of various organic electronic devices, such as OLEDs and solar cells. In an OLED, the phthalimide compounds may serve various functions, such as a host in the emissive layer, as a hole blocking material, or as an electron transport material. In a solar cell, the phthalimide compounds may serve various functions, such as an exciton blocking material. Various examples of phthalimide compounds which may be suitable for use in the present invention are disclosed.

In Silico target fishing: addressing a "Big Data" problem by ligand-based similarity rankings with data fusion.

PubMed

Liu, Xian; Xu, Yuan; Li, Shanshan; Wang, Yulan; Peng, Jianlong; Luo, Cheng; Luo, Xiaomin; Zheng, Mingyue; Chen, Kaixian; Jiang, Hualiang

2014-01-01

Ligand-based in silico target fishing can be used to identify the potential interacting target of bioactive ligands, which is useful for understanding the polypharmacology and safety profile of existing drugs. The underlying principle of the approach is that known bioactive ligands can be used as reference to predict the targets for a new compound. We tested a pipeline enabling large-scale target fishing and drug repositioning, based on simple fingerprint similarity rankings with data fusion. A large library containing 533 drug relevant targets with 179,807 active ligands was compiled, where each target was defined by its ligand set. For a given query molecule, its target profile is generated by similarity searching against the ligand sets assigned to each target, for which individual searches utilizing multiple reference structures are then fused into a single ranking list representing the potential target interaction profile of the query compound. The proposed approach was validated by 10-fold cross validation and two external tests using data from DrugBank and Therapeutic Target Database (TTD). The use of the approach was further demonstrated with some examples concerning the drug repositioning and drug side-effects prediction. The promising results suggest that the proposed method is useful for not only finding promiscuous drugs for their new usages, but also predicting some important toxic liabilities. With the rapid increasing volume and diversity of data concerning drug related targets and their ligands, the simple ligand-based target fishing approach would play an important role in assisting future drug design and discovery.

Competition between conceptual relations affects compound recognition: the role of entropy.

PubMed

Schmidtke, Daniel; Kuperman, Victor; Gagné, Christina L; Spalding, Thomas L

2016-04-01

Previous research has suggested that the conceptual representation of a compound is based on a relational structure linking the compound's constituents. Existing accounts of the visual recognition of modifier-head or noun-noun compounds posit that the process involves the selection of a relational structure out of a set of competing relational structures associated with the same compound. In this article, we employ the information-theoretic metric of entropy to gauge relational competition and investigate its effect on the visual identification of established English compounds. The data from two lexical decision megastudies indicates that greater entropy (i.e., increased competition) in a set of conceptual relations associated with a compound is associated with longer lexical decision latencies. This finding indicates that there exists competition between potential meanings associated with the same complex word form. We provide empirical support for conceptual composition during compound word processing in a model that incorporates the effect of the integration of co-activated and competing relational information.

Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data.

PubMed

Ribay, Kathryn; Kim, Marlene T; Wang, Wenyi; Pinolini, Daniel; Zhu, Hao

2016-03-01

Estrogen receptors (ERα) are a critical target for drug design as well as a potential source of toxicity when activated unintentionally. Thus, evaluating potential ERα binding agents is critical in both drug discovery and chemical toxicity areas. Using computational tools, e.g., Quantitative Structure-Activity Relationship (QSAR) models, can predict potential ERα binding agents before chemical synthesis. The purpose of this project was to develop enhanced predictive models of ERα binding agents by utilizing advanced cheminformatics tools that can integrate publicly available bioassay data. The initial ERα binding agent data set, consisting of 446 binders and 8307 non-binders, was obtained from the Tox21 Challenge project organized by the NIH Chemical Genomics Center (NCGC). After removing the duplicates and inorganic compounds, this data set was used to create a training set (259 binders and 259 non-binders). This training set was used to develop QSAR models using chemical descriptors. The resulting models were then used to predict the binding activity of 264 external compounds, which were available to us after the models were developed. The cross-validation results of training set [Correct Classification Rate (CCR) = 0.72] were much higher than the external predictivity of the unknown compounds (CCR = 0.59). To improve the conventional QSAR models, all compounds in the training set were used to search PubChem and generate a profile of their biological responses across thousands of bioassays. The most important bioassays were prioritized to generate a similarity index that was used to calculate the biosimilarity score between each two compounds. The nearest neighbors for each compound within the set were then identified and its ERα binding potential was predicted by its nearest neighbors in the training set. The hybrid model performance (CCR = 0.94 for cross validation; CCR = 0.68 for external prediction) showed significant improvement over the original QSAR models, particularly for the activity cliffs that induce prediction errors. The results of this study indicate that the response profile of chemicals from public data provides useful information for modeling and evaluation purposes. The public big data resources should be considered along with chemical structure information when predicting new compounds, such as unknown ERα binding agents.

Process for reducing aromatic compounds in ethylenediamine with calcium

DOEpatents

Benkeser, Robert A.; Laugal, James A.; Rappa, Angela

1985-01-01

Olefins are produced by containing an organic compound having at least one benzene ring with ethylenediamine and calcium metal, the calcium metal being used in large excess or alternatively in conjunction with an inert abrasive particulate substance. Substantially all of the organic compounds are converted to corresponding cyclic olefins, largely mono-olefins.

Process for reducing aromatic compounds in ethylenediamine with calcium

DOEpatents

Benkeser, R.A.; Laugal, J.A.; Rappa, A.

1985-08-06

Olefins are produced by containing an organic compound having at least one benzene ring with ethylenediamine and calcium metal, the calcium metal being used in large excess or alternatively in conjunction with an inert abrasive particulate substance. Substantially all of the organic compounds are converted to corresponding cyclic olefins, largely mono-olefins.

Prediction of rodent carcinogenicity bioassays from molecular structure using inductive logic programming

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

King, R.D.; Srinivasan, A.

1996-10-01

The machine learning program Progol was applied to the problem of forming the structure-activity relationship (SAR) for a set of compounds tested for carcinogenicity in rodent bioassays by the U.S. National Toxicology Program (NTP). Progol is the first inductive logic programming (ILP) algorithm to use a fully relational method for describing chemical structure in SARs, based on using atoms and their bond connectivities. Progol is well suited to forming SARs for carcinogenicity as it is designed to produce easily understandable rules (structural alerts) for sets of noncongeneric compounds. The Progol SAR method was tested by prediction of a set ofmore » compounds that have been widely predicted by other SAR methods (the compounds used in the NTP`s first round of carcinogenesis predictions). For these compounds no method (human or machine) was significantly more accurate than Progol. Progol was the most accurate method that did not use data from biological tests on rodents (however, the difference in accuracy is not significant). The Progol predictions were based solely on chemical structure and the results of tests for Salmonella mutagenicity. Using the full NTP database, the prediction accuracy of Progol was estimated to be 63% ({+-}3%) using 5-fold cross validation. A set of structural alerts for carcinogenesis was automatically generated and the chemical rationale for them investigated-these structural alerts are statistically independent of the Salmonella mutagenicity. Carcinogenicity is predicted for the compounds used in the NTP`s second round of carcinogenesis predictions. The results for prediction of carcinogenesis, taken together with the previous successful applications of predicting mutagenicity in nitroaromatic compounds, and inhibition of angiogenesis by suramin analogues, show that Progol has a role to play in understanding the SARs of cancer-related compounds. 29 refs., 2 figs., 4 tabs.« less

Herbal Compounds and Toxins Modulating TRP Channels

PubMed Central

Vriens, Joris; Nilius, Bernd; Vennekens, Rudi

2008-01-01

Although the benefits are sometimes obvious, traditional or herbal medicine is regarded with skepticism, because the mechanism through which plant compounds exert their powers are largely elusive. Recent studies have shown however that many of these plant compounds interact with specific ion channels and thereby modulate the sensing mechanism of the human body. Especially members of the Transient Receptor Potential (TRP) channels have drawn large attention lately as the receptors for plant-derived compounds such as capsaicin and menthol. TRP channels constitute a large and diverse family of channel proteins that can serve as versatile sensors that allow individual cells and entire organisms to detect changes in their environment. For this family, a striking number of empirical views have turned into mechanism-based actions of natural compounds. In this review we will give an overview of herbal compounds and toxins, which modulate TRP channels. PMID:19305789

Automatic Earthquake Detection by Active Learning

NASA Astrophysics Data System (ADS)

Bergen, K.; Beroza, G. C.

2017-12-01

In recent years, advances in machine learning have transformed fields such as image recognition, natural language processing and recommender systems. Many of these performance gains have relied on the availability of large, labeled data sets to train high-accuracy models; labeled data sets are those for which each sample includes a target class label, such as waveforms tagged as either earthquakes or noise. Earthquake seismologists are increasingly leveraging machine learning and data mining techniques to detect and analyze weak earthquake signals in large seismic data sets. One of the challenges in applying machine learning to seismic data sets is the limited labeled data problem; learning algorithms need to be given examples of earthquake waveforms, but the number of known events, taken from earthquake catalogs, may be insufficient to build an accurate detector. Furthermore, earthquake catalogs are known to be incomplete, resulting in training data that may be biased towards larger events and contain inaccurate labels. This challenge is compounded by the class imbalance problem; the events of interest, earthquakes, are infrequent relative to noise in continuous data sets, and many learning algorithms perform poorly on rare classes. In this work, we investigate the use of active learning for automatic earthquake detection. Active learning is a type of semi-supervised machine learning that uses a human-in-the-loop approach to strategically supplement a small initial training set. The learning algorithm incorporates domain expertise through interaction between a human expert and the algorithm, with the algorithm actively posing queries to the user to improve detection performance. We demonstrate the potential of active machine learning to improve earthquake detection performance with limited available training data.

STEPP--Search Tool for Exploration of Petri net Paths: a new tool for Petri net-based path analysis in biochemical networks.

PubMed

Koch, Ina; Schueler, Markus; Heiner, Monika

2005-01-01

To understand biochemical processes caused by, e. g., mutations or deletions in the genome, the knowledge of possible alternative paths between two arbitrary chemical compounds is of increasing interest for biotechnology, pharmacology, medicine, and drug design. With the steadily increasing amount of data from high-throughput experiments new biochemical networks can be constructed and existing ones can be extended, which results in many large metabolic, signal transduction, and gene regulatory networks. The search for alternative paths within these complex and large networks can provide a huge amount of solutions, which can not be handled manually. Moreover, not all of the alternative paths are generally of interest. Therefore, we have developed and implemented a method, which allows us to define constraints to reduce the set of all structurally possible paths to the truly interesting path set. The paper describes the search algorithm and the constraints definition language. We give examples for path searches using this dedicated special language for a Petri net model of the sucrose-to-starch breakdown in the potato tuber.

STEPP - Search Tool for Exploration of Petri net Paths: A New Tool for Petri Net-Based Path Analysis in Biochemical Networks.

PubMed

Koch, Ina; Schüler, Markus; Heiner, Monika

2011-01-01

To understand biochemical processes caused by, e.g., mutations or deletions in the genome, the knowledge of possible alternative paths between two arbitrary chemical compounds is of increasing interest for biotechnology, pharmacology, medicine, and drug design. With the steadily increasing amount of data from high-throughput experiments new biochemical networks can be constructed and existing ones can be extended, which results in many large metabolic, signal transduction, and gene regulatory networks. The search for alternative paths within these complex and large networks can provide a huge amount of solutions, which can not be handled manually. Moreover, not all of the alternative paths are generally of interest. Therefore, we have developed and implemented a method, which allows us to define constraints to reduce the set of all structurally possible paths to the truly interesting path set. The paper describes the search algorithm and the constraints definition language. We give examples for path searches using this dedicated special language for a Petri net model of the sucrose-to-starch breakdown in the potato tuber. http://sanaga.tfh-berlin.de/~stepp/

Quantitative prediction of solvation free energy in octanol of organic compounds.

PubMed

Delgado, Eduardo J; Jaña, Gonzalo A

2009-03-01

The free energy of solvation, DeltaGS0, in octanol of organic compounds is quantitatively predicted from the molecular structure. The model, involving only three molecular descriptors, is obtained by multiple linear regression analysis from a data set of 147 compounds containing diverse organic functions, namely, halogenated and non-halogenated alkanes, alkenes, alkynes, aromatics, alcohols, aldehydes, ketones, amines, ethers and esters; covering a DeltaGS0 range from about -50 to 0 kJ.mol(-1). The model predicts the free energy of solvation with a squared correlation coefficient of 0.93 and a standard deviation, 2.4 kJ.mol(-1), just marginally larger than the generally accepted value of experimental uncertainty. The involved molecular descriptors have definite physical meaning corresponding to the different intermolecular interactions occurring in the bulk liquid phase. The model is validated with an external set of 36 compounds not included in the training set.

Modification of the Sandwich Estimator in Generalized Estimating Equations with Correlated Binary Outcomes in Rare Event and Small Sample Settings

PubMed Central

Rogers, Paul; Stoner, Julie

2016-01-01

Regression models for correlated binary outcomes are commonly fit using a Generalized Estimating Equations (GEE) methodology. GEE uses the Liang and Zeger sandwich estimator to produce unbiased standard error estimators for regression coefficients in large sample settings even when the covariance structure is misspecified. The sandwich estimator performs optimally in balanced designs when the number of participants is large, and there are few repeated measurements. The sandwich estimator is not without drawbacks; its asymptotic properties do not hold in small sample settings. In these situations, the sandwich estimator is biased downwards, underestimating the variances. In this project, a modified form for the sandwich estimator is proposed to correct this deficiency. The performance of this new sandwich estimator is compared to the traditional Liang and Zeger estimator as well as alternative forms proposed by Morel, Pan and Mancl and DeRouen. The performance of each estimator was assessed with 95% coverage probabilities for the regression coefficient estimators using simulated data under various combinations of sample sizes and outcome prevalence values with an Independence (IND), Autoregressive (AR) and Compound Symmetry (CS) correlation structure. This research is motivated by investigations involving rare-event outcomes in aviation data. PMID:26998504

Target specific compound identification using a support vector machine.

PubMed

Plewczynski, Dariusz; von Grotthuss, Marcin; Spieser, Stephane A H; Rychlewski, Leszek; Wyrwicz, Lucjan S; Ginalski, Krzysztof; Koch, Uwe

2007-03-01

In many cases at the beginning of an HTS-campaign, some information about active molecules is already available. Often known active compounds (such as substrate analogues, natural products, inhibitors of a related protein or ligands published by a pharmaceutical company) are identified in low-throughput validation studies of the biochemical target. In this study we evaluate the effectiveness of a support vector machine applied for those compounds and used to classify a collection with unknown activity. This approach was aimed at reducing the number of compounds to be tested against the given target. Our method predicts the biological activity of chemical compounds based on only the atom pairs (AP) two dimensional topological descriptors. The supervised support vector machine (SVM) method herein is trained on compounds from the MDL drug data report (MDDR) known to be active for specific protein target. For detailed analysis, five different biological targets were selected including cyclooxygenase-2, dihydrofolate reductase, thrombin, HIV-reverse transcriptase and antagonists of the estrogen receptor. The accuracy of compound identification was estimated using the recall and precision values. The sensitivities for all protein targets exceeded 80% and the classification performance reached 100% for selected targets. In another application of the method, we addressed the absence of an initial set of active compounds for a selected protein target at the beginning of an HTS-campaign. In such a case, virtual high-throughput screening (vHTS) is usually applied by using a flexible docking procedure. However, the vHTS experiment typically contains a large percentage of false positives that should be verified by costly and time-consuming experimental follow-up assays. The subsequent use of our machine learning method was found to improve the speed (since the docking procedure was not required for all compounds from the database) and also the accuracy of the HTS hit lists (the enrichment factor).

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

Design of a fragment library that maximally represents available chemical space.

PubMed

Schulz, M N; Landström, J; Bright, K; Hubbard, R E

2011-07-01

Cheminformatics protocols have been developed and assessed that identify a small set of fragments which can represent the compounds in a chemical library for use in fragment-based ligand discovery. Six different methods have been implemented and tested on Input Libraries of compounds from three suppliers. The resulting Fragment Sets have been characterised on the basis of computed physico-chemical properties and their similarity to the Input Libraries. A method that iteratively identifies fragments with the maximum number of similar compounds in the Input Library (Nearest Neighbours) produces the most diverse library. This approach could increase the success of experimental ligand discovery projects, by providing fragments that can be progressed rapidly to larger compounds through access to available similar compounds (known as SAR by Catalog).

Cheminformatics Analysis of EPA ToxCast Chemical Libraries ...

EPA Pesticide Factsheets

An important goal of toxicology research is the development of robust methods that use in vitro and chemical structure information to predict in vivo toxicity endpoints. The US EPA ToxCast program is addressing this goal using ~600 in vitro assays to create bioactivity profiles on a set of 320 compounds, mostly pesticide actives, that have well characterized in vivo toxicity. These 320 compounds (EPA-320 set evaluated in Phase I of ToxCast) are a subset of a much larger set of ~10,000 candidates that are of interest to the EPA (called here EPA-10K). Predictive models of in vivo toxicity are being constructed from the in vitro assay data on the EPA-320 chemical set. These models require validation on additional chemicals prior to wide acceptance, and this will be carried out by evaluating compounds from EPA-10K in Phase II of ToxCast. We have used cheminformatics approaches including clustering, data visualization, and QSAR to develop models for EPA-320 that could help prioritizing EPA-10K validation chemicals. Both chemical descriptors, as well as calculated physicochemical properties have been used. Compounds from EPA-10K are prioritized based on their similarity to EPA-320 using different similarity metrics, with similarity thresholds defining the domain of applicability for the predictive models built for EPA-320 set. In addition, prioritized lists of compounds of increasing dissimilarity from the EPA-320 have been produced, to test the ability of the EPA-320

Survey of whole air data from the second airborne Biomass Burning and Lightning Experiment using principal component analysis

NASA Astrophysics Data System (ADS)

Choi, Yunsoo; Elliott, Scott; Simpson, Isobel J.; Blake, Donald R.; Colman, Jonah J.; Dubey, Manvendra K.; Meinardi, Simone; Rowland, F. Sherwood; Shirai, Tomoko; Smith, Felisa A.

2003-03-01

Hydrocarbon and halocarbon measurements collected during the second airborne Biomass Burning and Lightning Experiment (BIBLE-B) were subjected to a principal component analysis (PCA), to test the capability for identifying intercorrelated compounds within a large whole air data set. The BIBLE expeditions have sought to quantify and understand the products of burning, electrical discharge, and general atmospheric chemical processes during flights arrayed along the western edge of the Pacific. Principal component analysis was found to offer a compact method for identifying the major modes of composition encountered in the regional whole air data set. Transecting the continental monsoon, urban and industrial tracers (e.g., combustion byproducts, chlorinated methanes and ethanes, xylenes, and longer chain alkanes) dominated the observed variability. Pentane enhancements reflected vehicular emissions. In general, ethyl and propyl nitrate groupings indicated oxidation under nitrogen oxide (NOx) rich conditions and hence city or lightning influences. Over the tropical ocean, methyl nitrate grouped with brominated compounds and sometimes with dimethyl sulfide and methyl iodide. Biomass burning signatures were observed during flights over the Australian continent. Strong indications of wetland anaerobics (methane) or liquefied petroleum gas leakage (propane) were conspicuous by their absence. When all flights were considered together, sources attributable to human activity emerged as the most important. We suggest that factor reductions in general and PCA in particular may soon play a vital role in the analysis of regional whole air data sets, as a complement to more familiar methods.

Anomalous pressure dependence of thermal conductivities of large mass ratio compounds

DOE PAGES

Lindsay, Lucas R; Broido, David A.; Carrete, Jesus; ...

2015-03-27

The lattice thermal conductivities (k) of binary compound materials are examined as a function of hydrostatic pressure P using a first-principles approach. Compound materials with relatively small mass ratios, such as MgO, show an increase in k with P, consistent with measurements. Conversely, compounds with large mass ratios (e.g., BSb, BAs, BeTe, BeSe) exhibit decreasing with increasing P, a behavior that cannot be understood using simple theories of k. This anomalous P dependence of k arises from the fundamentally different nature of the intrinsic scattering processes for heat-carrying acoustic phonons in large mass ratio compounds compared to those with smallmore » mass ratios. We find this work demonstrates the power of first principles methods for thermal properties and advances the understanding of thermal transport in non-metals.« less

Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries.

PubMed

Ma, Xiao H; Jia, Jia; Zhu, Feng; Xue, Ying; Li, Ze R; Chen, Yu Z

2009-05-01

Machine learning methods have been explored as ligand-based virtual screening tools for facilitating drug lead discovery. These methods predict compounds of specific pharmacodynamic, pharmacokinetic or toxicological properties based on their structure-derived structural and physicochemical properties. Increasing attention has been directed at these methods because of their capability in predicting compounds of diverse structures and complex structure-activity relationships without requiring the knowledge of target 3D structure. This article reviews current progresses in using machine learning methods for virtual screening of pharmacodynamically active compounds from large compound libraries, and analyzes and compares the reported performances of machine learning tools with those of structure-based and other ligand-based (such as pharmacophore and clustering) virtual screening methods. The feasibility to improve the performance of machine learning methods in screening large libraries is discussed.

Investigating the different mechanisms of genotoxic and non-genotoxic carcinogens by a gene set analysis.

PubMed

Lee, Won Jun; Kim, Sang Cheol; Lee, Seul Ji; Lee, Jeongmi; Park, Jeong Hill; Yu, Kyung-Sang; Lim, Johan; Kwon, Sung Won

2014-01-01

Based on the process of carcinogenesis, carcinogens are classified as either genotoxic or non-genotoxic. In contrast to non-genotoxic carcinogens, many genotoxic carcinogens have been reported to cause tumor in carcinogenic bioassays in animals. Thus evaluating the genotoxicity potential of chemicals is important to discriminate genotoxic from non-genotoxic carcinogens for health care and pharmaceutical industry safety. Additionally, investigating the difference between the mechanisms of genotoxic and non-genotoxic carcinogens could provide the foundation for a mechanism-based classification for unknown compounds. In this study, we investigated the gene expression of HepG2 cells treated with genotoxic or non-genotoxic carcinogens and compared their mechanisms of action. To enhance our understanding of the differences in the mechanisms of genotoxic and non-genotoxic carcinogens, we implemented a gene set analysis using 12 compounds for the training set (12, 24, 48 h) and validated significant gene sets using 22 compounds for the test set (24, 48 h). For a direct biological translation, we conducted a gene set analysis using Globaltest and selected significant gene sets. To validate the results, training and test compounds were predicted by the significant gene sets using a prediction analysis for microarrays (PAM). Finally, we obtained 6 gene sets, including sets enriched for genes involved in the adherens junction, bladder cancer, p53 signaling pathway, pathways in cancer, peroxisome and RNA degradation. Among the 6 gene sets, the bladder cancer and p53 signaling pathway sets were significant at 12, 24 and 48 h. We also found that the DDB2, RRM2B and GADD45A, genes related to the repair and damage prevention of DNA, were consistently up-regulated for genotoxic carcinogens. Our results suggest that a gene set analysis could provide a robust tool in the investigation of the different mechanisms of genotoxic and non-genotoxic carcinogens and construct a more detailed understanding of the perturbation of significant pathways.

Investigating the Different Mechanisms of Genotoxic and Non-Genotoxic Carcinogens by a Gene Set Analysis

PubMed Central

Lee, Won Jun; Kim, Sang Cheol; Lee, Seul Ji; Lee, Jeongmi; Park, Jeong Hill; Yu, Kyung-Sang; Lim, Johan; Kwon, Sung Won

2014-01-01

Based on the process of carcinogenesis, carcinogens are classified as either genotoxic or non-genotoxic. In contrast to non-genotoxic carcinogens, many genotoxic carcinogens have been reported to cause tumor in carcinogenic bioassays in animals. Thus evaluating the genotoxicity potential of chemicals is important to discriminate genotoxic from non-genotoxic carcinogens for health care and pharmaceutical industry safety. Additionally, investigating the difference between the mechanisms of genotoxic and non-genotoxic carcinogens could provide the foundation for a mechanism-based classification for unknown compounds. In this study, we investigated the gene expression of HepG2 cells treated with genotoxic or non-genotoxic carcinogens and compared their mechanisms of action. To enhance our understanding of the differences in the mechanisms of genotoxic and non-genotoxic carcinogens, we implemented a gene set analysis using 12 compounds for the training set (12, 24, 48 h) and validated significant gene sets using 22 compounds for the test set (24, 48 h). For a direct biological translation, we conducted a gene set analysis using Globaltest and selected significant gene sets. To validate the results, training and test compounds were predicted by the significant gene sets using a prediction analysis for microarrays (PAM). Finally, we obtained 6 gene sets, including sets enriched for genes involved in the adherens junction, bladder cancer, p53 signaling pathway, pathways in cancer, peroxisome and RNA degradation. Among the 6 gene sets, the bladder cancer and p53 signaling pathway sets were significant at 12, 24 and 48 h. We also found that the DDB2, RRM2B and GADD45A, genes related to the repair and damage prevention of DNA, were consistently up-regulated for genotoxic carcinogens. Our results suggest that a gene set analysis could provide a robust tool in the investigation of the different mechanisms of genotoxic and non-genotoxic carcinogens and construct a more detailed understanding of the perturbation of significant pathways. PMID:24497971

Crystallization tendency of active pharmaceutical ingredients following rapid solvent evaporation--classification and comparison with crystallization tendency from undercooled melts.

PubMed

Van Eerdenbrugh, Bernard; Baird, Jared A; Taylor, Lynne S

2010-09-01

In this study, the crystallization behavior of a variety of compounds was studied following rapid solvent evaporation using spin coating. Initial screening to determine model compound suitability was performed using a structurally diverse set of 51 compounds in three different solvent systems [dichloromethane (DCM), a 1:1 (w/w) dichloromethane/ethanol mixture (MIX), and ethanol (EtOH)]. Of this starting set of 153 drug-solvent combinations, 93 (40 compounds) were selected for further evaluation based on solubility, chemical solution stability, and processability criteria. These systems were spin coated and their crystallization was monitored using polarized light microscopy (7 days, dry conditions). The crystallization behavior of the samples could be classified as rapid (Class I: 39 cases), intermediate (Class II: 23 cases), or slow (Class III: 31 cases). The solvent system employed influenced the classification outcome for only four of the compounds. The various compounds showed very diverse crystallization behavior. Upon comparison of classification results with those of a previous study, where cooling from the melt was used as a preparation technique, a good similarity was found whereby 68% of the cases were identically classified. Multivariate analysis was performed using a set of relevant physicochemical compound characteristics. It was found that a number of these parameters tended to differ between the different classes. These could be further interpreted in terms of the nature of the crystallization process. Additional multivariate analysis on the separate classes of compounds indicated some potential in predicting the crystallization tendency of a given compound.

Design of a general-purpose European compound screening library for EU-OPENSCREEN.

PubMed

Horvath, Dragos; Lisurek, Michael; Rupp, Bernd; Kühne, Ronald; Specker, Edgar; von Kries, Jens; Rognan, Didier; Andersson, C David; Almqvist, Fredrik; Elofsson, Mikael; Enqvist, Per-Anders; Gustavsson, Anna-Lena; Remez, Nikita; Mestres, Jordi; Marcou, Gilles; Varnek, Alexander; Hibert, Marcel; Quintana, Jordi; Frank, Ronald

2014-10-01

This work describes a collaborative effort to define and apply a protocol for the rational selection of a general-purpose screening library, to be used by the screening platforms affiliated with the EU-OPENSCREEN initiative. It is designed as a standard source of compounds for primary screening against novel biological targets, at the request of research partners. Given the general nature of the potential applications of this compound collection, the focus of the selection strategy lies on ensuring chemical stability, absence of reactive compounds, screening-compliant physicochemical properties, loose compliance to drug-likeness criteria (as drug design is a major, but not exclusive application), and maximal diversity/coverage of chemical space, aimed at providing hits for a wide spectrum of drugable targets. Finally, practical availability/cost issues cannot be avoided. The main goal of this publication is to inform potential future users of this library about its conception, sources, and characteristics. The outline of the selection procedure, notably of the filtering rules designed by a large committee of European medicinal chemists and chemoinformaticians, may be of general methodological interest for the screening/medicinal chemistry community. The selection task of 200K molecules out of a pre-filtered set of 1.4M candidates was shared by five independent European research groups, each picking a subset of 40K compounds according to their own in-house methodology and expertise. An in-depth analysis of chemical space coverage of the library serves not only to characterize the collection, but also to compare the various chemoinformatics-driven selection procedures of maximal diversity sets. Compound selections contributed by various participating groups were mapped onto general-purpose self-organizing maps (SOMs) built on the basis of marketed drugs and bioactive reference molecules. In this way, the occupancy of chemical space by the EU-OPENSCREEN library could be directly compared with distributions of known bioactives of various classes. This mapping highlights the relevance of the selection and shows how the consensus reached by merging the five different 40K selections contributes to achieve this relevance. The approach also allows one to readily identify subsets of target- or target-class-oriented compounds from the EU-OPENSCREEN library to suit the needs of the diverse range of potential users. The final EU-OPENSCREEN library, assembled by merging five independent selections of 40K compounds from various expert groups, represents an excellent example of a Europe-wide collaborative effort toward the common objective of building best-in-class European open screening platforms. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

One Way to Design a Valence-Skip Compound.

PubMed

Hase, I; Yanagisawa, T; Kawashima, K

2017-12-01

Valence-skip compound is a good candidate with high T c and low anisotropy because it has a large attractive interaction at the site of valence-skip atom. However, it is not easy to synthesize such compound because of (i) the instability of the skipping valence state, (ii) the competing charge order, and (iii) that formal valence may not be true in some compounds. In the present study, we show several examples of the valence-skip compounds and discuss how we can design them by first principles calculations. Furthermore, we calculated the electronic structure of a promising candidate of valence skipping compound RbTlCl 3 from first principles. We confirmed that the charge-density wave (CDW) is formed in this compound, and the Tl atoms in two crystallographic different sites take the valence Tl 1+ and Tl 3+ . Structure optimization study reveals that this CDW is stable at the ambient pressure, while this CDW gap can be collapsed when we apply pressure with several gigapascals. In this metallic phase, we can expect a large charge fluctuation and a large electron-phonon interaction.

Adaptive Landscape Flattening Accelerates Sampling of Alchemical Space in Multisite λ Dynamics.

PubMed

Hayes, Ryan L; Armacost, Kira A; Vilseck, Jonah Z; Brooks, Charles L

2017-04-20

Multisite λ dynamics (MSλD) is a powerful emerging method in free energy calculation that allows prediction of relative free energies for a large set of compounds from very few simulations. Calculating free energy differences between substituents that constitute large volume or flexibility jumps in chemical space is difficult for free energy methods in general, and for MSλD in particular, due to large free energy barriers in alchemical space. This study demonstrates that a simple biasing potential can flatten these barriers and introduces an algorithm that determines system specific biasing potential coefficients. Two sources of error, deep traps at the end points and solvent disruption by hard-core potentials, are identified. Both scale with the size of the perturbed substituent and are removed by sharp biasing potentials and a new soft-core implementation, respectively. MSλD with landscape flattening is demonstrated on two sets of molecules: derivatives of the heat shock protein 90 inhibitor geldanamycin and derivatives of benzoquinone. In the benzoquinone system, landscape flattening leads to 2 orders of magnitude improvement in transition rates between substituents and robust solvation free energies. Landscape flattening opens up new applications for MSλD by enabling larger chemical perturbations to be sampled with improved precision and accuracy.

Setting local rank constraints by orthogonal projections for image resolution analysis: application to the determination of a low dose pharmaceutical compound.

PubMed

Boiret, Mathieu; de Juan, Anna; Gorretta, Nathalie; Ginot, Yves-Michel; Roger, Jean-Michel

2015-09-10

Raman chemical imaging provides chemical and spatial information about pharmaceutical drug product. By using resolution methods on acquired spectra, the objective is to calculate pure spectra and distribution maps of image compounds. With multivariate curve resolution-alternating least squares, constraints are used to improve the performance of the resolution and to decrease the ambiguity linked to the final solution. Non negativity and spatial local rank constraints have been identified as the most powerful constraints to be used. In this work, an alternative method to set local rank constraints is proposed. The method is based on orthogonal projections pretreatment. For each drug product compound, raw Raman spectra are orthogonally projected to a basis including all the variability from the formulation compounds other than the product of interest. Presence or absence of the compound of interest is obtained by observing the correlations between the orthogonal projected spectra and a pure spectrum orthogonally projected to the same basis. By selecting an appropriate threshold, maps of presence/absence of compounds can be set up for all the product compounds. This method appears as a powerful approach to identify a low dose compound within a pharmaceutical drug product. The maps of presence/absence of compounds can be used as local rank constraints in resolution methods, such as multivariate curve resolution-alternating least squares process in order to improve the resolution of the system. The method proposed is particularly suited for pharmaceutical systems, where the identity of all compounds in the formulations is known and, therefore, the space of interferences can be well defined. Copyright © 2015 Elsevier B.V. All rights reserved.

Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

PubMed Central

Alves, Vinicius M.; Muratov, Eugene; Fourches, Denis; Strickland, Judy; Kleinstreuer, Nicole; Andrade, Carolina H.; Tropsha, Alexander

2015-01-01

Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using random forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers were 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the ScoreCard database of possible skin or sense organ toxicants as primary candidates for experimental validation. PMID:25560674

Total-energy Assisted Tight-binding Method Based on Local Density Approximation of Density Functional Theory

NASA Astrophysics Data System (ADS)

Fujiwara, Takeo; Nishino, Shinya; Yamamoto, Susumu; Suzuki, Takashi; Ikeda, Minoru; Ohtani, Yasuaki

2018-06-01

A novel tight-binding method is developed, based on the extended Hückel approximation and charge self-consistency, with referring the band structure and the total energy of the local density approximation of the density functional theory. The parameters are so adjusted by computer that the result reproduces the band structure and the total energy, and the algorithm for determining parameters is established. The set of determined parameters is applicable to a variety of crystalline compounds and change of lattice constants, and, in other words, it is transferable. Examples are demonstrated for Si crystals of several crystalline structures varying lattice constants. Since the set of parameters is transferable, the present tight-binding method may be applicable also to molecular dynamics simulations of large-scale systems and long-time dynamical processes.

Spectroscopic (FT-IR and UV-Vis) and theoretical (HF and DFT) investigation of 2-Ethyl-N-[(5-nitrothiophene-2-yl)methylidene]aniline

NASA Astrophysics Data System (ADS)

Ceylan, Ümit; Tarı, Gonca Özdemir; Gökce, Halil; Ağar, Erbil

2016-04-01

Crystal structure of the title compound, 2-Ethyl-N-[(5-nitrothiophene-2-yl)methylidene]aniline, C13H12N2O2S, has been synthesized and characterized by FT-IR and UV-Vis spectrum. The compound crystallized in the monoclinic space group P 21/c with a = 11.3578 (4) Å, b = 7.4923 (2) Å, c = 14.9676 (6) Å and β = 99.589 (3)° and Z = 4 in the unit cell. The molecular geometry was also calculated using the Gaussian 03 software and structure was optimized using the HF and DFT/B3LYP methods with the 6-311++G(d,p) basis set in ground state. Using the TD-DFT method, the electronic absorption spectra of the title compound was computed in both the gas phase and ethanol solvent. The harmonic vibrational frequencies of the title compound were calculated using the same methods with the 6-311++G(d,p) basis set. The calculated results were compared with the experimental determination results of the compound. It was seen that the optimized structure was in excellent agreement with the X-ray crystal structure. The energetic behaviors of the title compound in solvent media were examined using the HF and DFT/B3LYP methods with the 6-311++G(d,p) basis set applying the polarizable continuum model (PCM). In addition, the molecular orbitals (FMOs) analysis, molecular electrostatic potential (MEP), nonlinear optical and thermodynamic properties of the title compound were performed using the same methods with the 6-311++G(d,p) basis set.

Common y-intercept and single compound regressions of gas-particle partitioning data vs 1/T

NASA Astrophysics Data System (ADS)

Pankow, James F.

Confidence intervals are placed around the log Kp vs 1/ T correlation equations obtained using simple linear regressions (SLR) with the gas-particle partitioning data set of Yamasaki et al. [(1982) Env. Sci. Technol.16, 189-194]. The compounds and groups of compounds studied include the polycylic aromatic hydrocarbons phenanthrene + anthracene, me-phenanthrene + me-anthracene, fluoranthene, pyrene, benzo[ a]fluorene + benzo[ b]fluorene, chrysene + benz[ a]anthracene + triphenylene, benzo[ b]fluoranthene + benzo[ k]fluoranthene, and benzo[ a]pyrene + benzo[ e]pyrene (note: me = methyl). For any given compound, at equilibrium, the partition coefficient Kp equals ( F/ TSP)/ A where F is the particulate-matter associated concentration (ng m -3), A is the gas-phase concentration (ng m -3), and TSP is the concentration of particulate matter (μg m -3). At temperatures more than 10°C from the mean sampling temperature of 17°C, the confidence intervals are quite wide. Since theory predicts that similar compounds sorbing on the same particulate matter should possess very similar y-intercepts, the data set was also fitted using a special common y-intercept regression (CYIR). For most of the compounds, the CYIR equations fell inside of the SLR 95% confidence intervals. The CYIR y-intercept value is -18.48, and is reasonably close to the type of value that can be predicted for PAH compounds. The set of CYIR regression equations is probably more reliable than the set of SLR equations. For example, the CYIR-derived desorption enthalpies are much more highly correlated with vaporization enthalpies than are the SLR-derived desorption enthalpies. It is recommended that the CYIR approach be considered whenever analysing temperature-dependent gas-particle partitioning data.

Chemical landscape analysis with the OpenTox framework.

PubMed

Jeliazkova, Nina; Jeliazkov, Vedrin

2012-01-01

The Structure-Activity Relationships (SAR) landscape and activity cliffs concepts have their origins in medicinal chemistry and receptor-ligand interactions modelling. While intuitive, the definition of an activity cliff as a "pair of structurally similar compounds with large differences in potency" is commonly recognized as ambiguous. This paper proposes a new and efficient method for identifying activity cliffs and visualization of activity landscapes. The activity cliffs definition could be improved to reflect not the cliff steepness alone, but also the rate of the change of the steepness. The method requires explicitly setting similarity and activity difference thresholds, but provides means to explore multiple thresholds and to visualize in a single map how the thresholds affect the activity cliff identification. The identification of the activity cliffs is addressed by reformulating the problem as a statistical one, by introducing a probabilistic measure, namely, calculating the likelihood of a compound having large activity difference compared to other compounds, while being highly similar to them. The likelihood is effectively a quantification of a SAS Map with defined thresholds. Calculating the likelihood relies on four counts only, and does not require the pairwise matrix storage. This is a significant advantage, especially when processing large datasets. The method generates a list of individual compounds, ranked according to the likelihood of their involvement in the formation of activity cliffs, and goes beyond characterizing cliffs by structure pairs only. The visualisation is implemented by considering the activity plane fixed and analysing the irregularities of the similarity itself. It provides a convenient analogy to a topographic map and may help identifying the most appropriate similarity representation for each specific SAR space. The proposed method has been applied to several datasets, representing different biological activities. Finally, the method is implemented as part of an existing open source Ambit package and could be accessed via an OpenTox API compliant web service and via an interactive application, running within a modern, JavaScript enabled web browser. Combined with the functionalities already offered by the OpenTox framework, like data sharing and remote calculations, it could be a useful tool for exploring chemical landscapes online.

Exposure to potentially toxic hydrocarbons and halocarbons released from the dialyzer and tubing set during hemodialysis.

PubMed

Lee, Hyun Ji Julie; Meinardi, Simone; Pahl, Madeleine V; Vaziri, Nostratola D; Blake, Donald R

2012-10-01

Although much is known about the effect of chronic kidney failure and dialysis on the composition of solutes in plasma, little is known about their impact on the composition of gaseous compounds in exhaled breath. This study was designed to explore the effect of uremia and the hemodialysis (HD) procedure on the composition of exhaled breath. Breath samples were collected from 10 dialysis patients immediately before, during, and after a dialysis session. To determine the potential introduction of gaseous compounds from dialysis components, gasses emitted from dialyzers, tubing set, dialysate, and water supplies were collected. Prospective cohort study. 10 HD patients and 10 age-matched healthy individuals. Predictors include the dialyzers, tubing set, dialysate, and water supplies before, during, and after dialysis. Changes in the composition of exhaled breath. A 5-column/detector gas chromatography system was used to measure hydrocarbon, halocarbon, oxygenate, and alkyl nitrate compounds. Concentrations of 14 hydrocarbons and halocarbons in patients' breath rapidly increased after the onset of the HD treatment. All 14 compounds and 5 others not found in patients' breath were emitted from the dialyzers and tubing sets. Contrary to earlier reports, exhaled breath ethane concentrations in our dialysis patients were virtually unchanged during the HD treatment. Single-center study with a small sample size may limit the generalizability of the findings. The study documented the release of several potentially toxic hydrocarbons and halocarbons to patients from the dialyzer and tubing sets during the HD procedure. Because long-term exposure to these compounds may contribute to the morbidity and mortality in dialysis population, this issue should be considered in the manufacturing of the new generation of dialyzers and dialysis tubing sets. Copyright © 2012 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

Occurrence of PCDD/F, PCB, PBDE, PFAS, and organotin compounds in fish meal, fish oil and fish feed.

PubMed

Suominen, K; Hallikainen, A; Ruokojärvi, P; Airaksinen, R; Koponen, J; Rannikko, R; Kiviranta, H

2011-10-01

We analysed polychlorinated dibenzo-p-dioxins and furans (PCDD/F, dioxins), and polychlorinated biphenyls (PCB) in 13 fish meal, five fish oil, and seven fish feed samples. Polybrominated diphenyl ethers (PBDE), organotin compounds (OTC), and perfluoroalkylated substances (PFAS) were analysed in ten fish meal, two fish oil, and two fish feed samples. All measured TEQ concentrations of PCDD/F and PCB were below the maximum levels set by Directive 2002/32/EC. There was no correlation between concentrations of WHOPCDD/F-TEQ and indicator PCB in our samples. The most common congeners among PBDEs were BDE-47 and BDE-100. BDE-209 was present in five fish meals of the ten analysed. Tributyltin (TBT) was the predominant congener in all samples except in three fish meals, where monobutyltin (MBT) was the major congener. Perfluorooctane sulphonate (PFOS) was the predominant congener in six fish meals of the ten analysed. There was large variation in concentrations and congener distributions of the studied compounds between our samples. Our results underline a need to pay special attention to the origin and purity of feed raw material of marine origin. Copyright © 2011 Elsevier Ltd. 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.

51V solid-state NMR and density functional theory studies of vanadium environments in V(V)O2 dipicolinic acid complexes

NASA Astrophysics Data System (ADS)

Bolte, Stephanie E.; Ooms, Kristopher J.; Polenova, Tatyana; Baruah, Bharat; Crans, Debbie C.; Smee, Jason J.

2008-02-01

V51 solid-state NMR and density functional theory (DFT) investigations are reported for a series of pentacoordinate dioxovanadium(V)-dipicolinate [V(V )O2-dipicolinate] and heptacoordinate aquahydroxylamidooxovanadium(V)-dipicolinate [V(V)O-dipicolinate] complexes. These compounds are of interest because of their potency as phosphatase inhibitors as well as their insulin enhancing properties and potential for the treatment of diabetes. Experimental solid-state NMR results show that the electric field gradient tensors in the V(V )O2-dipicolinate derivatives are affected significantly by substitution on the dipicolinate ring and range from 5.8to8.3MHz. The chemical shift anisotropies show less dramatic variations with respect to the ligand changes and range between -550 and -600ppm. To gain insights on the origins of the NMR parameters, DFT calculations were conducted for an extensive series of the V(V )O2- and V(V)O-dipicolinate complexes. To assess the level of theory required for the accurate calculation of the V51 NMR parameters, different functionals, basis sets, and structural models were explored in the DFT study. It is shown that the original x-ray crystallographic geometries, including all counterions and solvation water molecules within 5Å of the vanadium, lead to the most accurate results. The choice of the functional and the basis set at a high level of theory has a relatively minor impact on the outcome of the chemical shift anisotropy calculations; however, the use of large basis sets is necessary for accurate calculations of the quadrupole coupling constants for several compounds of the V(V )O2 series. These studies demonstrate that even though the vanadium compounds under investigations exhibit distorted trigonal bipyramidal coordination geometry, they have a "perfect" trigonal bipyramidal electronic environment. This observation could potentially explain why vanadate and vanadium(V) adducts are often recognized as potent transition state analogs.

Prediction model of potential hepatocarcinogenicity of rat hepatocarcinogens using a large-scale toxicogenomics database

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

Uehara, Takeki, E-mail: takeki.uehara@shionogi.co.jp; Toxicogenomics Informatics Project, National Institute of Biomedical Innovation, 7-6-8 Asagi, Ibaraki, Osaka 567-0085; Minowa, Yohsuke

2011-09-15

The present study was performed to develop a robust gene-based prediction model for early assessment of potential hepatocarcinogenicity of chemicals in rats by using our toxicogenomics database, TG-GATEs (Genomics-Assisted Toxicity Evaluation System developed by the Toxicogenomics Project in Japan). The positive training set consisted of high- or middle-dose groups that received 6 different non-genotoxic hepatocarcinogens during a 28-day period. The negative training set consisted of high- or middle-dose groups of 54 non-carcinogens. Support vector machine combined with wrapper-type gene selection algorithms was used for modeling. Consequently, our best classifier yielded prediction accuracies for hepatocarcinogenicity of 99% sensitivity and 97% specificitymore » in the training data set, and false positive prediction was almost completely eliminated. Pathway analysis of feature genes revealed that the mitogen-activated protein kinase p38- and phosphatidylinositol-3-kinase-centered interactome and the v-myc myelocytomatosis viral oncogene homolog-centered interactome were the 2 most significant networks. The usefulness and robustness of our predictor were further confirmed in an independent validation data set obtained from the public database. Interestingly, similar positive predictions were obtained in several genotoxic hepatocarcinogens as well as non-genotoxic hepatocarcinogens. These results indicate that the expression profiles of our newly selected candidate biomarker genes might be common characteristics in the early stage of carcinogenesis for both genotoxic and non-genotoxic carcinogens in the rat liver. Our toxicogenomic model might be useful for the prospective screening of hepatocarcinogenicity of compounds and prioritization of compounds for carcinogenicity testing. - Highlights: >We developed a toxicogenomic model to predict hepatocarcinogenicity of chemicals. >The optimized model consisting of 9 probes had 99% sensitivity and 97% specificity. >This model enables us to detect genotoxic as well as non-genotoxic hepatocarcinogens.« less

Best of both worlds: combining pharma data and state of the art modeling technology to improve in Silico pKa prediction.

PubMed

Fraczkiewicz, Robert; Lobell, Mario; Göller, Andreas H; Krenz, Ursula; Schoenneis, Rolf; Clark, Robert D; Hillisch, Alexander

2015-02-23

In a unique collaboration between a software company and a pharmaceutical company, we were able to develop a new in silico pKa prediction tool with outstanding prediction quality. An existing pKa prediction method from Simulations Plus based on artificial neural network ensembles (ANNE), microstates analysis, and literature data was retrained with a large homogeneous data set of drug-like molecules from Bayer. The new model was thus built with curated sets of ∼14,000 literature pKa values (∼11,000 compounds, representing literature chemical space) and ∼19,500 pKa values experimentally determined at Bayer Pharma (∼16,000 compounds, representing industry chemical space). Model validation was performed with several test sets consisting of a total of ∼31,000 new pKa values measured at Bayer. For the largest and most difficult test set with >16,000 pKa values that were not used for training, the original model achieved a mean absolute error (MAE) of 0.72, root-mean-square error (RMSE) of 0.94, and squared correlation coefficient (R(2)) of 0.87. The new model achieves significantly improved prediction statistics, with MAE = 0.50, RMSE = 0.67, and R(2) = 0.93. It is commercially available as part of the Simulations Plus ADMET Predictor release 7.0. Good predictions are only of value when delivered effectively to those who can use them. The new pKa prediction model has been integrated into Pipeline Pilot and the PharmacophorInformatics (PIx) platform used by scientists at Bayer Pharma. Different output formats allow customized application by medicinal chemists, physical chemists, and computational chemists.

The composition of the primitive atmosphere and the synthesis of organic compounds on the early Earth

NASA Technical Reports Server (NTRS)

Bada, J. L.; Miller, S. L.

1985-01-01

The generally accepted theory for the origin of life on the Earth requires that a large variety of organic compounds be present to form the first living organisms and to provide the energy sources for primitive life either directly or through various fermentation reactions. This can provide a strong constraint on discussions of the formation of the Earth and on the composition of the primitive atmosphere. In order for substantial amounts of organic compounds to have been present on the prebiological Earth, certain conditions must have existed. There is a large body of literature on the prebiotic synthesis of organic compounds in various postulated atmospheres. In this mixture of abiotically synthesized organic compounds, the amino acids are of special interest since they are utilized by modern organisms to synthesize structural materials and a large array of catalytic peptides.

Plant growth inhibitors isolated from sugarcane (Saccharum officinarum) straw.

PubMed

Sampietro, Diego Alejandro; Vattuone, Marta Amelia; Isla, María Ines

2006-07-01

Several compounds related with plant defense and pharmacological activities have been isolated from sugarcane. Straw phytotoxins and their possible mechanisms of growth inhibition are largely unknown. A bioassay-guided fractionation of the phytotoxic constituents leachated from a sugarcane straw led to the isolation of trans-ferulic (trans-FA), cis-ferulic (cis-FA), vanillic (VA) and syringic (SA) acids. The straw leachates and their identified constituents significantly inhibited root growth of lettuce and four weeds. VA was more phytotoxic to root elongation than FA and SA. The identified phenolic compounds significantly increased leakage of root cell constituents, inhibited dehydrogenase activity and reduced chlorophyll content in lettuce. VA and FA inhibited mitotic index while SA increased cell division. Additive (VA-FA and FA-SA) and synergistic (VA-SA) interactions on root growth were observed at the response level of EC(25). Although the isolated compounds differed in their relative phytotoxic activities, the observed physiological responses suggest that they have a common mode of action. HPLC analysis indicated that sugarcane straw can potentially release 1.43 (ratio 2:1, trans:cis), 1.14 and 0.14mmolkg(-1) (straw dry weight) of FA, VA and SA, respectively. As phenolic acids are often found spatially concentrated in the top soil layers under plant straws, further studies are needed to establish the impact of these compounds in natural settings.

Effects of medicinal compounds on the differentiation of the eukaryotic microorganism dictyostelium discoideum: can this model be used as a screening test for reproductive toxicity in humans?

PubMed

Dannat, K; Tillner, J; Winckler, T; Weiss, M; Eger, K; Dingermann, T

2003-03-01

Dictyostelium discoideum is a single-cell, eukaryotic microorganism that can undergo multicellular development in order to produce dormant spores. We investigated the capacity of D. discoideum to be used as a rapid screening system for potential developmental toxicity of compounds under development as pharmaceuticals. We used a set of four transgenic D. discoideum strains that expressed a reporter gene under the control of promoters that are active at certain time periods and in distinct cell types during D. discoideum development. We found that teratogens such as valproic acid, tretinoin, or thalidomide interfered to various extents with D. discoideum development, and had different effects on prestalk and prespore cell-specific reporter gene expression. Phenytoin was inactive in this assay, which may point to limitations in metabolization of the compound in Dictyostelium required to exert developmental toxicity. D. discoideum cell culture is cheap and easy to handle compared to mammalian cell cultures or animal teratogenicity models. Although the Dictyostelium-based assay described in this report may not securely predict the teratogenic potential of these drugs in humans, this organism may be qualified for rapid large-scale screenings of synthetic compounds under development as new pharmaceuticals for their potential to interfere with developmental processes and thus help to reduce the amount of teratogenicity tests in animal models.

Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors

PubMed Central

Vilar, Santiago; Chakrabarti, Mayukh; Costanzi, Stefano

2010-01-01

The distribution of compounds between blood and brain is a very important consideration for new candidate drug molecules. In this paper, we describe the derivation of two linear discriminant analysis (LDA) models for the prediction of passive blood-brain partitioning, expressed in terms of log BB values. The models are based on computationally derived physicochemical descriptors, namely the octanol/water partition coefficient (log P), the topological polar surface area (TPSA) and the total number of acidic and basic atoms, and were obtained using a homogeneous training set of 307 compounds, for all of which the published experimental log BB data had been determined in vivo. In particular, since molecules with log BB > 0.3 cross the blood-brain barrier (BBB) readily while molecules with log BB < −1 are poorly distributed to the brain, on the basis of these thresholds we derived two distinct models, both of which show a percentage of good classification of about 80%. Notably, the predictive power of our models was confirmed by the analysis of a large external dataset of compounds with reported activity on the central nervous system (CNS) or lack thereof. The calculation of straightforward physicochemical descriptors is the only requirement for the prediction of the log BB of novel compounds through our models, which can be conveniently applied in conjunction with drug design and virtual screenings. PMID:20427217

Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors.

PubMed

Vilar, Santiago; Chakrabarti, Mayukh; Costanzi, Stefano

2010-06-01

The distribution of compounds between blood and brain is a very important consideration for new candidate drug molecules. In this paper, we describe the derivation of two linear discriminant analysis (LDA) models for the prediction of passive blood-brain partitioning, expressed in terms of logBB values. The models are based on computationally derived physicochemical descriptors, namely the octanol/water partition coefficient (logP), the topological polar surface area (TPSA) and the total number of acidic and basic atoms, and were obtained using a homogeneous training set of 307 compounds, for all of which the published experimental logBB data had been determined in vivo. In particular, since molecules with logBB>0.3 cross the blood-brain barrier (BBB) readily while molecules with logBB<-1 are poorly distributed to the brain, on the basis of these thresholds we derived two distinct models, both of which show a percentage of good classification of about 80%. Notably, the predictive power of our models was confirmed by the analysis of a large external dataset of compounds with reported activity on the central nervous system (CNS) or lack thereof. The calculation of straightforward physicochemical descriptors is the only requirement for the prediction of the logBB of novel compounds through our models, which can be conveniently applied in conjunction with drug design and virtual screenings. Published by Elsevier Inc.

Identification of (E,E)-2,4-undecadienal from coriander (Coriandrum sativum L.) as a highly effective deodorant compound against the offensive odor of porcine large intestine.

PubMed

Ikeura, Hiromi; Kohara, Kaori; Li, Xin-Xian; Kobayashi, Fumiyuki; Hayata, Yasuyoshi

2010-10-27

The leaves of coriander ( Coriandrum sativum L.) exhibited a strong deodorizing effect against porcine internal organs (large intestine). The effective deodorizing compounds of coriander were identified by separating the volatile component of coriander, testing the effectiveness of each fraction against the offensive odor of porcine large intestine, and then identifying the compounds by GC-MS. The volatile component of coriander was first separated into six fractions (A-F) by preparative gas chromatography, and the deodorizing activity of each of these fractions against the offensive odor was measured. Fraction D, which showed the strongest deodorizing effect, was then separated into 12 subfractions by preparative GC. The deodorant activity of each subfraction was evaluated, and the deodorant compounds were identified by GC-MS. It was discovered that (E,E)-2,4-undecadienal was the most effective deodorizing compound. The deodorizing activity of (E,E)-2,4-undecadienal on the porcine large intestine increased as with concentration, reaching almost complete deodorizing ability at 10 ppb.

SAMPL4, a blind challenge for computational solvation free energies: the compounds considered.

PubMed

Guthrie, J Peter

2014-03-01

For the fifth time I have provided a set of solvation energies (1 M gas to 1 M aqueous) for a SAMPL challenge. In this set there are 23 blind compounds and 30 supplementary compounds of related structure to one of the blind sets, but for which the solvation energy is readily available. The best current values of each compound are presented along with complete documentation of the experimental origins of the solvation energies. The calculations needed to go from reported data to solvation energies are presented, with particular attention to aspects which are new to this set. For some compounds the vapor pressures (VP) were reported for the liquid compound, which is solid at room temperature. To correct from VPsubcooled liquid to VPsublimation requires ΔSfusion, which is only known for mannitol. Estimated values were used for the others, all but one of which were benzene derivatives and expected to have very similar values. The final compound for which ΔSfusion was estimated was menthol, which melts at 42 °C so that modest errors in ΔSfusion will have little effect. It was also necessary to look into the effects of including estimated values of ΔCp on this correction. The approximate sizes of the effects of inclusion of ΔCp in the correction from VPsubcooled liquid to VPsublimation were estimated and it was noted that inclusion of ΔCp invariably makes ΔGS more positive. To extend the set of compounds for which the solvation energy could be calculated we explored the use of boiling point (b.p.) data from Reaxys/Beilstein as a substitute for studies of the VP as a function of temperature. B.p. data are not always reliable so it was necessary to develop a criterion for rejecting outliers. For two compounds (chlorinated guaiacols) it became clear that inclusion represented overreach; for each there were only two independent pressure, temperature points, which is too little for a trustworthy extrapolation. For a number of compounds the extrapolation from lowest temperature at which the VP was reported to 25 °C was long (sometimes over 100°) so that it was necessary to consider whether ΔCp might have significant effects. The problem is that there are no experimental values and possible intramolecular hydrogen bonds make estimation uncertain in some cases. The approximate sizes of the effects of ΔCp were estimated, and it was noted that inclusion of ΔCp in the extrapolation of VP down to room temperature invariably makes ΔGs more negative.

SAMPL4, a blind challenge for computational solvation free energies: the compounds considered

NASA Astrophysics Data System (ADS)

Guthrie, J. Peter

2014-03-01

For the fifth time I have provided a set of solvation energies (1 M gas to 1 M aqueous) for a SAMPL challenge. In this set there are 23 blind compounds and 30 supplementary compounds of related structure to one of the blind sets, but for which the solvation energy is readily available. The best current values of each compound are presented along with complete documentation of the experimental origins of the solvation energies. The calculations needed to go from reported data to solvation energies are presented, with particular attention to aspects which are new to this set. For some compounds the vapor pressures (VP) were reported for the liquid compound, which is solid at room temperature. To correct from VPsubcooled liquid to VPsublimation requires ΔSfusion, which is only known for mannitol. Estimated values were used for the others, all but one of which were benzene derivatives and expected to have very similar values. The final compound for which ΔSfusion was estimated was menthol, which melts at 42 °C so that modest errors in ΔSfusion will have little effect. It was also necessary to look into the effects of including estimated values of ΔCp on this correction. The approximate sizes of the effects of inclusion of ΔCp in the correction from VPsubcooled liquid to VPsublimation were estimated and it was noted that inclusion of ΔCp invariably makes ΔGS more positive. To extend the set of compounds for which the solvation energy could be calculated we explored the use of boiling point (b.p.) data from Reaxys/Beilstein as a substitute for studies of the VP as a function of temperature. B.p. data are not always reliable so it was necessary to develop a criterion for rejecting outliers. For two compounds (chlorinated guaiacols) it became clear that inclusion represented overreach; for each there were only two independent pressure, temperature points, which is too little for a trustworthy extrapolation. For a number of compounds the extrapolation from lowest temperature at which the VP was reported to 25 °C was long (sometimes over 100°) so that it was necessary to consider whether ΔCp might have significant effects. The problem is that there are no experimental values and possible intramolecular hydrogen bonds make estimation uncertain in some cases. The approximate sizes of the effects of ΔCp were estimated, and it was noted that inclusion of ΔCp in the extrapolation of VP down to room temperature invariably makes ΔGs more negative.

21 CFR 880.5440 - Intravascular administration set.

Code of Federal Regulations, 2012 CFR

2012-04-01

...) Classification. Class II (special controls). The special control for pharmacy compounding systems within this classification is the FDA guidance document entitled “Class II Special Controls Guidance Document: Pharmacy Compounding Systems; Final Guidance for Industry and FDA Reviewers.” Pharmacy compounding systems classified...

21 CFR 880.5440 - Intravascular administration set.

Code of Federal Regulations, 2011 CFR

2011-04-01

...) Classification. Class II (special controls). The special control for pharmacy compounding systems within this classification is the FDA guidance document entitled “Class II Special Controls Guidance Document: Pharmacy Compounding Systems; Final Guidance for Industry and FDA Reviewers.” Pharmacy compounding systems classified...

21 CFR 880.5440 - Intravascular administration set.

Code of Federal Regulations, 2013 CFR

2013-04-01

...) Classification. Class II (special controls). The special control for pharmacy compounding systems within this classification is the FDA guidance document entitled “Class II Special Controls Guidance Document: Pharmacy Compounding Systems; Final Guidance for Industry and FDA Reviewers.” Pharmacy compounding systems classified...

21 CFR 880.5440 - Intravascular administration set.

Code of Federal Regulations, 2014 CFR

2014-04-01

...) Classification. Class II (special controls). The special control for pharmacy compounding systems within this classification is the FDA guidance document entitled “Class II Special Controls Guidance Document: Pharmacy Compounding Systems; Final Guidance for Industry and FDA Reviewers.” Pharmacy compounding systems classified...

Extraction tools for identification of chemical contaminants in estuarine and coastal waters to determine toxic pressure on primary producers.

PubMed

Booij, Petra; Sjollema, Sascha B; Leonards, Pim E G; de Voogt, Pim; Stroomberg, Gerard J; Vethaak, A Dick; Lamoree, Marja H

2013-09-01

The extent to which chemical stressors affect primary producers in estuarine and coastal waters is largely unknown. However, given the large number of legacy pollutants and chemicals of emerging concern present in the environment, this is an important and relevant issue that requires further study. The purpose of our study was to extract and identify compounds which are inhibitors of photosystem II activity in microalgae from estuarine and coastal waters. Field sampling was conducted in the Western Scheldt estuary (Hansweert, The Netherlands). We compared four different commonly used extraction methods: passive sampling with silicone rubber sheets, polar organic integrative samplers (POCIS) and spot water sampling using two different solid phase extraction (SPE) cartridges. Toxic effects of extracts prepared from spot water samples and passive samplers were determined in the Pulse Amplitude Modulation (PAM) fluorometry bioassay. With target chemical analysis using LC-MS and GC-MS, a set of PAHs, PCBs and pesticides was determined in field samples. These compound classes are listed as priority substances for the marine environment by the OSPAR convention. In addition, recovery experiments with both SPE cartridges were performed to evaluate the extraction suitability of these methods. Passive sampling using silicone rubber sheets and POCIS can be applied to determine compounds with different structures and polarities for further identification and determination of toxic pressure on primary producers. The added value of SPE lies in its suitability for quantitative analysis; calibration of passive samplers still needs further investigation for quantification of field concentrations of contaminants. Copyright © 2013 Elsevier Ltd. All rights reserved.

MetaboID: a graphical user interface package for assignment of 1H NMR spectra of bodyfluids and tissues.

PubMed

MacKinnon, Neil; Somashekar, Bagganahalli S; Tripathi, Pratima; Ge, Wencheng; Rajendiran, Thekkelnaycke M; Chinnaiyan, Arul M; Ramamoorthy, Ayyalusamy

2013-01-01

Nuclear magnetic resonance based measurements of small molecule mixtures continues to be confronted with the challenge of spectral assignment. While multi-dimensional experiments are capable of addressing this challenge, the imposed time constraint becomes prohibitive, particularly with the large sample sets commonly encountered in metabolomic studies. Thus, one-dimensional spectral assignment is routinely performed, guided by two-dimensional experiments on a selected sample subset; however, a publicly available graphical interface for aiding in this process is currently unavailable. We have collected spectral information for 360 unique compounds from publicly available databases including chemical shift lists and authentic full resolution spectra, supplemented with spectral information for 25 compounds collected in-house at a proton NMR frequency of 900 MHz. This library serves as the basis for MetaboID, a Matlab-based user interface designed to aid in the one-dimensional spectral assignment process. The tools of MetaboID were built to guide resonance assignment in order of increasing confidence, starting from cursory compound searches based on chemical shift positions to analysis of authentic spike experiments. Together, these tools streamline the often repetitive task of spectral assignment. The overarching goal of the integrated toolbox of MetaboID is to centralize the one dimensional spectral assignment process, from providing access to large chemical shift libraries to providing a straightforward, intuitive means of spectral comparison. Such a toolbox is expected to be attractive to both experienced and new metabolomic researchers as well as general complex mixture analysts. Copyright © 2012 Elsevier Inc. All rights reserved.

Word Syntax of Nominal Compounds: Internal and Aphasiological Evidence from Turkish

ERIC Educational Resources Information Center

Tat, Deniz

2013-01-01

This dissertation is an analysis of two types of nominal compounds in Turkish, primary compounds and synthetic compounds within the framework of Distributed Morphology. A nominal primary compound is formed by two nouns, and its meaning is largely determined by world knowledge. A synthetic compound, on the other hand, is formed by a noun and a…

Novel chemical scaffolds of the tumor marker AKR1B10 inhibitors discovered by 3D QSAR pharmacophore modeling

PubMed Central

Kumar, Raj; Son, Minky; Bavi, Rohit; Lee, Yuno; Park, Chanin; Arulalapperumal, Venkatesh; Cao, Guang Ping; Kim, Hyong-ha; Suh, Jung-keun; Kim, Yong-seong; Kwon, Yong Jung; Lee, Keun Woo

2015-01-01

Aim: Recent evidence suggests that aldo-keto reductase family 1 B10 (AKR1B10) may be a potential diagnostic or prognostic marker of human tumors, and that AKR1B10 inhibitors offer a promising choice for treatment of many types of human cancers. The aim of this study was to identify novel chemical scaffolds of AKR1B10 inhibitors using in silico approaches. Methods: The 3D QSAR pharmacophore models were generated using HypoGen. A validated pharmacophore model was selected for virtual screening of 4 chemical databases. The best mapped compounds were assessed for their drug-like properties. The binding orientations of the resulting compounds were predicted by molecular docking. Density functional theory calculations were carried out using B3LYP. The stability of the protein-ligand complexes and the final binding modes of the hit compounds were analyzed using 10 ns molecular dynamics (MD) simulations. Results: The best pharmacophore model (Hypo 1) showed the highest correlation coefficient (0.979), lowest total cost (102.89) and least RMSD value (0.59). Hypo 1 consisted of one hydrogen-bond acceptor, one hydrogen-bond donor, one ring aromatic and one hydrophobic feature. This model was validated by Fischer's randomization and 40 test set compounds. Virtual screening of chemical databases and the docking studies resulted in 30 representative compounds. Frontier orbital analysis confirmed that only 3 compounds had sufficiently low energy band gaps. MD simulations revealed the binding modes of the 3 hit compounds: all of them showed a large number of hydrogen bonds and hydrophobic interactions with the active site and specificity pocket residues of AKR1B10. Conclusion: Three compounds with new structural scaffolds have been identified, which have stronger binding affinities for AKR1B10 than known inhibitors. PMID:26051108

A Compact Instrument for Remote Raman and Fluorescence Measurements to a Radial Distance of 100 m

NASA Technical Reports Server (NTRS)

Sharma, S. K.; Misra, A. K.; Lucey, P. g.; McKay, C. P.

2005-01-01

Compact remote spectroscopic instruments that could provide detailed information about mineralogy, organic and biomaterials on a planetary surface over a relatively large area are desirable for NASA s planetary exploration program. Ability to explore a large area on the planetary surfaces as well as in impact craters from a fixed location of a rover or lander will enhance the probability of selecting target rocks of high scientific contents as well as desirable sites in search of organic compounds and biomarkers on Mars and other planetary bodies. We have developed a combined remote inelastic scattering (Raman) and laser-induced fluorescence emission (LIFE) compact instrument capable of providing accurate information about minerals, organic and biogenic materials to a radial distance of 100 m. Here we present the Raman and LIFE (R-LIFE) data set.

Connectivity Mapping for Candidate Therapeutics Identification Using Next Generation Sequencing RNA-Seq Data

PubMed Central

McArt, Darragh G.; Dunne, Philip D.; Blayney, Jaine K.; Salto-Tellez, Manuel; Van Schaeybroeck, Sandra; Hamilton, Peter W.; Zhang, Shu-Dong

2013-01-01

The advent of next generation sequencing technologies (NGS) has expanded the area of genomic research, offering high coverage and increased sensitivity over older microarray platforms. Although the current cost of next generation sequencing is still exceeding that of microarray approaches, the rapid advances in NGS will likely make it the platform of choice for future research in differential gene expression. Connectivity mapping is a procedure for examining the connections among diseases, genes and drugs by differential gene expression initially based on microarray technology, with which a large collection of compound-induced reference gene expression profiles have been accumulated. In this work, we aim to test the feasibility of incorporating NGS RNA-Seq data into the current connectivity mapping framework by utilizing the microarray based reference profiles and the construction of a differentially expressed gene signature from a NGS dataset. This would allow for the establishment of connections between the NGS gene signature and those microarray reference profiles, alleviating the associated incurring cost of re-creating drug profiles with NGS technology. We examined the connectivity mapping approach on a publicly available NGS dataset with androgen stimulation of LNCaP cells in order to extract candidate compounds that could inhibit the proliferative phenotype of LNCaP cells and to elucidate their potential in a laboratory setting. In addition, we also analyzed an independent microarray dataset of similar experimental settings. We found a high level of concordance between the top compounds identified using the gene signatures from the two datasets. The nicotine derivative cotinine was returned as the top candidate among the overlapping compounds with potential to suppress this proliferative phenotype. Subsequent lab experiments validated this connectivity mapping hit, showing that cotinine inhibits cell proliferation in an androgen dependent manner. Thus the results in this study suggest a promising prospect of integrating NGS data with connectivity mapping. PMID:23840550

Prediction of Partition Coefficients of Organic Compounds between SPME/PDMS and Aqueous Solution

PubMed Central

Chao, Keh-Ping; Lu, Yu-Ting; Yang, Hsiu-Wen

2014-01-01

Polydimethylsiloxane (PDMS) is commonly used as the coated polymer in the solid phase microextraction (SPME) technique. In this study, the partition coefficients of organic compounds between SPME/PDMS and the aqueous solution were compiled from the literature sources. The correlation analysis for partition coefficients was conducted to interpret the effect of their physicochemical properties and descriptors on the partitioning process. The PDMS-water partition coefficients were significantly correlated to the polarizability of organic compounds (r = 0.977, p < 0.05). An empirical model, consisting of the polarizability, the molecular connectivity index, and an indicator variable, was developed to appropriately predict the partition coefficients of 61 organic compounds for the training set. The predictive ability of the empirical model was demonstrated by using it on a test set of 26 chemicals not included in the training set. The empirical model, applying the straightforward calculated molecular descriptors, for estimating the PDMS-water partition coefficient will contribute to the practical applications of the SPME technique. PMID:24534804

Comprehensive GC²/MS for the monitoring of aromatic tar oil constituents during biodegradation in a historically contaminated soil.

PubMed

Vasilieva, Viktoriya; Scherr, Kerstin E; Edelmann, Eva; Hasinger, Marion; Loibner, Andreas P

2012-02-20

The constituents of tar oil comprise a wide range of physico-chemically heterogeneous pollutants of environmental concern. Besides the sixteen polycyclic aromatic hydrocarbons defined as priority pollutants by the US-EPA (EPA-PAHs), a wide range of substituted (NSO-PAC) and alkylated (alkyl-PAC) aromatic tar oil compounds are gaining increased attention for their toxic, carcinogenic, mutagenic and/or teratogenic properties. Investigations on tar oil biodegradation in soil are in part hampered by the absence of an efficient analytical tool for the simultaneous analysis of this wide range of compounds with dissimilar analytical properties. Therefore, the present study sets out to explore the applicability of comprehensive two-dimensional gas chromatography (GC²/MS) for the simultaneous measurement of compounds with differing polarity or that are co-eluting in one-dimensional systems. Aerobic tar oil biodegradation in a historically contaminated soil was analyzed over 56 days in lab-scale bioslurry tests. Forty-three aromatic compounds were identified with GC²/MS in one single analysis. The number of alkyl chains on a molecule was found to prime over alkyl chain length in hampering compound biodegradation. In most cases, substitution of carbon with nitrogen and oxygen was related to increased compound degradation in comparison to unalkylated and sulphur- or unsubstituted PAH with a similar ring number.The obtained results indicate that GC²/MS can be employed for the rapid assessment of a large variety of structurally heterogeneous environmental contaminants. Its application can contribute to facilitate site assessment, development and control of microbial cleanup technologies for tar oil contaminated sites. Copyright © 2011 Elsevier B.V. All rights reserved.

eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC/MS-Based Metabolomics.

PubMed

Domingo-Almenara, Xavier; Brezmes, Jesus; Vinaixa, Maria; Samino, Sara; Ramirez, Noelia; Ramon-Krauel, Marta; Lerin, Carles; Díaz, Marta; Ibáñez, Lourdes; Correig, Xavier; Perera-Lluna, Alexandre; Yanes, Oscar

2016-10-04

Gas chromatography coupled to mass spectrometry (GC/MS) has been a long-standing approach used for identifying small molecules due to the highly reproducible ionization process of electron impact ionization (EI). However, the use of GC-EI MS in untargeted metabolomics produces large and complex data sets characterized by coeluting compounds and extensive fragmentation of molecular ions caused by the hard electron ionization. In order to identify and extract quantitative information on metabolites across multiple biological samples, integrated computational workflows for data processing are needed. Here we introduce eRah, a free computational tool written in the open language R composed of five core functions: (i) noise filtering and baseline removal of GC/MS chromatograms, (ii) an innovative compound deconvolution process using multivariate analysis techniques based on compound match by local covariance (CMLC) and orthogonal signal deconvolution (OSD), (iii) alignment of mass spectra across samples, (iv) missing compound recovery, and (v) identification of metabolites by spectral library matching using publicly available mass spectra. eRah outputs a table with compound names, matching scores and the integrated area of compounds for each sample. The automated capabilities of eRah are demonstrated by the analysis of GC-time-of-flight (TOF) MS data from plasma samples of adolescents with hyperinsulinaemic androgen excess and healthy controls. The quantitative results of eRah are compared to centWave, the peak-picking algorithm implemented in the widely used XCMS package, MetAlign, and ChromaTOF software. Significantly dysregulated metabolites are further validated using pure standards and targeted analysis by GC-triple quadrupole (QqQ) MS, LC-QqQ, and NMR. eRah is freely available at http://CRAN.R-project.org/package=erah .

TyPol - a new methodology for organic compounds clustering based on their molecular characteristics and environmental behavior.

PubMed

Servien, Rémi; Mamy, Laure; Li, Ziang; Rossard, Virginie; Latrille, Eric; Bessac, Fabienne; Patureau, Dominique; Benoit, Pierre

2014-09-01

Following legislation, the assessment of the environmental risks of 30000-100000 chemical substances is required for their registration dossiers. However, their behavior in the environment and their transfer to environmental components such as water or atmosphere are studied for only a very small proportion of the chemical in laboratory tests or monitoring studies because it is time-consuming and/or cost prohibitive. Therefore, the objective of this work was to develop a new methodology, TyPol, to classify organic compounds, and their degradation products, according to both their behavior in the environment and their molecular properties. The strategy relies on partial least squares analysis and hierarchical clustering. The calculation of molecular descriptors is based on an in silico approach, and the environmental endpoints (i.e. environmental parameters) are extracted from several available databases and literature. The classification of 215 organic compounds inputted in TyPol for this proof-of-concept study showed that the combination of some specific molecular descriptors could be related to a particular behavior in the environment. TyPol also provided an analysis of similarities (or dissimilarities) between organic compounds and their degradation products. Among the 24 degradation products that were inputted, 58% were found in the same cluster as their parents. The robustness of the method was tested and shown to be good. TyPol could help to predict the environmental behavior of a "new" compound (parent compound or degradation product) from its affiliation to one cluster, but also to select representative substances from a large data set in order to answer some specific questions regarding their behavior in the environment. Copyright © 2014 Elsevier Ltd. All rights reserved.

Application of Fragment Ion Information as Further Evidence in Probabilistic Compound Screening Using Bayesian Statistics and Machine Learning: A Leap Toward Automation.

PubMed

Woldegebriel, Michael; Zomer, Paul; Mol, Hans G J; Vivó-Truyols, Gabriel

2016-08-02

In this work, we introduce an automated, efficient, and elegant model to combine all pieces of evidence (e.g., expected retention times, peak shapes, isotope distributions, fragment-to-parent ratio) obtained from liquid chromatography-tandem mass spectrometry (LC-MS/MS/MS) data for screening purposes. Combining all these pieces of evidence requires a careful assessment of the uncertainties in the analytical system as well as all possible outcomes. To-date, the majority of the existing algorithms are highly dependent on user input parameters. Additionally, the screening process is tackled as a deterministic problem. In this work we present a Bayesian framework to deal with the combination of all these pieces of evidence. Contrary to conventional algorithms, the information is treated in a probabilistic way, and a final probability assessment of the presence/absence of a compound feature is computed. Additionally, all the necessary parameters except the chromatographic band broadening for the method are learned from the data in training and learning phase of the algorithm, avoiding the introduction of a large number of user-defined parameters. The proposed method was validated with a large data set and has shown improved sensitivity and specificity in comparison to a threshold-based commercial software package.

Access to Emissions Distributions and Related Ancillary Data through the ECCAD database

NASA Astrophysics Data System (ADS)

Darras, Sabine; Granier, Claire; Liousse, Catherine; De Graaf, Erica; Enriquez, Edgar; Boulanger, Damien; Brissebrat, Guillaume

2017-04-01

The ECCAD database (Emissions of atmospheric Compounds and Compilation of Ancillary Data) provides a user-friendly access to global and regional surface emissions for a large set of chemical compounds and ancillary data (land use, active fires, burned areas, population,etc). The emissions inventories are time series gridded data at spatial resolution from 1x1 to 0.1x0.1 degrees. ECCAD is the emissions database of the GEIA (Global Emissions InitiAtive) project and a sub-project of the French Atmospheric Data Center AERIS (http://www.aeris-data.fr). ECCAD has currently more than 2200 users originating from more than 80 countries. The project benefits from this large international community of users to expand the number of emission datasets made available. ECCAD provides detailed metadata for each of the datasets and various tools for data visualization, for computing global and regional totals and for interactive spatial and temporal analysis. The data can be downloaded as interoperable NetCDF CF-compliant files, i.e. the data are compatible with many other client interfaces. The presentation will provide information on the datasets available within ECCAD, as well as examples of the analysis work that can be done online through the website: http://eccad.aeris-data.fr.

Access to Emissions Distributions and Related Ancillary Data through the ECCAD database

NASA Astrophysics Data System (ADS)

Darras, Sabine; Enriquez, Edgar; Granier, Claire; Liousse, Catherine; Boulanger, Damien; Fontaine, Alain

2016-04-01

The ECCAD database (Emissions of atmospheric Compounds and Compilation of Ancillary Data) provides a user-friendly access to global and regional surface emissions for a large set of chemical compounds and ancillary data (land use, active fires, burned areas, population,etc). The emissions inventories are time series gridded data at spatial resolution from 1x1 to 0.1x0.1 degrees. ECCAD is the emissions database of the GEIA (Global Emissions InitiAtive) project and a sub-project of the French Atmospheric Data Center AERIS (http://www.aeris-data.fr). ECCAD has currently more than 2200 users originating from more than 80 countries. The project benefits from this large international community of users to expand the number of emission datasets made available. ECCAD provides detailed metadata for each of the datasets and various tools for data visualization, for computing global and regional totals and for interactive spatial and temporal analysis. The data can be downloaded as interoperable NetCDF CF-compliant files, i.e. the data are compatible with many other client interfaces. The presentation will provide information on the datasets available within ECCAD, as well as examples of the analysis work that can be done online through the website: http://eccad.aeris-data.fr.

Previously unknown class of metalorganic compounds revealed in meteorites

PubMed Central

Ruf, Alexander; Kanawati, Basem; Hertkorn, Norbert; Yin, Qing-Zhu; Moritz, Franco; Harir, Mourad; Lucio, Marianna; Michalke, Bernhard; Wimpenny, Joshua; Shilobreeva, Svetlana; Bronsky, Basil; Saraykin, Vladimir; Gabelica, Zelimir; Gougeon, Régis D.; Quirico, Eric; Ralew, Stefan; Jakubowski, Tomasz; Haack, Henning; Gonsior, Michael; Jenniskens, Peter; Hinman, Nancy W.; Schmitt-Kopplin, Philippe

2017-01-01

The rich diversity and complexity of organic matter found in meteorites is rapidly expanding our knowledge and understanding of extreme environments from which the early solar system emerged and evolved. Here, we report the discovery of a hitherto unknown chemical class, dihydroxymagnesium carboxylates [(OH)2MgO2CR]−, in meteoritic soluble organic matter. High collision energies, which are required for fragmentation, suggest substantial thermal stability of these Mg-metalorganics (CHOMg compounds). This was corroborated by their higher abundance in thermally processed meteorites. CHOMg compounds were found to be present in a set of 61 meteorites of diverse petrological classes. The appearance of this CHOMg chemical class extends the previously investigated, diverse set of CHNOS molecules. A connection between the evolution of organic compounds and minerals is made, as Mg released from minerals gets trapped into organic compounds. These CHOMg metalorganic compounds and their relation to thermal processing in meteorites might shed new light on our understanding of carbon speciation at a molecular level in meteorite parent bodies. PMID:28242686

[Safety and effectiveness of large dose compound Sophora flavescens Ait injection in the treatment of advanced malignant tumors].

PubMed

Li, Dao-rui; Lin, Hong-sheng

2011-04-01

To evaluate the effectiveness and safety of large dose compound Sophora flavescens Ait injection in the treatment of advanced malignant tumors. A non-randomized case control trial was conducted. Ninety six patients with pathologically confirmed advanced non-small-cell lung cancer, gastric cancer and colorectal cancer were divided into traditional Chinese medicine group and chemotherapy group, 48 cases each. Patients of the traditional Chinese medicine group received treatment with large dose of compound Sophora flavescens Ait injection (20 ml/d), and 21 days as a cycle. Forty-seven patients of the traditional Chinese medicine group and 46 patients of the chemotherapy group completed their treatment, respectively. The clinical benefit rate (CBR) in the traditional Chinese medicine group was 83.0%, significantly higher than that in the chemotherapy group (69.6%) (P < 0.01). The Karnofsky performance status and weight improvement in the traditional Chinese medicine group was superior to that in the chemotherapy group (P < 0.05). Except the skin irritation in one patient in the traditional Chinese medicine group, there were no other clinical adverse effects related with the large dose compound Sophora flavescens Ait injection. Large dose compound Sophora flavescens Ait injection in the treatment of advanced malignant tumors is safe and effective. The recommended dose is 20 ml/d.

3D-QSAR analysis of MCD inhibitors by CoMFA and CoMSIA.

PubMed

Pourbasheer, Eslam; Aalizadeh, Reza; Ebadi, Amin; Ganjali, Mohammad Reza

2015-01-01

Three-dimensional quantitative structure-activity relationship was developed for the series of compounds as malonyl-CoA decarboxylase antagonists (MCD) using the CoMFA and CoMSIA methods. The statistical parameters for CoMFA (q(2)=0.558, r(2)=0.841) and CoMSIA (q(2)= 0.615, r(2) = 0.870) models were derived based on 38 compounds as training set in the basis of the selected alignment. The external predictive abilities of the built models were evaluated by using the test set of nine compounds. From obtained results, the CoMSIA method was found to have highly predictive capability in comparison with CoMFA method. Based on the given results by CoMSIA and CoMFA contour maps, some features that can enhance the activity of compounds as MCD antagonists were introduced and used to design new compounds with better inhibition activity.

Plant defense compounds: systems approaches to metabolic analysis.

PubMed

Kliebenstein, Daniel J

2012-01-01

Systems biology attempts to answer biological questions by integrating across diverse genomic data sets. With the increasing ability to conduct genomics experiments, this integrative approach is being rapidly applied across numerous biological research communities. One of these research communities investigates how plants utilize secondary metabolites or defense metabolites to defend against attack by pathogens and other biotic organisms. This use of systems biology to integrate across transcriptomics, metabolomics, and genomics is significantly enhancing the rate of discovery of genes, metabolites, and bioactivities for plant defense compounds as well as extending our knowledge of how these compounds are regulated. Plant defense compounds are also providing a unique proving platform to develop new approaches that enhance the ability to conduct systems biology with existing and previously unforseen genomics data sets. This review attempts to illustrate both how systems biology is helping the study of plant defense compounds and vice versa.

Large Constituent Families Help Children Parse Compounds

ERIC Educational Resources Information Center

Krott, Andrea; Nicoladis, Elena

2005-01-01

The family size of the constituents of compound words, or the number of compounds sharing the constituents, has been shown to affect adults' access to compound words in the mental lexicon. The present study was designed to see if family size would affect children's segmentation of compounds. Twenty-five English-speaking children between 3;7 and…

Curcumin and Resveratrol as Promising Natural Remedies with Nanomedicine Approach for the Effective Treatment of Triple Negative Breast Cancer

PubMed Central

Shindikar, Amol; Singh, Akshita; Nobre, Malcolm; Kirolikar, Saurabh

2016-01-01

Researchers have made considerable progress in last few decades in understanding mechanisms underlying pathogenesis of breast cancer, its phenotypes, its molecular and genetic changes, its physiology, and its prognosis. This has allowed us to identify specific targets and design appropriate chemical entities for effective treatment of most breast cancer phenotypes, resulting in increased patient survivability. Unfortunately, these strategies have been largely ineffective in the treatment of triple negative breast cancer (TNBC). Hormonal receptors lacking render the conventional breast cancer drugs redundant, forcing scientists to identify novel targets for treatment of TNBC. Two natural compounds, curcumin and resveratrol, have been widely reported to have anticancer properties. In vitro and in vivo studies show promising results, though their effectiveness in clinical settings has been less than satisfactory, owing to their feeble pharmacokinetics. Here we discuss these naturally occurring compounds, their mechanism as anticancer agents, their shortcomings in translational research, and possible methodology to improve their pharmacokinetics/pharmacodynamics with advanced drug delivery systems. PMID:27242900

Absorption Spectroscopy of Polycyclic Aromatic Hydrocarbons under Interstellar Conditions

NASA Technical Reports Server (NTRS)

Stone, Bradley M.

1996-01-01

The presence and importance of polycyclic aromatic hydrocarbons (PAHs, a large family of organic compounds containing carbon and hydrogen) in the interstellar medium has already been well established. The Astrochemistry Laboratory at NASA Ames Research Center (under the direction of Louis Allamandola and Scott Sandford) has been the center of pioneering work in performing spectroscopy on these molecules under simulated interstellar conditions, and consequently in the identification of these species in the interstellar medium by comparison to astronomically obtained spectra. My project this summer was twofold: (1) We planned on obtaining absorption spectra of a number of PAHs and their cations in cold (4K) Ne matrices. The purpose of these experiments was to increase the number of different PAHs for which laboratory spectra have been obtained under these simulated interstellar conditions; and (2) I was to continue the planning and design of a new laser facility that is being established in the Astrochemistry laboratory. The laser-based experimental set-up will greatly enhance our capability in examining this astrophysically important class of compounds.

Experimental and Chemoinformatics Study of Tautomerism in a Database of Commercially Available Screening Samples

PubMed Central

2016-01-01

We investigated how many cases of the same chemical sold as different products (at possibly different prices) occurred in a prototypical large aggregated database and simultaneously tested the tautomerism definitions in the chemoinformatics toolkit CACTVS. We applied the standard CACTVS tautomeric transforms plus a set of recently developed ring–chain transforms to the Aldrich Market Select (AMS) database of 6 million screening samples and building blocks. In 30 000 cases, two or more AMS products were found to be just different tautomeric forms of the same compound. We purchased and analyzed 166 such tautomer pairs and triplets by 1H and 13C NMR to determine whether the CACTVS transforms accurately predicted what is the same “stuff in the bottle”. Essentially all prototropic transforms with examples in the AMS were confirmed. Some of the ring–chain transforms were found to be too “aggressive”, i.e. to equate structures with one another that were different compounds. PMID:27669079

Iron traps terrestrially derived dissolved organic matter at redox interfaces

PubMed Central

Riedel, Thomas; Zak, Dominik; Biester, Harald; Dittmar, Thorsten

2013-01-01

Reactive iron and organic carbon are intimately associated in soils and sediments. However, to date, the organic compounds involved are uncharacterized on the molecular level. At redox interfaces in peatlands, where the biogeochemical cycles of iron and dissolved organic matter (DOM) are coupled, this issue can readily be studied. We found that precipitation of iron hydroxides at the oxic surface layer of two rewetted fens removed a large fraction of DOM via coagulation. On aeration of anoxic fen pore waters, >90% of dissolved iron and 27 ± 7% (mean ± SD) of dissolved organic carbon were rapidly (within 24 h) removed. Using ultra-high-resolution MS, we show that vascular plant-derived aromatic and pyrogenic compounds were preferentially retained, whereas the majority of carboxyl-rich aliphatic acids remained in solution. We propose that redox interfaces, which are ubiquitous in marine and terrestrial settings, are selective yet intermediate barriers that limit the flux of land-derived DOM to oceanic waters. PMID:23733946

[Study on control and management for industrial volatile organic compounds (VOCs) in China].

PubMed

Wang, Hai-Lin; Zhang, Guo-Ning; Nei, Lei; Wang, Yu-Fei; Hao, Zheng-Ping

2011-12-01

Volatile organic compounds (VOCs) emitted from industrial sources account for a large percent of total anthropogenic VOCs. In this paper, VOCs emission characterization, control technologies and management were discussed. VOCs from industrial emissions were characterized by high intensity, wide range and uneven distribution, which focused on Bejing-Tianjin Joint Belt, Shangdong Peninsula, Yangtze River Delta and the Pearl River Delta. The current technologies for VOCs treatment include adsorption, catalytic combustion, bio-degradation and others, which were applied in petrochemical, oil vapor recovery, shipbuilding, printing, pharmaceutical, feather manufacturing and so on. The scarcity of related regulations/standards plus ineffective supervision make the VOCs management difficult. Therefore, it is suggested that VOCs treatment be firstly performed from key areas and industries, and then carried out step by step. By establishing of actual reducing amount control system and more detailed VOCs emission standards and regulations, applying practical technologies together with demonstration projects, and setting up VOCs emission registration and classification-related-charge system, VOCs could be reduced effectively.

Focus on Alectinib and Competitor Compounds for Second-Line Therapy in ALK-Rearranged NSCLC

PubMed Central

Tran, Phu N.; Klempner, Samuel J.

2016-01-01

The management of anaplastic lymphoma kinase rearranged (ALK+) non-small cell lung cancer (NSCLC) exemplifies the potential of a precision medicine approach to cancer care. The ALK inhibitor crizotinib has led to improved outcomes in the first- and second-line setting; however, toxicities, intracranial activity, and acquired resistance necessitated the advent of later generation ALK inhibitors. A large portion of acquired resistance to ALK inhibitors is caused by secondary mutations in the ALK kinase domain. Alectinib is a second-generation ALK inhibitor capable of overcoming multiple crizotinib-resistant ALK mutations and has demonstrated improved outcomes after crizotinib failure. Favorable toxicity profile and improved intracranial activity have spurred ongoing front-line trials and comparisons to other ALK inhibitors. However, important questions regarding comparability to competitor compounds, acquired alectinib resistance, and ALK inhibitor sequencing remain. Here, we review the key clinical data supporting alectinib in the second-line therapy of ALK+ NSCLC and provide context in comparison to other ALK inhibitors in development. PMID:27965961

Focus on Alectinib and Competitor Compounds for Second-Line Therapy in ALK-Rearranged NSCLC.

PubMed

Tran, Phu N; Klempner, Samuel J

2016-01-01

The management of anaplastic lymphoma kinase rearranged (ALK+) non-small cell lung cancer (NSCLC) exemplifies the potential of a precision medicine approach to cancer care. The ALK inhibitor crizotinib has led to improved outcomes in the first- and second-line setting; however, toxicities, intracranial activity, and acquired resistance necessitated the advent of later generation ALK inhibitors. A large portion of acquired resistance to ALK inhibitors is caused by secondary mutations in the ALK kinase domain. Alectinib is a second-generation ALK inhibitor capable of overcoming multiple crizotinib-resistant ALK mutations and has demonstrated improved outcomes after crizotinib failure. Favorable toxicity profile and improved intracranial activity have spurred ongoing front-line trials and comparisons to other ALK inhibitors. However, important questions regarding comparability to competitor compounds, acquired alectinib resistance, and ALK inhibitor sequencing remain. Here, we review the key clinical data supporting alectinib in the second-line therapy of ALK+ NSCLC and provide context in comparison to other ALK inhibitors in development.

Broth Microdilution In Vitro Screening: An Easy and Fast Method to Detect New Antifungal Compounds.

PubMed

de-Souza-Silva, Calliandra Maria; Guilhelmelli, Fernanda; Zamith-Miranda, Daniel; de Oliveira, Marco Antônio; Nosanchuk, Joshua Daniel; Silva-Pereira, Ildinete; Albuquerque, Patrícia

2018-02-14

Fungal infections have become an important medical condition in the last decades, but the number of available antifungal drugs is limited. In this scenario, the search for new antifungal drugs is necessary. The protocol reported here details a method to screen peptides for their antifungal properties. It is based on the broth microdilution susceptibility test from the Clinical and Laboratory Standards Institute (CLSI) M27-A3 guidelines with modifications to suit the research of antimicrobial peptides as potential new antifungals. This protocol describes a functional assay to evaluate the activity of antifungal compounds and may be easily modified to suit any particular class of molecules under investigation. Since the assays are performed in 96-well plates using small volumes, a large-scale screening can be completed in a short amount of time, especially if carried out in an automation setting. This procedure illustrates how a standardized and adjustable clinical protocol can help the bench-work pursuit of new molecules to improve the therapy of fungal diseases.

Occurrence and fate of antibiotic, analgesic/anti-inflammatory, and antifungal compounds in five wastewater treatment processes.

PubMed

Guerra, P; Kim, M; Shah, A; Alaee, M; Smyth, S A

2014-03-01

The presence of pharmaceuticals and personal care products (PPCPs) in the aquatic environment as a result of wastewater effluent discharge is a concern in many countries. In order to expand our understanding on the occurrence and fate of PPCPs during wastewater treatment processes, 62 antibiotic, analgesic/anti-inflammatory, and antifungal compounds were analyzed in 72 liquid and 24 biosolid samples from six wastewater treatment plants (WWTPs) during the summer and winter seasons of 2010-2012. This is the first scientific study to compare five different wastewater treatment processes: facultative and aerated lagoons, chemically-enhanced primary treatment, secondary activated sludge, and advanced biological nutrient removal. PPCPs were detected in all WWTP influents at median concentrations of 1.5 to 92,000 ng/L, with no seasonal differences. PPCPs were also found in all final effluents at median levels ranging from 3.6 to 4,200 ng/L with higher values during winter (p<0.05). Removal efficiencies ranged between -450% and 120%, depending on the compound, WWTP type, and season. Mass balance showed that the fate of analgesic/anti-inflammatory compounds was predominantly biodegradation during biological treatment, while antibiotics and antifungal compounds were more likely to sorb to sludge. However, some PPCPs remained soluble and were detected in effluent samples. Overall, this study highlighted the occurrence and behavior of a large set of PPCPs and determined how their removal is affected by environmental/operational factors in different WWTPs. Crown Copyright © 2013. Published by Elsevier B.V. All rights reserved.

Phytotoxicity of vulpia residues: III. Biological activity of identified allelochemicals from Vulpia myuros.

PubMed

An, M; Pratley, J E; Haig, T

2001-02-01

Twenty compounds identified in vulpia (Vulpia myuros) residues as allelochemicals were individually and collectively tested for biological activity. Each exhibited characteristic allelochemical behavior toward the test plant, i.e., inhibition at high concentrations and stimulation or no effect at low concentrations, but individual activities varied. Allelopathins present in large quantities, such as syringic, vanillic, and succinic acids, possessed low activity, while those present in small quantities, such as catechol and hydrocinnamic acid, possessed strong inhibitory activity. The concept of a phytotoxic strength index was developed for quantifying the biological properties of each individual allelopathin in a concise, comprehensive, and meaningful format. The individual contribution of each allelopathin, assessed by comparing the phytotoxic strength index to the overall toxicity of vulpia residues, was variable according to structure and was influenced by its relative proportion in the residue. The majority of compounds possessed low or medium biological activity and contributed most of the vulpia phytotoxicity, while compounds with high biological activity were in the minority and only present at low concentration. Artificial mixtures of these pure allelochemicals also produced phytotoxicity. There were additive/synergistic effects evident in the properties of these mixtures. One such mixture, formulated from allelochemicals found in the same proportions as occur in vulpia extract, produced stronger activity than another formulated from the same set of compounds but in equal proportions. These results suggest that the exploration of the relative composition of a cluster of allelopathins may be more important than simply focusing on the identification of one or two compounds with strong biological activity and that synergism is fundamental to the understanding of allelopathy.

Hazardous air pollutants and asthma.

PubMed

Leikauf, George D

2002-08-01

Asthma has a high prevalence in the United States, and persons with asthma may be at added risk from the adverse effects of hazardous air pollutants (HAPs). Complex mixtures (fine particulate matter and tobacco smoke) have been associated with respiratory symptoms and hospital admissions for asthma. The toxic ingredients of these mixtures are HAPs, but whether ambient HAP exposures can induce asthma remains unclear. Certain HAPs are occupational asthmagens, whereas others may act as adjuncts during sensitization. HAPs may exacerbate asthma because, once sensitized, individuals can respond to remarkably low concentrations, and irritants lower the bronchoconstrictive threshold to respiratory antigens. Adverse responses after ambient exposures to complex mixtures often occur at concentrations below those producing effects in controlled human exposures to a single compound. In addition, certain HAPs that have been associated with asthma in occupational settings may interact with criteria pollutants in ambient air to exacerbate asthma. Based on these observations and past experience with 188 HAPs, a list of 19 compounds that could have the highest impact on the induction or exacerbation of asthma was developed. Nine additional compounds were identified that might exacerbate asthma based on their irritancy, respirability, or ability to react with biological macromolecules. Although the ambient levels of these 28 compounds are largely unknown, estimated exposures from emissions inventories and limited air monitoring suggest that aldehydes (especially acrolein and formaldehyde) and metals (especially nickel and chromium compounds) may have possible health risk indices sufficient for additional attention. Recommendations for research are presented regarding exposure monitoring and evaluation of biologic mechanisms controlling how these substances induce and exacerbate asthma.

Hazardous air pollutants and asthma.

PubMed Central

Leikauf, George D

2002-01-01

Asthma has a high prevalence in the United States, and persons with asthma may be at added risk from the adverse effects of hazardous air pollutants (HAPs). Complex mixtures (fine particulate matter and tobacco smoke) have been associated with respiratory symptoms and hospital admissions for asthma. The toxic ingredients of these mixtures are HAPs, but whether ambient HAP exposures can induce asthma remains unclear. Certain HAPs are occupational asthmagens, whereas others may act as adjuncts during sensitization. HAPs may exacerbate asthma because, once sensitized, individuals can respond to remarkably low concentrations, and irritants lower the bronchoconstrictive threshold to respiratory antigens. Adverse responses after ambient exposures to complex mixtures often occur at concentrations below those producing effects in controlled human exposures to a single compound. In addition, certain HAPs that have been associated with asthma in occupational settings may interact with criteria pollutants in ambient air to exacerbate asthma. Based on these observations and past experience with 188 HAPs, a list of 19 compounds that could have the highest impact on the induction or exacerbation of asthma was developed. Nine additional compounds were identified that might exacerbate asthma based on their irritancy, respirability, or ability to react with biological macromolecules. Although the ambient levels of these 28 compounds are largely unknown, estimated exposures from emissions inventories and limited air monitoring suggest that aldehydes (especially acrolein and formaldehyde) and metals (especially nickel and chromium compounds) may have possible health risk indices sufficient for additional attention. Recommendations for research are presented regarding exposure monitoring and evaluation of biologic mechanisms controlling how these substances induce and exacerbate asthma. PMID:12194881

Diverse Neurotoxicants Target the Differentiation of Embryonic Neural Stem Cells into Neuronal and Glial Phenotypes

PubMed Central

Slotkin, Theodore A.; Skavicus, Samantha; Card, Jennifer; Levin, Edward D.; Seidler, Frederic J.

2016-01-01

The large number of compounds that need to be tested for developmental neurotoxicity drives the need to establish in vitro models to evaluate specific neurotoxic endpoints. We used neural stem cells derived from rat neuroepithelium on embryonic day 14 to evaluate the impact of diverse toxicants on their ability to differentiate into glia and neurons: a glucocorticoid (dexamethasone), organophosphate insecticides (chlorpyrifos, diazinon, parathion), insecticides targeting the GABAA receptor (dieldrin, fipronil), heavy metals (Ni2+, Ag+), nicotine and tobacco smoke extract. We found three broad groupings of effects. One diverse set of compounds, dexamethasone, the organophosphate pesticides, Ni2+ and nicotine, suppressed expression of the glial phenotype while having little or no effect on the neuronal phenotype. The second pattern was restricted to the pesticides acting on GABAA receptors. These compounds promoted the glial phenotype and suppressed the neuronal phenotype. Notably, the actions of compounds eliciting either of these differentiation patterns were clearly unrelated to deficits in cell numbers: dexamethasone, dieldrin and fipronil all reduced cell numbers, whereas organophosphates and Ni2+ had no effect. The third pattern, shared by Ag+ and tobacco smoke extract, clearly delineated cytotoxicity, characterized major cell loss with suppression of differentiation into both glial and neuronal phenotypes; but here again, there was some selectivity in that glia were suppressed more than neurons. Our results, from this survey with diverse compounds, point to convergence of neurotoxicant effects on a specific “decision node” that controls the emergence of neurons and glia from neural stem cells. PMID:27816694

A comparative bear model for immobility-induced osteopenia

NASA Technical Reports Server (NTRS)

Milbury, P. E.; Vaughan, M. R.; Farley, S.; Matula, G. J. Jr; Convertino, V. A.; Matson, W. R.

1998-01-01

The National Institutes of Health (NIH) and the National Aeronautics and Space Administration (NASA) are seeking solutions to the human problem of osteopenia, or immobility-induced bone loss. Bears, during winter dormancy, appear uniquely exempted from the debilitating effects of immobility osteopenia. NIH and ESA, Inc. are creating a large database of metabolic information on human ambulatory and bedrest plasma samples for comparison with metabolic data obtained from bear plasma samples collected in different seasons. The database generated from NASA's HR113 human bedrest study showed a clear difference between plasma samples of ambulatory and immobile subjects through cluster analysis using compounds determined by high performance liquid chromatography with coulometric electrochemical array detection (HPLC-EC). We collected plasma samples from black bears (Ursus americanus) across 4 seasons and from 3 areas and subjected them to similar analysis, with particular attention to compounds that changed significantly in the NASA human study. We found seasonal differences in 28 known compounds and 33 unknown compounds. A final database contained 40 known and 120 unknown peaks that were reliably assayed in all bear and human samples; these were the primary data set for interspecies comparison. Six unidentified compounds changed significantly but differentially in wintering bears and immobile humans. The data are discussed in light of current theories regarding dormancy, starvation, and anabolic metabolism. Work is in progress by ESA Laboratories on a larger database to confirm these findings prior to a chemical isolation and identification effort. This research could lead to new pharmaceuticals or dietary interventions for the treatment of immobility osteopenia.

Simplification and validation of a large volume polyurethane foam sampler for the analysis of persistent hydrophobic compounds in drinking water.

PubMed

Choi, J W; Lee, J H; Moon, B S; Kannan, K

2008-08-01

The use of a large volume polyurethane foam (PUF) sampler was validated for rapid extraction of persistent organic pollutants (POPs), such as polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), in raw water and treated water from drinking water plants. To validate the recovery of target compounds in the sampling process, a (37)Cl-labeled standard was spiked into the 1st PUF plug prior to filtration. An accelerated solvent extraction method, as a pressurized liquid extractor (PLE), was optimized to extract the PUF plug. For sample preparation, tandem column chromatography (TCC) clean-up was used for rapid analysis. The recoveries of labeled compounds in the analytical method were 80-110% (n = 9). The optimized PUF-PLE-TCC method was applied in the analysis of raw water and treated potable water from seven drinking water plants in South Korea. The sample volume used was between 18 and 102 L for raw water at a flow rate of 0.4-2 L min(-1), 95 and 107 L for treated water at a flow rate of 1.5-2.2 L min(-1). Limit of quantitation (LOQ) was a function of sample volume and it decreased with increasing sample volume. The LOQ of PCDD/Fs in raw waters analyzed by this method was 3-11 times lower than that described using large-size disk-type solid phase extraction (SPE) method. The LOQ of PCDD/F congeners in raw water and treated water were 0.022-3.9 ng L(-1) and 0.018-0.74 ng L(-1), respectively. Octachlorinated dibenzo-p-dioxin (OCDD) was found in some raw water samples, while their concentrations were well below the tentative criterion set by the Japanese Environmental Ministry for drinking water. OCDD was below the LOQ in the treated drinking water.

Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery.

PubMed

Fang, Jiansong; Yang, Ranyao; Gao, Li; Zhou, Dan; Yang, Shengqian; Liu, Ai-Lin; Du, Guan-hua

2013-11-25

Butyrylcholinesterase (BuChE, EC 3.1.1.8) is an important pharmacological target for Alzheimer's disease (AD) treatment. However, the currently available BuChE inhibitor screening assays are expensive, labor-intensive, and compound-dependent. It is necessary to develop robust in silico methods to predict the activities of BuChE inhibitors for the lead identification. In this investigation, support vector machine (SVM) models and naive Bayesian models were built to discriminate BuChE inhibitors (BuChEIs) from the noninhibitors. Each molecule was initially represented in 1870 structural descriptors (1235 from ADRIANA.Code, 334 from MOE, and 301 from Discovery studio). Correlation analysis and stepwise variable selection method were applied to figure out activity-related descriptors for prediction models. Additionally, structural fingerprint descriptors were added to improve the predictive ability of models, which were measured by cross-validation, a test set validation with 1001 compounds and an external test set validation with 317 diverse chemicals. The best two models gave Matthews correlation coefficient of 0.9551 and 0.9550 for the test set and 0.9132 and 0.9221 for the external test set. To demonstrate the practical applicability of the models in virtual screening, we screened an in-house data set with 3601 compounds, and 30 compounds were selected for further bioactivity assay. The assay results showed that 10 out of 30 compounds exerted significant BuChE inhibitory activities with IC50 values ranging from 0.32 to 22.22 μM, at which three new scaffolds as BuChE inhibitors were identified for the first time. To our best knowledge, this is the first report on BuChE inhibitors using machine learning approaches. The models generated from SVM and naive Bayesian approaches successfully predicted BuChE inhibitors. The study proved the feasibility of a new method for predicting bioactivities of ligands and discovering novel lead compounds.

Transmittance measurements of ultra violet and visible wavelength interference filters flown aboard LDEF

NASA Technical Reports Server (NTRS)

Mooney, Thomas A.; Smajkiewicz, Ali

1991-01-01

A set of ten interference filters for the UV and VIS spectral region were flown on the surface of the Long Duration Exposure Facility (LDEF) Tray B-8 along with earth radiation budget (ERB) components from the Eppley Laboratory. Transmittance changes and other degradation observed after the return of the filters to Barr are reported. Substrates, coatings, and (where applicable) cement materials are identified. In general, all filters except those containing lead compounds survived well. Metal dielectric filters for the UV developed large numbers of pinholes which caused an increase in transmittance. Band shapes and spectral positioning, however, did not change.

A high-throughput exploration of magnetic materials by using structure predicting methods

NASA Astrophysics Data System (ADS)

Arapan, S.; Nieves, P.; Cuesta-López, S.

2018-02-01

We study the capability of a structure predicting method based on genetic/evolutionary algorithm for a high-throughput exploration of magnetic materials. We use the USPEX and VASP codes to predict stable and generate low-energy meta-stable structures for a set of representative magnetic structures comprising intermetallic alloys, oxides, interstitial compounds, and systems containing rare-earths elements, and for both types of ferromagnetic and antiferromagnetic ordering. We have modified the interface between USPEX and VASP codes to improve the performance of structural optimization as well as to perform calculations in a high-throughput manner. We show that exploring the structure phase space with a structure predicting technique reveals large sets of low-energy metastable structures, which not only improve currently exiting databases, but also may provide understanding and solutions to stabilize and synthesize magnetic materials suitable for permanent magnet applications.

Recent Developments in Carbonylation Chemistry Using [13 C]CO, [11 C]CO and [14 C]CO.

PubMed

Nielsen, Dennis U; Neumann, Karoline T; Lindhardt, Anders T; Skrydstrup, Troels

2018-06-01

Carbon monoxide represents the most important C1-building block for the chemical industry, both for the production of bulk and fine chemicals, but also for synthetic fuels. Yet, its toxicity and subsequently its cautious handling has limited its applications in medicinal chemistry research and in particular for the synthesis of pharmaceutically relevant molecules. Recent years have nevertheless witnessed a considerable headway on the development of carbon monoxide surrogates and reactor systems, which provide an ideal setting for performing carbonylation chemistry with stoichiometric and sub-stoichiometric carbon monoxide. Such set-ups are particularly ideal for the introduction of isotope labels such as carbon-11, carbon-13 and carbon-14 into bioactive compounds. This review summarizes this growing field and examines the large number of carbonylation reactions that can be exploited for the introduction of a carbon isotope. This article is protected by copyright. All rights reserved.

Butyl rubber O-ring seals: Revision of test procedures for stockpile materials

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

Domeier, L.A.; Wagter, K.R.

1996-12-01

Extensive testing showed little correlation between test slab and O-ring performance. New procedures, comparable to those used with the traditional test slabs, were defined for hardness, compression set, and tensile property testing on sacrificial O-ring specimens. Changes in target performance values were made as needed and were, in one case, tightened to reflect the O-ring performance data. An additional study was carried out on O-ring and slab performance vs cure cycle and showed little sensitivity of material performance to large changes in curing time. Aging and spectra of certain materials indicated that two sets of test slabs from current vendormore » were accidently made from EPDM rather than butyl rubber. Random testing found no O-rings made from EPDM. As a result, and additional spectroscope test will be added to the product acceptance procedures to verify the type of rubber compound used.« less

Solid fat content as a substitute for total polar compound analysis in edible oils

USDA-ARS?s Scientific Manuscript database

The solid fat contents (SFC) of heated edible oil samples were measured and found to correlate positively with total polar compounds (TPC) and inversely with triglyceride concentration. Traditional methods for determination of total polar compounds require a laboratory setting and are time intensiv...

77 FR 52606 - Approval and Promulgation of Air Quality Implementation Plans; Indiana; Volatile Organic...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-30

... Promulgation of Air Quality Implementation Plans; Indiana; Volatile Organic Compounds; Architectural and... sets limits on the amount of volatile organic compounds (VOC) in architectural and industrial... Indiana SIP a new rule within Title 326, Article 8 ``Volatile Organic Compound Rules'' that limits the VOC...

Content changes of bitter compounds in 'Guoqing No.1' Satsuma mandarin (Citrus unshiu Marc.) during fruit development of consecutive 3 seasons.

PubMed

Li, Shaojie; Wang, Zhuang; Ding, Fan; Sun, Da; Ma, Zhaocheng; Cheng, Yunjiang; Xu, Juan

2014-02-15

The main bitter compounds (nomilin, limonin and naringin) in the fruit tissues of 'Guoqing No.1' Satsuma mandarin (Citrus unshiu Marc.) were determined throughout the fruit development of 3 consecutive growing seasons. Although fluctuating largely at the corresponding developing stages of the 3 years, the contents of these compounds in fruit tissues mostly displayed a declining trend, which implied that the rhythm of the metabolism of these bitter compounds was not consistent among years and was largely growing season dependent. Regarding their distribution, fruit flavedo might be a weak sink that contained the lowest level of naringin, while the segment membrane accumulated large amount of limonin and nomilin, which indicated a possible tissue bias pattern for biosynthesis or accumulation of those compounds. Partial correlation coefficient analysis revealed a synergistic accumulation of naringin and the two limonoid aglycones in fruit tissues during fruit development, indicating an integrated metabolism of flavonoids and limonoids. Copyright © 2013 Elsevier Ltd. All rights reserved.

Rapid volatile metabolomics and genomics in large strawberry populations segregating for aroma

USDA-ARS?s Scientific Manuscript database

Volatile organic compounds (VOCs) in strawberry (Fragaria spp.) represent a large portion of the fruit secondary metabolome, and contribute significantly to aroma, flavor, disease resistance, pest resistance and overall fruit quality. Understanding the basis for volatile compound biosynthesis and it...

QSPR modeling of octanol/water partition coefficient for vitamins by optimal descriptors calculated with SMILES.

PubMed

Toropov, A A; Toropova, A P; Raska, I

2008-04-01

Simplified molecular input line entry system (SMILES) has been utilized in constructing quantitative structure-property relationships (QSPR) for octanol/water partition coefficient of vitamins and organic compounds of different classes by optimal descriptors. Statistical characteristics of the best model (vitamins) are the following: n=17, R(2)=0.9841, s=0.634, F=931 (training set); n=7, R(2)=0.9928, s=0.773, F=690 (test set). Using this approach for modeling octanol/water partition coefficient for a set of organic compounds gives a model that is statistically characterized by n=69, R(2)=0.9872, s=0.156, F=5184 (training set) and n=70, R(2)=0.9841, s=0.179, F=4195 (test set).

Ultra-performance liquid chromatography/tandem mass spectrometric quantification of structurally diverse drug mixtures using an ESI-APCI multimode ionization source.

PubMed

Yu, Kate; Di, Li; Kerns, Edward; Li, Susan Q; Alden, Peter; Plumb, Robert S

2007-01-01

We report in this paper an ultra-performance liquid chromatography/tandem mass spectrometric (UPLC(R)/MS/MS) method utilizing an ESI-APCI multimode ionization source to quantify structurally diverse analytes. Eight commercial drugs were used as test compounds. Each LC injection was completed in 1 min using a UPLC system coupled with MS/MS multiple reaction monitoring (MRM) detection. Results from three separate sets of experiments are reported. In the first set of experiments, the eight test compounds were analyzed as a single mixture. The mass spectrometer was switching rapidly among four ionization modes (ESI+, ESI-, APCI-, and APCI+) during an LC run. Approximately 8-10 data points were collected across each LC peak. This was insufficient for a quantitative analysis. In the second set of experiments, four compounds were analyzed as a single mixture. The mass spectrometer was switching rapidly among four ionization modes during an LC run. Approximately 15 data points were obtained for each LC peak. Quantification results were obtained with a limit of detection (LOD) as low as 0.01 ng/mL. For the third set of experiments, the eight test compounds were analyzed as a batch. During each LC injection, a single compound was analyzed. The mass spectrometer was detecting at a particular ionization mode during each LC injection. More than 20 data points were obtained for each LC peak. Quantification results were also obtained. This single-compound analytical method was applied to a microsomal stability test. Compared with a typical HPLC method currently used for the microsomal stability test, the injection-to-injection cycle time was reduced to 1.5 min (UPLC method) from 3.5 min (HPLC method). The microsome stability results were comparable with those obtained by traditional HPLC/MS/MS.

Tackling the conformational sampling of larger flexible compounds and macrocycles in pharmacology and drug discovery.

PubMed

Chen, I-Jen; Foloppe, Nicolas

2013-12-15

Computational conformational sampling underpins much of molecular modeling and design in pharmaceutical work. The sampling of smaller drug-like compounds has been an active area of research. However, few studies have tested in details the sampling of larger more flexible compounds, which are also relevant to drug discovery, including therapeutic peptides, macrocycles, and inhibitors of protein-protein interactions. Here, we investigate extensively mainstream conformational sampling methods on three carefully curated compound sets, namely the 'Drug-like', larger 'Flexible', and 'Macrocycle' compounds. These test molecules are chemically diverse with reliable X-ray protein-bound bioactive structures. The compared sampling methods include Stochastic Search and the recent LowModeMD from MOE, all the low-mode based approaches from MacroModel, and MD/LLMOD recently developed for macrocycles. In addition to default settings, key parameters of the sampling protocols were explored. The performance of the computational protocols was assessed via (i) the reproduction of the X-ray bioactive structures, (ii) the size, coverage and diversity of the output conformational ensembles, (iii) the compactness/extendedness of the conformers, and (iv) the ability to locate the global energy minimum. The influence of the stochastic nature of the searches on the results was also examined. Much better results were obtained by adopting search parameters enhanced over the default settings, while maintaining computational tractability. In MOE, the recent LowModeMD emerged as the method of choice. Mixed torsional/low-mode from MacroModel performed as well as LowModeMD, and MD/LLMOD performed well for macrocycles. The low-mode based approaches yielded very encouraging results with the flexible and macrocycle sets. Thus, one can productively tackle the computational conformational search of larger flexible compounds for drug discovery, including macrocycles. Copyright © 2013 Elsevier Ltd. All rights reserved.

Developing Enhanced Blood–Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling

PubMed Central

Wang, Wenyi; Kim, Marlene T.; Sedykh, Alexander

2015-01-01

Purpose Experimental Blood–Brain Barrier (BBB) permeability models for drug molecules are expensive and time-consuming. As alternative methods, several traditional Quantitative Structure-Activity Relationship (QSAR) models have been developed previously. In this study, we aimed to improve the predictivity of traditional QSAR BBB permeability models by employing relevant public bio-assay data in the modeling process. Methods We compiled a BBB permeability database consisting of 439 unique compounds from various resources. The database was split into a modeling set of 341 compounds and a validation set of 98 compounds. Consensus QSAR modeling workflow was employed on the modeling set to develop various QSAR models. A five-fold cross-validation approach was used to validate the developed models, and the resulting models were used to predict the external validation set compounds. Furthermore, we used previously published membrane transporter models to generate relevant transporter profiles for target compounds. The transporter profiles were used as additional biological descriptors to develop hybrid QSAR BBB models. Results The consensus QSAR models have R2=0.638 for fivefold cross-validation and R2=0.504 for external validation. The consensus model developed by pooling chemical and transporter descriptors showed better predictivity (R2=0.646 for five-fold cross-validation and R2=0.526 for external validation). Moreover, several external bio-assays that correlate with BBB permeability were identified using our automatic profiling tool. Conclusions The BBB permeability models developed in this study can be useful for early evaluation of new compounds (e.g., new drug candidates). The combination of chemical and biological descriptors shows a promising direction to improve the current traditional QSAR models. PMID:25862462

Calibration and validation of toxicokinetic-toxicodynamic models for three neonicotinoids and some aquatic macroinvertebrates.

PubMed

Focks, Andreas; Belgers, Dick; Boerwinkel, Marie-Claire; Buijse, Laura; Roessink, Ivo; Van den Brink, Paul J

2018-05-01

Exposure patterns in ecotoxicological experiments often do not match the exposure profiles for which a risk assessment needs to be performed. This limitation can be overcome by using toxicokinetic-toxicodynamic (TKTD) models for the prediction of effects under time-variable exposure. For the use of TKTD models in the environmental risk assessment of chemicals, it is required to calibrate and validate the model for specific compound-species combinations. In this study, the survival of macroinvertebrates after exposure to the neonicotinoid insecticide was modelled using TKTD models from the General Unified Threshold models of Survival (GUTS) framework. The models were calibrated on existing survival data from acute or chronic tests under static exposure regime. Validation experiments were performed for two sets of species-compound combinations: one set focussed on multiple species sensitivity to a single compound: imidacloprid, and the other set on the effects of multiple compounds for a single species, i.e., the three neonicotinoid compounds imidacloprid, thiacloprid and thiamethoxam, on the survival of the mayfly Cloeon dipterum. The calibrated models were used to predict survival over time, including uncertainty ranges, for the different time-variable exposure profiles used in the validation experiments. From the comparison between observed and predicted survival, it appeared that the accuracy of the model predictions was acceptable for four of five tested species in the multiple species data set. For compounds such as neonicotinoids, which are known to have the potential to show increased toxicity under prolonged exposure, the calibration and validation of TKTD models for survival needs to be performed ideally by considering calibration data from both acute and chronic tests.

Streamlined system for purifying and quantifying a diverse library of compounds and the effect of compound concentration measurements on the accurate interpretation of biological assay results.

PubMed

Popa-Burke, Ioana G; Issakova, Olga; Arroway, James D; Bernasconi, Paul; Chen, Min; Coudurier, Louis; Galasinski, Scott; Jadhav, Ajit P; Janzen, William P; Lagasca, Dennis; Liu, Darren; Lewis, Roderic S; Mohney, Robert P; Sepetov, Nikolai; Sparkman, Darren A; Hodge, C Nicholas

2004-12-15

As part of an overall systems approach to generating highly accurate screening data across large numbers of compounds and biological targets, we have developed and implemented streamlined methods for purifying and quantitating compounds at various stages of the screening process, coupled with automated "traditional" storage methods (DMSO, -20 degrees C). Specifically, all of the compounds in our druglike library are purified by LC/MS/UV and are then controlled for identity and concentration in their respective DMSO stock solutions by chemiluminescent nitrogen detection (CLND)/evaporative light scattering detection (ELSD) and MS/UV. In addition, the compound-buffer solutions used in the various biological assays are quantitated by LC/UV/CLND to determine the concentration of compound actually present during screening. Our results show that LC/UV/CLND/ELSD/MS is a widely applicable method that can be used to purify, quantitate, and identify most small organic molecules from compound libraries. The LC/UV/CLND technique is a simple and sensitive method that can be easily and cost-effectively employed to rapidly determine the concentrations of even small amounts of any N-containing compound in aqueous solution. We present data to establish error limits for concentration determination that are well within the overall variability of the screening process. This study demonstrates that there is a significant difference between the predicted amount of soluble compound from stock DMSO solutions following dilution into assay buffer and the actual amount present in assay buffer solutions, even at the low concentrations employed for the assays. We also demonstrate that knowledge of the concentrations of compounds to which the biological target is exposed is critical for accurate potency determinations. Accurate potency values are in turn particularly important for drug discovery, for understanding structure-activity relationships, and for building useful empirical models of protein-ligand interactions. Our new understanding of relative solubility demonstrates that most, if not all, decisions that are made in early discovery are based upon missing or inaccurate information. Finally, we demonstrate that careful control of compound handling and concentration, coupled with accurate assay methods, allows the use of both positive and negative data in analyzing screening data sets for structure-activity relationships that determine potency and selectivity.

Hyperpolarizable compounds and devices fabricated therefrom

DOEpatents

Therien, Michael J.; DiMagno, Stephen G.

1998-01-01

Substituted compounds having relatively large molecular first order hyperpolarizabilities are provided, along with devices and materials containing them. In general, the compounds bear electron-donating and electron-withdrawing chemical substituents on a polyheterocyclic core.

BitterSweetForest: A random forest based binary classifier to predict bitterness and sweetness of chemical compounds

NASA Astrophysics Data System (ADS)

Banerjee, Priyanka; Preissner, Robert

2018-04-01

Taste of a chemical compounds present in food stimulates us to take in nutrients and avoid poisons. However, the perception of taste greatly depends on the genetic as well as evolutionary perspectives. The aim of this work was the development and validation of a machine learning model based on molecular fingerprints to discriminate between sweet and bitter taste of molecules. BitterSweetForest is the first open access model based on KNIME workflow that provides platform for prediction of bitter and sweet taste of chemical compounds using molecular fingerprints and Random Forest based classifier. The constructed model yielded an accuracy of 95% and an AUC of 0.98 in cross-validation. In independent test set, BitterSweetForest achieved an accuracy of 96 % and an AUC of 0.98 for bitter and sweet taste prediction. The constructed model was further applied to predict the bitter and sweet taste of natural compounds, approved drugs as well as on an acute toxicity compound data set. BitterSweetForest suggests 70% of the natural product space, as bitter and 10 % of the natural product space as sweet with confidence score of 0.60 and above. 77 % of the approved drug set was predicted as bitter and 2% as sweet with a confidence scores of 0.75 and above. Similarly, 75% of the total compounds from acute oral toxicity class were predicted only as bitter with a minimum confidence score of 0.75, revealing toxic compounds are mostly bitter. Furthermore, we applied a Bayesian based feature analysis method to discriminate the most occurring chemical features between sweet and bitter compounds from the feature space of a circular fingerprint.

A Chemogenomic Analysis of Ionization Constants - Implications for Drug Discovery

PubMed Central

Manallack, David T.; Prankerd, Richard J.; Nassta, Gemma C.; Ursu, Oleg; Oprea, Tudor I.; Chalmers, David K.

2013-01-01

Chemogenomics methods seek to characterize the interaction between drugs and biological systems and are an important guide for the selection of screening compounds. The acid/base character of drugs has a profound influence on their affinity for the receptor, on their absorption, distribution, metabolism, excretion and toxicity (ADMET) profile and the way the drug can be formulated. In particular, the charge state of a molecule greatly influences its lipophilicity and biopharmaceutical characteristics. This study investigates the acid/base profile of human small molecule drugs, chemogenomics datasets and screening compounds including a natural products set. We estimate the ionization constants (pKa values) of these compounds and determine the identity of the ionizable functional groups in each set. We find substantial differences in acid/base profiles of the chemogenomic classes. In many cases, these differences can be linked to the nature of the target binding site and the corresponding functional groups needed for recognition of the ligand. Clear differences are also observed between the acid/base characteristics of drugs and screening compounds. For example, the proportion of drugs containing a carboxylic acid was 20%, in stark contrast to a value of 2.4% for the screening set sample. The proportion of aliphatic amines was 27% for drugs and only 3.4% for screening compounds. This suggests that there is a mismatch between commercially available screening compounds and the compounds that are likely to interact with a given chemogenomic target family. Our analysis provides a guide for the selection of screening compounds to better target specific chemogenomic families with regard to the overall balance of acids, bases and pKa distributions. PMID:23303535

BitterSweetForest: A Random Forest Based Binary Classifier to Predict Bitterness and Sweetness of Chemical Compounds

PubMed Central

Banerjee, Priyanka; Preissner, Robert

2018-01-01

Taste of a chemical compound present in food stimulates us to take in nutrients and avoid poisons. However, the perception of taste greatly depends on the genetic as well as evolutionary perspectives. The aim of this work was the development and validation of a machine learning model based on molecular fingerprints to discriminate between sweet and bitter taste of molecules. BitterSweetForest is the first open access model based on KNIME workflow that provides platform for prediction of bitter and sweet taste of chemical compounds using molecular fingerprints and Random Forest based classifier. The constructed model yielded an accuracy of 95% and an AUC of 0.98 in cross-validation. In independent test set, BitterSweetForest achieved an accuracy of 96% and an AUC of 0.98 for bitter and sweet taste prediction. The constructed model was further applied to predict the bitter and sweet taste of natural compounds, approved drugs as well as on an acute toxicity compound data set. BitterSweetForest suggests 70% of the natural product space, as bitter and 10% of the natural product space as sweet with confidence score of 0.60 and above. 77% of the approved drug set was predicted as bitter and 2% as sweet with a confidence score of 0.75 and above. Similarly, 75% of the total compounds from acute oral toxicity class were predicted only as bitter with a minimum confidence score of 0.75, revealing toxic compounds are mostly bitter. Furthermore, we applied a Bayesian based feature analysis method to discriminate the most occurring chemical features between sweet and bitter compounds using the feature space of a circular fingerprint. PMID:29696137

Identifying natural compounds as multi-target-directed ligands against Alzheimer's disease: an in silico approach.

PubMed

Ambure, Pravin; Bhat, Jyotsna; Puzyn, Tomasz; Roy, Kunal

2018-04-23

Alzheimer's disease (AD) is a multi-factorial disease, which can be simply outlined as an irreversible and progressive neurodegenerative disorder with an unclear root cause. It is a major cause of dementia in old aged people. In the present study, utilizing the structural and biological activity information of ligands for five important and mostly studied vital targets (i.e. cyclin-dependant kinase 5, β-secretase, monoamine oxidase B, glycogen synthase kinase 3β, acetylcholinesterase) that are believed to be effective against AD, we have developed five classification models using linear discriminant analysis (LDA) technique. Considering the importance of data curation, we have given more attention towards the chemical and biological data curation, which is a difficult task especially in case of big data-sets. Thus, to ease the curation process we have designed Konstanz Information Miner (KNIME) workflows, which are made available at http://teqip.jdvu.ac.in/QSAR_Tools/ . The developed models were appropriately validated based on the predictions for experiment derived data from test sets, as well as true external set compounds including known multi-target compounds. The domain of applicability for each classification model was checked based on a confidence estimation approach. Further, these validated models were employed for screening of natural compounds collected from the InterBioScreen natural database ( https://www.ibscreen.com/natural-compounds ). Further, the natural compounds that were categorized as 'actives' in at least two classification models out of five developed models were considered as multi-target leads, and these compounds were further screened using the drug-like filter, molecular docking technique and then thoroughly analyzed using molecular dynamics studies. Finally, the most potential multi-target natural compounds against AD are suggested.

Use of Cell Viability Assay Data Improves the Prediction Accuracy of Conventional Quantitative Structure–Activity Relationship Models of Animal Carcinogenicity

PubMed Central

Zhu, Hao; Rusyn, Ivan; Richard, Ann; Tropsha, Alexander

2008-01-01

Background To develop efficient approaches for rapid evaluation of chemical toxicity and human health risk of environmental compounds, the National Toxicology Program (NTP) in collaboration with the National Center for Chemical Genomics has initiated a project on high-throughput screening (HTS) of environmental chemicals. The first HTS results for a set of 1,408 compounds tested for their effects on cell viability in six different cell lines have recently become available via PubChem. Objectives We have explored these data in terms of their utility for predicting adverse health effects of the environmental agents. Methods and results Initially, the classification k nearest neighbor (kNN) quantitative structure–activity relationship (QSAR) modeling method was applied to the HTS data only, for a curated data set of 384 compounds. The resulting models had prediction accuracies for training, test (containing 275 compounds together), and external validation (109 compounds) sets as high as 89%, 71%, and 74%, respectively. We then asked if HTS results could be of value in predicting rodent carcinogenicity. We identified 383 compounds for which data were available from both the Berkeley Carcinogenic Potency Database and NTP–HTS studies. We found that compounds classified by HTS as “actives” in at least one cell line were likely to be rodent carcinogens (sensitivity 77%); however, HTS “inactives” were far less informative (specificity 46%). Using chemical descriptors only, kNN QSAR modeling resulted in 62.3% prediction accuracy for rodent carcinogenicity applied to this data set. Importantly, the prediction accuracy of the model was significantly improved (72.7%) when chemical descriptors were augmented by HTS data, which were regarded as biological descriptors. Conclusions Our studies suggest that combining NTP–HTS profiles with conventional chemical descriptors could considerably improve the predictive power of computational approaches in toxicology. PMID:18414635

Dry selection and wet evaluation for the rational discovery of new anthelmintics

NASA Astrophysics Data System (ADS)

Marrero-Ponce, Yovani; Castañeda, Yeniel González; Vivas-Reyes, Ricardo; Vergara, Fredy Máximo; Arán, Vicente J.; Castillo-Garit, Juan A.; Pérez-Giménez, Facundo; Torrens, Francisco; Le-Thi-Thu, Huong; Pham-The, Hai; Montenegro, Yolanda Vera; Ibarra-Velarde, Froylán

2017-09-01

Helminths infections remain a major problem in medical and public health. In this report, atom-based 2D bilinear indices, a TOMOCOMD-CARDD (QuBiLs-MAS module) molecular descriptor family and linear discriminant analysis (LDA) were used to find models that differentiate among anthelmintic and non-anthelmintic compounds. Two classification models obtained by using non-stochastic and stochastic 2D bilinear indices, classified correctly 86.64% and 84.66%, respectively, in the training set. Equation 1(2) correctly classified 141(135) out of 165 [85.45%(81.82%)] compounds in external validation set. Another LDA models were performed in order to get the most likely mechanism of action of anthelmintics. The model shows an accuracy of 86.84% in the training set and 94.44% in the external prediction set. Finally, we carry out an experiment to predict the biological profile of our 'in-house' collections of indole, indazole, quinoxaline and cinnoline derivatives (∼200 compounds). Subsequently, we selected a group of nine of the theoretically most active structures. Then, these chemicals were tested in an in vitro assay and one good candidate (VA5-5c) as fasciolicide compound (100% of reduction at concentrations of 50 and 10 mg/L) was discovered.

Defect Genome of Cubic Perovskites for Fuel Cell Applications

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

Balachandran, Janakiraman; Lin, Lianshan; Anchell, Jonathan S.

Heterogeneities such as point defects, inherent to material systems, can profoundly influence material functionalities critical for numerous energy applications. This influence in principle can be identified and quantified through development of large defect data sets which we call the defect genome, employing high-throughput ab initio calculations. However, high-throughput screening of material models with point defects dramatically increases the computational complexity and chemical search space, creating major impediments toward developing a defect genome. In this paper, we overcome these impediments by employing computationally tractable ab initio models driven by highly scalable workflows, to study formation and interaction of various point defectsmore » (e.g., O vacancies, H interstitials, and Y substitutional dopant), in over 80 cubic perovskites, for potential proton-conducting ceramic fuel cell (PCFC) applications. The resulting defect data sets identify several promising perovskite compounds that can exhibit high proton conductivity. Furthermore, the data sets also enable us to identify and explain, insightful and novel correlations among defect energies, material identities, and defect-induced local structural distortions. Finally, such defect data sets and resultant correlations are necessary to build statistical machine learning models, which are required to accelerate discovery of new materials.« less

Defect Genome of Cubic Perovskites for Fuel Cell Applications

DOE PAGES

Balachandran, Janakiraman; Lin, Lianshan; Anchell, Jonathan S.; ...

2017-10-10

Heterogeneities such as point defects, inherent to material systems, can profoundly influence material functionalities critical for numerous energy applications. This influence in principle can be identified and quantified through development of large defect data sets which we call the defect genome, employing high-throughput ab initio calculations. However, high-throughput screening of material models with point defects dramatically increases the computational complexity and chemical search space, creating major impediments toward developing a defect genome. In this paper, we overcome these impediments by employing computationally tractable ab initio models driven by highly scalable workflows, to study formation and interaction of various point defectsmore » (e.g., O vacancies, H interstitials, and Y substitutional dopant), in over 80 cubic perovskites, for potential proton-conducting ceramic fuel cell (PCFC) applications. The resulting defect data sets identify several promising perovskite compounds that can exhibit high proton conductivity. Furthermore, the data sets also enable us to identify and explain, insightful and novel correlations among defect energies, material identities, and defect-induced local structural distortions. Finally, such defect data sets and resultant correlations are necessary to build statistical machine learning models, which are required to accelerate discovery of new materials.« less

Hyperpolarizable compounds and devices fabricated therefrom

DOEpatents

Therien, M.J.; DiMagno, S.G.

1998-07-21

Substituted compounds having relatively large molecular first order hyperpolarizabilities are provided, along with devices and materials containing them. In general, the compounds bear electron-donating and electron-withdrawing chemical substituents on a polyheterocyclic core. 13 figs.

Features of Pharmaceutical Compounding in the Republic of Tajikistan.

PubMed

Alfred-Ugbenbo, D S; Valiev, A H; Zdoryk, O A; Georgiyants, V A

2017-01-01

Despite the deep assortment of finished pharmaceutical products and the reduction in the number of compounding and hospital pharmacies in the Republic of Tajikistan, the need for extemporal medicinal products is still preserved and remains relevant. This article discusses the practice of compounding in the Republic of Tajikistan. History, laws, limits, regulatory institutions, protocols for compounding pharmacy set up, challenges, equipment, extemporaneous formulations, quality control, and storage within regulatory framework are discussed. Copyright© by International Journal of Pharmaceutical Compounding, Inc.

Large displacement vertical translational actuator based on piezoelectric thin films.

PubMed

Qiu, Zhen; Pulskamp, Jeffrey S; Lin, Xianke; Rhee, Choong-Ho; Wang, Thomas; Polcawich, Ronald G; Oldham, Kenn

2010-07-01

A novel vertical translational microactuator based on thin-film piezoelectric actuation is presented, using a set of four compound bend-up/bend-down unimorphs to produce translational motion of a moving platform or stage. The actuation material is a chemical-solution deposited lead-zirconate-titanate (PZT) thin film. Prototype designs have shown as much as 120 μ m of static displacement, with 80-90 μ m displacements being typical, using four 920 μ m long by 70 μ m legs. Analytical models are presented that accurately describe nonlinear behavior in both static and dynamic operation of prototype stages when the dependence of piezoelectric coefficients on voltage is known. Resonance of the system is observed at a frequency of 200 Hz. The large displacement and high bandwidth of the actuators at low-voltage and low-power levels should make them useful to a variety of optical applications, including endoscopic microscopy.

Analysis of pharmaceutical and other organic wastewater compounds in filtered and unfiltered water samples by gas chromatography/mass spectrometry

USGS Publications Warehouse

Zaugg, Steven D.; Phillips, Patrick J.; Smith, Steven G.

2014-01-01

Research on the effects of exposure of stream biota to complex mixtures of pharmaceuticals and other organic compounds associated with wastewater requires the development of additional analytical capabilities for these compounds in water samples. Two gas chromatography/mass spectrometry (GC/MS) analytical methods used at the U.S. Geological Survey National Water Quality Laboratory (NWQL) to analyze organic compounds associated with wastewater were adapted to include additional pharmaceutical and other organic compounds beginning in 2009. This report includes a description of method performance for 42 additional compounds for the filtered-water method (hereafter referred to as the filtered method) and 46 additional compounds for the unfiltered-water method (hereafter referred to as the unfiltered method). The method performance for the filtered method described in this report has been published for seven of these compounds; however, the addition of several other compounds to the filtered method and the addition of the compounds to the unfiltered method resulted in the need to document method performance for both of the modified methods. Most of these added compounds are pharmaceuticals or pharmaceutical degradates, although two nonpharmaceutical compounds are included in each method. The main pharmaceutical compound classes added to the two modified methods include muscle relaxants, opiates, analgesics, and sedatives. These types of compounds were added to the original filtered and unfiltered methods largely in response to the tentative identification of a wide range of pharmaceutical and other organic compounds in samples collected from wastewater-treatment plants. Filtered water samples are extracted by vacuum through disposable solid-phase cartridges that contain modified polystyrene-divinylbenzene resin. Unfiltered samples are extracted by using continuous liquid-liquid extraction with dichloromethane. The compounds of interest for filtered and unfiltered sample types were determined by use of the capillary-column gas chromatography/mass spectrometry. The performance of each method was assessed by using data on recoveries of compounds in fortified surface-water, wastewater, and reagent-water samples. These experiments (referred to as spike experiments) consist of fortifying (or spiking) samples with known amounts of target analytes. Surface-water-spike experiments were performed by using samples obtained from a stream in Colorado (unfiltered method) and a stream in New York (filtered method). Wastewater spike experiments for both the filtered and unfiltered methods were performed by using a treated wastewater obtained from a single wastewater treatment plant in New York. Surface water and wastewater spike experiments were fortified at both low and high concentrations and termed low- and high-level spikes, respectively. Reagent water spikes were assessed in three ways: (1) set spikes, (2) a low-concentration fortification experiment, and (3) a high-concentration fortification experiment. Set spike samples have been determined since 2009, and consist of analysis of fortified reagent water for target compounds included for each group of 10 to18 environmental samples analyzed at the NWQL. The low-concentration and high-concentration reagent spike experiments, by contrast, represent a one-time assessment of method performance. For each spike experiment, mean recoveries ranging from 60 to 130 percent indicate low bias, and relative standard deviations (RSDs) less than ( Of the compounds included in the filtered method, 21 had mean recoveries ranging from 63 to 129 percent for the low-level and high-level surface-water spikes, and had low ()132 percent]. For wastewater spikes, 24 of the compounds included in the filtered method had recoveries ranging from 61 to 130 percent for the low-level and high-level spikes. RSDs were 130 percent) or variable recoveries (RSDs >30 percent) for low-level wastewater spikes, or low recoveries ( Of the compounds included in the unfiltered method, 17 had mean spike recoveries ranging from 74 to 129 percent and RSDs ranging from 5 to 25 percent for low-level and high-level surface water spikes. The remaining compounds had poor mean recoveries (130 percent), or high RSDs (>29 percent) for these spikes. For wastewater, 14 of the compounds included in the unfiltered method had mean recoveries ranging from 62 to 127 percent and RSDs 130 percent), or low mean recoveries (33 percent) for the low-level wastewater spikes. Of the compounds found in wastewater, 24 had mean set spike recoveries ranging from 64 to 104 percent and RSDs Separate method detection limits (MDLs) were computed for surface water and wastewater for both the filtered and unfiltered methods. Filtered method MDLs ranged from 0.007 to 0.14 microgram per liter (μg/L) for the surface water matrix and from 0.004 to 0.62 μg/L for the wastewater matrix. Unfiltered method MDLs ranged from 0.014 to 0.33 μg/L for the surface water matrix and from 0.008 to 0.36 μg/L for the wastewater matrix.

Kohonen and counterpropagation neural networks applied for mapping and interpretation of IR spectra.

PubMed

Novic, Marjana

2008-01-01

The principles of learning strategy of Kohonen and counterpropagation neural networks are introduced. The advantages of unsupervised learning are discussed. The self-organizing maps produced in both methods are suitable for a wide range of applications. Here, we present an example of Kohonen and counterpropagation neural networks used for mapping, interpretation, and simulation of infrared (IR) spectra. The artificial neural network models were trained for prediction of structural fragments of an unknown compound from its infrared spectrum. The training set contained over 3,200 IR spectra of diverse compounds of known chemical structure. The structure-spectra relationship was encompassed by the counterpropagation neural network, which assigned structural fragments to individual compounds within certain probability limits, assessed from the predictions of test compounds. The counterpropagation neural network model for prediction of fragments of chemical structure is reversible, which means that, for a given structural domain, limited to the training data set in the study, it can be used to simulate the IR spectrum of a chemical defined with a set of structural fragments.

Thermodynamic properties of arsenic compounds and the heat of formation of the As atom from high level electronic structure calculations.

PubMed

Feller, David; Vasiliu, Monica; Grant, Daniel J; Dixon, David A

2011-12-29

Structures, vibrational frequencies, atomization energies at 0 K, and heats of formation at 0 and 298 K are predicted for the compounds As(2), AsH, AsH(2), AsH(3), AsF, AsF(2), and AsF(3) from frozen core coupled cluster theory calculations performed with large correlation consistent basis sets, up through augmented sextuple zeta quality. The coupled cluster calculations involved up through quadruple excitations. For As(2) and the hydrides, it was also possible to examine the impact of full configuration interaction on some of the properties. In addition, adjustments were incorporated to account for extrapolation to the frozen core complete basis set limit, core/valence correlation, scalar relativistic effects, the diagonal Born-Oppenheimer correction, and atomic spin orbit corrections. Based on our best theoretical D(0)(As(2)) and the experimental heat of formation of As(2), we propose a revised 0 K arsenic atomic heat of formation of 68.86 ± 0.8 kcal/mol. While generally good agreement was found between theory and experiment, the heat of formation of AsF(3) was an exception. Our best estimate is more than 7 kcal/mol more negative than the single available experimental value, which argues for a re-examination of that measurement. © 2011 American Chemical Society

Establishment of alternative potency test for botulinum toxin type A using compound muscle action potential (CMAP) in rats.

PubMed

Torii, Yasushi; Goto, Yoshitaka; Nakahira, Shinji; Ginnaga, Akihiro

2014-11-01

The biological activity of botulinum toxin type A has been evaluated using the mouse intraperitoneal (ip) LD50 test. This method requires a large number of mice to precisely determine toxin activity, and, as such, poses problems with regard to animal welfare. We previously developed a compound muscle action potential (CMAP) assay using rats as an alternative method to the mouse ip LD50 test. In this study, to evaluate this quantitative method of measuring toxin activity using CMAP, we assessed the parameters necessary for quantitative tests according to ICH Q2 (R1). This assay could be used to evaluate the activity of the toxin, even when inactive toxin was mixed with the sample. To reduce the number of animals needed, this assay was set to measure two samples per animal. Linearity was detected over a range of 0.1-12.8 U/mL, and the measurement range was set at 0.4-6.4 U/mL. The results for accuracy and precision showed low variability. The body weight was selected as a variable factor, but it showed no effect on the CMAP amplitude. In this study, potency tests using the rat CMAP assay of botulinum toxin type A demonstrated that it met the criteria for a quantitative analysis method. Copyright © 2014 Elsevier Ltd. All rights reserved.

Experimentally determined soil organic matter-water sorption coefficients for different classes of natural toxins and comparison with estimated numbers.

PubMed

Schenzel, Judith; Goss, Kai-Uwe; Schwarzenbach, René P; Bucheli, Thomas D; Droge, Steven T J

2012-06-05

Although natural toxins, such as mycotoxins or phytoestrogens are widely studied and were recently identified as micropollutants in the environment, many of their environmentally relevant physicochemical properties have not yet been determined. Here, the sorption affinity to Pahokee peat, a model sorbent for soil organic matter, was investigated for 29 mycotoxins and two phytoestrogens. Sorption coefficients (K(oc)) were determined with a dynamic HPLC-based column method using a fully aqueous mobile phase with 5 mM CaCl(2) at pH 4.5. Sorption coefficients varied from less than 10(0.7) L/kg(oc) (e.g., all type B trichothecenes) to 10(4.0) L/kg(oc) (positively charged ergot alkaloids). For the neutral compounds the experimental sorption data set was compared with predicted sorption coefficients using various models, based on molecular fragment approaches (EPISuite's KOCWIN or SPARC), poly parameter linear free energy relationship (pp-LFER) in combination with predicted descriptors, and quantum-chemical based software (COSMOtherm)). None of the available models was able to adequately predict absolute K(oc) numbers and relative differences in sorption affinity for the whole set of neutral toxins, largely because mycotoxins exhibit highly complex structures. Hence, at present, for such compounds fast and consistent experimental techniques for determining sorption coefficients, as the one used in this study, are required.

Large-scale annotation of small-molecule libraries using public databases.

PubMed

Zhou, Yingyao; Zhou, Bin; Chen, Kaisheng; Yan, S Frank; King, Frederick J; Jiang, Shumei; Winzeler, Elizabeth A

2007-01-01

While many large publicly accessible databases provide excellent annotation for biological macromolecules, the same is not true for small chemical compounds. Commercial data sources also fail to encompass an annotation interface for large numbers of compounds and tend to be cost prohibitive to be widely available to biomedical researchers. Therefore, using annotation information for the selection of lead compounds from a modern day high-throughput screening (HTS) campaign presently occurs only under a very limited scale. The recent rapid expansion of the NIH PubChem database provides an opportunity to link existing biological databases with compound catalogs and provides relevant information that potentially could improve the information garnered from large-scale screening efforts. Using the 2.5 million compound collection at the Genomics Institute of the Novartis Research Foundation (GNF) as a model, we determined that approximately 4% of the library contained compounds with potential annotation in such databases as PubChem and the World Drug Index (WDI) as well as related databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and ChemIDplus. Furthermore, the exact structure match analysis showed 32% of GNF compounds can be linked to third party databases via PubChem. We also showed annotations such as MeSH (medical subject headings) terms can be applied to in-house HTS databases in identifying signature biological inhibition profiles of interest as well as expediting the assay validation process. The automated annotation of thousands of screening hits in batch is becoming feasible and has the potential to play an essential role in the hit-to-lead decision making process.

Determination of Bimolecular Rate Constants for Reactions of Hydroxyl Radical with Pharmaceutical and Cosmetics Chemicals - Implications to the Fate in the Aquatic Environment

NASA Astrophysics Data System (ADS)

Nakajima, H.; Arakaki, T.; Anastasio, C.

2008-12-01

Large organic compounds such as hyaluronic acid and chondroitin sulfate are often used in pharmaceutical and cosmetics products, but their chemical degradation pathways are not well understood. To better elucidate their fate in the aquatic environment, we initiated a study to determine bimolecular rate constants between these organic compounds and hydroxyl radical (OH), which is a potent oxidant in the environment. The lifetimes of many organic compounds are determined by reactions with OH radicals, and the lifetime of OH is often controlled by reactions with organic compounds. To determine these bimolecular rate constants we used a competition kinetics technique with either hydrogen peroxide or nitrate as a source of OH and benzoate as the competing sink. Since the molecular weights of some of the large organic compounds we studied were not known, we used dissolved organic carbon (DOC) concentrations to determine mole-carbon based bimolecular rate constants, instead of the commonly used molar-based bimolecular rate constants. We will report the mole-carbon based bimolecular rate constants of OH, determined at room temperature, with hyaluronic acid, chondroitin sulfate and some other large organic compounds.

Basis set and electron correlation effects on the polarizability and second hyperpolarizability of model open-shell π-conjugated systems

NASA Astrophysics Data System (ADS)

Champagne, Benoı̂t; Botek, Edith; Nakano, Masayoshi; Nitta, Tomoshige; Yamaguchi, Kizashi

2005-03-01

The basis set and electron correlation effects on the static polarizability (α) and second hyperpolarizability (γ) are investigated ab initio for two model open-shell π-conjugated systems, the C5H7 radical and the C6H8 radical cation in their doublet state. Basis set investigations evidence that the linear and nonlinear responses of the radical cation necessitate the use of a less extended basis set than its neutral analog. Indeed, double-zeta-type basis sets supplemented by a set of d polarization functions but no diffuse functions already provide accurate (hyper)polarizabilities for C6H8 whereas diffuse functions are compulsory for C5H7, in particular, p diffuse functions. In addition to the 6-31G*+pd basis set, basis sets resulting from removing not necessary diffuse functions from the augmented correlation consistent polarized valence double zeta basis set have been shown to provide (hyper)polarizability values of similar quality as more extended basis sets such as augmented correlation consistent polarized valence triple zeta and doubly augmented correlation consistent polarized valence double zeta. Using the selected atomic basis sets, the (hyper)polarizabilities of these two model compounds are calculated at different levels of approximation in order to assess the impact of including electron correlation. As a function of the method of calculation antiparallel and parallel variations have been demonstrated for α and γ of the two model compounds, respectively. For the polarizability, the unrestricted Hartree-Fock and unrestricted second-order Møller-Plesset methods bracket the reference value obtained at the unrestricted coupled cluster singles and doubles with a perturbative inclusion of the triples level whereas the projected unrestricted second-order Møller-Plesset results are in much closer agreement with the unrestricted coupled cluster singles and doubles with a perturbative inclusion of the triples values than the projected unrestricted Hartree-Fock results. Moreover, the differences between the restricted open-shell Hartree-Fock and restricted open-shell second-order Møller-Plesset methods are small. In what concerns the second hyperpolarizability, the unrestricted Hartree-Fock and unrestricted second-order Møller-Plesset values remain of similar quality while using spin-projected schemes fails for the charged system but performs nicely for the neutral one. The restricted open-shell schemes, and especially the restricted open-shell second-order Møller-Plesset method, provide for both compounds γ values close to the results obtained at the unrestricted coupled cluster level including singles and doubles with a perturbative inclusion of the triples. Thus, to obtain well-converged α and γ values at low-order electron correlation levels, the removal of spin contamination is a necessary but not a sufficient condition. Density-functional theory calculations of α and γ have also been carried out using several exchange-correlation functionals. Those employing hybrid exchange-correlation functionals have been shown to reproduce fairly well the reference coupled cluster polarizability and second hyperpolarizability values. In addition, inclusion of Hartree-Fock exchange is of major importance for determining accurate polarizability whereas for the second hyperpolarizability the gradient corrections are large.

Large First Hyperpolarizabilities in Push-Pull Polyenes by Tuning Bond Length Alternation and Aromaticity

NASA Technical Reports Server (NTRS)

Marder, S. R.; Tiemann, B. G.; Friedli, A. C.; Cheng, L. -T.; Blanchard-Desce, M.

1993-01-01

Conjugated organic compounds with 3-phenyl-5-isoxazolone, or N, N'-diethylthiobarbituric acid acceptors have large first molecular hyperpolarizabilities in comparison to compounds with 4-nitrophenyl acceptors as measured by electric feld induced second harmonic generation, (EFISH), in chloroform, with 1.907 micron fundamental radiation.

In silico study toward the identification of new and safe potential inhibitors of photosynthetic electron transport.

PubMed

Ribeiro, Taisa Pereira Piacentini; Manarin, Flávia Giovana; Borges de Melo, Eduardo

2018-05-30

To address the rising global demand for food, it is necessary to search for new herbicides that can control resistant weeds. We performed a 2D-quantitative structure-activity relationship (QSAR) study to predict compounds with photosynthesis-inhibitory activity. A data set of 44 compounds (quinolines and naphthalenes), which are described as photosynthetic electron transport (PET) inhibitors, was used. The obtained model was approved in internal and external validation tests. 2D Similarity-based virtual screening was performed and 64 compounds were selected from the ZINC database. By using the VEGA QSAR software, 48 compounds were shown to have potential toxic effects (mutagenicity and carcinogenicity). Therefore, the model was also tested using a set of 16 molecules obtained by a similarity search of the ZINC database. Six compounds showed good predicted inhibition of PET. The obtained model shows potential utility in the design of new PET inhibitors, and the hit compounds found by virtual screening are novel bicyclic scaffolds of this class. Copyright © 2018 Elsevier Inc. All rights reserved.

Discrete Fourier Transform-Based Multivariate Image Analysis: Application to Modeling of Aromatase Inhibitory Activity.

PubMed

Barigye, Stephen J; Freitas, Matheus P; Ausina, Priscila; Zancan, Patricia; Sola-Penna, Mauro; Castillo-Garit, Juan A

2018-02-12

We recently generalized the formerly alignment-dependent multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR) method through the application of the discrete Fourier transform (DFT), allowing for its application to noncongruent and structurally diverse chemical compound data sets. Here we report the first practical application of this method in the screening of molecular entities of therapeutic interest, with human aromatase inhibitory activity as the case study. We developed an ensemble classification model based on the two-dimensional (2D) DFT MIA-QSAR descriptors, with which we screened the NCI Diversity Set V (1593 compounds) and obtained 34 chemical compounds with possible aromatase inhibitory activity. These compounds were docked into the aromatase active site, and the 10 most promising compounds were selected for in vitro experimental validation. Of these compounds, 7419 (nonsteroidal) and 89 201 (steroidal) demonstrated satisfactory antiproliferative and aromatase inhibitory activities. The obtained results suggest that the 2D-DFT MIA-QSAR method may be useful in ligand-based virtual screening of new molecular entities of therapeutic utility.

Comparative study of biodegradability prediction of chemicals using decision trees, functional trees, and logistic regression.

PubMed

Chen, Guangchao; Li, Xuehua; Chen, Jingwen; Zhang, Ya-Nan; Peijnenburg, Willie J G M

2014-12-01

Biodegradation is the principal environmental dissipation process of chemicals. As such, it is a dominant factor determining the persistence and fate of organic chemicals in the environment, and is therefore of critical importance to chemical management and regulation. In the present study, the authors developed in silico methods assessing biodegradability based on a large heterogeneous set of 825 organic compounds, using the techniques of the C4.5 decision tree, the functional inner regression tree, and logistic regression. External validation was subsequently carried out by 2 independent test sets of 777 and 27 chemicals. As a result, the functional inner regression tree exhibited the best predictability with predictive accuracies of 81.5% and 81.0%, respectively, on the training set (825 chemicals) and test set I (777 chemicals). Performance of the developed models on the 2 test sets was subsequently compared with that of the Estimation Program Interface (EPI) Suite Biowin 5 and Biowin 6 models, which also showed a better predictability of the functional inner regression tree model. The model built in the present study exhibits a reasonable predictability compared with existing models while possessing a transparent algorithm. Interpretation of the mechanisms of biodegradation was also carried out based on the models developed. © 2014 SETAC.

Toxins and bioactive compounds from cyanobacteria and their implications on human health.

PubMed

Rao, P V Lakshmana; Gupta, Nidhi; Bhaskar, A S B; Jayaraj, R

2002-07-01

Many species of cyanobacteria (blue-green algae) produce secondary metabolites with potent biotoxic or cytotoxic properties. These metabolites differ from the intermediates and cofactor compounds that are essential for cell structural synthesis and energy transduction. The mass growth of cyanobacteria which develop in fresh, brackish and, marine waters commonly contain potent toxins. Cyanobacterial toxins or cyanotoxins are responsible for or implicated in animal poisoning, human gastroenteritis, dermal contact irritations and primary liver cancer in humans. These toxins (microcystins, nodularins, saxitoxins, anatoxin-a, anatoxin-a(s), cylindrospermopsin) are structurally diverse and their effects range from liver damage, including liver cancer to neurotoxicity. Several incidents of human illness and more recently, the death of 60 haemodialysis patients in Caruaru, Brazil, have been linked to the presence of microcystins in water. In response to the growing concern about the non-lethal acute and chronic effects of microcystins, World Health Organization has recently set a new provisional guideline value for microcystin-LR of 1.0 microg/L in drinking water. Cyanobacteria including microcystin-producing strains produce a large number of peptide compounds, e.g. micropeptins, cyanopeptolins, microviridin, circinamide, aeruginosin, with varying bioactivities and potential pharmacological application. This article discusses briefly cyanobacterial toxins and their implications on human health.

Genome Analysis of the Fruiting Body-Forming Myxobacterium Chondromyces crocatus Reveals High Potential for Natural Product Biosynthesis

PubMed Central

Zaburannyi, Nestor; Bunk, Boyke; Maier, Josef; Overmann, Jörg

2016-01-01

Here, we report the complete genome sequence of the type strain of the myxobacterial genus Chondromyces, Chondromyces crocatus Cm c5. It presents one of the largest prokaryotic genomes featuring a single circular chromosome and no plasmids. Analysis revealed an enlarged set of tRNA genes, along with reduced pressure on preferred codon usage compared to that of other bacterial genomes. The large coding capacity and the plethora of encoded secondary metabolite biosynthetic gene clusters are in line with the capability of Cm c5 to produce an arsenal of antibacterial, antifungal, and cytotoxic compounds. Known pathways of the ajudazol, chondramide, chondrochloren, crocacin, crocapeptin, and thuggacin compound families are complemented by many more natural compound biosynthetic gene clusters in the chromosome. Whole-genome comparison of the fruiting-body-forming type strain (Cm c5, DSM 14714) to an accustomed laboratory strain which has lost this ability (nonfruiting phenotype, Cm c5 fr−) revealed genetic changes in three loci. In addition to the low synteny found with the closest sequenced representative of the same family, Sorangium cellulosum, extensive genetic information duplication and broad application of eukaryotic-type signal transduction systems are hallmarks of this 11.3-Mbp prokaryotic genome. PMID:26773087

Effect of bottling and storage on the migration of plastic constituents in Spanish bottled waters.

PubMed

Guart, Albert; Bono-Blay, Francisco; Borrell, Antonio; Lacorte, Silvia

2014-08-01

Bottled water is packaged in either glass or, to a large extent, in plastic bottles with metallic or plastic caps of different material, shape and colour. Plastic materials are made of one or more monomers and several additives that can eventually migrate into water, either during bottle manufacturing, water filling or storage. The main objective of the present study was to carry out a comprehensive assessment of the quality of the Spanish bottled water market in terms of (i) migration of plastic components or additives during bottling and during storage and (ii) evaluation of the effect of the packaging material and bottle format on the migration potential. The compounds investigated were 5 phthalates, diethylhexyl adipate, alkylphenols and bisphenol A. A set of 362 bottled water samples corresponding to 131 natural mineral waters and spring waters sources and 3 treated waters of several commercial brands were analysed immediately after bottling and after one-year storage (a total of 724 samples). Target compounds were detected in 5.6% of the data values, with diethyl hexyl phthalate and bisphenol A being the most ubiquitous compounds detected. The total daily intake was estimated and a comparison with reference values was indicated. Copyright © 2014 Elsevier Ltd. All rights reserved.

High-Throughput In Vivo Genotoxicity Testing: An Automated Readout System for the Somatic Mutation and Recombination Test (SMART)

PubMed Central

Kwak, Jihoon; Genovesio, Auguste; Kang, Myungjoo; Hansen, Michael Adsett Edberg; Han, Sung-Jun

2015-01-01

Genotoxicity testing is an important component of toxicity assessment. As illustrated by the European registration, evaluation, authorization, and restriction of chemicals (REACH) directive, it concerns all the chemicals used in industry. The commonly used in vivo mammalian tests appear to be ill adapted to tackle the large compound sets involved, due to throughput, cost, and ethical issues. The somatic mutation and recombination test (SMART) represents a more scalable alternative, since it uses Drosophila, which develops faster and requires less infrastructure. Despite these advantages, the manual scoring of the hairs on Drosophila wings required for the SMART limits its usage. To overcome this limitation, we have developed an automated SMART readout. It consists of automated imaging, followed by an image analysis pipeline that measures individual wing genotoxicity scores. Finally, we have developed a wing score-based dose-dependency approach that can provide genotoxicity profiles. We have validated our method using 6 compounds, obtaining profiles almost identical to those obtained from manual measures, even for low-genotoxicity compounds such as urethane. The automated SMART, with its faster and more reliable readout, fulfills the need for a high-throughput in vivo test. The flexible imaging strategy we describe and the analysis tools we provide should facilitate the optimization and dissemination of our methods. PMID:25830368

Docking ligands into flexible and solvated macromolecules. 7. Impact of protein flexibility and water molecules on docking-based virtual screening accuracy.

PubMed

Therrien, Eric; Weill, Nathanael; Tomberg, Anna; Corbeil, Christopher R; Lee, Devin; Moitessier, Nicolas

2014-11-24

The use of predictive computational methods in the drug discovery process is in a state of continual growth. Over the last two decades, an increasingly large number of docking tools have been developed to identify hits or optimize lead molecules through in-silico screening of chemical libraries to proteins. In recent years, the focus has been on implementing protein flexibility and water molecules. Our efforts led to the development of Fitted first reported in 2007 and further developed since then. In this study, we wished to evaluate the impact of protein flexibility and occurrence of water molecules on the accuracy of the Fitted docking program to discriminate active compounds from inactive compounds in virtual screening (VS) campaigns. For this purpose, a total of 171 proteins cocrystallized with small molecules representing 40 unique enzymes and receptors as well as sets of known ligands and decoys were selected from the Protein Data Bank (PDB) and the Directory of Useful Decoys (DUD), respectively. This study revealed that implementing displaceable crystallographic or computationally placed particle water molecules and protein flexibility can improve the enrichment in active compounds. In addition, an informed decision based on library diversity or research objectives (hit discovery vs lead optimization) on which implementation to use may lead to significant improvements.

Comparison of the chemical composition of dissolved organic matter in three lakes in Minnesota

USGS Publications Warehouse

Cao, Xiaoyan; Aiken, George R.; Butler, Kenna D.; Mao, Jingdong; Schmidt-Rohr, Klaus

2018-01-01

New information on the chemical composition of dissolved organic matter (DOM) in three lakes in Minnesota has been gained from spectral editing and two-dimensional nuclear magnetic resonance (NMR) methods, indicating the effects of lake hydrological settings on DOM composition. Williams Lake (WL), Shingobee Lake (SL), and Manganika Lake (ML) had different source inputs, and the lake water residence time (WRT) of WL was markedly longer than that of SL and ML. The hydrophobic organic acid (HPOA) and transphilic organic acid (TPIA) fractions combined comprised >50% of total DOM in these lakes, and contained carboxyl-rich alicyclic molecules (CRAM), aromatics, carbohydrates, and N-containing compounds. The previously understudied TPIA fractions contained fewer aromatics, more oxygen-rich CRAM, and more N-containing compounds compared to the corresponding HPOA. CRAM represented the predominant component in DOM from all lakes studied, and more so in WL than in SL and ML. Aromatics including lignin residues and phenols decreased in relative abundances from ML to SL and WL. Carbohydrates and N-containing compounds were minor components in both HPOA and TPIA and did not show large variations among the three lakes. The increased relative abundances of CRAM in DOM from ML, SL to WL suggested the selective preservation of CRAM with increased residence time.

Promises of Machine Learning Approaches in Prediction of Absorption of Compounds.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2018-01-01

The Machine Learning (ML) is one of the fastest developing techniques in the prediction and evaluation of important pharmacokinetic properties such as absorption, distribution, metabolism and excretion. The availability of a large number of robust validation techniques for prediction models devoted to pharmacokinetics has significantly enhanced the trust and authenticity in ML approaches. There is a series of prediction models generated and used for rapid screening of compounds on the basis of absorption in last one decade. Prediction of absorption of compounds using ML models has great potential across the pharmaceutical industry as a non-animal alternative to predict absorption. However, these prediction models still have to go far ahead to develop the confidence similar to conventional experimental methods for estimation of drug absorption. Some of the general concerns are selection of appropriate ML methods and validation techniques in addition to selecting relevant descriptors and authentic data sets for the generation of prediction models. The current review explores published models of ML for the prediction of absorption using physicochemical properties as descriptors and their important conclusions. In addition, some critical challenges in acceptance of ML models for absorption are also discussed. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Online and offline experimental techniques for polycyclic aromatic hydrocarbons recovery and measurement.

PubMed

Comandini, A; Malewicki, T; Brezinsky, K

2012-03-01

The implementation of techniques aimed at improving engine performance and reducing particulate matter (PM) pollutant emissions is strongly influenced by the limited understanding of the polycyclic aromatic hydrocarbons (PAH) formation chemistry, in combustion devices, that produces the PM emissions. New experimental results which examine the formation of multi-ring compounds are required. The present investigation focuses on two techniques for such an experimental examination by recovery of PAH compounds from a typical combustion oriented experimental apparatus. The online technique discussed constitutes an optimal solution but not always feasible approach. Nevertheless, a detailed description of a new online sampling system is provided which can serve as reference for future applications to different experimental set-ups. In comparison, an offline technique, which is sometimes more experimentally feasible but not necessarily optimal, has been studied in detail for the recovery of a variety of compounds with different properties, including naphthalene, biphenyl, and iodobenzene. The recovery results from both techniques were excellent with an error in the total carbon balance of around 10% for the online technique and an uncertainty in the measurement of the single species of around 7% for the offline technique. Although both techniques proved to be suitable for measurement of large PAH compounds, the online technique represents the optimal solution in view of the simplicity of the corresponding experimental procedure. On the other hand, the offline technique represents a valuable solution in those cases where the online technique cannot be implemented.

Photochemical oxidation and changes in molecular composition of organic aerosol in the regional context

NASA Astrophysics Data System (ADS)

Robinson, Allen L.; Donahue, Neil M.; Rogge, Wolfgang F.

2006-02-01

This paper presents evidence that condensed-phase organic compounds are significantly oxidized in regional air masses and in locations affected by regional transport, especially during the summer. The core of the paper examines a large data set of ambient organic aerosol concentrations for removal of reactive compounds relative to less-reactive compounds. The approach allows visualization of both photochemistry and mixing of emissions from multiple sources in order to differentiate between the two phenomena. The focus is on hopanes and alkenoic acids, important markers for motor vehicle and cooking emissions. Ambient data from Pittsburgh, PA and the Southeastern United States contain evidence for significant photochemical oxidation of these compounds in the summertime. There is a strong seasonal pattern in the ratio of different hopanes to elemental carbon consistent with oxidation. In addition, measurements at rural sites indicate that hopanes are severely depleted in the regional air mass during the summer. Alkenoic acids also appear to be photochemically oxidized during the summertime; however, the oxidation rate appears to be much slower than that inferred from laboratory experiments. The significance of photochemistry is supported by rudimentary calculations which indicate substantial oxidation by OH radicals and ozone on a time scale of a few days or so, comparable to time scales for regional transport. Oxidation is non-linear; therefore it represents a very substantial complication to linear source apportionment techniques such as the Chemical Mass Balance model.

Co-culturing of Fungal Strains Against Botrytis cinerea as a Model for the Induction of Chemical Diversity and Therapeutic Agents.

PubMed

Serrano, Rachel; González-Menéndez, Víctor; Rodríguez, Lorena; Martín, Jesús; Tormo, José R; Genilloud, Olga

2017-01-01

New fungal SMs (SMs) have been successfully described to be produced by means of in vitro -simulated microbial community interactions. Co-culturing of fungi has proved to be an efficient way to induce cell-cell interactions that can promote the activation of cryptic pathways, frequently silent when the strains are grown in laboratory conditions. Filamentous fungi represent one of the most diverse microbial groups known to produce bioactive natural products. Triggering the production of novel antifungal compounds in fungi could respond to the current needs to fight health compromising pathogens and provide new therapeutic solutions. In this study, we have selected the fungus Botrytis cinerea as a model to establish microbial interactions with a large set of fungal strains related to ecosystems where they can coexist with this phytopathogen, and to generate a collection of extracts, obtained from their antagonic microbial interactions and potentially containing new bioactive compounds. The antifungal specificity of the extracts containing compounds induced after B. cinerea interaction was determined against two human fungal pathogens ( Candida albicans and Aspergillus fumigatus ) and three phytopathogens ( Colletotrichum acutatum , Fusarium proliferatum , and Magnaporthe grisea ). In addition, their cytotoxicity was also evaluated against the human hepatocellular carcinoma cell line (HepG2). We have identified by LC-MS the production of a wide variety of known compounds induced from these fungal interactions, as well as novel molecules that support the potential of this approach to generate new chemical diversity and possible new therapeutic agents.

Relationships for the impact sensitivities of energetic C-nitro compounds based on bond dissociation energy.

PubMed

Li, Jinshan

2010-02-18

The ZPE-corrected C-NO(2) bond dissociation energies (BDEs(ZPE)) of a series of model C-nitro compounds and 26 energetic C-nitro compounds have been calculated using density functional theory methods. Computed results show that for C-nitro compounds the UB3LYP calculated BDE(ZPE) is less than the UB3P86 using the 6-31G** basis set, and the UB3P86 BDE(ZPE) changes slightly with the basis set varying from 6-31G** to 6-31++G**. For the series of model C-nitro compounds with different chemical skeletons, it is drawn from NBO analysis that the order of BDE(ZPE) is not only in line with that of the NAO bond order but also with that of the energy gap between C-NO(2) bonding and antibonding orbitals. It is found that for the energetic C-nitro compounds whose drop energies (Es(dr)) are below 24.5 J a good linear correlation exists between E(dr) and BDE(ZPE), implying that these compounds ignite through the C-NO(2) dissociation mechanism. After excluding the so-called trinitrotoluene mechanism compounds, a polynomial correlation of ln(E(dr)) with the BDE(ZPE) calculated at density functional theory levels has been established successfully for the 18 C-NO(2) dissociation energetic C-nitro compounds.

Prioritizing Environmental Risk of Prescription Pharmaceuticals

PubMed Central

Dong, Zhao; Senn, David B.; Moran, Rebecca E.

2015-01-01

Low levels of pharmaceutical compounds have been detected in aquatic environments worldwide, but their human and ecological health risks associated with low dose environmental exposure is largely unknown due to the large number of these compounds and a lack of information. Therefore prioritization and ranking methods are needed for screening target compounds for research and risk assessment. Previous efforts to rank pharmaceutical compounds have often focused on occurrence data and have paid less attention to removal mechanisms such as human metabolism. This study proposes a simple prioritization approach based on number of prescriptions and toxicity information, accounting for metabolism and wastewater treatment removal, and can be applied to unmeasured compounds. The approach was performed on the 200 most-prescribed drugs in the U.S. in 2009. Our results showed that under-studied compounds such as levothyroxine and montelukast sodium received the highest scores, suggesting the importance of removal mechanisms in influencing the ranking, and the need for future environmental research to include other less-studied but potentially harmful pharmaceutical compounds. PMID:22813724

On the enrichment of hydrophobic organic compounds in fog droplets

NASA Astrophysics Data System (ADS)

Valsaraj, K. T.; Thoma, G. J.; Reible, D. D.; Thibodeaux, L. J.

The unusual degree of enrichment of hydrophobic organics in fogwater droplets reported by several investigators can be interpreted as a result of (a) the effects of temperature correction on the reported enrichment factors, (b) the effects of colloidal organic matter (both filterable and non-filterable) in fog water and (c) the effects of the large air-water interfacial adsorption of neutral hydrophobic organics on the tiny fog droplets. The enrichment factor was directly correlated to the hydrophobicity (or the activity coefficient in water) of the compounds, as indicated by their octanol-water partition constants. Compounds with large octanol-water partition coefficients (high activity coefficients in water) showed the largest enrichment. Available experimental data on the adsorption of hydrophobic compounds at the air-water interface and on colloidal organic carbon were used to show that the large specific air-water interfacial areas of fog droplets contribute significantly to the enrichment factor.

A modification of the Hammett equation for predicting ionisation constants of p-vinyl phenols.

PubMed

Sipilä, Julius; Nurmi, Harri; Kaukonen, Ann Marie; Hirvonen, Jouni; Taskinen, Jyrki; Yli-Kauhaluoma, Jari

2005-01-01

Currently there are several compounds used as drugs or studied as new chemical entities, which have an electron withdrawing group connected to a vinylic double bond in a phenolic or catecholic core structure. These compounds share a common feature--current computational methods utilizing the Hammett type equation for the prediction of ionisation constants fail to give accurate prediction of pK(a)'s for compounds containing the vinylic moiety. The hypothesis was that the effect of electron-withdrawing substituents on the pK(a) of p-vinyl phenols is due to the delocalized electronic structure of these compounds. Thus, this effect should be additive for multiple substituents attached to the vinylic double bond and quantifiable by LFER-based methods. The aim of this study was to produce an improved equation with a reduced tendency to underestimate the effect of the double bond on the ionisation of the phenolic hydroxyl. To this end a set of 19 para-substituted vinyl phenols was used. The ionisation constants were measured potentiometrically, and a training set of 10 compounds was selected to build a regression model (r2 = 0.987 and S.E. = 0.09). The average error with an external test set of six compounds was 0.19 for our model and 1.27 for the ACD-labs 7.0. Thus, we have been able to significantly improve the existing model for prediction of the ionisation constants of substituted p-vinyl phenols.

A High-Content Live-Cell Viability Assay and Its Validation on a Diverse 12K Compound Screen.

PubMed

Chiaravalli, Jeanne; Glickman, J Fraser

2017-08-01

We have developed a new high-content cytotoxicity assay using live cells, called "ImageTOX." We used a high-throughput fluorescence microscope system, image segmentation software, and the combination of Hoechst 33342 and SYTO 17 to simultaneously score the relative size and the intensity of the nuclei, the nuclear membrane permeability, and the cell number in a 384-well microplate format. We then performed a screen of 12,668 diverse compounds and compared the results to a standard cytotoxicity assay. The ImageTOX assay identified similar sets of compounds to the standard cytotoxicity assay, while identifying more compounds having adverse effects on cell structure, earlier in treatment time. The ImageTOX assay uses inexpensive commercially available reagents and facilitates the use of live cells in toxicity screens. Furthermore, we show that we can measure the kinetic profile of compound toxicity in a high-content, high-throughput format, following the same set of cells over an extended period of time.

BAC-MP4 predictions of thermochemistry for the gas-phase tin compounds in the Sn-H-C-Cl system.

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

Allendorf, Mark D.; Melius, Carl F.

2004-09-01

In this work, the BAC-MP4 method is extended for the first time to compounds in the fourth row of the periodic table, resulting in a self-consistent set of thermochemical data for 56 tin-containing molecules in the Sn-H-C-Cl system. The BAC-MP4 method combines ab initio electronic structure calculations with empirical corrections to obtain accurate heats of formation. To obtain electronic energies for tin-containing species, the standard 6-31G(d,p) basis set used in BAC-MP4 calculations is augmented with a relativistic effective core potential to describe the electronic structure of the tin atom. Both stable compounds and radical species are included in this study.more » Trends within homologous series and calculated bond dissociation energies are consistent with previous BAC-MP4 predictions for group 14 compounds and the limited data available from the literature, indicating that the method is performing well for these compounds.« less

Quantitative Prediction of Solvation Free Energy in Octanol of Organic Compounds

PubMed Central

Delgado, Eduardo J.; Jaña, Gonzalo A.

2009-01-01

The free energy of solvation, ΔGS0, in octanol of organic compunds is quantitatively predicted from the molecular structure. The model, involving only three molecular descriptors, is obtained by multiple linear regression analysis from a data set of 147 compounds containing diverse organic functions, namely, halogenated and non-halogenated alkanes, alkenes, alkynes, aromatics, alcohols, aldehydes, ketones, amines, ethers and esters; covering a ΔGS0 range from about −50 to 0 kJ·mol−1. The model predicts the free energy of solvation with a squared correlation coefficient of 0.93 and a standard deviation, 2.4 kJ·mol−1, just marginally larger than the generally accepted value of experimental uncertainty. The involved molecular descriptors have definite physical meaning corresponding to the different intermolecular interactions occurring in the bulk liquid phase. The model is validated with an external set of 36 compounds not included in the training set. PMID:19399236

Investigation of miscellaneous hERG inhibition in large diverse compound collection using automated patch-clamp assay

PubMed Central

Yu, Hai-bo; Zou, Bei-yan; Wang, Xiao-liang; Li, Min

2016-01-01

Aim: hERG potassium channels display miscellaneous interactions with diverse chemical scaffolds. In this study we assessed the hERG inhibition in a large compound library of diverse chemical entities and provided data for better understanding of the mechanisms underlying promiscuity of hERG inhibition. Methods: Approximately 300 000 compounds contained in Molecular Library Small Molecular Repository (MLSMR) library were tested. Compound profiling was conducted on hERG-CHO cells using the automated patch-clamp platform–IonWorks Quattro™. Results: The compound library was tested at 1 and 10 μmol/L. IC50 values were predicted using a modified 4-parameter logistic model. Inhibitor hits were binned into three groups based on their potency: high (IC50<1 μmol/L), intermediate (1 μmol/L< IC50<10 μmol/L), and low (IC50>10 μmol/L) with hit rates of 1.64%, 9.17% and 16.63%, respectively. Six physiochemical properties of each compound were acquired and calculated using ACD software to evaluate the correlation between hERG inhibition and the properties: hERG inhibition was positively correlative to the physiochemical properties ALogP, molecular weight and RTB, and negatively correlative to TPSA. Conclusion: Based on a large diverse compound collection, this study provides experimental evidence to understand the promiscuity of hERG inhibition. This study further demonstrates that hERG liability compounds tend to be more hydrophobic, high-molecular, flexible and polarizable. PMID:26725739

A Comparative Analysis of Synthetic Quorum Sensing Modulators in Pseudomonas aeruginosa: New Insights into Mechanism, Active Efflux Susceptibility, Phenotypic Response, and Next-Generation Ligand Design.

PubMed

Moore, Joseph D; Rossi, Francis M; Welsh, Michael A; Nyffeler, Kayleigh E; Blackwell, Helen E

2015-11-25

Quorum sensing (QS) is a chemical signaling mechanism that allows bacterial populations to coordinate gene expression in response to social and environmental cues. Many bacterial pathogens use QS to initiate infection at high cell densities. Over the past two decades, chemical antagonists of QS in pathogenic bacteria have attracted substantial interest for use both as tools to further elucidate QS mechanisms and, with further development, potential anti-infective agents. Considerable recent research has been devoted to the design of small molecules capable of modulating the LasR QS receptor in the opportunistic pathogen Pseudomonas aeruginosa. These molecules hold significant promise in a range of contexts; however, as most compounds have been developed independently, comparative activity data for these compounds are scarce. Moreover, the mechanisms by which the bulk of these compounds act are largely unknown. This paucity of data has stalled the choice of an optimal chemical scaffold for further advancement. Herein, we submit the best-characterized LasR modulators to standardized cell-based reporter and QS phenotypic assays in P. aeruginosa, and we report the first comprehensive set of comparative LasR activity data for these compounds. Our experiments uncovered multiple interesting mechanistic phenomena (including a potential alternative QS-modulatory ligand binding site/partner) that provide new, and unexpected, insights into the modes by which many of these LasR ligands act. The lead compounds, data trends, and mechanistic insights reported here will significantly aid the design of new small molecule QS inhibitors and activators in P. aeruginosa, and in other bacteria, with enhanced potencies and defined modes of action.

Efforts to develop a cultured sponge cell line: revisiting an intractable problem.

PubMed

Grasela, James J; Pomponi, Shirley A; Rinkevich, Buki; Grima, Jennifer

2012-01-01

Residents of the marine environment, sponges (Porifera) have the ability to produce organic compounds known as secondary metabolites, which are not directly involved in the normal growth, development, or reproduction of an organism. Because of their sessile nature, the production of these bioactive compounds has been interpreted as a functional adaptation to serve in an important survival role as a means to counter various environmental stress factors such as predation, overgrowth by fouling organisms, or competition for limited space. Regardless of the reasons for this adaptation, a variety of isolated compounds have already proven to demonstrate remarkable anticancer, fungicidal, and antibiotic properties. A major obstacle to the isolation and production of novel compounds from sponges is the lack of a large, reliable source of sponge material. Sponge collection from the sea would be environmentally detrimental to the already stressed and sparse sponge populations. Sponge production in an aquaculture setting might appear to be an ideal alternative but would also be cost-ineffective and sponge growth is extremely slow. A third approach involves the development of a sponge cell culture system capable of producing the necessary cell numbers to harvest for research purposes as well as for the eventual commercial-scale production of promising bioactive compounds. Unfortunately, little progress has been made in this direction other than the establishment of temporary cultures containing aggregates and fragments of cells. One impediment toward successful sponge cell culture might be ascribed to a lack of published knowledge of failed methodologies, and thus, time and effort is wasted on continued reinvention of the same methods and procedures. Consequently, we have undertaken here to chart some of our unsuccessful research efforts, our methodology, and results to provide the sponge research community with knowledge to assist them to better avoid taking the same failed pathways.

Very large virtual compound spaces: construction, storage and utility in drug discovery.

PubMed

Peng, Zhengwei

2013-09-01

Recent activities in the construction, storage and exploration of very large virtual compound spaces are reviewed by this report. As expected, the systematic exploration of compound spaces at the highest resolution (individual atoms and bonds) is intrinsically intractable. By contrast, by staying within a finite number of reactions and a finite number of reactants or fragments, several virtual compound spaces have been constructed in a combinatorial fashion with sizes ranging from 10(11)11 to 10(20)20 compounds. Multiple search methods have been developed to perform searches (e.g. similarity, exact and substructure) into those compound spaces without the need for full enumeration. The up-front investment spent on synthetic feasibility during the construction of some of those virtual compound spaces enables a wider adoption by medicinal chemists to design and synthesize important compounds for drug discovery. Recent activities in the area of exploring virtual compound spaces via the evolutionary approach based on Genetic Algorithm also suggests a positive shift of focus from method development to workflow, integration and ease of use, all of which are required for this approach to be widely adopted by medicinal chemists.

Discovery of novel mGluR1 antagonists: a multistep virtual screening approach based on an SVM model and a pharmacophore hypothesis significantly increases the hit rate and enrichment factor.

PubMed

Li, Guo-Bo; Yang, Ling-Ling; Feng, Shan; Zhou, Jian-Ping; Huang, Qi; Xie, Huan-Zhang; Li, Lin-Li; Yang, Sheng-Yong

2011-03-15

Development of glutamate non-competitive antagonists of mGluR1 (Metabotropic glutamate receptor subtype 1) has increasingly attracted much attention in recent years due to their potential therapeutic application for various nervous disorders. Since there is no crystal structure reported for mGluR1, ligand-based virtual screening (VS) methods, typically pharmacophore-based VS (PB-VS), are often used for the discovery of mGluR1 antagonists. Nevertheless, PB-VS usually suffers a lower hit rate and enrichment factor. In this investigation, we established a multistep ligand-based VS approach that is based on a support vector machine (SVM) classification model and a pharmacophore model. Performance evaluation of these methods in virtual screening against a large independent test set, M-MDDR, show that the multistep VS approach significantly increases the hit rate and enrichment factor compared with the individual SB-VS and PB-VS methods. The multistep VS approach was then used to screen several large chemical libraries including PubChem, Specs, and Enamine. Finally a total of 20 compounds were selected from the top ranking compounds, and shifted to the subsequent in vitro and in vivo studies, which results will be reported in the near future. Copyright © 2011 Elsevier Ltd. All rights reserved.

Sensing a Changing Chemical Mixture Using an Electronic Nose

NASA Technical Reports Server (NTRS)

Duong, Tuan; Ryan, Margaret

2008-01-01

A method of using an electronic nose to detect an airborne mixture of known chemical compounds and measure the temporally varying concentrations of the individual compounds is undergoing development. In a typical intended application, the method would be used to monitor the air in an inhabited space (e.g., the interior of a building) for the release of solvents, toxic fumes, and other compounds that are regarded as contaminants. At the present state of development, the method affords a capability for identifying and quantitating one or two compounds that are members of a set of some number (typically of the order of a dozen) known compounds. In principle, the method could be extended to enable monitoring of more than two compounds. An electronic nose consists of an array of sensors, typically made from polymer carbon composites, the electrical resistances of which change upon exposure to a variety of chemicals. By design, each sensor is unique in its responses to these chemicals: some or all of the sensitivities of a given sensor to the various vapors differ from the corresponding sensitivities of other sensors. In general, the responses of the sensors are nonlinear functions of the concentrations of the chemicals. Hence, mathematically, the monitoring problem is to solve the set of time-dependent nonlinear equations for the sensor responses to obtain the time dependent concentrations of individual compounds. In the present developmental method, successive approximations of the solution are generated by a learning algorithm based on independent-component analysis (ICA) an established information theoretic approach for transforming a vector of observed interdependent signals into a set of signals that are as nearly statistically independent as possible.

Water-quality assessment of the largely urban blue river basin, Metropolitan Kansas City, USA, 1998 to 2007

USGS Publications Warehouse

Wilkison, D.H.; Armstrong, D.J.; Hampton, S.A.

2009-01-01

From 1998 through 2007, over 750 surface-water or bed-sediment samples in the Blue River Basin - a largely urban basin in metropolitan Kansas City - were analyzed for more than 100 anthropogenic compounds. Compounds analyzed included nutrients, fecal-indicator bacteria, suspended sediment, pharmaceuticals and personal care products. Non-point source runoff, hydrologic alterations, and numerous waste-water discharge points resulted in the routine detection of complex mixtures of anthropogenic compounds in samples from basin stream sites. Temporal and spatial variations in concentrations and loads of nutrients, pharmaceuticals, and organic wastewater compounds were observed, primarily related to a site's proximity to point-source discharges and stream-flow dynamics. ?? 2009 ASCE.

Quantum chemical calculations of glycine glutaric acid

NASA Astrophysics Data System (ADS)

Arioǧlu, ćaǧla; Tamer, Ömer; Avci, Davut; Atalay, Yusuf

2017-02-01

Density functional theory (DFT) calculations of glycine glutaric acid were performed by using B3LYP levels with 6-311++G(d,p) basis set. The theoretical structural parameters such as bond lengths and bond angles are in a good agreement with the experimental values of the title compound. HOMO and LUMO energies were calculated, and the obtained energy gap shows that charge transfer occurs in the title compound. Vibrational frequencies were calculated and compare with experimental ones. 3D molecular surfaces of the title compound were simulated using the same level and basis set. Finally, the 13C and 1H NMR chemical shift values were calculated by the application of the gauge independent atomic orbital (GIAO) method.

Ground-water contamination at an inactive coal and oil gasification plant site, Gas Works Park, Seattle, Washington

USGS Publications Warehouse

Turney, G.L.; Goerlitz, D.F.

1989-01-01

Gas Works Park, in Seattle, Washington, is located on the site of a coal and oil gasification plant that ceased operation in 1956. During operation, many types of wastes, including coal, tar, and oil, accumulated on site. The park soil is presently (1986) contaminated with compounds such as polynuclear aromatic hydrocarbons, volatile organic compounds, trace metals, and cyanide. Analyses of water samples from a network of observation wells in the park indicate that these compounds are also present in the groundwater. Polynuclear aromatic hydrocarbons and volatile organic compounds were identified in groundwater samples in concentrations as large as 200 mg/L. Concentrations of organic compounds were largest where groundwater was in contact with a nonaqueous phase liquid in the soil. Concentrations in groundwater were much smaller where no nonaqueous phase liquid was present, even if the groundwater was in contact with contaminated soils. This condition is attributed to weathering processes at the site, such as dissolution, volatilization, and biodegradation. Soluble, volatile, low-molecular-weight organic compounds are preferentially dissolved from the nonaqueous phase liquid into the groundwater. Where no nonaqueous phase liquid is present, only stained soils containing relatively insoluble, high-molecular-weight compounds remain; therefore, contaminant concentrations in the groundwater are much smaller. Concentrations of organic contaminants in the soils may still remain large. Values of specific conductance were as large as 5,280 microsiemens/cm, well above a background of 242 microsiemens/cm, suggesting large concentrations of minerals in the groundwater. Trace metal concentrations, however , were generally < 0.010 mg/L, and below limits of US EPA drinking water standards. Cyanide was present in groundwater samples from throughout the park, ranging in concentration from 0.01 to 8.6 mg/L. (Author 's abstract)

Determination of the asphaltene and carboxylic acid content of a heavy oil using a microfluidic device.

PubMed

Bowden, Stephen A; Wilson, Rab; Parnell, John; Cooper, Jonathan M

2009-03-21

Heavy oil utilisation is set to increase over the coming decades as reserves of conventional oil decline. Heavy oil differs from conventional oil in containing relatively large quantities of asphaltene and carboxylic acids. The proportions of these compounds greatly influence how oil behaves during production and its utilisation as a fuel or feedstock. We report the development of a microfluidic technique, based on a H-cell, that can extract the carboxylic acid components of an oil and assess its asphaltene content. Ultimately this technology could yield a field-deployable device capable of performing measurements that facilitate improved resource management at the point of resource-extraction.

Evaluation of a focused virtual library of heterobifunctional ligands for Clostridium difficile toxins.

PubMed

Sanhueza, Carlos A; Cartmell, Jonathan; El-Hawiet, Amr; Szpacenko, Adam; Kitova, Elena N; Daneshfar, Rambod; Klassen, John S; Lang, Dean E; Eugenio, Luiz; Ng, Kenneth K-S; Kitov, Pavel I; Bundle, David R

2015-01-07

A focused library of virtual heterobifunctional ligands was generated in silico and a set of ligands with recombined fragments was synthesized and evaluated for binding to Clostridium difficile toxins. The position of the trisaccharide fragment was used as a reference for filtering docked poses during virtual screening to match the trisaccharide ligand in a crystal structure. The peptoid, a diversity fragment probing the protein surface area adjacent to a known binding site, was generated by a multi-component Ugi reaction. Our approach combines modular fragment-based design with in silico screening of synthetically feasible compounds and lays the groundwork for future efforts in development of composite bifunctional ligands for large clostridial toxins.

Global simulation of aromatic volatile organic compounds in the atmosphere

NASA Astrophysics Data System (ADS)

Cabrera Perez, David; Taraborrelli, Domenico; Pozzer, Andrea

2015-04-01

Among the large number of chemical compounds in the atmosphere, the organic group plays a key role in the tropospheric chemistry. Specifically the subgroup called aromatics is of great interest. Aromatics are the predominant trace gases in urban areas due to high emissions, primarily by vehicle exhausts and fuel evaporation. They are also present in areas where biofuel is used (i.e residential wood burning). Emissions of aromatic compounds are a substantial fraction of the total emissions of the volatile organic compounds (VOC). Impact of aromatics on human health is very important, as they do not only contribute to the ozone formation in the urban environment, but they are also highly toxic themselves, especially in the case of benzene which is able to trigger a range of illness under long exposure, and of nitro-phenols which cause detrimental for humans and vegetation even at very low concentrations. The aim of this work is to assess the atmospheric impacts of aromatic compounds on the global scale. The main goals are: lifetime and budget estimation, mixing ratios distribution, net effect on ozone production and OH loss for the most emitted aromatic compounds (benzene, toluene, xylenes, ethylbenzene, styrene and trimethylbenzenes). For this purpose, we use the numerical chemistry and climate simulation ECHAM/MESSy Atmospheric Chemistry (EMAC) model to build the global atmospheric budget for the most emitted and predominant aromatic compounds in the atmosphere. A set of emissions was prepared in order to include biomass burning, vegetation and anthropogenic sources of aromatics into the model. A chemical mechanism based on the Master Chemical Mechanism (MCM) was developed to describe the chemical oxidation in the gas phase of these aromatic compounds. MCM have been reduced in terms of number of chemical equation and species in order to make it affordable in a 3D model. Additionally other features have been added, for instance the production of HONO via ortho-nitrophenols photolysis. The model results are compared with observations from different surface and aircraft campaigns in order to estimate the accuracy of the model.

Compound windows of the Hénon-map

NASA Astrophysics Data System (ADS)

Lorenz, Edward N.

2008-08-01

For the two-parameter second-order Hénon map, the shapes and locations of the periodic windows-continua of parameter values for which solutions x0,x1,… can be stably periodic, embedded in larger regions where chaotic solutions or solutions of other periods prevail-are found by a random searching procedure and displayed graphically. Many windows have a typical shape, consisting of a central “body” from which four narrow “antennae” extend. Such windows, to be called compound windows, are often arranged in bands, to be called window streets, that are made up largely of small detected but poorly resolved compound windows. For each fundamental subwindow-the portion of a window where a fundamental period prevails-a stability measure U is introduced; where the solution is stable, |U|<1. Curves of constant U are found by numerical integration. Along one line in parameter space the Hénon-map reduces to the one-parameter first-order logistic map, and two antennae from each compound window intersect this line. The curves where U=1 and U=-1 that bound either antenna are close together within these intersections, but, as either curve with U=-1 leaves the line, it diverges from the curve where U=1, crosses the other curve where U=-1, and nears the other curve where U=1, forming another antenna. The region bounded by the numerically determined curves coincides with the subwindow as found by random searching. A fourth-degree equation for an idealized curve of constant U is established. Points in parameter space producing periodic solutions where x0=xm=0, for given values of m, are found to lie on Cantor sets of curves that closely fit the window streets. Points producing solutions where x0=xm=0 and satisfying a third condition, approximating the condition that xn be bounded as n→-∞, lie on curves, to be called street curves of order m, that approximate individual members of the Cantor set and individual window streets. Compound windows of period m+m‧ tend to occur near the intersections of street curves of orders m and m‧. Some exceptions to what appear to be fairly general results are noted. The exceptions render it difficult to establish general theorems.

Bond-based linear indices in QSAR: computational discovery of novel anti-trichomonal compounds

NASA Astrophysics Data System (ADS)

Marrero-Ponce, Yovani; Meneses-Marcel, Alfredo; Rivera-Borroto, Oscar M.; García-Domenech, Ramón; De Julián-Ortiz, Jesus Vicente; Montero, Alina; Escario, José Antonio; Barrio, Alicia Gómez; Pereira, David Montero; Nogal, Juan José; Grau, Ricardo; Torrens, Francisco; Vogel, Christian; Arán, Vicente J.

2008-08-01

Trichomonas vaginalis ( Tv) is the causative agent of the most common, non-viral, sexually transmitted disease in women and men worldwide. Since 1959, metronidazole (MTZ) has been the drug of choice in the systemic treatment of trichomoniasis. However, resistance to MTZ in some patients and the great cost associated with the development of new trichomonacidals make necessary the development of computational methods that shorten the drug discovery pipeline. Toward this end, bond-based linear indices, new TOMOCOMD-CARDD molecular descriptors, and linear discriminant analysis were used to discover novel trichomonacidal chemicals. The obtained models, using non-stochastic and stochastic indices, are able to classify correctly 89.01% (87.50%) and 82.42% (84.38%) of the chemicals in the training (test) sets, respectively. These results validate the models for their use in the ligand-based virtual screening. In addition, they show large Matthews' correlation coefficients ( C) of 0.78 (0.71) and 0.65 (0.65) for the training (test) sets, correspondingly. The result of predictions on the 10% full-out cross-validation test also evidences the robustness of the obtained models. Later, both models are applied to the virtual screening of 12 compounds already proved against Tv. As a result, they correctly classify 10 out of 12 (83.33%) and 9 out of 12 (75.00%) of the chemicals, respectively; which is the most important criterion for validating the models. Besides, these classification functions are applied to a library of seven chemicals in order to find novel antitrichomonal agents. These compounds are synthesized and tested for in vitro activity against Tv. As a result, experimental observations approached to theoretical predictions, since it was obtained a correct classification of 85.71% (6 out of 7) of the chemicals. Moreover, out of the seven compounds that are screened, synthesized and biologically assayed, six compounds (VA7-34, VA7-35, VA7-37, VA7-38, VA7-68, VA7-70) show pronounced cytocidal activity at the concentration of 100 μg/ml at 24 h (48 h) within the range of 98.66%-100% (99.40%-100%), while only two molecules (chemicals VA7-37 and VA7-38) show high cytocidal activity at the concentration of 10 μg/ml at 24 h (48 h): 98.38% (94.23%) and 97.59% (98.10%), correspondingly. The LDA-assisted QSAR models presented here could significantly reduce the number of synthesized and tested compounds and could increase the chance of finding new chemical entities with anti-trichomonal activity.

Bond-based linear indices in QSAR: computational discovery of novel anti-trichomonal compounds.

PubMed

Marrero-Ponce, Yovani; Meneses-Marcel, Alfredo; Rivera-Borroto, Oscar M; García-Domenech, Ramón; De Julián-Ortiz, Jesus Vicente; Montero, Alina; Escario, José Antonio; Barrio, Alicia Gómez; Pereira, David Montero; Nogal, Juan José; Grau, Ricardo; Torrens, Francisco; Vogel, Christian; Arán, Vicente J

2008-08-01

Trichomonas vaginalis (Tv) is the causative agent of the most common, non-viral, sexually transmitted disease in women and men worldwide. Since 1959, metronidazole (MTZ) has been the drug of choice in the systemic treatment of trichomoniasis. However, resistance to MTZ in some patients and the great cost associated with the development of new trichomonacidals make necessary the development of computational methods that shorten the drug discovery pipeline. Toward this end, bond-based linear indices, new TOMOCOMD-CARDD molecular descriptors, and linear discriminant analysis were used to discover novel trichomonacidal chemicals. The obtained models, using non-stochastic and stochastic indices, are able to classify correctly 89.01% (87.50%) and 82.42% (84.38%) of the chemicals in the training (test) sets, respectively. These results validate the models for their use in the ligand-based virtual screening. In addition, they show large Matthews' correlation coefficients (C) of 0.78 (0.71) and 0.65 (0.65) for the training (test) sets, correspondingly. The result of predictions on the 10% full-out cross-validation test also evidences the robustness of the obtained models. Later, both models are applied to the virtual screening of 12 compounds already proved against Tv. As a result, they correctly classify 10 out of 12 (83.33%) and 9 out of 12 (75.00%) of the chemicals, respectively; which is the most important criterion for validating the models. Besides, these classification functions are applied to a library of seven chemicals in order to find novel antitrichomonal agents. These compounds are synthesized and tested for in vitro activity against Tv. As a result, experimental observations approached to theoretical predictions, since it was obtained a correct classification of 85.71% (6 out of 7) of the chemicals. Moreover, out of the seven compounds that are screened, synthesized and biologically assayed, six compounds (VA7-34, VA7-35, VA7-37, VA7-38, VA7-68, VA7-70) show pronounced cytocidal activity at the concentration of 100 mug/ml at 24 h (48 h) within the range of 98.66%-100% (99.40%-100%), while only two molecules (chemicals VA7-37 and VA7-38) show high cytocidal activity at the concentration of 10 mug/ml at 24 h (48 h): 98.38% (94.23%) and 97.59% (98.10%), correspondingly. The LDA-assisted QSAR models presented here could significantly reduce the number of synthesized and tested compounds and could increase the chance of finding new chemical entities with anti-trichomonal activity.

Freely available compound data sets and software tools for chemoinformatics and computational medicinal chemistry applications

PubMed Central

Bajorath, Jurgen

2012-01-01

We have generated a number of  compound data sets and programs for different types of applications in pharmaceutical research. These data sets and programs were originally designed for our research projects and are made publicly available. Without consulting original literature sources, it is difficult to understand specific features of data sets and software tools, basic ideas underlying their design, and applicability domains. Currently, 30 different entries are available for download from our website. In this data article, we provide an overview of the data and tools we make available and designate the areas of research for which they should be useful. For selected data sets and methods/programs, detailed descriptions are given. This article should help interested readers to select data and tools for specific computational investigations. PMID:24358818

Conceptual Design and Performance Analysis for a Large Civil Compound Helicopter

NASA Technical Reports Server (NTRS)

Russell, Carl; Johnson, Wayne

2012-01-01

A conceptual design study of a large civil compound helicopter is presented. The objective is to determine how a compound helicopter performs when compared to both a conventional helicopter and a tiltrotor using a design mission that is shorter than optimal for a tiltrotor and longer than optimal for a helicopter. The designs are generated and analyzed using conceptual design software and are further evaluated with a comprehensive rotorcraft analysis code. Multiple metrics are used to determine the suitability of each design for the given mission. Plots of various trade studies and parameter sweeps as well as comprehensive analysis results are presented. The results suggest that the compound helicopter examined for this study would not be competitive with a tiltrotor or conventional helicopter, but multiple possibilities are identified for improving the performance of the compound helicopter in future research.

QSAR Classification Model for Antibacterial Compounds and Its Use in Virtual Screening

DTIC Science & Technology

2012-09-26

test set molecules that were not used to train the models . This allowed us to more accurately estimate the prediction power of the models . As...pathogens and deposited in PubChem Bioassays. Ultimately, the main purpose of this model is to make predictions , based on known antibacterial and non...the model built form the remaining compounds is used to predict the left out compound. Once all the compounds pass through this cycle of prediction , a

Influence of Antimony-Halogen Additives on Flame Propagation.

PubMed

Babushok, Valeri I; Deglmann, Peter; Krämer, Roland; Linteris, Gregory T

2017-01-01

A kinetic model for flame inhibition by antimony-halogen compounds in hydrocarbon flames is developed. Thermodynamic data for the relevant species are assembled from the literature, and calculations are performed for a large set of additional species of Sb-Br-C-H-O system. The main Sb- and Br-containing species in the combustion products and reaction zone are determined using flame equilibrium calculations with a set of possible Sb-Br-C-H-O species, and these are used to develop the species and reactions in a detailed kinetic model for antimony flame inhibition. The complete thermodynamic data set and kinetic mechanism are presented. Laminar burning velocity simulations are used to validate the mechanism against available data in the literature, as well as to explore the relative performance of the antimony-halogen compounds. Further analysis of the premixed flame simulations has unraveled the catalytic radical recombination cycle of antimony. It includes (primarily) the species Sb, SbO, SbO 2 , and HOSbO, and the reactions: Sb+O+M=SbO+M; Sb+O 2 +M=SbO 2 +M; SbO+H=Sb+OH; SbO+O=Sb+O 2 ; SbO+OH+M=HOSbO+M; SbO 2 +H 2 O=HOSbO+OH; HOSbO+H=SbO+H 2 O; SbO+O+M=SbO 2 +M. The inhibition cycles of antimony are shown to be more effective than those of bromine, and intermediate between the highly effective agents CF 3 Br and trimethylphosphate. Preliminary examination of a Sb/Br gas-phase system did not show synergism in the gas-phase catalytic cycles (i.e., they acted essentially independently).

Influence of Antimony-Halogen Additives on Flame Propagation*

PubMed Central

Babushok, Valeri I.; Deglmann, Peter; Krämer, Roland; Linteris, Gregory T.

2016-01-01

A kinetic model for flame inhibition by antimony-halogen compounds in hydrocarbon flames is developed. Thermodynamic data for the relevant species are assembled from the literature, and calculations are performed for a large set of additional species of Sb-Br-C-H-O system. The main Sb- and Br-containing species in the combustion products and reaction zone are determined using flame equilibrium calculations with a set of possible Sb-Br-C-H-O species, and these are used to develop the species and reactions in a detailed kinetic model for antimony flame inhibition. The complete thermodynamic data set and kinetic mechanism are presented. Laminar burning velocity simulations are used to validate the mechanism against available data in the literature, as well as to explore the relative performance of the antimony-halogen compounds. Further analysis of the premixed flame simulations has unraveled the catalytic radical recombination cycle of antimony. It includes (primarily) the species Sb, SbO, SbO2, and HOSbO, and the reactions: Sb+O+M=SbO+M; Sb+O2+M=SbO2+M; SbO+H=Sb+OH; SbO+O=Sb+O2; SbO+OH+M=HOSbO+M; SbO2+H2O=HOSbO+OH; HOSbO+H=SbO+H2O; SbO+O+M=SbO2+M. The inhibition cycles of antimony are shown to be more effective than those of bromine, and intermediate between the highly effective agents CF3Br and trimethylphosphate. Preliminary examination of a Sb/Br gas-phase system did not show synergism in the gas-phase catalytic cycles (i.e., they acted essentially independently). PMID:28133390

Iterative Refinement of a Binding Pocket Model: Active Computational Steering of Lead Optimization

PubMed Central

2012-01-01

Computational approaches for binding affinity prediction are most frequently demonstrated through cross-validation within a series of molecules or through performance shown on a blinded test set. Here, we show how such a system performs in an iterative, temporal lead optimization exercise. A series of gyrase inhibitors with known synthetic order formed the set of molecules that could be selected for “synthesis.” Beginning with a small number of molecules, based only on structures and activities, a model was constructed. Compound selection was done computationally, each time making five selections based on confident predictions of high activity and five selections based on a quantitative measure of three-dimensional structural novelty. Compound selection was followed by model refinement using the new data. Iterative computational candidate selection produced rapid improvements in selected compound activity, and incorporation of explicitly novel compounds uncovered much more diverse active inhibitors than strategies lacking active novelty selection. PMID:23046104

Deuterium permeation through EPDM rubber compounds

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

Zapp, P.E.

1988-01-01

The permeation of deuterium through a specially formulated compound of ethylene propylene diene rubber was measured in the temperature range of 26/degree/C to 120/degree/C. The results were similar to permeation through two commercial compounds of this elastomer. Permeation was reduced after gamma irradiation (in the presence of hydrogen gas to simulate a tritium exposure). However the reduction was smaller than that experienced by the two commercial compounds. Radiation damage is apparently less severe in the special compound. It is possible that mechanical properties such as compression set may be influenced less by ionizing radiation in this compound as compared withmore » the commercial compounds. 4 figs., 1 tab.« less

Similarities among receptor pockets and among compounds: analysis and application to in silico ligand screening.

PubMed

Fukunishi, Yoshifumi; Mikami, Yoshiaki; Nakamura, Haruki

2005-09-01

We developed a new method to evaluate the distances and similarities between receptor pockets or chemical compounds based on a multi-receptor versus multi-ligand docking affinity matrix. The receptors were classified by a cluster analysis based on calculations of the distance between receptor pockets. A set of low homologous receptors that bind a similar compound could be classified into one cluster. Based on this line of reasoning, we proposed a new in silico screening method. According to this method, compounds in a database were docked to multiple targets. The new docking score was a slightly modified version of the multiple active site correction (MASC) score. Receptors that were at a set distance from the target receptor were not included in the analysis, and the modified MASC scores were calculated for the selected receptors. The choice of the receptors is important to achieve a good screening result, and our clustering of receptors is useful to this purpose. This method was applied to the analysis of a set of 132 receptors and 132 compounds, and the results demonstrated that this method achieves a high hit ratio, as compared to that of a uniform sampling, using a receptor-ligand docking program, Sievgene, which was newly developed with a good docking performance yielding 50.8% of the reconstructed complexes at a distance of less than 2 A RMSD.

An expanded calibration study of the explicitly correlated CCSD(T)-F12b method using large basis set standard CCSD(T) atomization energies.

PubMed

Feller, David; Peterson, Kirk A

2013-08-28

The effectiveness of the recently developed, explicitly correlated coupled cluster method CCSD(T)-F12b is examined in terms of its ability to reproduce atomization energies derived from complete basis set extrapolations of standard CCSD(T). Most of the standard method findings were obtained with aug-cc-pV7Z or aug-cc-pV8Z basis sets. For a few homonuclear diatomic molecules it was possible to push the basis set to the aug-cc-pV9Z level. F12b calculations were performed with the cc-pVnZ-F12 (n = D, T, Q) basis set sequence and were also extrapolated to the basis set limit using a Schwenke-style, parameterized formula. A systematic bias was observed in the F12b method with the (VTZ-F12/VQZ-F12) basis set combination. This bias resulted in the underestimation of reference values associated with small molecules (valence correlation energies <0.5 E(h)) and an even larger overestimation of atomization energies for bigger systems. Consequently, caution should be exercised in the use of F12b for high accuracy studies. Root mean square and mean absolute deviation error metrics for this basis set combination were comparable to complete basis set values obtained with standard CCSD(T) and the aug-cc-pVDZ through aug-cc-pVQZ basis set sequence. However, the mean signed deviation was an order of magnitude larger. Problems partially due to basis set superposition error were identified with second row compounds which resulted in a weak performance for the smaller VDZ-F12/VTZ-F12 combination of basis sets.

Partition coefficients of organic compounds between water and imidazolium-, pyridinium-, and phosphonium-based ionic liquids.

PubMed

Padró, Juan M; Pellegrino Vidal, Rocío B; Reta, Mario

2014-12-01

The partition coefficients, P IL/w, of several compounds, some of them of biological and pharmacological interest, between water and room-temperature ionic liquids based on the imidazolium, pyridinium, and phosphonium cations, namely 1-octyl-3-methylimidazolium hexafluorophosphate, N-octylpyridinium tetrafluorophosphate, trihexyl(tetradecyl)phosphonium chloride, trihexyl(tetradecyl)phosphonium bromide, trihexyl(tetradecyl)phosphonium bis(trifluoromethylsulfonyl)imide, and trihexyl(tetradecyl)phosphonium dicyanamide, were accurately measured. In this way, we extended our database of partition coefficients in room-temperature ionic liquids previously reported. We employed the solvation parameter model with different probe molecules (the training set) to elucidate the chemical interactions involved in the partition process and discussed the most relevant differences among the three types of ionic liquids. The multiparametric equations obtained with the aforementioned model were used to predict the partition coefficients for compounds (the test set) not present in the training set, most being of biological and pharmacological interest. An excellent agreement between calculated and experimental log P IL/w values was obtained. Thus, the obtained equations can be used to predict, a priori, the extraction efficiency for any compound using these ionic liquids as extraction solvents in liquid-liquid extractions.

Effect of training data size and noise level on support vector machines virtual screening of genotoxic compounds from large compound libraries.

PubMed

Kumar, Pankaj; Ma, Xiaohua; Liu, Xianghui; Jia, Jia; Bucong, Han; Xue, Ying; Li, Ze Rong; Yang, Sheng Yong; Wei, Yu Quan; Chen, Yu Zong

2011-05-01

Various in vitro and in-silico methods have been used for drug genotoxicity tests, which show limited genotoxicity (GT+) and non-genotoxicity (GT-) identification rates. New methods and combinatorial approaches have been explored for enhanced collective identification capability. The rates of in-silco methods may be further improved by significantly diversified training data enriched by the large number of recently reported GT+ and GT- compounds, but a major concern is the increased noise levels arising from high false-positive rates of in vitro data. In this work, we evaluated the effect of training data size and noise level on the performance of support vector machines (SVM) method known to tolerate high noise levels in training data. Two SVMs of different diversity/noise levels were developed and tested. H-SVM trained by higher diversity higher noise data (GT+ in any in vivo or in vitro test) outperforms L-SVM trained by lower noise lower diversity data (GT+ in in vivo or Ames test only). H-SVM trained by 4,763 GT+ compounds reported before 2008 and 8,232 GT- compounds excluding clinical trial drugs correctly identified 81.6% of the 38 GT+ compounds reported since 2008, predicted 83.1% of the 2,008 clinical trial drugs as GT-, and 23.96% of 168 K MDDR and 27.23% of 17.86M PubChem compounds as GT+. These are comparable to the 43.1-51.9% GT+ and 75-93% GT- rates of existing in-silico methods, 58.8% GT+ and 79% GT- rates of Ames method, and the estimated percentages of 23% in vivo and 31-33% in vitro GT+ compounds in the "universe of chemicals". There is a substantial level of agreement between H-SVM and L-SVM predicted GT+ and GT- MDDR compounds and the prediction from TOPKAT. SVM showed good potential in identifying GT+ compounds from large compound libraries based on higher diversity and higher noise training data.

Follow up: Compound data sets and software tools for chemoinformatics and medicinal chemistry applications: update and data transfer.

PubMed

Hu, Ye; Bajorath, Jürgen

2014-01-01

In 2012, we reported 30 compound data sets and/or programs developed in our laboratory in a data article and made them freely available to the scientific community to support chemoinformatics and computational medicinal chemistry applications. These data sets and computational tools were provided for download from our website. Since publication of this data article, we have generated 13 new data sets with which we further extend our collection of publicly available data and tools. Due to changes in web servers and website architectures, data accessibility has recently been limited at times. Therefore, we have also transferred our data sets and tools to a public repository to ensure full and stable accessibility. To aid in data selection, we have classified the data sets according to scientific subject areas. Herein, we describe new data sets, introduce the data organization scheme, summarize the database content and provide detailed access information in ZENODO (doi: 10.5281/zenodo.8451 and doi:10.5281/zenodo.8455).

Sentential Context and the Interpretation of Familiar Open-Compounds and Novel Modifier-Noun Phrases

ERIC Educational Resources Information Center

Gagne, Christina L.; Spalding, Thomas L.; Gorrie, Melissa C.

2005-01-01

Two experiments investigated the influence of sentential context on the relative ease of deriving a particular meaning for novel and familiar compounds. Experiment 1 determined which of two possible meanings was preferred for a set of novel phrases. Experiment 2 used both novel (e.g., "brain sponge") and familiar compounds (e.g., "bug spray"). The…

A ranking method for the concurrent learning of compounds with various activity profiles.

PubMed

Dörr, Alexander; Rosenbaum, Lars; Zell, Andreas

2015-01-01

In this study, we present a SVM-based ranking algorithm for the concurrent learning of compounds with different activity profiles and their varying prioritization. To this end, a specific labeling of each compound was elaborated in order to infer virtual screening models against multiple targets. We compared the method with several state-of-the-art SVM classification techniques that are capable of inferring multi-target screening models on three chemical data sets (cytochrome P450s, dehydrogenases, and a trypsin-like protease data set) containing three different biological targets each. The experiments show that ranking-based algorithms show an increased performance for single- and multi-target virtual screening. Moreover, compounds that do not completely fulfill the desired activity profile are still ranked higher than decoys or compounds with an entirely undesired profile, compared to other multi-target SVM methods. SVM-based ranking methods constitute a valuable approach for virtual screening in multi-target drug design. The utilization of such methods is most helpful when dealing with compounds with various activity profiles and the finding of many ligands with an already perfectly matching activity profile is not to be expected.

SensiPath: computer-aided design of sensing-enabling metabolic pathways.

PubMed

Delépine, Baudoin; Libis, Vincent; Carbonell, Pablo; Faulon, Jean-Loup

2016-07-08

Genetically-encoded biosensors offer a wide range of opportunities to develop advanced synthetic biology applications. Circuits with the ability of detecting and quantifying intracellular amounts of a compound of interest are central to whole-cell biosensors design for medical and environmental applications, and they also constitute essential parts for the selection and regulation of high-producer strains in metabolic engineering. However, the number of compounds that can be detected through natural mechanisms, like allosteric transcription factors, is limited; expanding the set of detectable compounds is therefore highly desirable. Here, we present the SensiPath web server, accessible at http://sensipath.micalis.fr SensiPath implements a strategy to enlarge the set of detectable compounds by screening for multi-step enzymatic transformations converting non-detectable compounds into detectable ones. The SensiPath approach is based on the encoding of reactions through signature descriptors to explore sensing-enabling metabolic pathways, which are putative biochemical transformations of the target compound leading to known effectors of transcription factors. In that way, SensiPath enlarges the design space by broadening the potential use of biosensors in synthetic biology applications. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

NMR-based urine analysis in rats: prediction of proximal tubule kidney toxicity and phospholipidosis.

PubMed

Lienemann, Kai; Plötz, Thomas; Pestel, Sabine

2008-01-01

The aim of safety pharmacology is early detection of compound-induced side-effects. NMR-based urine analysis followed by multivariate data analysis (metabonomics) identifies efficiently differences between toxic and non-toxic compounds; but in most cases multiple administrations of the test compound are necessary. We tested the feasibility of detecting proximal tubule kidney toxicity and phospholipidosis with metabonomics techniques after single compound administration as an early safety pharmacology approach. Rats were treated orally, intravenously, inhalatively or intraperitoneally with different test compounds. Urine was collected at 0-8 h and 8-24 h after compound administration, and (1)H NMR-patterns were recorded from the samples. Variation of post-processing and feature extraction methods led to different views on the data. Support Vector Machines were trained on these different data sets and then aggregated as experts in an Ensemble. Finally, validity was monitored with a cross-validation study using a training, validation, and test data set. Proximal tubule kidney toxicity could be predicted with reasonable total classification accuracy (85%), specificity (88%) and sensitivity (78%). In comparison to alternative histological studies, results were obtained quicker, compound need was reduced, and very importantly fewer animals were needed. In contrast, the induction of phospholipidosis by the test compounds could not be predicted using NMR-based urine analysis or the previously published biomarker PAG. NMR-based urine analysis was shown to effectively predict proximal tubule kidney toxicity after single compound administration in rats. Thus, this experimental design allows early detection of toxicity risks with relatively low amounts of compound in a reasonably short period of time.

Large-Scale Bioinformatics Analysis of Bacillus Genomes Uncovers Conserved Roles of Natural Products in Bacterial Physiology.

PubMed

Grubbs, Kirk J; Bleich, Rachel M; Santa Maria, Kevin C; Allen, Scott E; Farag, Sherif; Shank, Elizabeth A; Bowers, Albert A

2017-01-01

Bacteria possess an amazing capacity to synthesize a diverse range of structurally complex, bioactive natural products known as specialized (or secondary) metabolites. Many of these specialized metabolites are used as clinical therapeutics, while others have important ecological roles in microbial communities. The biosynthetic gene clusters (BGCs) that generate these metabolites can be identified in bacterial genome sequences using their highly conserved genetic features. We analyzed an unprecedented 1,566 bacterial genomes from Bacillus species and identified nearly 20,000 BGCs. By comparing these BGCs to one another as well as a curated set of known specialized metabolite BGCs, we discovered that the majority of Bacillus natural products are comprised of a small set of highly conserved, well-distributed, known natural product compounds. Most of these metabolites have important roles influencing the physiology and development of Bacillus species. We identified, in addition to these characterized compounds, many unique, weakly conserved BGCs scattered across the genus that are predicted to encode unknown natural products. Many of these "singleton" BGCs appear to have been acquired via horizontal gene transfer. Based on this large-scale characterization of metabolite production in the Bacilli , we go on to connect the alkylpyrones, natural products that are highly conserved but previously biologically uncharacterized, to a role in Bacillus physiology: inhibiting spore development. IMPORTANCE Bacilli are capable of producing a diverse array of specialized metabolites, many of which have gained attention for their roles as signals that affect bacterial physiology and development. Up to this point, however, the Bacillus genus's metabolic capacity has been underexplored. We undertook a deep genomic analysis of 1,566 Bacillus genomes to understand the full spectrum of metabolites that this bacterial group can make. We discovered that the majority of the specialized metabolites produced by Bacillus species are highly conserved, known compounds with important signaling roles in the physiology and development of this bacterium. Additionally, there is significant unique biosynthetic machinery distributed across the genus that might lead to new, unknown metabolites with diverse biological functions. Inspired by the findings of our genomic analysis, we speculate that the highly conserved alkylpyrones might have an important biological activity within this genus. We go on to validate this prediction by demonstrating that these natural products are developmental signals in Bacillus and act by inhibiting sporulation.

Identification of novel drugs to target dormant micrometastases.

PubMed

Hurst, Robert E; Hauser, Paul J; You, Youngjae; Bailey-Downs, Lora C; Bastian, Anja; Matthews, Stephen M; Thorpe, Jessica; Earle, Christine; Bourguignon, Lilly Y W; Ihnat, Michael A

2015-05-14

Cancer-specific survival has changed remarkably little over the past half century, mainly because metastases that are occult at diagnosis and generally resistant to chemotherapy subsequently develop months, years or even decades following definitive therapy. Targeting the dormant micrometastases responsible for these delayed or occult metastases would represent a major new tool in cancer patient management. Our hypothesis is that these metastases develop from micrometastatic cells that are suppressed by normal extracellular matrix (ECM). A new screening method was developed that compared the effect of drugs on the proliferation of cells grown on a normal ECM gel (small intestine submucosa, SISgel) to cells grown on plastic cell culture plates. The desired endpoint was that cells on SISgel were more sensitive than the same cells grown as monolayers. Known cancer chemotherapeutic agents show the opposite pattern. Screening 13,000 compounds identified two leads with low toxicity in mice and EC50 values in the range of 3-30 μM, depending on the cell line, and another two leads that were too toxic to mice to be useful. In a novel flank xenograft method of suppressed/dormant cells co-injected with SISgel into the flank, the lead compounds significantly eliminated the suppressed cells, whereas conventional chemotherapeutics were ineffective. Using a 4T1 triple negative breast cancer model, modified for physiological metastatic progression, as predicted, both lead compounds reduced the number of large micrometastases/macrometastases in the lung. One of the compounds also targeted cancer stem cells (CSC) isolated from the parental line. The CSC also retained their stemness on SISgel. Mechanistic studies showed a mild, late apoptotic response and depending on the compound, a mild arrest either at S or G2/M in the cell cycle. In summary we describe a novel, first in class set of compounds that target micrometastatic cells and prevent their reactivation to form recurrent tumors/macrometastases.

Discovering new materials and new phenomena with evolutionary algorithms

NASA Astrophysics Data System (ADS)

Oganov, Artem

Thanks to powerful evolutionary algorithms, in particular the USPEX method, it is now possible to predict both the stable compounds and their crystal structures at arbitrary conditions, given just the set of chemical elements. Recent developments include major increases of efficiency and extensions to low-dimensional systems and molecular crystals (which allowed large structures to be handled easily, e.g. Mg(BH4)2 and H2O-H2) and new techniques called evolutionary metadynamics and Mendelevian search. Some of the results that I will discuss include: 1. Theoretical and experimental evidence for a new partially ionic phase of boron, γ-B and an insulating and optically transparent form of sodium. 2. Predicted stability of ``impossible'' chemical compounds that become stable under pressure - e.g. Na3Cl, Na2Cl, Na3Cl2, NaCl3, NaCl7, Mg3O2 and MgO2. 3. Novel surface phases (e.g. boron surface reconstructions). 4. Novel dielectric polymers, and novel permanent magnets confirmed by experiment and ready for applications. 5. Prediction of new ultrahard materials and computational proof that diamond is the hardest possible material.

Enumeration of Ring–Chain Tautomers Based on SMIRKS Rules

PubMed Central

2015-01-01

A compound exhibits (prototropic) tautomerism if it can be represented by two or more structures that are related by a formal intramolecular movement of a hydrogen atom from one heavy atom position to another. When the movement of the proton is accompanied by the opening or closing of a ring it is called ring–chain tautomerism. This type of tautomerism is well observed in carbohydrates, but it also occurs in other molecules such as warfarin. In this work, we present an approach that allows for the generation of all ring–chain tautomers of a given chemical structure. Based on Baldwin’s Rules estimating the likelihood of ring closure reactions to occur, we have defined a set of transform rules covering the majority of ring–chain tautomerism cases. The rules automatically detect substructures in a given compound that can undergo a ring–chain tautomeric transformation. Each transformation is encoded in SMIRKS line notation. All work was implemented in the chemoinformatics toolkit CACTVS. We report on the application of our ring–chain tautomerism rules to a large database of commercially available screening samples in order to identify ring–chain tautomers. PMID:25158156

In silico quantitative structure-toxicity relationship study of aromatic nitro compounds.

PubMed

Pasha, Farhan Ahmad; Neaz, Mohammad Morshed; Cho, Seung Joo; Ansari, Mohiuddin; Mishra, Sunil Kumar; Tiwari, Sharvan

2009-05-01

Small molecules often have toxicities that are a function of molecular structural features. Minor variations in structural features can make large difference in such toxicity. Consequently, in silico techniques may be used to correlate such molecular toxicities with their structural features. Relative to nine different sets of aromatic nitro compounds having known observed toxicities against different targets, we developed ligand-based 2D quantitative structure-toxicity relationship models using 20 selected topological descriptors. The topological descriptors have several advantages such as conformational independency, facile and less time-consuming computation to yield good results. Multiple linear regression analysis was used to correlate variations of toxicity with molecular properties. The information index on molecular size, lopping centric index and Kier flexibility index were identified as fundamental descriptors for different kinds of toxicity, and further showed that molecular size, branching and molecular flexibility might be particularly important factors in quantitative structure-toxicity relationship analysis. This study revealed that topological descriptor-guided quantitative structure-toxicity relationship provided a very useful, cost and time-efficient, in silico tool for describing small-molecule toxicities.

Lantibiotics produced by Actinobacteria and their potential applications (a review).

PubMed

Gomes, Karen Machado; Duarte, Rafael Silva; de Freire Bastos, Maria do Carmo

2017-02-01

The phylum Actinobacteria, which comprises a great variety of Gram-positive bacteria with a high G+C content in their genomes, is known for its large production of bioactive compounds, including those with antimicrobial activity. Among the antimicrobials, bacteriocins, ribosomally synthesized peptides, represent an important arsenal of potential new drugs to face the increasing prevalence of resistance to antibiotics among microbial pathogens. The actinobacterial bacteriocins form a heterogeneous group of substances that is difficult to adapt to most proposed classification schemes. However, recent updates have accommodated efficiently the diversity of bacteriocins produced by this phylum. Among the bacteriocins, the lantibiotics represent a source of new antimicrobials to control infections caused mainly by Gram-positive bacteria and with a low propensity for resistance development. Moreover, some of these compounds have additional biological properties, exhibiting activity against viruses and tumour cells and having also potential to be used in blood pressure or inflammation control and in pain relief. Thus, lantibiotics already described in Actinobacteria exhibit potential practical applications in medical settings, food industry and agriculture, with examples at different stages of pre-clinical and clinical trials.

Efficient hit-finding approaches for histone methyltransferases: the key parameters.

PubMed

Ahrens, Thomas; Bergner, Andreas; Sheppard, David; Hafenbradl, Doris

2012-01-01

For many novel epigenetics targets the chemical ligand space and structural information were limited until recently and are still largely unknown for some targets. Hit-finding campaigns are therefore dependent on large and chemically diverse libraries. In the specific case of the histone methyltransferase G9a, the authors have been able to apply an efficient process of intelligent selection of compounds for primary screening, rather than screening the full diverse deck of 900 000 compounds to identify hit compounds. A number of different virtual screening methods have been applied for the compound selection, and the results have been analyzed in the context of their individual success rates. For the primary screening of 2112 compounds, a FlashPlate assay format and full-length histone H3.1 substrate were employed. Validation of hit compounds was performed using the orthogonal fluorescence lifetime technology. Rated by purity and IC(50) value, 18 compounds (0.9% of compound screening deck) were finally considered validated primary G9a hits. The hit-finding approach has led to novel chemotypes being identified, which can facilitate hit-to-lead projects. This study demonstrates the power of virtual screening technologies for novel, therapeutically relevant epigenetics protein targets.

Analyzing chromatographic data using multilevel modeling.

PubMed

Wiczling, Paweł

2018-06-01

It is relatively easy to collect chromatographic measurements for a large number of analytes, especially with gradient chromatographic methods coupled with mass spectrometry detection. Such data often have a hierarchical or clustered structure. For example, analytes with similar hydrophobicity and dissociation constant tend to be more alike in their retention than a randomly chosen set of analytes. Multilevel models recognize the existence of such data structures by assigning a model for each parameter, with its parameters also estimated from data. In this work, a multilevel model is proposed to describe retention time data obtained from a series of wide linear organic modifier gradients of different gradient duration and different mobile phase pH for a large set of acids and bases. The multilevel model consists of (1) the same deterministic equation describing the relationship between retention time and analyte-specific and instrument-specific parameters, (2) covariance relationships relating various physicochemical properties of the analyte to chromatographically specific parameters through quantitative structure-retention relationship based equations, and (3) stochastic components of intra-analyte and interanalyte variability. The model was implemented in Stan, which provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods. Graphical abstract Relationships between log k and MeOH content for acidic, basic, and neutral compounds with different log P. CI credible interval, PSA polar surface area.

Diazo compounds in continuous-flow technology.

PubMed

Müller, Simon T R; Wirth, Thomas

2015-01-01

Diazo compounds are very versatile reagents in organic chemistry and meet the challenge of selective assembly of structurally complex molecules. Their leaving group is dinitrogen; therefore, they are very clean and atom-efficient reagents. However, diazo compounds are potentially explosive and extremely difficult to handle on an industrial scale. In this review, it is discussed how continuous flow technology can help to make these powerful reagents accessible on large scale. Microstructured devices can improve heat transfer greatly and help with the handling of dangerous reagents safely. The in situ formation and subsequent consumption of diazo compounds are discussed along with advances in handling diazomethane and ethyl diazoacetate. The potential large-scale applications of a given methodology is emphasized. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Virtual screening methods as tools for drug lead discovery from large chemical libraries.

PubMed

Ma, X H; Zhu, F; Liu, X; Shi, Z; Zhang, J X; Yang, S Y; Wei, Y Q; Chen, Y Z

2012-01-01

Virtual screening methods have been developed and explored as useful tools for searching drug lead compounds from chemical libraries, including large libraries that have become publically available. In this review, we discussed the new developments in exploring virtual screening methods for enhanced performance in searching large chemical libraries, their applications in screening libraries of ~ 1 million or more compounds in the last five years, the difficulties in their applications, and the strategies for further improving these methods.

A web-based platform for virtual screening.

PubMed

Watson, Paul; Verdonk, Marcel; Hartshorn, Michael J

2003-09-01

A fully integrated, web-based, virtual screening platform has been developed to allow rapid virtual screening of large numbers of compounds. ORACLE is used to store information at all stages of the process. The system includes a large database of historical compounds from high throughput screenings (HTS) chemical suppliers, ATLAS, containing over 3.1 million unique compounds with their associated physiochemical properties (ClogP, MW, etc.). The database can be screened using a web-based interface to produce compound subsets for virtual screening or virtual library (VL) enumeration. In order to carry out the latter task within ORACLE a reaction data cartridge has been developed. Virtual libraries can be enumerated rapidly using the web-based interface to the cartridge. The compound subsets can be seamlessly submitted for virtual screening experiments, and the results can be viewed via another web-based interface allowing ad hoc querying of the virtual screening data stored in ORACLE.

Single-Chain Magnets Based on Octacyanotungstate with the Highest Energy Barriers for Cyanide Compounds.

PubMed

Wei, Rong-Min; Cao, Fan; Li, Jing; Yang, Li; Han, Yuan; Zhang, Xiu-Ling; Zhang, Zaichao; Wang, Xin-Yi; Song, You

2016-04-13

By introducing large counter cations as the spacer, two isolated 3, 3-ladder compounds, (Ph4P)[Co(II)(3-Mepy)2.7(H2O)0.3W(V)(CN)8] · 0.6H2O (1) and (Ph4As)[Co(II)(3-Mepy)3W(V)(CN)8] (2, 3-Mepy = 3-methylpyridine), were synthesized and characterized. Static and dynamic magnetic characterizations reveal that compounds 1 and 2 both behave as the single-chain magnets (SCMs) with very high energy barriers: 252(9) K for 1 and 224(7) K for 2, respectively. These two compounds display the highest relaxation barriers for cyano-bridged SCMs and are preceded only by two cobalt(II)-radical compounds among all SCMs. Meanwhile, a large coercive field of 26.2 kOe (1) and 22.6 kOe (2) were observed at 1.8 K.

Unraveling signatures of biogeochemical processes and the depositional setting in the molecular composition of pore water DOM across different marine environments

NASA Astrophysics Data System (ADS)

Schmidt, Frauke; Koch, Boris P.; Goldhammer, Tobias; Elvert, Marcus; Witt, Matthias; Lin, Yu-Shih; Wendt, Jenny; Zabel, Matthias; Heuer, Verena B.; Hinrichs, Kai-Uwe

2017-06-01

Dissolved organic matter (DOM) in marine sediment pore waters derives largely from decomposition of particulate organic matter and its composition is influenced by various biogeochemical and oceanographic processes in yet undetermined ways. Here, we determine the molecular inventory of pore water DOM in marine sediments of contrasting depositional regimes with ultrahigh-resolution mass spectrometry and complementary bulk chemical analyses in order to elucidate the factors that shape DOM composition. Our sample sets from the Mediterranean, Marmara and Black Seas covered different sediment depths, ages and a range of marine environments with different (i) organic matter sources, (ii) balances of organic matter production and preservation, and (iii) geochemical conditions in sediment and water column including anoxic, sulfidic and hypersaline conditions. Pore water DOM had a higher molecular formula richness than overlying water with up to 11,295 vs. 2114 different molecular formulas in the mass range of 299-600 Da and covered a broader range of element ratios (H/C = 0.35-2.19, O/C = 0.03-1.19 vs. H/C = 0.56-2.13, O/C = 0.15-1.14). Formula richness was independent of concentrations of DOC and TOC. Near-surface pore water DOM was more similar to water column DOM than to deep pore water DOM from the same core with respect to formula richness and the molecular composition, suggesting exchange at the sediment-water interface. The DOM composition in the deeper sediments was controlled by organic matter source, selective decomposition of specific DOM fractions and early diagenetic molecule transformations. Compounds in pelagic sediment pore waters were predominantly highly unsaturated and N-bearing formulas, whereas oxygen-rich CHO-formulas and aromatic compounds were more abundant in pore water DOM from terrigenous sediments. The increase of S-bearing molecular formulas in the water column and pore waters of the Black Sea and the Mediterranean Discovery Basin was consistent with elevated HS- concentrations reflecting the incorporation of sulfur into biomolecules during early diagenesis. Sulfurization resulted in an increased average molecular mass of DOM and higher formula richness (up to 5899 formulas per sample). In sediments from the methanogenic zone in the Black Sea, the DOM pool was distinctly more reduced than overlying sediments from the sulfate-reducing zone. Bottom and pore water DOM from the Discovery Basin contained the highest abundances of aliphatic compounds in the entire dataset; a large fraction of abundant N-bearing formulas possibly represented peptide and nucleotide formulas suggesting preservation of these molecules in the life inhibiting environment of the Discovery Basin. Our unique data set provides the basis for a comprehensive understanding of the molecular signatures in pore water DOM and the turnover of sedimentary organic matter in marine sediments.

Analysis of volatile organic compounds from illicit cocaine samples

NASA Astrophysics Data System (ADS)

Robins, W. H.; Wright, Bob W.

1994-10-01

Detection of illicit cocaine hydrochloride shipments can be improved if there is a greater understanding of the identity and quantity of volatile compounds present. This study provides preliminary data concerning the volatile organic compounds detected in a limited set of cocaine hydrochloride samples. In all cases, cocaine was one of the major volatile compounds detected. Other tropeines were detected in almost all samples. Low concentrations of compounds which may be residues of processing solvents were observed in some samples. The equilibrium emissivity of cocaine from cocaine hydrochloride was investigated and a value of 83 parts-per-trillion was determined.

Studies on in vitro biostability and blood compatibility of polyurethane potting compound based on aromatic polymeric MDI for extracorporeal devices.

PubMed

Hridya, V K; Jayabalan, M

2009-12-01

Polyurethane potting compound based on aromatic isocyanurate of polymeric MDI, poly propylene glycol (PPG400) and trimethylol propane (TMP) has significant favourable properties, good pot life and setting characteristics. The cured potting compound of this formulation has appreciable thermal stability and mechanical properties. In vitro biostability of cured potting compound has been found to be excellent without any significant degradation in simulated physiological media and chemical environment. Studies on blood-material interaction and cytotoxicity reveal in vitro blood compatibility and compatibility with cells of this potting compound.

Structure-activity relationship and docking studies of thiazolidinedione-type compounds with monoamine oxidase B.

PubMed

Carroll, Richard T; Dluzen, Dean E; Stinnett, Hilary; Awale, Prabha S; Funk, Max O; Geldenhuys, Werner J

2011-08-15

The neuroprotective activity of pioglitazone and rosiglitazone in the MPTP parkinsonian mouse prompted us to evaluate a set of thiazolidinedione (TZD) type compounds for monoamine oxidase A and B inhibition activity. These compounds were able to inhibit MAO-B over several log units of magnitude (82 nM to 600 μM). Initial structure-activity relationship studies identified key areas to modify the aromatic substituted TZD compounds. Primarily, substitutions on the aromatic group and the TZD nitrogen were key areas where activity was enhanced within this group of compounds. Copyright © 2011 Elsevier Ltd. All rights reserved.

Investigating secondary aerosol formation from agricultural amines and reduced sulfur compounds

USDA-ARS?s Scientific Manuscript database

Gas phase amines and reduced sulfur compounds are often co-emitted from agricultural processes. Amines have been recently recognized as potentially major sources of agricultural aerosol formation, while the reduced sulfur compounds are largely ignored. There is a severe lack of knowledge and under...

Antiprotozoan lead discovery by aligning dry and wet screening: prediction, synthesis, and biological assay of novel quinoxalinones.

PubMed

Martins Alho, Miriam A; Marrero-Ponce, Yovani; Barigye, Stephen J; Meneses-Marcel, Alfredo; Machado Tugores, Yanetsy; Montero-Torres, Alina; Gómez-Barrio, Alicia; Nogal, Juan J; García-Sánchez, Rory N; Vega, María Celeste; Rolón, Miriam; Martínez-Fernández, Antonio R; Escario, José A; Pérez-Giménez, Facundo; Garcia-Domenech, Ramón; Rivera, Norma; Mondragón, Ricardo; Mondragón, Mónica; Ibarra-Velarde, Froylán; Lopez-Arencibia, Atteneri; Martín-Navarro, Carmen; Lorenzo-Morales, Jacob; Cabrera-Serra, Maria Gabriela; Piñero, Jose; Tytgat, Jan; Chicharro, Roberto; Arán, Vicente J

2014-03-01

Protozoan parasites have been one of the most significant public health problems for centuries and several human infections caused by them have massive global impact. Most of the current drugs used to treat these illnesses have been used for decades and have many limitations such as the emergence of drug resistance, severe side-effects, low-to-medium drug efficacy, administration routes, cost, etc. These drugs have been largely neglected as models for drug development because they are majorly used in countries with limited resources and as a consequence with scarce marketing possibilities. Nowadays, there is a pressing need to identify and develop new drug-based antiprotozoan therapies. In an effort to overcome this problem, the main purpose of this study is to develop a QSARs-based ensemble classifier for antiprotozoan drug-like entities from a heterogeneous compounds collection. Here, we use some of the TOMOCOMD-CARDD molecular descriptors and linear discriminant analysis (LDA) to derive individual linear classification functions in order to discriminate between antiprotozoan and non-antiprotozoan compounds as a way to enable the computational screening of virtual combinatorial datasets and/or drugs already approved. Firstly, we construct a wide-spectrum benchmark database comprising of 680 organic chemicals with great structural variability (254 of them antiprotozoan agents and 426 to drugs having other clinical uses). This series of compounds was processed by a k-means cluster analysis in order to design training and predicting sets. In total, seven discriminant functions were obtained, by using the whole set of atom-based linear indices. All the LDA-based QSAR models show accuracies above 85% in the training set and values of Matthews correlation coefficients (C) vary from 0.70 to 0.86. The external validation set shows rather-good global classifications of around 80% (92.05% for best equation). Later, we developed a multi-agent QSAR classification system, in which the individual QSAR outputs are the inputs of the aforementioned fusion approach. Finally, the fusion model was used for the identification of a novel generation of lead-like antiprotozoan compounds by using ligand-based virtual screening of 'available' small molecules (with synthetic feasibility) in our 'in-house' library. A new molecular subsystem (quinoxalinones) was then theoretically selected as a promising lead series, and its derivatives subsequently synthesized, structurally characterized, and experimentally assayed by using in vitro screening that took into consideration a battery of five parasite-based assays. The chemicals 11(12) and 16 are the most active (hits) against apicomplexa (sporozoa) and mastigophora (flagellata) subphylum parasites, respectively. Both compounds depicted good activity in every protozoan in vitro panel and they did not show unspecific cytotoxicity on the host cells. The described technical framework seems to be a promising QSAR-classifier tool for the molecular discovery and development of novel classes of broad-antiprotozoan-spectrum drugs, which may meet the dual challenges posed by drug-resistant parasites and the rapid progression of protozoan illnesses. Copyright © 2014 Elsevier Ltd. All rights reserved.

Long-term stability measurements of low concentration Volatile Organic Compound gas mixtures

NASA Astrophysics Data System (ADS)

Allen, Nick; Amico di Meane, Elena; Brewer, Paul; Ferracci, Valerio; Corbel, Marivon; Worton, David

2017-04-01

VOCs (Volatile Organic Compounds) are a class of compounds with significant influence on the atmosphere due to their large anthropogenic and biogenic emission sources. VOC emissions have a significant impact on the atmospheric hydroxyl budget and nitrogen reservoir species, while also contributing indirectly to the production of tropospheric ozone and secondary organic aerosol. However, the global budget of many of these species are poorly constrained. Moreover, the World Meteorological Organization's (WMO) Global Atmosphere Watch (GAW) have set challenging data quality objectives for atmospheric monitoring programmes for these classes of traceable VOCs, despite the lack of available stable gas standards. The Key-VOCs Joint Research Project is an ongoing three-year collaboration with the aim of improving the measurement infrastructure of important atmospheric VOCs by providing traceable and comparable reference gas standards and by validating new measurement systems in support of the air monitoring networks. It focuses on VOC compounds that are regulated by European legislation, that are relevant for indoor air monitoring and for air quality and climate monitoring programmes like the VOC programme established by the WMO GAW and the European Monitoring and Evaluation Programme (EMEP). These VOCs include formaldehyde, oxy[genated]-VOCs (acetone, ethanol and methanol) and terpenes (a-pinene, 1,8-cineole, δ-3-carene and R-limonene). Here we present the results of a novel long term stability study for low concentration formaldehyde, oxy-VOC and terpenes gas mixtures produced by the Key-VOCs consortium with discussion regarding the implementation of improved preparation techniques and the use of novel cylinder passivation chemistries to guarantee mixture stability.

Sulfated steroids as natural ligands of mouse pheromone-sensing neurons

PubMed Central

Nodari, Francesco; Hsu, Fong-Fu; Fu, Xiaoyan; Holekamp, Terrence F.; Kao, Lung-Fa; Turk, John; Holy, Timothy E.

2009-01-01

Among mice, pheromones and other social odor cues convey information about sex, social status, and identity; however, the molecular nature of these cues is largely unknown. To identify these cues, we screened chromatographic fractions of female mouse urine for their ability to cause reproducible firing rate increases in the pheromone-detecting vomeronasal sensory neurons (VSNs) using multielectrode array (MEA) recording. Active compounds were found to be remarkably homogenous in their basic properties, with most being of low molecular weight, moderate hydrophobicity, low volatility, and possessing a negative electric charge. Purification and structural analysis of active compounds revealed multiple sulfated steroids, of which two were identified as sulfated glucocorticoids, including corticosterone 21-sulfate. Sulfatase-treated urine extracts lost more than 80% of their activity, indicating that sulfated compounds are the predominant VSN ligands in female mouse urine. As measured by MEA recording, a collection of 31 synthetic sulfated steroids triggered responses 30-fold more frequently than did a similarly-sized stimulus set containing the majority of all previously-reported VSN ligands. Collectively, VSNs detected all major classes of sulfated steroids, but individual neurons were sensitive to small variations in chemical structure. VSNs from both males and females detected sulfated steroids, but knockouts for the sensory transduction channel TRPC2 did not detect these compounds. Urine concentrations of the two sulfated glucocorticoids increased many-fold in stressed animals, indicating that information about physiological status is encoded by the urine concentration of particular sulfated steroids. These results provide an unprecedented characterization of the signals available for chemical communication among mice. PMID:18562612

Monitoring variations of dimethyl sulfide and dimethylsulfoniopropionate in seawater and the atmosphere based on sequential vapor generation and ion molecule reaction mass spectrometry.

PubMed

Iyadomi, Satoshi; Ezoe, Kentaro; Ohira, Shin-Ichi; Toda, Kei

2016-04-01

To monitor the fluctuations of dimethyl sulfur compounds at the seawater/atmosphere interface, an automated system was developed based on sequential injection analysis coupled with vapor generation-ion molecule reaction mass spectrometry (SIA-VG-IMRMS). Using this analytical system, dissolved dimethyl sulfide (DMS(aq)) and dimethylsulfoniopropionate (DMSP), a precursor to DMS in seawater, were monitored together sequentially with atmospheric dimethyl sulfide (DMS(g)). A shift from the equilibrium point between DMS(aq) and DMS(g) results in the emission of DMS to the atmosphere. Atmospheric DMS emitted from seawater plays an important role as a source of cloud condensation nuclei, which influences the oceanic climate. Water samples were taken periodically and dissolved DMS(aq) was vaporized for analysis by IMRMS. After that, DMSP was hydrolyzed to DMS and acrylic acid, and analyzed in the same manner as DMS(aq). The vaporization behavior and hydrolysis of DMSP to DMS were investigated to optimize these conditions. Frequent (every 30 min) determination of the three components, DMS(aq)/DMSP (nanomolar) and DMS(g) (ppbv), was carried out by SIA-VG-IMRMS. Field analysis of the dimethyl sulfur compounds was undertaken at a coastal station, which succeeded in showing detailed variations of the compounds in a natural setting. Observed concentrations of the dimethyl sulfur compounds both in the atmosphere and seawater largely changed with time and similar variations were repeatedly observed over several days, suggesting diurnal variations in the DMS flux at the seawater/atmosphere interface.

Co-culturing of Fungal Strains Against Botrytis cinerea as a Model for the Induction of Chemical Diversity and Therapeutic Agents

PubMed Central

Serrano, Rachel; González-Menéndez, Víctor; Rodríguez, Lorena; Martín, Jesús; Tormo, José R.; Genilloud, Olga

2017-01-01

New fungal SMs (SMs) have been successfully described to be produced by means of in vitro-simulated microbial community interactions. Co-culturing of fungi has proved to be an efficient way to induce cell–cell interactions that can promote the activation of cryptic pathways, frequently silent when the strains are grown in laboratory conditions. Filamentous fungi represent one of the most diverse microbial groups known to produce bioactive natural products. Triggering the production of novel antifungal compounds in fungi could respond to the current needs to fight health compromising pathogens and provide new therapeutic solutions. In this study, we have selected the fungus Botrytis cinerea as a model to establish microbial interactions with a large set of fungal strains related to ecosystems where they can coexist with this phytopathogen, and to generate a collection of extracts, obtained from their antagonic microbial interactions and potentially containing new bioactive compounds. The antifungal specificity of the extracts containing compounds induced after B. cinerea interaction was determined against two human fungal pathogens (Candida albicans and Aspergillus fumigatus) and three phytopathogens (Colletotrichum acutatum, Fusarium proliferatum, and Magnaporthe grisea). In addition, their cytotoxicity was also evaluated against the human hepatocellular carcinoma cell line (HepG2). We have identified by LC-MS the production of a wide variety of known compounds induced from these fungal interactions, as well as novel molecules that support the potential of this approach to generate new chemical diversity and possible new therapeutic agents. PMID:28469610

The micro-environmental impact of volatile organic compound emissions from large-scale assemblies of people in a confined space

USDA-ARS?s Scientific Manuscript database

Large-scale assemblies of people in a con'ned space can exert signi'cant impacts on the local air chemistry due to human emissions of volatile organics. Variations of air-quality in such small scale can be studied by quantifying 'ngerprint volatile organic compounds (VOCs) such as acetone, toluene, ...

COMPACT, CONTINUOUS MONITORING FOR VOLATILE ORGANIC COMPOUNDS - PHASE I

EPA Science Inventory

Improved methods for onsite measurement of multiple volatile organic compounds are needed for process control, monitoring, and remediation. This Phase I SBIR project sets forth an optical measurement method that meets these needs. The proposed approach provides an instantaneous m...

Automated segmentation of knee and ankle regions of rats from CT images to quantify bone mineral density for monitoring treatments of rheumatoid arthritis

NASA Astrophysics Data System (ADS)

Cruz, Francisco; Sevilla, Raquel; Zhu, Joe; Vanko, Amy; Lee, Jung Hoon; Dogdas, Belma; Zhang, Weisheng

2014-03-01

Bone mineral density (BMD) obtained from a CT image is an imaging biomarker used pre-clinically for characterizing the Rheumatoid arthritis (RA) phenotype. We use this biomarker in animal studies for evaluating disease progression and for testing various compounds. In the current setting, BMD measurements are obtained manually by selecting the regions of interest from three-dimensional (3-D) CT images of rat legs, which results in a laborious and low-throughput process. Combining image processing techniques, such as intensity thresholding and skeletonization, with mathematical techniques in curve fitting and curvature calculations, we developed an algorithm for quick, consistent, and automatic detection of joints in large CT data sets. The implemented algorithm has reduced analysis time for a study with 200 CT images from 10 days to 3 days and has improved the robust detection of the obtained regions of interest compared with manual segmentation. This algorithm has been used successfully in over 40 studies.

Discovery of a new chemical series of BRD4(1) inhibitors using protein-ligand docking and structure-guided design.

PubMed

Duffy, Bryan C; Liu, Shuang; Martin, Gregory S; Wang, Ruifang; Hsia, Ming Min; Zhao, He; Guo, Cheng; Ellis, Michael; Quinn, John F; Kharenko, Olesya A; Norek, Karen; Gesner, Emily M; Young, Peter R; McLure, Kevin G; Wagner, Gregory S; Lakshminarasimhan, Damodharan; White, Andre; Suto, Robert K; Hansen, Henrik C; Kitchen, Douglas B

2015-07-15

Bromodomains are key transcriptional regulators that are thought to be druggable epigenetic targets for cancer, inflammation, diabetes and cardiovascular therapeutics. Of particular importance is the first of two bromodomains in bromodomain containing 4 protein (BRD4(1)). Protein-ligand docking in BRD4(1) was used to purchase a small, focused screening set of compounds possessing a large variety of core structures. Within this set, a small number of weak hits each contained a dihydroquinoxalinone ring system. We purchased other analogs with this ring system and further validated the new hit series and obtained improvement in binding inhibition. Limited exploration by new analog synthesis showed that the binding inhibition in a FRET assay could be improved to the low μM level making this new core a potential hit-to-lead series. Additionally, the predicted geometries of the initial hit and an improved analog were confirmed by X-ray co-crystallography with BRD4(1). Copyright © 2015 Elsevier Ltd. All rights reserved.

Sizing and Discovery of Nanosized Polyoxometalate Clusters by Mass Spectrometry

PubMed Central

2016-01-01

Ion mobility-mass spectrometry (IM-MS) is a powerful technique for structural characterization, e.g., sizing and conformation, particularly when combined with quantitative modeling and comparison to theoretical values. Traveling wave IM-MS (TW-IM-MS) has recently become commercially available to nonspecialist groups and has been exploited in the structural study of large biomolecules, however reliable calibrants for large anions have not been available. Polyoxometalate (POM) species—nanoscale inorganic anions—share many of the facets of large biomolecules, however, the full potential of IM-MS in their study has yet to be realized due to a lack of suitable calibration data or validated theoretical models. Herein we address these limitations by reporting DT-IM (drift tube) data for a set of POM clusters {M12} Keggin 1, {M18} Dawson 2, and two {M7} Anderson derivatives 3 and 4 which demonstrate their use as a TW-IM-MS calibrant set to facilitate characterization of very large (ca. 1–4 nm) anionic species. The data was also used to assess the validity of standard techniques to model the collision cross sections of large inorganic anions using the nanoscale family of compounds based upon the {Se2W29} unit including the trimer, {Se8W86O299} A, tetramer, {Se8W116O408} B, and hexamer {Se12W174O612} C, including their relative sizing in solution. Furthermore, using this data set, we demonstrated how IM-MS can be used to conveniently characterize and identify the synthesis of two new, i.e., previously unreported POM species, {P8W116}, unknown D, and {Te8W116}, unknown E, which are not amenable to analysis by other means with the approximate formulation of [H34W118X8M2O416]44–, where X = P and M = Co for D and X = Te and M = Mn for E. This work establishes a new type of inorganic calibrant for IM-MS allowing sizing, structural analysis, and discovery of molecular nanostructures directly from solution. PMID:26906879

Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations

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

Dral, Pavlo O.; von Lilienfeld, O. Anatole; Thiel, Walter

2015-05-12

We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempiricalmore » OM2 method using a set of 6095 constitutional isomers C7H10O2, for which accurate ab initio atomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules.« less

Toxicity challenges in environmental chemicals: Prediction of ...

EPA Pesticide Factsheets

Physiologically based pharmacokinetic (PBPK) models bridge the gap between in vitro assays and in vivo effects by accounting for the adsorption, distribution, metabolism, and excretion of xenobiotics, which is especially useful in the assessment of human toxicity. Quantitative structure-activity relationships (QSAR) serve as a vital tool for the high-throughput prediction of chemical-specific PBPK parameters, such as the fraction of a chemical unbound by plasma protein (Fub). The presented work explores the merit of utilizing experimental pharmaceutical Fub data for the construction of a universal QSAR model, in order to compensate for the limited range of high-quality experimental Fub data for environmentally relevant chemicals, such as pollutants, pesticides, and consumer products. Independent QSAR models were constructed with three machine-learning algorithms, k nearest neighbors (kNN), random forest (RF), and support vector machine (SVM) regression, from a large pharmaceutical training set (~1000) and assessed with independent test sets of pharmaceuticals (~200) and environmentally relevant chemicals in the ToxCast program (~400). Small descriptor sets yielded the optimal balance of model complexity and performance, providing insight into the biochemical factors of plasma protein binding, while preventing over fitting to the training set. Overlaps in chemical space between pharmaceutical and environmental compounds were considered through applicability of do

Machine learning of parameters for accurate semiempirical quantum chemical calculations

DOE PAGES

Dral, Pavlo O.; von Lilienfeld, O. Anatole; Thiel, Walter

2015-04-14

We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempiricalmore » OM2 method using a set of 6095 constitutional isomers C 7H 10O 2, for which accurate ab initio atomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules.« less

Radial basis function neural networks in non-destructive determination of compound aspirin tablets on NIR spectroscopy.

PubMed

Dou, Ying; Mi, Hong; Zhao, Lingzhi; Ren, Yuqiu; Ren, Yulin

2006-09-01

The application of the second most popular artificial neural networks (ANNs), namely, the radial basis function (RBF) networks, has been developed for quantitative analysis of drugs during the last decade. In this paper, the two components (aspirin and phenacetin) were simultaneously determined in compound aspirin tablets by using near-infrared (NIR) spectroscopy and RBF networks. The total database was randomly divided into a training set (50) and a testing set (17). Different preprocessing methods (standard normal variate (SNV), multiplicative scatter correction (MSC), first-derivative and second-derivative) were applied to two sets of NIR spectra of compound aspirin tablets with different concentrations of two active components and compared each other. After that, the performance of RBF learning algorithm adopted the nearest neighbor clustering algorithm (NNCA) and the criterion for selection used a cross-validation technique. Results show that using RBF networks to quantificationally analyze tablets is reliable, and the best RBF model was obtained by first-derivative spectra.

QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation.

PubMed

Votano, Joseph R; Parham, Marc; Hall, L Mark; Hall, Lowell H; Kier, Lemont B; Oloff, Scott; Tropsha, Alexander

2006-11-30

Four modeling techniques, using topological descriptors to represent molecular structure, were employed to produce models of human serum protein binding (% bound) on a data set of 1008 experimental values, carefully screened from publicly available sources. To our knowledge, this data is the largest set on human serum protein binding reported for QSAR modeling. The data was partitioned into a training set of 808 compounds and an external validation test set of 200 compounds. Partitioning was accomplished by clustering the compounds in a structure descriptor space so that random sampling of 20% of the whole data set produced an external test set that is a good representative of the training set with respect to both structure and protein binding values. The four modeling techniques include multiple linear regression (MLR), artificial neural networks (ANN), k-nearest neighbors (kNN), and support vector machines (SVM). With the exception of the MLR model, the ANN, kNN, and SVM QSARs were ensemble models. Training set correlation coefficients and mean absolute error ranged from r2=0.90 and MAE=7.6 for ANN to r2=0.61 and MAE=16.2 for MLR. Prediction results from the validation set yielded correlation coefficients and mean absolute errors which ranged from r2=0.70 and MAE=14.1 for ANN to a low of r2=0.59 and MAE=18.3 for the SVM model. Structure descriptors that contribute significantly to the models are discussed and compared with those found in other published models. For the ANN model, structure descriptor trends with respect to their affects on predicted protein binding can assist the chemist in structure modification during the drug design process.

Tautomerism in o-hydroxyanilino-1,4-naphthoquinone derivatives: Structure, NMR, HPLC and density functional theoretic investigations

NASA Astrophysics Data System (ADS)

Bhand, Sujit; Patil, Rishikesh; Shinde, Yogesh; Lande, Dipali N.; Rao, Soniya S.; Kathawate, Laxmi; Gejji, Shridhar P.; Weyhermüller, Thomas; Salunke-Gawali, Sunita

2016-11-01

Structure and spectral characteristics of 'Ortho' ((E)-4-hydroxy-2-(2‧-(4‧-R)-hydroxyphenyl)-imino)-naphthalen-1(2H)-one) and 'para' (2-(2‧-(4‧-R)-hydroxyphenyl)-amino)-1,4-naphthoquinone) tautomers of o-hydroxyanilino-1,4-naphthoquinone derivatives (Rdbnd H, 1A; sbnd CH3, 2A; and -Cl, 3A) are investigated using the 1H, 13C, DEPT, gDQCOSY, gHSQCAD NMR, HPLC, cyclic voltammetry techniques combined with the density functional theory. The compound 2A crystallizes in monoclinic space group P21/c. wherein the polymer chain is facilitated via Osbnd H⋯O and Csbnd H⋯O intermolecular hydrogen bonding. Marginal variations in bond distances in quinonoid and aminophenol moieties render structural flexibility to these compounds those in solution exist as exist in 'ortho - para' tautomers. 1H and 13C NMR spectra in DMSO-d6 showed two sets of peaks in all compounds; whereas only the para tautomer of for 1A and 2A, the para tautomer is predominant in CD3CN solution. Further the ortho-para interconversion is accompanied by a large up-field signals for C(3)sbnd H(3) in their 1H and 13C NMR spectra. These inferences are corroborated by the density functional theoretic calculations.

Enabling Efficient and Confident Annotation of LC−MS Metabolomics Data through MS1 Spectrum and Time Prediction

DOE PAGES

Broeckling, Corey D.; Ganna, Andrea; Layer, Mark; ...

2016-09-08

Liquid chromatography coupled to electrospray ionization-mass spectrometry (LC–ESI-MS) is a versatile and robust platform for metabolomic analysis. However, while ESI is a soft ionization technique, in-source phenomena including multimerization, nonproton cation adduction, and in-source fragmentation complicate interpretation of MS data. Here, we report chromatographic and mass spectrometric behavior of 904 authentic standards collected under conditions identical to a typical nontargeted profiling experiment. The data illustrate that the often high level of complexity in MS spectra is likely to result in misinterpretation during the annotation phase of the experiment and a large overestimation of the number of compounds detected. However, ourmore » analysis of this MS spectral library data indicates that in-source phenomena are not random but depend at least in part on chemical structure. These nonrandom patterns enabled predictions to be made as to which in-source signals are likely to be observed for a given compound. Using the authentic standard spectra as a training set, we modeled the in-source phenomena for all compounds in the Human Metabolome Database to generate a theoretical in-source spectrum and retention time library. A novel spectral similarity matching platform was developed to facilitate efficient spectral searching for nontargeted profiling applications. Taken together, this collection of experimental spectral data, predictive modeling, and informatic tools enables more efficient, reliable, and transparent metabolite annotation.« less

Enabling Efficient and Confident Annotation of LC-MS Metabolomics Data through MS1 Spectrum and Time Prediction

DOE PAGES

Broeckling, Corey D.; Ganna, Andrea; Layer, Mark; ...

2016-08-25

Liquid chromatography coupled to electrospray ionization-mass spectrometry (LC–ESI-MS) is a versatile and robust platform for metabolomic analysis. However, while ESI is a soft ionization technique, in-source phenomena including multimerization, nonproton cation adduction, and in-source fragmentation complicate interpretation of MS data. Here, we report chromatographic and mass spectrometric behavior of 904 authentic standards collected under conditions identical to a typical nontargeted profiling experiment. The data illustrate that the often high level of complexity in MS spectra is likely to result in misinterpretation during the annotation phase of the experiment and a large overestimation of the number of compounds detected. However, ourmore » analysis of this MS spectral library data indicates that in-source phenomena are not random but depend at least in part on chemical structure. These nonrandom patterns enabled predictions to be made as to which in-source signals are likely to be observed for a given compound. Using the authentic standard spectra as a training set, we modeled the in-source phenomena for all compounds in the Human Metabolome Database to generate a theoretical in-source spectrum and retention time library. A novel spectral similarity matching platform was developed to facilitate efficient spectral searching for nontargeted profiling applications. Taken together, this collection of experimental spectral data, predictive modeling, and informatic tools enables more efficient, reliable, and transparent metabolite annotation.« less

Insight into the structural requirements of proton pump inhibitors based on CoMFA and CoMSIA studies.

PubMed

Nayana, M Ravi Shashi; Sekhar, Y Nataraja; Nandyala, Haritha; Muttineni, Ravikumar; Bairy, Santosh Kumar; Singh, Kriti; Mahmood, S K

2008-10-01

In the present study, a series of 179 quinoline and quinazoline heterocyclic analogues exhibiting inhibitory activity against Gastric (H+/K+)-ATPase were investigated using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) methods. Both the models exhibited good correlation between the calculated 3D-QSAR fields and the observed biological activity for the respective training set compounds. The most optimal CoMFA and CoMSIA models yielded significant leave-one-out cross-validation coefficient, q(2) of 0.777, 0.744 and conventional cross-validation coefficient, r(2) of 0.927, 0.914 respectively. The predictive ability of generated models was tested on a set of 52 compounds having broad range of activity. CoMFA and CoMSIA yielded predicted activities for test set compounds with r(pred)(2) of 0.893 and 0.917 respectively. These validation tests not only revealed the robustness of the models but also demonstrated that for our models r(pred)(2) based on the mean activity of test set compounds can accurately estimate external predictivity. The factors affecting activity were analyzed carefully according to standard coefficient contour maps of steric, electrostatic, hydrophobic, acceptor and donor fields derived from the CoMFA and CoMSIA. These contour plots identified several key features which explain the wide range of activities. The results obtained from models offer important structural insight into designing novel peptic-ulcer inhibitors prior to their synthesis.

Managing expectations: assessment of chemistry databases generated by automated extraction of chemical structures from patents.

PubMed

Senger, Stefan; Bartek, Luca; Papadatos, George; Gaulton, Anna

2015-12-01

First public disclosure of new chemical entities often takes place in patents, which makes them an important source of information. However, with an ever increasing number of patent applications, manual processing and curation on such a large scale becomes even more challenging. An alternative approach better suited for this large corpus of documents is the automated extraction of chemical structures. A number of patent chemistry databases generated by using the latter approach are now available but little is known that can help to manage expectations when using them. This study aims to address this by comparing two such freely available sources, SureChEMBL and IBM SIIP (IBM Strategic Intellectual Property Insight Platform), with manually curated commercial databases. When looking at the percentage of chemical structures successfully extracted from a set of patents, using SciFinder as our reference, 59 and 51 % were also found in our comparison in SureChEMBL and IBM SIIP, respectively. When performing this comparison with compounds as starting point, i.e. establishing if for a list of compounds the databases provide the links between chemical structures and patents they appear in, we obtained similar results. SureChEMBL and IBM SIIP found 62 and 59 %, respectively, of the compound-patent pairs obtained from Reaxys. In our comparison of automatically generated vs. manually curated patent chemistry databases, the former successfully provided approximately 60 % of links between chemical structure and patents. It needs to be stressed that only a very limited number of patents and compound-patent pairs were used for our comparison. Nevertheless, our results will hopefully help to manage expectations of users of patent chemistry databases of this type and provide a useful framework for more studies like ours as well as guide future developments of the workflows used for the automated extraction of chemical structures from patents. The challenges we have encountered whilst performing this study highlight that more needs to be done to make such assessments easier. Above all, more adequate, preferably open access to relevant 'gold standards' is required.

Study of multi-channel optical system based on the compound eye

NASA Astrophysics Data System (ADS)

Zhao, Yu; Fu, Yuegang; Liu, Zhiying; Dong, Zhengchao

2014-09-01

As an important part of machine vision, compound eye optical systems have the characteristics of high resolution and large FOV. By applying the compound eye optical systems to target detection and recognition, the contradiction between large FOV and high resolution in the traditional single aperture optical systems could be solved effectively and also the parallel processing ability of the optical systems could be sufficiently shown. In this paper, the imaging features of the compound eye optical systems are analyzed. After discussing the relationship between the FOV in each subsystem and the contact ratio of the FOV in the whole system, a method to define the FOV of the subsystem is presented. And a compound eye optical system is designed, which is based on the large FOV synthesized of multi-channels. The compound eye optical system consists with a central optical system and array subsystem, in which the array subsystem is used to capture the target. The high resolution image of the target could be achieved by the central optical system. With the advantage of small volume, light weight and rapid response speed, the optical system could detect the objects which are in 3km and FOV of 60°without any scanning device. The objects in the central field 2w=5.1°could be imaged with high resolution so that the objects could be recognized.

Migration from printing inks in multilayer food packaging materials by GC-MS analysis and pattern recognition with chemometrics.

PubMed

Clemente, Isabel; Aznar, Margarita; Nerín, Cristina; Bosetti, Osvaldo

2016-01-01

Inks and varnishes used in food packaging multilayer materials can contain different substances that are potential migrants when packaging is in contact with food. Although printing inks are applied on the external layer, they can migrate due to set-off phenomena. In order to assess food safety, migration tests were performed from two materials sets: set A based on paper and set B based on PET; both contained inks. Migration was performed to four food simulants (EtOH 50%, isooctane, EtOH 95% and Tenax(®)) and the volatile compounds profile was analysed by GC-MS. The effect of presence/absence of inks and varnishes and also their position in the material was studied. A total of 149 volatile compounds were found in migration from set A and 156 from set B materials, some of them came from inks. Quantitative analysis and a principal component analysis were performed in order to identify patterns among sample groups.

Enthalpies of Formation of Hydrazine and Its Derivatives.

PubMed

Dorofeeva, Olga V; Ryzhova, Oxana N; Suchkova, Taisiya A

2017-07-20

Enthalpies of formation, Δ f H 298 ° , in both the gas and condensed phase, and enthalpies of sublimation or vaporization have been estimated for hydrazine, NH 2 NH 2 , and its 36 various derivatives using quantum chemical calculations. The composite G4 method has been used along with isodesmic reaction schemes to derive a set of self-consistent high-accuracy gas-phase enthalpies of formation. To estimate the enthalpies of sublimation and vaporization with reasonable accuracy (5-20 kJ/mol), the method of molecular electrostatic potential (MEP) has been used. The value of Δ f H 298 ° (NH 2 NH 2 ,g) = 97.0 ± 3.0 kJ/mol was determined from 75 isogyric reactions involving about 50 reference species; for most of these species, the accurate Δ f H 298 ° (g) values are available in Active Thermochemical Tables (ATcT). The calculated value is in excellent agreement with the reported results of the most accurate models based on coupled cluster theory (97.3 kJ/mol, the average of six calculations). Thus, the difference between the values predicted by high-level theoretical calculations and the experimental value of Δ f H 298 ° (NH 2 NH 2 ,g) = 95.55 ± 0.19 kJ/mol recommended in the ATcT and other comprehensive reference sources is sufficiently large and requires further investigation. Different hydrazine derivatives have been also considered in this work. For some of them, both the enthalpy of formation in the condensed phase and the enthalpy of sublimation or vaporization are available; for other compounds, experimental data for only one of these properties exist. Evidence of accuracy of experimental data for the first group of compounds was provided by the agreement with theoretical Δ f H 298 ° (g) value. The unknown property for the second group of compounds was predicted using the MEP model. This paper presents a systematic comparison of experimentally determined enthalpies of formation and enthalpies of sublimation or vaporization with the results of calculations. Because of relatively large uncertainty in the estimated enthalpies of sublimation, it was not always possible to evaluate the accuracy of the experimental values; however, this model allowed us to detect large errors in the experimental data, as in the case of 5,5'-hydrazinebistetrazole. The enthalpies of formation and enthalpies of sublimation or vaporization have been predicted for the first time for ten hydrazine derivatives with no experimental data. A recommended set of self-consistent experimental and calculated gas-phase enthalpies of formation of hydrazine derivatives can be used as reference Δ f H 298 ° (g) values to predict the enthalpies of formation of various hydrazines by means of isodesmic reactions.

A Mössbauer study of some new trinuclear Fe-S cluster compounds

NASA Astrophysics Data System (ADS)

Zhang, Jing-Kun; Song, Li-Cheng; Zhang, Ze-Min; Liu, Rong-Gon; Cheng, Zheng-Zhung; Wang, Ji-Tao

1988-02-01

The reaction of (u-RS)2 (XMgS) Fe2 (CO)2 with CpFe (CO)2I gave thirteen new compounds (u-RS) [CpFe (CO)2S] Fe2 (CO)4. Mossbauer spectra were obtained at 80K. Two quadrupote doubles (A set and B set) were present. The ratio of areas between A set and B set was close to 2∶1. The molecule of every compound contained two Fe (2+) which were in the same chemical environment of low spin state with a coordination number of six, and the Mossbauer parameters of the two Fe (2+), IS=0.2 0.3 mm/s, QS=0.7 0.8 mm/s. In addition, the molecule contained a Fe (3+) in low spin state which was proved by ESR. Its Mossbauer parameters, IS=0.4 0.5 mm/s. QS=1.5±1.6 mm/s, The molecular structure of (u-MeS) [u-CpFe (CO)2S] Fe2 (CO)4 was determined by X-ray diffraction, monoclinic form, space group P21/n z=4, unit cell parameters, a=7.90A, b=10.77A, c=22.53A.

A pharmacological profile of the aldehyde receptor repertoire in rat olfactory epithelium

PubMed Central

Araneda, Ricardo C; Peterlin, Zita; Zhang, Xinmin; Chesler, Alex; Firestein, Stuart

2004-01-01

Several lines of evidence suggest that odorants are recognized through a combinatorial process in the olfactory system; a single odorant is recognized by multiple receptors and multiple odorants are recognized by the same receptor. However few details of how this might actually function for any particular odour set or receptor family are available. Approaching the problem from the ligands rather than the receptors, we used the response to a common odorant, octanal, as the basis for defining multiple receptor profiles. Octanal and other aldehydes induce large EOG responses in the rodent olfactory epithelium, suggesting that these compounds activate a large number of odour receptors (ORs). Here, we have determined and compared the pharmacological profile of different octanal receptors using Ca2+ imaging in isolated olfactory sensory neurones (OSNs). It is believed that each OSN expresses only one receptor, thus the response profile of each cell corresponds to the pharmacological profile of one particular receptor. We stimulated the cells with a panel of nine odorants, which included octanal, octanoic acid, octanol and cinnamaldehyde among others (all at 30μm). Cluster analysis revealed several distinct pharmacological profiles for cells that were all sensitive to octanal. Some receptors had a broad molecular range, while others were activated only by octanal. Comparison of the profiles with that of the one identified octanal receptor, OR-I7, indicated several differences. While OR-I7 is activated by low concentrations of octanal and blocked by citral, other receptors were less sensitive to octanal and not blocked by citral. A lower estimate for the maximal number of octanal receptors is between 33 and 55. This large number of receptors for octanal suggests that, although the peripheral olfactory system is endowed with high sensitivity, discrimination among different compounds probably requires further central processing. PMID:14724183

Molecular Structure-Based Large-Scale Prediction of Chemical-Induced Gene Expression Changes.

PubMed

Liu, Ruifeng; AbdulHameed, Mohamed Diwan M; Wallqvist, Anders

2017-09-25

The quantitative structure-activity relationship (QSAR) approach has been used to model a wide range of chemical-induced biological responses. However, it had not been utilized to model chemical-induced genomewide gene expression changes until very recently, owing to the complexity of training and evaluating a very large number of models. To address this issue, we examined the performance of a variable nearest neighbor (v-NN) method that uses information on near neighbors conforming to the principle that similar structures have similar activities. Using a data set of gene expression signatures of 13 150 compounds derived from cell-based measurements in the NIH Library of Integrated Network-based Cellular Signatures program, we were able to make predictions for 62% of the compounds in a 10-fold cross validation test, with a correlation coefficient of 0.61 between the predicted and experimentally derived signatures-a reproducibility rivaling that of high-throughput gene expression measurements. To evaluate the utility of the predicted gene expression signatures, we compared the predicted and experimentally derived signatures in their ability to identify drugs known to cause specific liver, kidney, and heart injuries. Overall, the predicted and experimentally derived signatures had similar receiver operating characteristics, whose areas under the curve ranged from 0.71 to 0.77 and 0.70 to 0.73, respectively, across the three organ injury models. However, detailed analyses of enrichment curves indicate that signatures predicted from multiple near neighbors outperformed those derived from experiments, suggesting that averaging information from near neighbors may help improve the signal from gene expression measurements. Our results demonstrate that the v-NN method can serve as a practical approach for modeling large-scale, genomewide, chemical-induced, gene expression changes.

The observed correlation between in vivo clinical pharmacokinetic parameters and in vitro potency of VEGFR-2 inhibitors. Can this be used as a prospective guide for the development of novel compounds?

PubMed

Benjamin, B; Sahu, M; Bhatnagar, U; Abhyankar, D; Srinivas, N R

2012-04-01

Literature data on the clinical pharmacokinetics of various VEGFR-2 inhibitors along with in vitro potency data were correlated and a linear relationship was established in spite of limited data set. In this work, a model set comprised of axitinib, recentin, sunitinib, pazopanib, and sorafenib were used. The in vitro potencies of the model set compounds were correlated with the published unbound plasma concentrations (Cmax, Cavg, Ctrough). The established linear regression (r2>0.90) equation was used to predict Cmax, Cavg, Ctrough of the 'prediction set' (motesanib, telatinib, CP547632, vatalanib, vandetanib) using in vitro potency and unbound protein free fraction. Cavg and Ctrough of prediction set were closely matched (0.2-1.8 fold of reported), demonstrating the usefulness of such predictions for tracking the target related modulation and/or efficacy signals within the clinically optimized population average. In case of Cmax where correlation was least anticipated, the predicted values were within 0.1-1.1 fold of those reported. Such predictions of appropriate parameters would provide rough estimates of whether or not therapeutically relevant dose(s) have been administered when clinical investigations of novel agents of this class are being performed. Therefore, it may aid in increasing clinical doses to a desired level if safety of the compound does not compromise such dose increases. In conclusion, the proposed model may prospectively guide the dosing strategies and would greatly aid the development of novel compounds in this class. © Georg Thieme Verlag KG Stuttgart · New York.

Model based on GRID-derived descriptors for estimating CYP3A4 enzyme stability of potential drug candidates

NASA Astrophysics Data System (ADS)

Crivori, Patrizia; Zamora, Ismael; Speed, Bill; Orrenius, Christian; Poggesi, Italo

2004-03-01

A number of computational approaches are being proposed for an early optimization of ADME (absorption, distribution, metabolism and excretion) properties to increase the success rate in drug discovery. The present study describes the development of an in silico model able to estimate, from the three-dimensional structure of a molecule, the stability of a compound with respect to the human cytochrome P450 (CYP) 3A4 enzyme activity. Stability data were obtained by measuring the amount of unchanged compound remaining after a standardized incubation with human cDNA-expressed CYP3A4. The computational method transforms the three-dimensional molecular interaction fields (MIFs) generated from the molecular structure into descriptors (VolSurf and Almond procedures). The descriptors were correlated to the experimental metabolic stability classes by a partial least squares discriminant procedure. The model was trained using a set of 1800 compounds from the Pharmacia collection and was validated using two test sets: the first one including 825 compounds from the Pharmacia collection and the second one consisting of 20 known drugs. This model correctly predicted 75% of the first and 85% of the second test set and showed a precision above 86% to correctly select metabolically stable compounds. The model appears a valuable tool in the design of virtual libraries to bias the selection toward more stable compounds. Abbreviations: ADME - absorption, distribution, metabolism and excretion; CYP - cytochrome P450; MIFs - molecular interaction fields; HTS - high throughput screening; DDI - drug-drug interactions; 3D - three-dimensional; PCA - principal components analysis; CPCA - consensus principal components analysis; PLS - partial least squares; PLSD - partial least squares discriminant; GRIND - grid independent descriptors; GRID - software originally created and developed by Professor Peter Goodford.

Characteristics of Elastomer Seals Exposed to Space Environments

NASA Technical Reports Server (NTRS)

Daniels, Christopher C.; deGroh, Henry, III; Dunlap, Patrick H., Jr.; Finkbeiner, Joshua R.; Steinetz, Bruce M.; Bastrzyk, Marta B.; Oswald, Jay J.; Banks, Bruce A.; Dever, Joyce A.; Miller, Sharon K.;

Improved outcomes in auditory brainstem implantation with the use of near-field electrical compound action potentials.

PubMed

Mandalà, Marco; Colletti, Liliana; Colletti, Giacomo; Colletti, Vittorio

2014-12-01

To compare the outcomes (auditory threshold and open-set speech perception at 48-month follow-up) of a new near-field monitoring procedure, electrical compound action potential, on positioning the auditory brainstem implant electrode array on the surface of the cochlear nuclei versus the traditional far-field electrical auditory brainstem response. Retrospective study. Tertiary referral center. Among the 202 patients with auditory brainstem implants fitted and monitored with electrical auditory brainstem response during implant fitting, 9 also underwent electrical compound action potential recording. These subjects were matched retrospectively with a control group of 9 patients in whom only the electrical auditory brainstem response was recorded. Electrical compound action potentials were obtained using a cotton-wick recording electrode located near the surface of the cochlear nuclei and on several cranial nerves. Significantly lower potential thresholds were observed with the recording electrode located on the cochlear nuclei surface compared with the electrical auditory brainstem response (104.4 ± 32.5 vs 158.9 ± 24.2, P = .0030). Electrical brainstem response and compound action potentials identified effects on the neighboring cranial nerves on 3.2 ± 2.4 and 7.8 ± 3.2 electrodes, respectively (P = .0034). Open-set speech perception outcomes at 48-month follow-up had improved significantly in the near- versus far-field recording groups (78.9% versus 56.7%; P = .0051). Electrical compound action potentials during auditory brainstem implantation significantly improved the definition of the potential threshold and the number of auditory and extra-auditory waves generated. It led to the best coupling between the electrode array and cochlear nuclei, significantly improving the overall open-set speech perception. © American Academy of Otolaryngology—Head and Neck Surgery Foundation 2014.

Three-dimensional aromatic networks.

PubMed

Toyota, Shinji; Iwanaga, Tetsuo

2014-01-01

Three-dimensional (3D) networks consisting of aromatic units and linkers are reviewed from various aspects. To understand principles for the construction of such compounds, we generalize the roles of building units, the synthetic approaches, and the classification of networks. As fundamental compounds, cyclophanes with large aromatic units and aromatic macrocycles with linear acetylene linkers are highlighted in terms of transannular interactions between aromatic units, conformational preference, and resolution of chiral derivatives. Polycyclic cage compounds are constructed from building units by linkages via covalent bonds, metal-coordination bonds, or hydrogen bonds. Large cage networks often include a wide range of guest species in their cavity to afford novel inclusion compounds. Topological isomers consisting of two or more macrocycles are formed by cyclization of preorganized species. Some complicated topological networks are constructed by self-assembly of simple building units.

ForceGen 3D structure and conformer generation: from small lead-like molecules to macrocyclic drugs

NASA Astrophysics Data System (ADS)

Cleves, Ann E.; Jain, Ajay N.

2017-05-01

We introduce the ForceGen method for 3D structure generation and conformer elaboration of drug-like small molecules. ForceGen is novel, avoiding use of distance geometry, molecular templates, or simulation-oriented stochastic sampling. The method is primarily driven by the molecular force field, implemented using an extension of MMFF94s and a partial charge estimator based on electronegativity-equalization. The force field is coupled to algorithms for direct sampling of realistic physical movements made by small molecules. Results are presented on a standard benchmark from the Cambridge Crystallographic Database of 480 drug-like small molecules, including full structure generation from SMILES strings. Reproduction of protein-bound crystallographic ligand poses is demonstrated on four carefully curated data sets: the ConfGen Set (667 ligands), the PINC cross-docking benchmark (1062 ligands), a large set of macrocyclic ligands (182 total with typical ring sizes of 12-23 atoms), and a commonly used benchmark for evaluating macrocycle conformer generation (30 ligands total). Results compare favorably to alternative methods, and performance on macrocyclic compounds approaches that observed on non-macrocycles while yielding a roughly 100-fold speed improvement over alternative MD-based methods with comparable performance.

Identification of Key Odorants in Used Disposable Absorbent Incontinence Products

PubMed Central

Hall, Gunnar; Forsgren-Brusk, Ulla

2017-01-01

PURPOSE: The purpose of this study was to identify key odorants in used disposable absorbent incontinence products. DESIGN: Descriptive in vitro study SUBJECTS AND SETTING: Samples of used incontinence products were collected from 8 residents with urinary incontinence living in geriatric nursing homes in the Gothenburg area of Sweden. Products were chosen from a larger set of products that had previously been characterized by descriptive odor analysis. METHODS: Pieces of the used incontinence products were cut from the wet area, placed in glass bottles, and kept frozen until dynamic headspace sampling of volatile compounds was completed. Gas chromatography–olfactometry was used to identify which compounds contributed most to the odors in the samples. Compounds were identified by gas chromatography–mass spectrometry. RESULTS: Twenty-eight volatiles were found to be key odorants in the used incontinence products. Twenty-six were successfully identified. They belonged to the following classes of chemical compounds: aldehydes (6); amines (1); aromatics (3); isothiocyanates (1); heterocyclics (2); ketones (6); sulfur compounds (6); and terpenes (1). CONCLUSION: Nine of the 28 key odorants were considered to be of particular importance to the odor of the used incontinence products: 3-methylbutanal, trimethylamine, cresol, guaiacol, 4,5-dimethylthiazole-S-oxide, diacetyl, dimethyl trisulfide, 5-methylthio-4-penten-2-ol, and an unidentified compound. PMID:28328644

Designing Multi-target Compound Libraries with Gaussian Process Models.

PubMed

Bieler, Michael; Reutlinger, Michael; Rodrigues, Tiago; Schneider, Petra; Kriegl, Jan M; Schneider, Gisbert

2016-05-01

We present the application of machine learning models to selecting G protein-coupled receptor (GPCR)-focused compound libraries. The library design process was realized by ant colony optimization. A proprietary Boehringer-Ingelheim reference set consisting of 3519 compounds tested in dose-response assays at 11 GPCR targets served as training data for machine learning and activity prediction. We compared the usability of the proprietary data with a public data set from ChEMBL. Gaussian process models were trained to prioritize compounds from a virtual combinatorial library. We obtained meaningful models for three of the targets (5-HT2c , MCH, A1), which were experimentally confirmed for 12 of 15 selected and synthesized or purchased compounds. Overall, the models trained on the public data predicted the observed assay results more accurately. The results of this study motivate the use of Gaussian process regression on public data for virtual screening and target-focused compound library design. © 2016 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA. This is an open access article under the terms of the Creative Commons Attribution Non-Commercial NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Mapping of the Available Chemical Space versus the Chemical Universe of Lead-Like Compounds.

PubMed

Lin, Arkadii; Horvath, Dragos; Afonina, Valentina; Marcou, Gilles; Reymond, Jean-Louis; Varnek, Alexandre

2018-03-20

This is, to our knowledge, the most comprehensive analysis to date based on generative topographic mapping (GTM) of fragment-like chemical space (40 million molecules with no more than 17 heavy atoms, both from the theoretically enumerated GDB-17 and real-world PubChem/ChEMBL databases). The challenge was to prove that a robust map of fragment-like chemical space can actually be built, in spite of a limited (≪10 5 ) maximal number of compounds ("frame set") usable for fitting the GTM manifold. An evolutionary map building strategy has been updated with a "coverage check" step, which discards manifolds failing to accommodate compounds outside the frame set. The evolved map has a good propensity to separate actives from inactives for more than 20 external structure-activity sets. It was proven to properly accommodate the entire collection of 40 m compounds. Next, it served as a library comparison tool to highlight biases of real-world molecules (PubChem and ChEMBL) versus the universe of all possible species represented by FDB-17, a fragment-like subset of GDB-17 containing 10 million molecules. Specific patterns, proper to some libraries and absent from others (diversity holes), were highlighted. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors

NASA Astrophysics Data System (ADS)

Briard, Jennie G.; Fernandez, Michael; de Luna, Phil; Woo, Tom. K.; Ben, Robert N.

2016-05-01

Ice recrystallization is the main contributor to cell damage and death during the cryopreservation of cells and tissues. Over the past five years, many small carbohydrate-based molecules were identified as ice recrystallization inhibitors and several were shown to reduce cryoinjury during the cryopreservation of red blood cells (RBCs) and hematopoietic stems cells (HSCs). Unfortunately, clear structure-activity relationships have not been identified impeding the rational design of future compounds possessing ice recrystallization inhibition (IRI) activity. A set of 124 previously synthesized compounds with known IRI activities were used to calibrate 3D-QSAR classification models using GRid INdependent Descriptors (GRIND) derived from DFT level quantum mechanical calculations. Partial least squares (PLS) model was calibrated with 70% of the data set which successfully identified 80% of the IRI active compounds with a precision of 0.8. This model exhibited good performance in screening the remaining 30% of the data set with 70% of active additives successfully recovered with a precision of ~0.7 and specificity of 0.8. The model was further applied to screen a new library of aryl-alditol molecules which were then experimentally synthesized and tested with a success rate of 82%. Presented is the first computer-aided high-throughput experimental screening for novel IRI active compounds.

In vitro transcriptomic prediction of hepatotoxicity for early drug discovery

PubMed Central

Cheng, Feng; Theodorescu, Dan; Schulman, Ira G.; Lee, Jae K.

2012-01-01

Liver toxicity (hepatotoxicity) is a critical issue in drug discovery and development. Standard preclinical evaluation of drug hepatotoxicity is generally performed using in vivo animal systems. However, only a small number of preselected compounds can be examined in vivo due to high experimental costs. A more efficient yet accurate screening technique which can identify potentially hepatotoxic compounds in the early stages of drug development would thus be valuable. Here, we develop and apply a novel genomic prediction technique for screening hepatotoxic compounds based on in vitro human liver cell tests. Using a training set of in vivo rodent experiments for drug hepatotoxicity evaluation, we discovered common biomarkers of drug-induced liver toxicity among six heterogeneous compounds. This gene set was further triaged to a subset of 32 genes that can be used as a multi-gene expression signature to predict hepatotoxicity. This multi-gene predictor was independently validated and showed consistently high prediction performance on five test sets of in vitro human liver cell and in vivo animal toxicity experiments. The predictor also demonstrated utility in evaluating different degrees of toxicity in response to drug concentrations which may be useful not only for discerning a compound’s general hepatotoxicity but also for determining its toxic concentration. PMID:21884709

QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors

PubMed Central

Briard, Jennie G.; Fernandez, Michael; De Luna, Phil; Woo, Tom. K.; Ben, Robert N.

2016-01-01

Ice recrystallization is the main contributor to cell damage and death during the cryopreservation of cells and tissues. Over the past five years, many small carbohydrate-based molecules were identified as ice recrystallization inhibitors and several were shown to reduce cryoinjury during the cryopreservation of red blood cells (RBCs) and hematopoietic stems cells (HSCs). Unfortunately, clear structure-activity relationships have not been identified impeding the rational design of future compounds possessing ice recrystallization inhibition (IRI) activity. A set of 124 previously synthesized compounds with known IRI activities were used to calibrate 3D-QSAR classification models using GRid INdependent Descriptors (GRIND) derived from DFT level quantum mechanical calculations. Partial least squares (PLS) model was calibrated with 70% of the data set which successfully identified 80% of the IRI active compounds with a precision of 0.8. This model exhibited good performance in screening the remaining 30% of the data set with 70% of active additives successfully recovered with a precision of ~0.7 and specificity of 0.8. The model was further applied to screen a new library of aryl-alditol molecules which were then experimentally synthesized and tested with a success rate of 82%. Presented is the first computer-aided high-throughput experimental screening for novel IRI active compounds. PMID:27216585

QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors.

PubMed

Briard, Jennie G; Fernandez, Michael; De Luna, Phil; Woo, Tom K; Ben, Robert N

2016-05-24

Ice recrystallization is the main contributor to cell damage and death during the cryopreservation of cells and tissues. Over the past five years, many small carbohydrate-based molecules were identified as ice recrystallization inhibitors and several were shown to reduce cryoinjury during the cryopreservation of red blood cells (RBCs) and hematopoietic stems cells (HSCs). Unfortunately, clear structure-activity relationships have not been identified impeding the rational design of future compounds possessing ice recrystallization inhibition (IRI) activity. A set of 124 previously synthesized compounds with known IRI activities were used to calibrate 3D-QSAR classification models using GRid INdependent Descriptors (GRIND) derived from DFT level quantum mechanical calculations. Partial least squares (PLS) model was calibrated with 70% of the data set which successfully identified 80% of the IRI active compounds with a precision of 0.8. This model exhibited good performance in screening the remaining 30% of the data set with 70% of active additives successfully recovered with a precision of ~0.7 and specificity of 0.8. The model was further applied to screen a new library of aryl-alditol molecules which were then experimentally synthesized and tested with a success rate of 82%. Presented is the first computer-aided high-throughput experimental screening for novel IRI active compounds.

Hamiltonian Dynamics of Spider-Type Multirotor Rigid Bodies Systems

NASA Astrophysics Data System (ADS)

Doroshin, Anton V.

2010-03-01

This paper sets out to develop a spider-type multiple-rotor system which can be used for attitude control of spacecraft. The multirotor system contains a large number of rotor-equipped rays, so it was called a ``Spider-type System,'' also it can be called ``Rotary Hedgehog.'' These systems allow using spinups and captures of conjugate rotors to perform compound attitude motion of spacecraft. The paper describes a new method of spacecraft attitude reorientation and new mathematical model of motion in Hamilton form. Hamiltonian dynamics of the system is investigated with the help of Andoyer-Deprit canonical variables. These variables allow obtaining exact solution for hetero- and homoclinic orbits in phase space of the system motion, which are very important for qualitative analysis.

Discovery of ferromagnetism with large magnetic anisotropy in ZrMnP and HfMnP

DOE PAGES

Lamichhane, Tej N.; Taufour, Valentin; Masters, Morgan W.; ...

2016-08-29

Here, ZrMnP and HfMnP single crystals are grown by a self-flux growth technique, and structural as well as temperature dependent magnetic and transport properties are studied. Both compounds have an orthorhombic crystal structure. ZrMnP and HfMnP are ferromagnetic with Curie temperatures around 370 K and 320 K, respectively. The spontaneous magnetizations of ZrMnP and HfMnP are determined to be 1.9 μ B/f.u. and 2.1 μ B/f.u., respectively, at 50 K. The magnetocaloric effect of ZrMnP in terms of entropy change (Δ S) is estimated to be –6.7 kJ m –3 K –1 around 369 K. The easy axis of magnetizationmore » is [100] for both compounds, with a small anisotropy relative to the [010] axis. At 50 K, the anisotropy field along the [001] axis is ~4.6 T for ZrMnP and ~10 T for HfMnP. Such large magnetic anisotropy is remarkable considering the absence of rare-earth elements in these compounds. The first principle calculation correctly predicts the magnetization and hard axis orientation for both compounds, and predicts the experimental HfMnP anisotropy field within 25%. More importantly, our calculations suggest that the large magnetic anisotropy comes primarily from the Mn atoms, suggesting that similarly large anisotropies may be found in other 3d transition metal compounds.« less

Large unexplained suite of chemically reactive compounds present in ambient air due to biomass fires.

PubMed

Kumar, V; Chandra, B P; Sinha, V

2018-01-12

Biomass fires impact global atmospheric chemistry. The reactive compounds emitted and formed due to biomass fires drive ozone and organic aerosol formation, affecting both air quality and climate. Direct hydroxyl (OH) Reactivity measurements quantify total gaseous reactive pollutant loadings and comparison with measured compounds yields the fraction of unmeasured compounds. Here, we quantified the magnitude and composition of total OH reactivity in the north-west Indo-Gangetic Plain. More than 120% increase occurred in total OH reactivity (28 s -1 to 64 s -1 ) and from no missing OH reactivity in the normal summertime air, the missing OH reactivity fraction increased to ~40 % in the post-harvest summertime period influenced by large scale biomass fires highlighting presence of unmeasured compounds. Increased missing OH reactivity between the two summertime periods was associated with increased concentrations of compounds with strong photochemical source such as acetaldehyde, acetone, hydroxyacetone, nitromethane, amides, isocyanic acid and primary emissions of acetonitrile and aromatic compounds. Currently even the most detailed state-of-the art atmospheric chemistry models exclude formamide, acetamide, nitromethane and isocyanic acid and their highly reactive precursor alkylamines (e.g. methylamine, ethylamine, dimethylamine, trimethylamine). For improved understanding of atmospheric chemistry-air quality-climate feedbacks in biomass-fire impacted atmospheric environments, future studies should include these compounds.

Mid-infrared hyperspectral imaging for the detection of explosive compounds

NASA Astrophysics Data System (ADS)

Ruxton, K.; Robertson, G.; Miller, W.; Malcolm, G. P. A.; Maker, G. T.

2012-10-01

Active hyperspectral imaging is a valuable tool in a wide range of applications. A developing market is the detection and identification of energetic compounds through analysis of the resulting absorption spectrum. This work presents a selection of results from a prototype mid-infrared (MWIR) hyperspectral imaging instrument that has successfully been used for compound detection at a range of standoff distances. Active hyperspectral imaging utilises a broadly tunable laser source to illuminate the scene with light over a range of wavelengths. While there are a number of illumination methods, this work illuminates the scene by raster scanning the laser beam using a pair of galvanometric mirrors. The resulting backscattered light from the scene is collected by the same mirrors and directed and focussed onto a suitable single-point detector, where the image is constructed pixel by pixel. The imaging instrument that was developed in this work is based around a MWIR optical parametric oscillator (OPO) source with broad tunability, operating at 2.6 μm to 3.7 μm. Due to material handling procedures associated with explosive compounds, experimental work was undertaken initially using simulant compounds. A second set of compounds that was tested alongside the simulant compounds is a range of confusion compounds. By having the broad wavelength tunability of the OPO, extended absorption spectra of the compounds could be obtained to aid in compound identification. The prototype imager instrument has successfully been used to record the absorption spectra for a range of compounds from the simulant and confusion sets and current work is now investigating actual explosive compounds. The authors see a very promising outlook for the MWIR hyperspectral imager. From an applications point of view this format of imaging instrument could be used for a range of standoff, improvised explosive device (IED) detection applications and potential incident scene forensic investigation.

Analyzing large-scale spiking neural data with HRLAnalysis™

PubMed Central

Thibeault, Corey M.; O'Brien, Michael J.; Srinivasa, Narayan

2014-01-01

The additional capabilities provided by high-performance neural simulation environments and modern computing hardware has allowed for the modeling of increasingly larger spiking neural networks. This is important for exploring more anatomically detailed networks but the corresponding accumulation in data can make analyzing the results of these simulations difficult. This is further compounded by the fact that many existing analysis packages were not developed with large spiking data sets in mind. Presented here is a software suite developed to not only process the increased amount of spike-train data in a reasonable amount of time, but also provide a user friendly Python interface. We describe the design considerations, implementation and features of the HRLAnalysis™ suite. In addition, performance benchmarks demonstrating the speedup of this design compared to a published Python implementation are also presented. The result is a high-performance analysis toolkit that is not only usable and readily extensible, but also straightforward to interface with existing Python modules. PMID:24634655

An Update on ToxCast™ | Science Inventory | US EPA

EPA Pesticide Factsheets

In its first phase, ToxCast™ is profiling over 300 well-characterized chemicals (primarily pesticides) in over 400 HTS endpoints. These endpoints include biochemical assays of protein function, cell-based transcriptional reporter assays, multi-cell interaction assays, transcriptomics on primary cell cultures, and developmental assays in zebrafish embryos. Almost all of the compounds being examined in Phase 1 of ToxCast™ have been tested in traditional toxicology tests, including developmental toxicity, multi-generation studies, and sub-chronic and chronic rodent bioassays Lessons learned to date for ToxCast: Large amounts of quality HTS data can be economically obtained. Large scale data sets will be required to understand potential for biological activity. Value in having multiple assays with overlapping coverage of biological pathways and a variety of methodologies Concentration-response will be important for ultimate interpretation Data transparency will be important for acceptance. Metabolic capabilities and coverage of developmental toxicity pathways will need additional attention. Need to define the gold standard Partnerships are needed to bring critical mass and expertise.

Method of immobilizing water-soluble bioorganic compounds on a capillary-porous carrier

DOEpatents

Ershov, Gennady Moiseevich; Timofeev, Eduard Nikolaevich; Ivanov, Igor Borisovich; Florentiev, Vladimir Leonidovich; Mirzabekov, Andrei Darievich

1998-01-01

The method for immobilizing water-soluble bioorganic compounds to capillary-porous carrier comprises application of solutions of water-soluble bioorganic compounds onto a capillary-porous carrier, setting the carrier temperature equal to or below the dew point of the ambient air, keeping the carrier till appearance of water condensate and complete swelling of the carrier, whereupon the carrier surface is coated with a layer of water-immiscible nonluminescent inert oil and is allowed to stand till completion of the chemical reaction of bonding the bioorganic compounds with the carrier.

Carboplatin: the clinical spectrum to date.

PubMed

Canetta, R; Rozencweig, M; Carter, S K

1985-09-01

The existing literature data base on carboplatin updated to June, 1985 has been reviewed. The compound seems to retain the same spectrum of activity as cisplatin, and a definite set of efficacy data is available for ovarian cancer of epithelial origin, small cell carcinoma of the lung and epidermoid carcinoma of the head and neck. A yet unpublished toxicity data base on carboplatin suggests that the compound has an improved therapeutic index over the parent compound, cisplatin, and that it does not seem inferior to another platinum coordination compound currently in clinical trials, iproplatin.

Nuclear magnetic resonance shielding constants and chemical shifts in linear 199Hg compounds: a comparison of three relativistic computational methods.

PubMed

Arcisauskaite, Vaida; Melo, Juan I; Hemmingsen, Lars; Sauer, Stephan P A

2011-07-28

We investigate the importance of relativistic effects on NMR shielding constants and chemical shifts of linear HgL(2) (L = Cl, Br, I, CH(3)) compounds using three different relativistic methods: the fully relativistic four-component approach and the two-component approximations, linear response elimination of small component (LR-ESC) and zeroth-order regular approximation (ZORA). LR-ESC reproduces successfully the four-component results for the C shielding constant in Hg(CH(3))(2) within 6 ppm, but fails to reproduce the Hg shielding constants and chemical shifts. The latter is mainly due to an underestimation of the change in spin-orbit contribution. Even though ZORA underestimates the absolute Hg NMR shielding constants by ∼2100 ppm, the differences between Hg chemical shift values obtained using ZORA and the four-component approach without spin-density contribution to the exchange-correlation (XC) kernel are less than 60 ppm for all compounds using three different functionals, BP86, B3LYP, and PBE0. However, larger deviations (up to 366 ppm) occur for Hg chemical shifts in HgBr(2) and HgI(2) when ZORA results are compared with four-component calculations with non-collinear spin-density contribution to the XC kernel. For the ZORA calculations it is necessary to use large basis sets (QZ4P) and the TZ2P basis set may give errors of ∼500 ppm for the Hg chemical shifts, despite deceivingly good agreement with experimental data. A Gaussian nucleus model for the Coulomb potential reduces the Hg shielding constants by ∼100-500 ppm and the Hg chemical shifts by 1-143 ppm compared to the point nucleus model depending on the atomic number Z of the coordinating atom and the level of theory. The effect on the shielding constants of the lighter nuclei (C, Cl, Br, I) is, however, negligible. © 2011 American Institute of Physics

Deep UV Raman spectroscopy for planetary exploration: The search for in situ organics

NASA Astrophysics Data System (ADS)

Abbey, William J.; Bhartia, Rohit; Beegle, Luther W.; DeFlores, Lauren; Paez, Veronica; Sijapati, Kripa; Sijapati, Shakher; Williford, Kenneth; Tuite, Michael; Hug, William; Reid, Ray

2017-07-01

Raman spectroscopy has emerged as a powerful, non-contact, non-destructive technique for detection and characterization of in situ organic compounds. Excitation using deep UV wavelengths (< 250 nm), in particular, offers the benefits of spectra obtained in a largely fluorescence-free region while taking advantage of signal enhancing resonance Raman effects for key classes of organic compounds, such as the aromatics. In order to demonstrate the utility of this technique for planetary exploration and astrobiological applications, we interrogated three sets of samples using a custom built Raman instrument equipped with a deep UV (248.6 nm) excitation source. The sample sets included: (1) the Mojave Mars Simulant, a well characterized basaltic sample used as an analog for Martian regolith, in which we detected ∼0.04 wt% of condensed carbon; (2) a suite of organic (aromatic hydrocarbons, carboxylic acids, and amino acids) and astrobiologically relevant inorganic (sulfates, carbonates, phosphates, nitrates and perchlorate) standards, many of which have not had deep UV Raman spectra in the solid phase previously reported in the literature; and (3) Mojave Mars Simulant spiked with a representative selection of these standards, at a concentration of 1 wt%, in order to investigate natural 'real world' matrix effects. We were able to resolve all of the standards tested at this concentration. Some compounds, such as the aromatic hydrocarbons, have especially strong signals due to resonance effects even when present in trace amounts. Phenanthrene, one of the aromatic hydrocarbons, was also examined at a concentration of 0.1 wt% and even at this level was found to have a strong signal-to-noise ratio. It should be noted that the instrument utilized in this study was designed to approximate the operation of a 'fieldable' spectrometer in order to test astrobiological applications both here on Earth as well as for current and future planetary missions. It is the foundation of SHERLOC, an arm mounted instrument recently selected by NASA to fly on the next rover mission to Mars in 2020.

Quantitative High-Throughput Screen Identifies Inhibitors of the Schistosoma mansoni Redox Cascade

PubMed Central

Simeonov, Anton; Jadhav, Ajit; Sayed, Ahmed A.; Wang, Yuhong; Nelson, Michael E.; Thomas, Craig J.; Inglese, James; Williams, David L.; Austin, Christopher P.

2008-01-01

Schistosomiasis is a tropical disease associated with high morbidity and mortality, currently affecting over 200 million people worldwide. Praziquantel is the only drug used to treat the disease, and with its increased use the probability of developing drug resistance has grown significantly. The Schistosoma parasites can survive for up to decades in the human host due in part to a unique set of antioxidant enzymes that continuously degrade the reactive oxygen species produced by the host's innate immune response. Two principal components of this defense system have been recently identified in S. mansoni as thioredoxin/glutathione reductase (TGR) and peroxiredoxin (Prx) and as such these enzymes present attractive new targets for anti-schistosomiasis drug development. Inhibition of TGR/Prx activity was screened in a dual-enzyme format with reducing equivalents being transferred from NADPH to glutathione via a TGR-catalyzed reaction and then to hydrogen peroxide via a Prx-catalyzed step. A fully automated quantitative high-throughput (qHTS) experiment was performed against a collection of 71,028 compounds tested as 7- to 15-point concentration series at 5 µL reaction volume in 1536-well plate format. In order to generate a robust data set and to minimize the effect of compound autofluorescence, apparent reaction rates derived from a kinetic read were utilized instead of end-point measurements. Actives identified from the screen, along with previously untested analogues, were subjected to confirmatory experiments using the screening assay and subsequently against the individual targets in secondary assays. Several novel active series were identified which inhibited TGR at a range of potencies, with IC50s ranging from micromolar to the assay response limit (∼25 nM). This is, to our knowledge, the first report of a large-scale HTS to identify lead compounds for a helminthic disease, and provides a paradigm that can be used to jump-start development of novel therapeutics for other neglected tropical diseases. PMID:18235848

Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

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

Alves, Vinicius M.; Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599; Muratov, Eugene

Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putativemore » sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using Random Forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers was 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR Toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the Scorecard database of possible skin or sense organ toxicants as primary candidates for experimental validation. - Highlights: • It was compiled the largest publicly-available skin sensitization dataset. • Predictive QSAR models were developed for skin sensitization. • Developed models have higher prediction accuracy than OECD QSAR Toolbox. • Putative chemical hazards in the Scorecard database were found using our models.« less

Optical XOR gate

DOEpatents

Vawter, G. Allen

2013-11-12

An optical XOR gate is formed as a photonic integrated circuit (PIC) from two sets of optical waveguide devices on a substrate, with each set of the optical waveguide devices including an electroabsorption modulator electrically connected in series with a waveguide photodetector. The optical XOR gate utilizes two digital optical inputs to generate an XOR function digital optical output. The optical XOR gate can be formed from III-V compound semiconductor layers which are epitaxially deposited on a III-V compound semiconductor substrate, and operates at a wavelength in the range of 0.8-2.0 .mu.m.

Efficient quantification of the health-relevant anthocyanin and phenolic acid profiles in commercial cultivars and breeding selections of blueberries ( Vaccinium spp.).

PubMed

Yousef, Gad G; Brown, Allan F; Funakoshi, Yayoi; Mbeunkui, Flaubert; Grace, Mary H; Ballington, James R; Loraine, Ann; Lila, Mary A

2013-05-22

Anthocyanins and phenolic acids are major secondary metabolites in blueberry with important implications for human health maintenance. An improved protocol was developed for the accurate, efficient, and rapid comparative screening for large blueberry sample sets. Triplicates of six commercial cultivars and four breeding selections were analyzed using the new method. The compound recoveries ranged from 94.2 to 97.5 ± 5.3% when samples were spiked with commercial standards prior to extraction. Eighteen anthocyanins and 4 phenolic acids were quantified in frozen and freeze-dried fruits. Large variations for individual and total anthocyanins, ranging from 201.4 to 402.8 mg/100 g, were assayed in frozen fruits. The total phenolic acid content ranged from 23.6 to 61.7 mg/100 g in frozen fruits. Across all genotypes, freeze-drying resulted in minor reductions in anthocyanin concentration (3.9%) compared to anthocyanins in frozen fruits. However, phenolic acids increased by an average of 1.9-fold (±0.3) in the freeze-dried fruit. Different genotypes frequently had comparable overall levels of total anthocyanins and phenolic acids, but differed dramatically in individual profiles of compounds. Three of the genotypes contained markedly higher concentrations of delphinidin 3-O-glucoside, cyanidin 3-O-glucoside, and malvidin 3-O-glucoside, which have previously been implicated as bioactive principles in this fruit. The implications of these findings for human health benefits are discussed.

Altered transport and metabolism of phenolic compounds in obesity and diabetes: implications for functional food development and assessment

USDA-ARS?s Scientific Manuscript database

Interest in application of phenolic compounds from diet or supplements for prevention of chronic diseases has grown significantly, but efficacy of such approaches in humans is largely dependent on the bioavailability and metabolism of these compounds. While food and dietary factors have been the foc...

CO-OCCURRENCE OF METHYL- TERT-BUTYL ETHER (MTBE) AND BTEX COMPOUNDS AT MARINAS IN A LARGE RESEVOIR

EPA Science Inventory

Methyl tert-butyl ether (MTBE) is released into the environment as one of some gasoline components, not as a pure compound. BTEX compounds (benzene, tolune, ethylbenzene, and xylenes) are major volatile constituents found in gasoline and are water soluble and mobile. This study...

High Throughput, Label-free Screening Small Molecule Compound Libraries for Protein-Ligands using Combination of Small Molecule Microarrays and a Special Ellipsometry-based Optical Scanner.

PubMed

Landry, James P; Fei, Yiyan; Zhu, X D

2011-12-01

Small-molecule compounds remain the major source of therapeutic and preventative drugs. Developing new drugs against a protein target often requires screening large collections of compounds with diverse structures for ligands or ligand fragments that exhibit sufficiently affinity and desirable inhibition effect on the target before further optimization and development. Since the number of small molecule compounds is large, high-throughput screening (HTS) methods are needed. Small-molecule microarrays (SMM) on a solid support in combination with a suitable binding assay form a viable HTS platform. We demonstrate that by combining an oblique-incidence reflectivity difference optical scanner with SMM we can screen 10,000 small-molecule compounds on a single glass slide for protein ligands without fluorescence labeling. Furthermore using such a label-free assay platform we can simultaneously acquire binding curves of a solution-phase protein to over 10,000 immobilized compounds, thus enabling full characterization of protein-ligand interactions over a wide range of affinity constants.

Applicability domains for classification problems: benchmarking of distance to models for AMES mutagenicity set

EPA Science Inventory

For QSAR and QSPR modeling of biological and physicochemical properties, estimating the accuracy of predictions is a critical problem. The “distance to model” (DM) can be defined as a metric that defines the similarity between the training set molecules and the test set compound ...

Assessment of the significance of patent-derived information for the early identification of compound-target interaction hypotheses.

PubMed

Senger, Stefan

2017-04-21

Patents are an important source of information for effective decision making in drug discovery. Encouragingly, freely accessible patent-chemistry databases are now in the public domain. However, at present there is still a wide gap between relatively low coverage-high quality manually-curated data sources and high coverage data sources that use text mining and automated extraction of chemical structures. To secure much needed funding for further research and an improved infrastructure, hard evidence is required to demonstrate the significance of patent-derived information in drug discovery. Surprisingly little such evidence has been reported so far. To address this, the present study attempts to quantify the relevance of patents for formulating and substantiating hypotheses for compound-target interactions. A manually-curated set of 130 compound-target interaction pairs annotated with what are considered to be the earliest patent and publication has been produced. The analysis of this set revealed that in stark contrast to what has been reported for novel chemical structures, only about 10% of the compound-target interaction pairs could be found in publications in the scientific literature within one year of being reported in patents. The average delay across all interaction pairs is close to 4 years. In an attempt to benchmark current capabilities, it was also examined how much of the benefit of using patent-derived information can be retained when a bioannotated version of SureChEMBL is used as secondary source for the patent literature. Encouragingly, this approach found the patents in the annotated set for 72% of the compound-target interaction pairs. Similarly, the effect of using the bioactivity database ChEMBL as secondary source for the scientific literature was studied. Here, the publications from the annotated set were only found for 46% of the compound-target interaction pairs. Patent-derived information is a significant enabler for formulating compound-target interaction hypotheses even in cases where the respective interaction is later reported in the scientific literature. The findings of this study clearly highlight the significance of future investments in the development and provision of databases and tools that will allow scientists to search patent information in a comprehensive, reliable, and efficient manner.

An Assessment of the Intestinal Lumen as a Site for Intervention in Reducing Body Burdens of Organochlorine Compounds

PubMed Central

Jandacek, Ronald J.; Genuis, Stephen J.

2013-01-01

Many individuals maintain a persistent body burden of organochlorine compounds (OCs) as well as other lipophilic compounds, largely as a result of airborne and dietary exposures. Ingested OCs are typically absorbed from the small intestine along with dietary lipids. Once in the body, stored OCs can mobilize from adipose tissue storage sites and, along with circulating OCs, are delivered into the small intestine via hepatic processing and biliary transport. Retained OCs are also transported into both the large and small intestinal lumen via non-biliary mechanisms involving both secretion and desquamation from enterocytes. OCs and some other toxicants can be reabsorbed from the intestine, however, they take part in enterohepatic circulation(EHC). While dietary fat facilitates the absorption of OCs from the small intestine, it has little effect on OCs within the large intestine. Non-absorbable dietary fats and fat absorption inhibitors, however, can reduce the re-absorption of OCs and other lipophiles involved in EHC and may enhance the secretion of these compounds into the large intestine—thereby hastening their elimination. Clinical studies are currently underway to determine the efficacy of using non-absorbable fats and inhibitors of fat absorption in facilitating the elimination of persistent body burdens of OCs and other lipophilic human contaminants. PMID:23476122

An assessment of the intestinal lumen as a site for intervention in reducing body burdens of organochlorine compounds.

PubMed

Jandacek, Ronald J; Genuis, Stephen J

2013-01-01

Many individuals maintain a persistent body burden of organochlorine compounds (OCs) as well as other lipophilic compounds, largely as a result of airborne and dietary exposures. Ingested OCs are typically absorbed from the small intestine along with dietary lipids. Once in the body, stored OCs can mobilize from adipose tissue storage sites and, along with circulating OCs, are delivered into the small intestine via hepatic processing and biliary transport. Retained OCs are also transported into both the large and small intestinal lumen via non-biliary mechanisms involving both secretion and desquamation from enterocytes. OCs and some other toxicants can be reabsorbed from the intestine, however, they take part in enterohepatic circulation(EHC). While dietary fat facilitates the absorption of OCs from the small intestine, it has little effect on OCs within the large intestine. Non-absorbable dietary fats and fat absorption inhibitors, however, can reduce the re-absorption of OCs and other lipophiles involved in EHC and may enhance the secretion of these compounds into the large intestine--thereby hastening their elimination. Clinical studies are currently underway to determine the efficacy of using non-absorbable fats and inhibitors of fat absorption in facilitating the elimination of persistent body burdens of OCs and other lipophilic human contaminants.

Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations

NASA Astrophysics Data System (ADS)

Ward, Logan; Liu, Ruoqian; Krishna, Amar; Hegde, Vinay I.; Agrawal, Ankit; Choudhary, Alok; Wolverton, Chris

2017-07-01

While high-throughput density functional theory (DFT) has become a prevalent tool for materials discovery, it is limited by the relatively large computational cost. In this paper, we explore using DFT data from high-throughput calculations to create faster, surrogate models with machine learning (ML) that can be used to guide new searches. Our method works by using decision tree models to map DFT-calculated formation enthalpies to a set of attributes consisting of two distinct types: (i) composition-dependent attributes of elemental properties (as have been used in previous ML models of DFT formation energies), combined with (ii) attributes derived from the Voronoi tessellation of the compound's crystal structure. The ML models created using this method have half the cross-validation error and similar training and evaluation speeds to models created with the Coulomb matrix and partial radial distribution function methods. For a dataset of 435 000 formation energies taken from the Open Quantum Materials Database (OQMD), our model achieves a mean absolute error of 80 meV/atom in cross validation, which is lower than the approximate error between DFT-computed and experimentally measured formation enthalpies and below 15% of the mean absolute deviation of the training set. We also demonstrate that our method can accurately estimate the formation energy of materials outside of the training set and be used to identify materials with especially large formation enthalpies. We propose that our models can be used to accelerate the discovery of new materials by identifying the most promising materials to study with DFT at little additional computational cost.

Anhydrous 1:1 proton-transfer compounds of isonipecotamide with picric acid and 3,5-dinitrosalicylic acid: 4-carbamoylpiperidinium 2,4,6-trinitrophenolate and two polymorphs of 4-carbamoylpiperidinium 2-carboxy-4,6-dinitrophenolate.

PubMed

Smith, Graham; Wermuth, Urs D

2010-12-01

The structures of the anhydrous 1:1 proton-transfer compounds of isonipecotamide (piperidine-4-carboxamide) with picric acid and 3,5-dinitrosalicylic acid, namely 4-carbamoylpiperidinium 2,4,6-trinitrophenolate, C(6)H(13)N(2)O(+)·C(6)H(2)N(3)O(7)(-), (I), and 4-carbamoylpiperidinium 2-carboxy-4,6-dinitrophenolate [two forms of which were found, the monoclinic α-polymorph, (II), and the triclinic β-polymorph, (III)], C(6)H(13)N(2)O(+)·C(7)H(3)N(2)O(7)(-), have been determined at 200 K. All three compounds form hydrogen-bonded structures, viz. one-dimensional in (II), two-dimensional in (I) and three-dimensional in (III). In (I), the cations form centrosymmetric cyclic head-to-tail hydrogen-bonded homodimers [graph set R(2)(2)(14)] through lateral duplex piperidinium-amide N-H...O interactions. These dimers are extended into a two-dimensional network structure through further interactions with phenolate and nitro O-atom acceptors, including a direct symmetric piperidinium-phenol/nitro N-H...O,O cation-anion association [graph set R(1)(2)(6)]. The monoclinic polymorph, (II), has a similar R(1)(2)(6) cation-anion hydrogen-bonding interaction to (I) but with an additional conjoint symmetrical R(1)(2)(4) interaction as well as head-to-tail piperidinium-amide N-H...O,O hydrogen bonds and amide-carboxyl N-H...O hydrogen bonds, giving a network structure which includes large R(4)(3)(20) rings. The hydrogen bonding in the triclinic polymorph, (III), is markedly different from that of monoclinic (II). The asymmetric unit contains two independent cation-anion pairs which associate through cyclic piperidinium-carboxyl N-H...O,O' interactions [graph set R(1)(2)(4)]. The cations also show the zigzag head-to-tail piperidinium-amide N-H...O hydrogen-bonded chain substructures found in (II), but in addition feature amide-nitro and amide-phenolate N-H...O associations. As well, there is a centrosymmetric double-amide N-H...O(carboxyl) bridged bis(cation-anion) ring system [graph set R(4)(2)(8)] in the three-dimensional framework. The structures reported here demonstrate the utility of the isonipecotamide cation as a synthon with previously unrecognized potential for structure assembly applications. Furthermore, the structures of the two polymorphic 3,5-dinitrosalicylic acid salts show an unusual dissimilarity in hydrogen-bonding characteristics, considering that both were obtained from identical solvent systems.

A-site cationic disorder induced significantly large magnetoresistance in polycrystalline La0.2Gd0.5Ba0.3MnO3 compound

NASA Astrophysics Data System (ADS)

Saha, Suvayan; Das, Kalipada; Bandyopadhyay, Sudipta; Das, I.

2017-11-01

The observation of significantly large magnetoresistance at the liquid nitrogen temperature range in the polycrystalline La0.2Gd0.5Ba0.3MnO3 (LGBMO) compound has been addressed in the present manuscript. The motivation of considering LGBMO sample is the average 'A' site ionic radius 〈rA 〉 and tolerance factor (t), almost same as that of La0.7Sr0.3MnO3 (LSMO), which is a well studied colossal magnetoresistive material. Magnetoresistance of the LGBMO compound has been compared with the LSMO as well as parent compound La0.7Ba0.3MnO3(LBMO) to show the enhancement of magnetoresistance in LGBMO compound. This observed nature has been elucidated considering the disorder induced short range magnetic interaction due to the enhance size disorder parameter (σ2). Our study revels that, size disorder parameter plays the crucial role for enhancing the colossal magnetoresistance.

Benzimidazobenzothiazole-based highly-efficient thermally activated delayed fluorescence emitters for organic light-emitting diodes: A quantum-chemical TD-DFT study

NASA Astrophysics Data System (ADS)

Zhu, Qiuling; Wen, Keke; Feng, Songyan; Guo, Xugeng; Zhang, Jinglai

2018-03-01

Based upon two thermally activated delayed fluorescence (TADF) emitters 1 and 2, compounds 3-6 have been designed by replacing the carbazol group with the bis(4-biphenyl)amine one (3 and 4) and introducing the electron-withdrawing CF3 group into the acceptor unit of 3 and 4 (5 and 6). It is found that the present calculations predict comparable but relatively large energy differences (approximate 0.5 eV) between the lowest singlet S1 and triplet T1 states (Δ EST) for the six targeted compounds. In order to explain the highly-efficient TADF behavior observed in compounds 1 and 2, the"triplet reservoir" mechanism has been proposed. In addition, the fluorescence rates of all six compounds are very large, in 107-108 orders of magnitude. According to the present calculations, it is a reasonable assumption that the newly designed compounds 3-6 could be considered as the potential TADF emitters, which needs to be further verified by experimental techniques.

Effectively identifying compound-protein interactions by learning from positive and unlabeled examples.

PubMed

Cheng, Zhanzhan; Zhou, Shuigeng; Wang, Yang; Liu, Hui; Guan, Jihong; Chen, Yi-Ping Phoebe

2016-05-18

Prediction of compound-protein interactions (CPIs) is to find new compound-protein pairs where a protein is targeted by at least a compound, which is a crucial step in new drug design. Currently, a number of machine learning based methods have been developed to predict new CPIs in the literature. However, as there is not yet any publicly available set of validated negative CPIs, most existing machine learning based approaches use the unknown interactions (not validated CPIs) selected randomly as the negative examples to train classifiers for predicting new CPIs. Obviously, this is not quite reasonable and unavoidably impacts the CPI prediction performance. In this paper, we simply take the unknown CPIs as unlabeled examples, and propose a new method called PUCPI (the abbreviation of PU learning for Compound-Protein Interaction identification) that employs biased-SVM (Support Vector Machine) to predict CPIs using only positive and unlabeled examples. PU learning is a class of learning methods that leans from positive and unlabeled (PU) samples. To the best of our knowledge, this is the first work that identifies CPIs using only positive and unlabeled examples. We first collect known CPIs as positive examples and then randomly select compound-protein pairs not in the positive set as unlabeled examples. For each CPI/compound-protein pair, we extract protein domains as protein features and compound substructures as chemical features, then take the tensor product of the corresponding compound features and protein features as the feature vector of the CPI/compound-protein pair. After that, biased-SVM is employed to train classifiers on different datasets of CPIs and compound-protein pairs. Experiments over various datasets show that our method outperforms six typical classifiers, including random forest, L1- and L2-regularized logistic regression, naive Bayes, SVM and k-nearest neighbor (kNN), and three types of existing CPI prediction models. Source code, datasets and related documents of PUCPI are available at: http://admis.fudan.edu.cn/projects/pucpi.html.

Hydrodesulfurization catalysis by Chevrel phase compounds

DOEpatents

McCarty, Kevin F.; Schrader, Glenn L.

1985-12-24

A process is disclosed for the hydrodesulfurization of sulfur-containing hydrocarbon fuel with reduced ternary molybdenum sulfides, known as Chevrel phase compounds. Chevrel phase compounds of the general composition M.sub.x Mo.sub.6 S.sub.8, with M being Ho, Pb, Sn, Ag, In, Cu, Fe, Ni, or Co, were found to have hydrodesulfurization activities comparable to model unpromoted and cobalt-promoted MoS.sub.2 catalysts. The most active catalysts were the "large" cation compounds (Ho, Pb, Sn), and the least active catalysts were the "small" cation compounds (Cu, Fe, Ni, Co.).

Hydrodesulfurization catalyst by Chevrel phase compounds

DOEpatents

McCarty, K.F.; Schrader, G.L.

1985-05-20

A process is disclosed for the hydrodesulfurization of sulfur-containing hydrocarbon fuel with reduced ternary molybdenum sulfides, known as Chevrel phase compounds. Chevrel phase compounds of the general composition M/sub x/Mo/sub 6/S/sub 8/, with M being Ho, Pb, Sn, Ag, In, Cu, Fe, Ni, or Co, were found to have hydrodesulfurization activities comparable to model unpromoted and cobalt-promoted MoS/sub 2/ catalysts. The most active catalysts were the ''large'' cation compounds (Ho, Pb, Sn), and the least active catalysts were the ''small'' cation compounds (Cu, Fe, Ni, Co.).

SING: Subgraph search In Non-homogeneous Graphs

PubMed Central

2010-01-01

Background Finding the subgraphs of a graph database that are isomorphic to a given query graph has practical applications in several fields, from cheminformatics to image understanding. Since subgraph isomorphism is a computationally hard problem, indexing techniques have been intensively exploited to speed up the process. Such systems filter out those graphs which cannot contain the query, and apply a subgraph isomorphism algorithm to each residual candidate graph. The applicability of such systems is limited to databases of small graphs, because their filtering power degrades on large graphs. Results In this paper, SING (Subgraph search In Non-homogeneous Graphs), a novel indexing system able to cope with large graphs, is presented. The method uses the notion of feature, which can be a small subgraph, subtree or path. Each graph in the database is annotated with the set of all its features. The key point is to make use of feature locality information. This idea is used to both improve the filtering performance and speed up the subgraph isomorphism task. Conclusions Extensive tests on chemical compounds, biological networks and synthetic graphs show that the proposed system outperforms the most popular systems in query time over databases of medium and large graphs. Other specific tests show that the proposed system is effective for single large graphs. PMID:20170516

Compound Walls For Vacuum Chambers

NASA Technical Reports Server (NTRS)

Frazer, Robert E.

1988-01-01

Proposed compound-wall configuration enables construction of large high-vacuum chambers without having to use thick layers of expensive material to obtain necessary strength. Walls enclose chambers more than 1 m in diameter and several kilometers long. Compound wall made of strong outer layer of structural-steel culvert pipe welded to thin layer of high-quality, low-outgassing stainless steel.

Comparison and Analysis of Toxcast Data with In Vivo Data for Food-Relevant Compounds Using The Risk21 Approach

EPA Science Inventory

The ToxCast program has generated a great wealth of in vitro high throughput screening (HTS) data on a large number of compounds, providing a unique resource of information on the bioactivity of these compounds. However, analysis of these data are ongoing, and interpretation and ...

Editor's highlight: Evaluation of a Microelectrode Array-based Assay for Neural Network Ontogeny using Training Set Chemicals

EPA Science Inventory

Thousands of compounds in the environment have not been characterized for developmental neurotoxicity (DNT) hazard. To address this issue, methods to screen compounds rapidly for DNT hazard evaluation are necessary and are being developed for key neurodevelopmental processes. In...

77 FR 52630 - Approval and Promulgation of Air Quality Implementation Plans; Indiana; Volatile Organic...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-30

... Promulgation of Air Quality Implementation Plans; Indiana; Volatile Organic Compounds; Architectural and... rule that sets emissions limits on the amount of volatile organic compounds in architectural and... period. Any parties interested in commenting on this action should do so at this time. Please note that...

Estimation of Henry's Law Constant for a Diverse Set of Organic Compounds from Molecular Structure

EPA Science Inventory

The SPARC (SPARC Performs Automated Reasoning in Chemistry) vapor pressure and activity coefficient models were coupled to estimate Henry’s Law Constant (HLC) in water and in hexadecane for a wide range of non-polar and polar organic compounds without modification or additional p...

A general structure-property relationship to predict the enthalpy of vaporisation at ambient temperatures.

PubMed

Oberg, T

2007-01-01

The vapour pressure is the most important property of an anthropogenic organic compound in determining its partitioning between the atmosphere and the other environmental media. The enthalpy of vaporisation quantifies the temperature dependence of the vapour pressure and its value around 298 K is needed for environmental modelling. The enthalpy of vaporisation can be determined by different experimental methods, but estimation methods are needed to extend the current database and several approaches are available from the literature. However, these methods have limitations, such as a need for other experimental results as input data, a limited applicability domain, a lack of domain definition, and a lack of predictive validation. Here we have attempted to develop a quantitative structure-property relationship (QSPR) that has general applicability and is thoroughly validated. Enthalpies of vaporisation at 298 K were collected from the literature for 1835 pure compounds. The three-dimensional (3D) structures were optimised and each compound was described by a set of computationally derived descriptors. The compounds were randomly assigned into a calibration set and a prediction set. Partial least squares regression (PLSR) was used to estimate a low-dimensional QSPR model with 12 latent variables. The predictive performance of this model, within the domain of application, was estimated at n=560, q2Ext=0.968 and s=0.028 (log transformed values). The QSPR model was subsequently applied to a database of 100,000+ structures, after a similar 3D optimisation and descriptor generation. Reliable predictions can be reported for compounds within the previously defined applicability domain.

A kinase-focused compound collection: compilation and screening strategy.

PubMed

Sun, Dongyu; Chuaqui, Claudio; Deng, Zhan; Bowes, Scott; Chin, Donovan; Singh, Juswinder; Cullen, Patrick; Hankins, Gretchen; Lee, Wen-Cherng; Donnelly, Jason; Friedman, Jessica; Josiah, Serene

2006-06-01

Lead identification by high-throughput screening of large compound libraries has been supplemented with virtual screening and focused compound libraries. To complement existing approaches for lead identification at Biogen Idec, a kinase-focused compound collection was designed, developed and validated. Two strategies were adopted to populate the compound collection: a ligand shape-based virtual screening and a receptor-based approach (structural interaction fingerprint). Compounds selected with the two approaches were cherry-picked from an existing high-throughput screening compound library, ordered from suppliers and supplemented with specific medicinal compounds from internal programs. Promising hits and leads have been generated from the kinase-focused compound collection against multiple kinase targets. The principle of the collection design and screening strategy was validated and the use of the kinase-focused compound collection for lead identification has been added to existing strategies.

A comparative study of DFT calculated and experimental UV/Visible spectra for thirty carboline and carbazole based compounds

NASA Astrophysics Data System (ADS)

Zara, Zeenat; Iqbal, Javed; Ayub, Khurshid; Irfan, Muhammad; Mahmood, Athar; Khera, Rasheed Ahmad; Eliasson, Bertil

2017-12-01

A comparative study of UV/Visible spectra of carboline and carbazole derivatives was conducted by employing the Density Functional Theory (DFT) approach. In this study, the geometries of ground and excited states, excitation energy and absorption spectra were estimated by using seven different DFT functional; CAM-B3LYP, B3LYP, MPW1PW91, PBE, B3PW91, WB97XD and HSE06 with 6-31G basis set. Moreover, five different basis sets 3-21G, 6-31G, DGDZVP, DGTZVP and SDD were also investigated with the CAM-B3LYP and WB97XD functional to take out the best combination of functional and basis set. CAM-B3LYP/6-31G and WB97XD/DGDZVP combination were found to have closest agreement with the experimental values of β-carboline derivatives and carbazole derivatives, respectively. This study provided an insight about the electronic characteristics of the selected compounds and provided an effective tool for developing and designing the better UV absorber compounds.

Estimating the octanol/water partition coefficient for aliphatic organic compounds using semi-empirical electrotopological index.

PubMed

Souza, Erica Silva; Zaramello, Laize; Kuhnen, Carlos Alberto; Junkes, Berenice da Silva; Yunes, Rosendo Augusto; Heinzen, Vilma Edite Fonseca

2011-01-01

A new possibility for estimating the octanol/water coefficient (log P) was investigated using only one descriptor, the semi-empirical electrotopological index (I(SET)). The predictability of four octanol/water partition coefficient (log P) calculation models was compared using a set of 131 aliphatic organic compounds from five different classes. Log P values were calculated employing atomic-contribution methods, as in the Ghose/Crippen approach and its later refinement, AlogP; using fragmental methods through the ClogP method; and employing an approach considering the whole molecule using topological indices with the MlogP method. The efficiency and the applicability of the I(SET) in terms of calculating log P were demonstrated through good statistical quality (r > 0.99; s < 0.18), high internal stability and good predictive ability for an external group of compounds in the same order as the widely used models based on the fragmental method, ClogP, and the atomic contribution method, AlogP, which are among the most used methods of predicting log P.

Adhesives, fillers and potting compounds. Second progress report, December 1, 1967--April 1, 1968

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

Lichte, H.W.; Akst, I.B.

1968-12-31

Progress in the development program whose immediate purpose is to reduce set time of a silicone compound is described. Data are presented showing that a formulation of a current RTV silicone rubber with dibutyltin diacetate has a profitably lower set time than the same rubber in the present formulation which uses dibutyltin dilaurate, without increase in probability of either reversion or penalty to other weapons components. Time to set sufficiently to allow the next assembly step is 2 to 4 hours, compared to the 16 to 24 hours presently allowed or the 8 to 12 hours minimum attainable with themore » present formulation. The reduction is of the magnitude set as a goal, the attainment of which would increase production capacity enough to reduce the amount of new construction planned to accommodate weapons assembly programs.« less

Assessment of Metabolome Annotation Quality: A Method for Evaluating the False Discovery Rate of Elemental Composition Searches

PubMed Central

Matsuda, Fumio; Shinbo, Yoko; Oikawa, Akira; Hirai, Masami Yokota; Fiehn, Oliver; Kanaya, Shigehiko; Saito, Kazuki

2009-01-01

Background In metabolomics researches using mass spectrometry (MS), systematic searching of high-resolution mass data against compound databases is often the first step of metabolite annotation to determine elemental compositions possessing similar theoretical mass numbers. However, incorrect hits derived from errors in mass analyses will be included in the results of elemental composition searches. To assess the quality of peak annotation information, a novel methodology for false discovery rates (FDR) evaluation is presented in this study. Based on the FDR analyses, several aspects of an elemental composition search, including setting a threshold, estimating FDR, and the types of elemental composition databases most reliable for searching are discussed. Methodology/Principal Findings The FDR can be determined from one measured value (i.e., the hit rate for search queries) and four parameters determined by Monte Carlo simulation. The results indicate that relatively high FDR values (30–50%) were obtained when searching time-of-flight (TOF)/MS data using the KNApSAcK and KEGG databases. In addition, searches against large all-in-one databases (e.g., PubChem) always produced unacceptable results (FDR >70%). The estimated FDRs suggest that the quality of search results can be improved not only by performing more accurate mass analysis but also by modifying the properties of the compound database. A theoretical analysis indicates that FDR could be improved by using compound database with smaller but higher completeness entries. Conclusions/Significance High accuracy mass analysis, such as Fourier transform (FT)-MS, is needed for reliable annotation (FDR <10%). In addition, a small, customized compound database is preferable for high-quality annotation of metabolome data. PMID:19847304

Ionothermal synthesis of discrete supertetrahedral Tn (n = 4, 5) clusters with tunable components, band gaps, and fluorescence properties.

PubMed

Yang, Dan-Dan; Li, Wei; Xiong, Wei-Wei; Li, Jian-Rong; Huang, Xiao-Ying

2018-05-01

The preparation of crystalline molecularly supertetrahedral Tn clusters with variable sizes and components is of vital importance for the fundamental study of their physicochemical properties. However, setting up an efficient method to stabilize large discrete Tn clusters is a challenge due to their high negative charges and polymerization nature. In this work, we report on the ionothermal synthesis of three discrete T4 cluster compounds, namely [Bmmim]5[(CH3)2NH2]4[NH4][M4In16S31(SH)4]·6H2O (M = Mn (1), Zn (2), Cd (3), Bmmim = 1-buty-2,3-dimethyl-imidazolium), and four discrete T5 cluster compounds, namely [Bmmim]10[NH4]3[Cu5Ga30-xInxS52(SH)4] (x = 6.6 (5), 14.5 (6), 23.8 (7), and 30 (8)). The compound [Bmmim]10[NH4]3[Cu5Ga30S52(SH)4] (4) previously reported by us features a discrete T5 cluster. The steep UV-Vis absorption edges indicate band gaps of 2.20 eV for 1, 2.64 eV for 2, 2.69 eV for 3, 3.04 eV for 4, 2.65 eV for 5, 2.48 eV for 6, 2.32 eV for 7, and 2.30 eV for 8. The compositions of T5 clusters could be varied with the ratios of Ga : In in the starting reagents, providing an opportunity to systematically control the band gaps and fluorescence performances of T5 cluster-based compounds. This research might advance the understanding of the ionothermal preparation and functionality tuning of crystalline chalcogenides.

3D QSAR studies on binding affinities of coumarin natural products for glycosomal GAPDH of Trypanosoma cruzi

NASA Astrophysics Data System (ADS)

Menezes, Irwin R. A.; Lopes, Julio C. D.; Montanari, Carlos A.; Oliva, Glaucius; Pavão, Fernando; Castilho, Marcelo S.; Vieira, Paulo C.; Pupo, M.^onica T.

2003-05-01

Drug design strategies based on Comparative Molecular Field Analysis (CoMFA) have been used to predict the activity of new compounds. The major advantage of this approach is that it permits the analysis of a large number of quantitative descriptors and uses chemometric methods such as partial least squares (PLS) to correlate changes in bioactivity with changes in chemical structure. Because it is often difficult to rationalize all variables affecting the binding affinity of compounds using CoMFA solely, the program GRID was used to describe ligands in terms of their molecular interaction fields, MIFs. The program VolSurf that is able to compress the relevant information present in 3D maps into a few descriptors can treat these GRID fields. The binding affinities of a new set of compounds consisting of 13 coumarins, for one of which the three-dimensional ligand-enzyme bound structure is known, were studied. A final model based on the mentioned programs was independently validated by synthesizing and testing new coumarin derivatives. By relying on our knowledge of the real physical data (i.e., combining crystallographic and binding affinity results), it is also shown that ligand-based design agrees with structure-based design. The compound with the highest binding affinity was the coumarin chalepin, isolated from Rutaceae species, with an IC50 value of 55.5 μM towards the enzyme glyceraldehyde-3-phosphate dehydrogenase (gGAPDH) from glycosomes of the parasite Trypanosoma cruzi, the causative agent of Chagas' disease. The proposed models from GRID MIFs have revealed the importance of lipophilic interactions in modulating the inhibition, but without excluding the dependence on stereo-electronic properties as found from CoMFA fields.

Virtual screening for novel Staphylococcus Aureus NorA efflux pump inhibitors from natural products.

PubMed

Thai, Khac-Minh; Ngo, Trieu-Du; Phan, Thien-Vy; Tran, Thanh-Dao; Nguyen, Ngoc-Vinh; Nguyen, Thien-Hai; Le, Minh-Tri

2015-01-01

NorA is a member of the Major Facilitator Superfamily (MFS) drug efflux pumps that have been shown to mediate antibiotic resistance in Staphylococcus aureus (SA). In this study, QSAR analysis, virtual screening and molecular docking were implemented in an effort to discover novel SA NorA efflux pump inhibitors. Originally, a set of 47 structurally diverse compounds compiled from the literature was used to develop linear QSAR models and another set of 15 different compounds were chosen for extra validation. The final model which was estimated by statistical values for the full data set (n = 45, Q(2) = 0.80, RMSE = 0.20) and for the external test set (n = 15, R(2) = 0.60, |res|max = 0.75, |res|min = 0.02) was applied on the collection of 182 flavonoides and the traditional Chinese medicine (TCM) database to screen for novel NorA inhibitors. Finally, 33 lead compounds that met the Lipinski's rules of five/three and had good predicted pIC50 values from in silico screening process were employed to analyze the binding ability by docking studies on NorA homology model in place of its unavailable crystal structures at two active sites, the central channel and the Walker B.

Evaluating the Predictivity of Virtual Screening for Abl Kinase Inhibitors to Hinder Drug Resistance

PubMed Central

Gani, Osman A B S M; Narayanan, Dilip; Engh, Richard A

2013-01-01

Virtual screening methods are now widely used in early stages of drug discovery, aiming to rank potential inhibitors. However, any practical ligand set (of active or inactive compounds) chosen for deriving new virtual screening approaches cannot fully represent all relevant chemical space for potential new compounds. In this study, we have taken a retrospective approach to evaluate virtual screening methods for the leukemia target kinase ABL1 and its drug-resistant mutant ABL1-T315I. ‘Dual active’ inhibitors against both targets were grouped together with inactive ligands chosen from different decoy sets and tested with virtual screening approaches with and without explicit use of target structures (docking). We show how various scoring functions and choice of inactive ligand sets influence overall and early enrichment of the libraries. Although ligand-based methods, for example principal component analyses of chemical properties, can distinguish some decoy sets from active compounds, the addition of target structural information via docking improves enrichment, and explicit consideration of multiple target conformations (i.e. types I and II) achieves best enrichment of active versus inactive ligands, even without assuming knowledge of the binding mode. We believe that this study can be extended to other therapeutically important kinases in prospective virtual screening studies. PMID:23746052

Theoretical study of thermopower behavior of LaFeO3 compound in high temperature region

NASA Astrophysics Data System (ADS)

Singh, Saurabh; Shastri, Shivprasad S.; Pandey, Sudhir K.

2018-04-01

The electronic structure and thermopower (α) behavior of LaFeO3 compound were investigated by combining the ab-initio electronic structures and Boltzmann transport calculations. LSDA plus Hubbard U (U = 5 eV) calculation on G-type anti-ferromagnetic (AFM) configuration gives an energy gap of ˜2 eV, which is very close to the experimentally reported energy gap. The calculated values of effective mass of holes (mh*) in valance band (VB) are found ˜4 times that of the effective mass of electrons (me*) in conduction band (CB). The large effective masses of holes are responsible for the large and positive thermopower exhibited by this compound. The calculated values of α using BoltzTraP code are found to be large and positive in the 300-1200 K temperature range, which is in agreement with the experimentally reported data.

Graphene-Based Chemical Vapor Sensors for Electronic Nose Applications

NASA Astrophysics Data System (ADS)

Nallon, Eric C.

An electronic nose (e-nose) is a biologically inspired device designed to mimic the operation of the olfactory system. The e-nose utilizes a chemical sensor array consisting of broadly responsive vapor sensors, whose combined response produces a unique pattern for a given compound or mixture. The sensor array is inspired by the biological function of the receptor neurons found in the human olfactory system, which are inherently cross-reactive and respond to many different compounds. The use of an e-nose is an attractive approach to predict unknown odors and is used in many fields for quantitative and qualitative analysis. If properly designed, an e-nose has the potential to adapt to new odors it was not originally designed for through laboratory training and algorithm updates. This would eliminate the lengthy and costly R&D costs associated with materiel and product development. Although e-nose technology has been around for over two decades, much research is still being undertaken in order to find new and more diverse types of sensors. Graphene is a single-layer, 2D material comprised of carbon atoms arranged in a hexagonal lattice, with extraordinary electrical, mechanical, thermal and optical properties due to its 2D, sp2-bonded structure. Graphene has much potential as a chemical sensing material due to its 2D structure, which provides a surface entirely exposed to its surrounding environment. In this configuration, every carbon atom in graphene is a surface atom, providing the greatest possible surface area per unit volume, so that electron transport is highly sensitive to adsorbed molecular species. Graphene has gained much attention since its discovery in 2004, but has not been realized in many commercial electronics. It has the potential to be a revolutionary material for use in chemical sensors due to its excellent conductivity, large surface area, low noise, and versatile surface for functionalization. In this work, graphene is incorporated into a chemiresistor device and used as a chemical sensor, where its resistance is temporarily modified while exposed to chemical compounds. The inherent, broad selective nature of graphene is demonstrated by testing a sensor against a diverse set of volatile organic compounds and also against a set of chemically similar compounds. The sensor exhibits excellent selectivity and is capable of achieving high classification accuracies. The kinetics of the sensor's response are further investigated revealing a relationship between the transient behavior of the response curve and physiochemical properties of the compounds, such as the molar mass and vapor pressure. This kinetic information is also shown to provide important information for further pattern recognition and classification, which is demonstrated by increased classification accuracy of very similar compounds. Covalent modification of the graphene surface is demonstrated by means of plasma treatment and free radical exchange, and sensing performance compared to an unmodified graphene sensor. Finally, the first example of a graphene-based, cross-reactive chemical sensor array is demonstrated by applying various polymers as coatings over an array of graphene sensors. The sensor array is tested against a variety of compounds, including the complex odor of Scotch whiskies, where it is capable of perfect classification of 10 Scotch whiskey variations.

Methods, compounds and systems for detecting a microorganism in a sample

DOEpatents

Colston, Jr, Bill W.; Fitch, J. Patrick; Gardner, Shea N.; Williams, Peter L.; Wagner, Mark C.

2016-09-06

Methods to identify a set of probe polynucleotides suitable for detecting a set of targets and in particular methods for identification of primers suitable for detection of target microorganisms related polynucleotides, set of polynucleotides and compositions, and related methods and systems for detection and/or identification of microorganisms in a sample.

Summer Research Program (1992). Summer Faculty Research Program (SFRP) Reports. Volume 5B. Wright Laboratory

DTIC Science & Technology

1992-12-01

composite system was made of carbon black-filled proprietary rubber compound matrix and 1260/2 39- 6 nylon cord reinforcement laid at an angle of +/-38...1.5 urn. 2) These are ternary compounds without the additional complication of added phosphorous as in the common compound InGaAsP. 3) Recent...theoretical coirputations indicate that these compounds may have large optical nonlinearities. For thin layers the lattice mismatch induces internal strain

Comparative Analysis of Phenolic Compound Characterization and Their Biosynthesis Genes between Two Diverse Bread Wheat (Triticum aestivum) Varieties Differing for Chapatti (Unleavened Flat Bread) Quality.

PubMed

Sharma, Monica; Sandhir, Rajat; Singh, Anuradha; Kumar, Pankaj; Mishra, Ankita; Jachak, Sanjay; Singh, Sukhvinder P; Singh, Jagdeep; Roy, Joy

2016-01-01

Phenolic compounds (PCs) affect the bread quality and can also affect the other types of end-use food products such as chapatti (unleavened flat bread), now globally recognized wheat-based food product. The detailed analysis of PCs and their biosynthesis genes in diverse bread wheat ( Triticum aestivum ) varieties differing for chapatti quality have not been studied. In this study, the identification and quantification of PCs using UPLC-QTOF-MS and/or MS/MS and functional genomics techniques such as microarrays and qRT-PCR of their biosynthesis genes have been studied in a good chapatti variety, "C 306" and a poor chapatti variety, "Sonalika." About 80% (69/87) of plant phenolic compounds were tentatively identified in these varieties. Nine PCs (hinokinin, coutaric acid, fertaric acid, p-coumaroylqunic acid, kaempferide, isorhamnetin, epigallocatechin gallate, methyl isoorientin-2'-O-rhamnoside, and cyanidin-3-rutinoside) were identified only in the good chapatti variety and four PCs (tricin, apigenindin, quercetin-3-O-glucuronide, and myricetin-3-glucoside) in the poor chapatti variety. Therefore, about 20% of the identified PCs are unique to each other and may be "variety or genotype" specific PCs. Fourteen PCs used for quantification showed high variation between the varieties. The microarray data of 44 phenolic compound biosynthesis genes and 17 of them on qRT-PCR showed variation in expression level during seed development and majority of them showed low expression in the good chapatti variety. The expression pattern in the good chapatti variety was largely in agreement with that of phenolic compounds. The level of variation of 12 genes was high between the good and poor chapatti quality varieties and has potential in development of markers. The information generated in this study can be extended onto a larger germplasm set for development of molecular markers using QTL and/or association mapping approaches for their application in wheat breeding.

Comparative Analysis of Phenolic Compound Characterization and Their Biosynthesis Genes between Two Diverse Bread Wheat (Triticum aestivum) Varieties Differing for Chapatti (Unleavened Flat Bread) Quality

PubMed Central

Sharma, Monica; Sandhir, Rajat; Singh, Anuradha; Kumar, Pankaj; Mishra, Ankita; Jachak, Sanjay; Singh, Sukhvinder P.; Singh, Jagdeep; Roy, Joy

2016-01-01

Phenolic compounds (PCs) affect the bread quality and can also affect the other types of end-use food products such as chapatti (unleavened flat bread), now globally recognized wheat-based food product. The detailed analysis of PCs and their biosynthesis genes in diverse bread wheat (Triticum aestivum) varieties differing for chapatti quality have not been studied. In this study, the identification and quantification of PCs using UPLC-QTOF-MS and/or MS/MS and functional genomics techniques such as microarrays and qRT-PCR of their biosynthesis genes have been studied in a good chapatti variety, “C 306” and a poor chapatti variety, “Sonalika.” About 80% (69/87) of plant phenolic compounds were tentatively identified in these varieties. Nine PCs (hinokinin, coutaric acid, fertaric acid, p-coumaroylqunic acid, kaempferide, isorhamnetin, epigallocatechin gallate, methyl isoorientin-2′-O-rhamnoside, and cyanidin-3-rutinoside) were identified only in the good chapatti variety and four PCs (tricin, apigenindin, quercetin-3-O-glucuronide, and myricetin-3-glucoside) in the poor chapatti variety. Therefore, about 20% of the identified PCs are unique to each other and may be “variety or genotype” specific PCs. Fourteen PCs used for quantification showed high variation between the varieties. The microarray data of 44 phenolic compound biosynthesis genes and 17 of them on qRT-PCR showed variation in expression level during seed development and majority of them showed low expression in the good chapatti variety. The expression pattern in the good chapatti variety was largely in agreement with that of phenolic compounds. The level of variation of 12 genes was high between the good and poor chapatti quality varieties and has potential in development of markers. The information generated in this study can be extended onto a larger germplasm set for development of molecular markers using QTL and/or association mapping approaches for their application in wheat breeding. PMID:28018403