QSAR Study for Carcinogenic Potency of Aromatic Amines Based on GEP and MLPs

PubMed Central

Song, Fucheng; Zhang, Anling; Liang, Hui; Cui, Lianhua; Li, Wenlian; Si, Hongzong; Duan, Yunbo; Zhai, Honglin

2016-01-01

A new analysis strategy was used to classify the carcinogenicity of aromatic amines. The physical-chemical parameters are closely related to the carcinogenicity of compounds. Quantitative structure activity relationship (QSAR) is a method of predicting the carcinogenicity of aromatic amine, which can reveal the relationship between carcinogenicity and physical-chemical parameters. This study accessed gene expression programming by APS software, the multilayer perceptrons by Weka software to predict the carcinogenicity of aromatic amines, respectively. All these methods relied on molecular descriptors calculated by CODESSA software and eight molecular descriptors were selected to build function equations. As a remarkable result, the accuracy of gene expression programming in training and test sets are 0.92 and 0.82, the accuracy of multilayer perceptrons in training and test sets are 0.84 and 0.74 respectively. The precision of the gene expression programming is obviously superior to multilayer perceptrons both in training set and test set. The QSAR application in the identification of carcinogenic compounds is a high efficiency method. PMID:27854309

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).

Prediction of biodegradability of aromatics in water using QSAR modeling.

PubMed

Cvetnic, Matija; Juretic Perisic, Daria; Kovacic, Marin; Kusic, Hrvoje; Dermadi, Jasna; Horvat, Sanja; Bolanca, Tomislav; Marin, Vedrana; Karamanis, Panaghiotis; Loncaric Bozic, Ana

2017-05-01

The study was aimed at developing models for predicting the biodegradability of aromatic water pollutants. For that purpose, 36 single-benzene ring compounds, with different type, number and position of substituents, were used. The biodegradability was estimated according to the ratio of the biochemical (BOD 5 ) and chemical (COD) oxygen demand values determined for parent compounds ((BOD 5 /COD) 0 ), as well as for their reaction mixtures in half-life achieved by UV-C/H 2 O 2 process ((BOD 5 /COD) t1/2 ). The models correlating biodegradability and molecular structure characteristics of studied pollutants were derived using quantitative structure-activity relationship (QSAR) principles and tools. Upon derivation of the models and calibration on the training and subsequent testing on the test set, 3- and 5-variable models were selected as the most predictive for (BOD 5 /COD) 0 and (BOD 5 /COD) t1/2 , respectively, according to the values of statistical parameters R 2 and Q 2 . Hence, 3-variable model predicting (BOD 5 /COD) 0 possessed R 2 =0.863 and Q 2 =0.799 for training set, and R 2 =0.710 for test set, while 5-variable model predicting (BOD 5 /COD) 1/2 possessed R 2 =0.886 and Q 2 =0.788 for training set, and R 2 =0.564 for test set. The selected models are interpretable and transparent, reflecting key structural features that influence targeted biodegradability and can be correlated with the degradation mechanisms of studied compounds by UV-C/H 2 O 2 . Copyright © 2017 Elsevier Inc. All rights reserved.

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.

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.

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.

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 ...

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

The Vitotox and ToxTracker assays: A two-test combination for quick and reliable assessment of genotoxic hazards.

PubMed

Ates, Gamze; Favyts, Dorien; Hendriks, Giel; Derr, Remco; Mertens, Birgit; Verschaeve, Luc; Rogiers, Vera; Y Doktorova, Tatyana

2016-11-01

To ensure safety for humans, it is essential to characterize the genotoxic potential of new chemical entities, such as pharmaceutical and cosmetic substances. In a first tier, a battery of in vitro tests is recommended by international regulatory agencies. However, these tests suffer from inadequate specificity: compounds may be wrongly categorized as genotoxic, resulting in unnecessary, time-consuming, and expensive in vivo follow-up testing. In the last decade, novel assays (notably, reporter-based assays) have been developed in an attempt to overcome these drawbacks. Here, we have investigated the performance of two in vitro reporter-based assays, Vitotox and ToxTracker. A set of reference compounds was selected to span a variety of mechanisms of genotoxic action and applicability domains (e.g., pharmaceutical and cosmetic ingredients). Combining the performance of the two assays, we achieved 93% sensitivity and 79% specificity for prediction of gentoxicity for this set of compounds. Both assays permit quick high-throughput analysis of drug candidates, while requiring only small quantities of the test substances. Our study shows that these two assays, when combined, can be a reliable method for assessment of genotoxicity hazard. Copyright © 2016 Elsevier B.V. All rights reserved.

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

Reference compounds for alternative test methods to indicate developmental neurotoxicity (DNT) potential of chemicals: example lists and criteria for their selection and use.

PubMed

Aschner, Michael; Ceccatelli, Sandra; Daneshian, Mardas; Fritsche, Ellen; Hasiwa, Nina; Hartung, Thomas; Hogberg, Helena T; Leist, Marcel; Li, Abby; Mundi, William R; Padilla, Stephanie; Piersma, Aldert H; Bal-Price, Anna; Seiler, Andrea; Westerink, Remco H; Zimmer, Bastian; Lein, Pamela J

2017-01-01

There is a paucity of information concerning the developmental neurotoxicity (DNT) hazard posed by industrial and environmental chemicals. New testing approaches will most likely be based on batteries of alternative and complementary (non-animal) tests. As DNT is assumed to result from the modulation of fundamental neurodevelopmental processes (such as neuronal differentiation, precursor cell migration or neuronal network formation) by chemicals, the first generation of alternative DNT tests target these processes. The advantage of such types of assays is that they capture toxicants with multiple targets and modes-of-action. Moreover, the processes modelled by the assays can be linked to toxicity endophenotypes, i.e., alterations in neural connectivity that form the basis for neurofunctional deficits in man. The authors of this review convened in a workshop to define criteria for the selection of positive/negative controls, to prepare recommendations on their use, and to initiate the setup of a directory of reference chemicals. For initial technical optimization of tests, a set of > 50 endpoint-specific control compounds was identified. For further test development, an additional "test" set of 33 chemicals considered to act directly as bona fide DNT toxicants is proposed, and each chemical is annotated to the extent it fulfills these criteria. A tabular compilation of the original literature used to select the test set chemicals provides information on statistical procedures, and toxic/non-toxic doses (both for pups and dams). Suggestions are provided on how to use the > 100 compounds (including negative controls) compiled here to address specificity, adversity and use of alternative test systems.

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.

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

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.

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.

Reference compounds for alternative test methods to indicate developmental neurotoxicity (DNT) potential of chemicals: example lists and criteria for their selection and use

PubMed Central

Aschner, Michael; Ceccatelli, Sandra; Daneshian, Mardas; Fritsche, Ellen; Hasiwa, Nina; Hartung, Thomas; Hogberg, Helena T.; Leist, Marcel; Li, Abby; Mundy, William R.; Padilla, Stephanie; Piersma, Aldert H.; Bal-Price, Anna; Seiler, Andrea; Westerink, Remco H.; Zimmer, Bastian; Lein, Pamela J.

2016-01-01

Summary There is a paucity of information concerning the developmental neurotoxicity (DNT) hazard posed by industrial and environmental chemicals. New testing approaches will most likely be based on batteries of alternative and complementary (non-animal) tests. As DNT is assumed to result from the modulation of fundamental neurodevelopmental processes (such as neuronal differentiation, precursor cell migration or neuronal network formation) by chemicals, the first generation of alternative DNT tests target these processes. The advantage of such types of assays is that they capture toxicants with multiple targets and modes-of-action. Moreover, the processes modelled by the assays can be linked to toxicity endophenotypes, i.e. alterations in neural connectivity that form the basis for neurofunctional deficits in man. The authors of this review convened in a workshop to define criteria for the selection of positive/negative controls, to prepare recommendations on their use, and to initiate the setup of a directory of reference chemicals. For initial technical optimization of tests, a set of >50 endpoint-specific control compounds was identified. For further test development, an additional “test” set of 33 chemicals considered to act directly as bona fide DNT toxicants is proposed, and each chemical is annotated to the extent it fulfills these criteria. A tabular compilation of the original literature used to select the test set chemicals provides information on statistical procedures, and toxic/non-toxic doses (both for pups and dams). Suggestions are provided on how to use the >100 compounds (including negative controls) compiled here to address specificity, adversity and use of alternative test systems. PMID:27452664

A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts

PubMed Central

Pizzo, Fabiola; Lombardo, Anna; Manganaro, Alberto; Benfenati, Emilio

2016-01-01

The prompt identification of chemical molecules with potential effects on liver may help in drug discovery and in raising the levels of protection for human health. Besides in vitro approaches, computational methods in toxicology are drawing attention. We built a structure-activity relationship (SAR) model for evaluating hepatotoxicity. After compiling a data set of 950 compounds using data from the literature, we randomly split it into training (80%) and test sets (20%). We also compiled an external validation set (101 compounds) for evaluating the performance of the model. To extract structural alerts (SAs) related to hepatotoxicity and non-hepatotoxicity we used SARpy, a statistical application that automatically identifies and extracts chemical fragments related to a specific activity. We also applied the chemical grouping approach for manually identifying other SAs. We calculated accuracy, specificity, sensitivity and Matthews correlation coefficient (MCC) on the training, test and external validation sets. Considering the complexity of the endpoint, the model performed well. In the training, test and external validation sets the accuracy was respectively 81, 63, and 68%, specificity 89, 33, and 33%, sensitivity 93, 88, and 80% and MCC 0.63, 0.27, and 0.13. Since it is preferable to overestimate hepatotoxicity rather than not to recognize unsafe compounds, the model's architecture followed a conservative approach. As it was built using human data, it might be applied without any need for extrapolation from other species. This model will be freely available in the VEGA platform. PMID:27920722

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

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.

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

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.

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.

Classification of Breast Cancer Resistant Protein (BCRP) Inhibitors and Non-Inhibitors Using Machine Learning Approaches.

PubMed

Belekar, Vilas; Lingineni, Karthik; Garg, Prabha

2015-01-01

The breast cancer resistant protein (BCRP) is an important transporter and its inhibitors play an important role in cancer treatment by improving the oral bioavailability as well as blood brain barrier (BBB) permeability of anticancer drugs. In this work, a computational model was developed to predict the compounds as BCRP inhibitors or non-inhibitors. Various machine learning approaches like, support vector machine (SVM), k-nearest neighbor (k-NN) and artificial neural network (ANN) were used to develop the models. The Matthews correlation coefficients (MCC) of developed models using ANN, k-NN and SVM are 0.67, 0.71 and 0.77, and prediction accuracies are 85.2%, 88.3% and 90.8% respectively. The developed models were tested with a test set of 99 compounds and further validated with external set of 98 compounds. Distribution plot analysis and various machine learning models were also developed based on druglikeness descriptors. Applicability domain is used to check the prediction reliability of the new molecules.

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

Minimizing DILI risk in drug discovery - A screening tool for drug candidates.

PubMed

Schadt, S; Simon, S; Kustermann, S; Boess, F; McGinnis, C; Brink, A; Lieven, R; Fowler, S; Youdim, K; Ullah, M; Marschmann, M; Zihlmann, C; Siegrist, Y M; Cascais, A C; Di Lenarda, E; Durr, E; Schaub, N; Ang, X; Starke, V; Singer, T; Alvarez-Sanchez, R; Roth, A B; Schuler, F; Funk, C

2015-12-25

Drug-induced liver injury (DILI) is a leading cause of acute hepatic failure and a major reason for market withdrawal of drugs. Idiosyncratic DILI is multifactorial, with unclear dose-dependency and poor predictability since the underlying patient-related susceptibilities are not sufficiently understood. Because of these limitations, a pharmaceutical research option would be to reduce the compound-related risk factors in the drug-discovery process. Here we describe the development and validation of a methodology for the assessment of DILI risk of drug candidates. As a training set, 81 marketed or withdrawn compounds with differing DILI rates - according to the FDA categorization - were tested in a combination of assays covering different mechanisms and endpoints contributing to human DILI. These include the generation of reactive metabolites (CYP3A4 time-dependent inhibition and glutathione adduct formation), inhibition of the human bile salt export pump (BSEP), mitochondrial toxicity and cytotoxicity (fibroblasts and human hepatocytes). Different approaches for dose- and exposure-based calibrations were assessed and the same parameters applied to a test set of 39 different compounds. We achieved a similar performance to the training set with an overall accuracy of 79% correctly predicted, a sensitivity of 76% and a specificity of 82%. This test system may be applied in a prospective manner to reduce the risk of idiosyncratic DILI of drug candidates. Copyright © 2015 Elsevier B.V. All rights reserved.

Design Considerations of a Compounded Sterile Preparations Course

PubMed Central

Petraglia, Christine; Mattison, Melissa J.

2016-01-01

Objective. To design a comprehensive learning and assessment environment for the practical application of compounded sterile preparations using a constructivist approach. Design. Compounded Sterile Preparations Laboratory is a required 1-credit course that builds upon the themes of training aseptic technique typically used in health system settings and threads application of concepts from other courses in the curriculum. Students used critical-thinking skills to devise appropriate strategies to compound sterile preparations. Assessment. Aseptic technique skills were assessed with objective, structured, checklist-based rubrics. Most students successfully completed practical assessments using appropriate technique (mean assessment grade=83.2%). Almost all students passed the practical media fill (98%) and gloved fingertip sampling (86%) tests on the first attempt; all passed on the second attempt. Conclusion. Employing a constructivist scaffold approach to teaching proper hygiene and aseptic technique prepared students to pass media fill and gloved fingertip tests and to perform well on practical compounding assessments. PMID:26941438

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

The dose-response relationship between the patch test and ROAT and the potential use for regulatory purposes.

PubMed

Fischer, Louise Arup; Voelund, Aage; Andersen, Klaus Ejner; Menné, Torkil; Johansen, Jeanne Duus

2009-10-01

Allergic contact dermatitis is common and can be prevented. The relationship between thresholds for patch tests and the repeated open application test (ROAT) is unclear. It would be desirable if patch test and ROAT data from already sensitized individuals could be used in prevention. The aim was to develop an equation that could predict the response to an allergen in a ROAT based on the dose-response curve derived by patch testing. Results from two human experimental elicitation studies with non-volatile allergens, nickel and the preservative methyldibromo glutaronitrile (MDBGN), were analysed by logistic dose-response statistics. The relation for volatile compounds was investigated using the results from experiments with the fragrance chemicals hydroxyisohexyl 3-cyclohexene carboxaldehyde and isoeugenol. For non-volatile compounds, the outcome of a ROAT can be estimated from the patch test by: ED(xx)(ROAT) = 0.0296 ED(xx)(patch test). For volatile compounds, the equation predicts that the response in the ROAT is more severe than the patch test response, but it overestimates the response. This equation may be used for non-volatile compounds other than nickel and MDBGN, after further validation. The relationship between the patch test and the ROAT can be used for prevention, to set safe levels of allergen exposure based on patch test data.

Electrochemical research on corrosion behavior of A3 steel in compound sodium molybadate and organic inhibitor solution

NASA Astrophysics Data System (ADS)

Sun, C. X.; Chen, Y. M.; Xu, H. W.; Zhang, M.; Chen, M.; Xue, M.; Wu, J. Y.; Huang, C. S.

2015-07-01

The electrochemical corrosion behavior of A3 in compound sodium molybdate and organic inhibitor solution was tested by the electrochemical workstation method. The concentration of the compound inhibitor set to range 250 mg/L to 3000 mg/L. The polarization curve results of A3 in different concentration inhibitor solutions show that the inhibitor markedly represses the anodic processes. The EIS has two time constant. The extreme concentration is 1500 mg/L.

Laboratory studies of Aedes aegypti (L.) attraction to ketones, sulfides and primary chloroalkanes tested alone and in combination with l-lactic acid

USDA-ARS?s Scientific Manuscript database

The attraction of female Aedes aegypti to single compounds and binary compositions comprised of L-lactic acid and an additional saturated compound from a set of ketones, sulfides, and chloroalkanes was studied using a triple-cage dual-port olfactometer. These chemical classes were studied because o...

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

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.

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.

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.

New public QSAR model for carcinogenicity

PubMed Central

2010-01-01

Background One of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fulfill the gaps in data concerned with properties of chemicals affecting the human health. (Q)SAR models are accepted as a suitable source of information. The EU funded CAESAR project aimed to develop models for prediction of 5 endpoints for regulatory purposes. Carcinogenicity is one of the endpoints under consideration. Results Models for prediction of carcinogenic potency according to specific requirements of Chemical regulation were developed. The dataset of 805 non-congeneric chemicals extracted from Carcinogenic Potency Database (CPDBAS) was used. Counter Propagation Artificial Neural Network (CP ANN) algorithm was implemented. In the article two alternative models for prediction carcinogenicity are described. The first model employed eight MDL descriptors (model A) and the second one twelve Dragon descriptors (model B). CAESAR's models have been assessed according to the OECD principles for the validation of QSAR. For the model validity we used a wide series of statistical checks. Models A and B yielded accuracy of training set (644 compounds) equal to 91% and 89% correspondingly; the accuracy of the test set (161 compounds) was 73% and 69%, while the specificity was 69% and 61%, respectively. Sensitivity in both cases was equal to 75%. The accuracy of the leave 20% out cross validation for the training set of models A and B was equal to 66% and 62% respectively. To verify if the models perform correctly on new compounds the external validation was carried out. The external test set was composed of 738 compounds. We obtained accuracy of external validation equal to 61.4% and 60.0%, sensitivity 64.0% and 61.8% and specificity equal to 58.9% and 58.4% respectively for models A and B. Conclusion Carcinogenicity is a particularly important endpoint and it is expected that QSAR models will not replace the human experts opinions and conventional methods. However, we believe that combination of several methods will provide useful support to the overall evaluation of carcinogenicity. In present paper models for classification of carcinogenic compounds using MDL and Dragon descriptors were developed. Models could be used to set priorities among chemicals for further testing. The models at the CAESAR site were implemented in java and are publicly accessible. PMID:20678182

Determining the Basis of Homodesmotic Reactions of Cyclic Organic Compounds by Means of Graph Theory

NASA Astrophysics Data System (ADS)

Khursan, S. L.; Ismagilova, A. S.; Akhmetyanova, A. I.

2018-07-01

Comparative calculations based on the use of a homodesmotic reaction (HDR)—an isodesmic process with the additional requirement for group balance—is used to analyze the thermochemical characteristics of cyclic organic compounds exemplified by bicyclo[2.1.0]pentene-2. To avoid confusion in selecting HDRs, an algorithm is developed for determining the HDR basis, i.e., the set of all possible independent homodesmotic reactions. The algorithm for constructing the set of HDRs is based on an analysis and transformations of the bond graph of groups for the investigated chemical compound. The use of graph theory allows us to automate the procedure for deriving the basis of homodesmotic reactions, and to obtain a visual geometric interpretation of the basis, which is important for subsequent physicochemical analysis. The energetics of bicyclo[2.1.0]pentene-2 is investigated using the proposed approach, and the independent basis of HDRs is found to include 19 formal transformations. Standard enthalpies for the test compound and the participants of homodesmotic reactions are calculated using the G3 composite approach. Thermochemical analysis of the obtained data allows us to determine the standard enthalpy of formation of the bicycle (Δf H° = 336.4 kJ/mol) and value Δf H° of a number of cyclic and acyclic alkenes and alkadienes that are products of theoretical decomposition of the test compound. The proposed method is shown to be extremely effective in analyzing the effects of nonbonded interactions in the structure of organic molecules. The ring strain energy of the bicycle is calculated or the test compound: E S = 295.2± 2.2 kJ/mol.

Biodegradation of natural reinforcing fillers for polymer composites

NASA Astrophysics Data System (ADS)

Mastalygina, E. E.; Pantyukhov, P. V.; Popov, A. A.

2018-05-01

Twelve different natural raw materials were selected as possible fillers for eco-friendly biocomposites. The target was to find the most biodegradable ones. Two mycological tests were held: in the aqueous and agar media. It was found that two tests showed different results. In aqueous media, the fillers with a high content of water-soluble and easy-hydrolysed compounds demostrated the most intensive biofouling. In agar media, the entire filler was exposed to biodigestion by fungi. Therefore, multi-compound fillers with a set of different macro- and microelements were more biodegradable than others.

In silico models for predicting ready biodegradability under REACH: a comparative study.

PubMed

Pizzo, Fabiola; Lombardo, Anna; Manganaro, Alberto; Benfenati, Emilio

2013-10-01

REACH (Registration Evaluation Authorization and restriction of Chemicals) legislation is a new European law which aims to raise the human protection level and environmental health. Under REACH all chemicals manufactured or imported for more than one ton per year must be evaluated for their ready biodegradability. Ready biodegradability is also used as a screening test for persistent, bioaccumulative and toxic (PBT) substances. REACH encourages the use of non-testing methods such as QSAR (quantitative structure-activity relationship) models in order to save money and time and to reduce the number of animals used for scientific purposes. Some QSAR models are available for predicting ready biodegradability. We used a dataset of 722 compounds to test four models: VEGA, TOPKAT, BIOWIN 5 and 6 and START and compared their performance on the basis of the following parameters: accuracy, sensitivity, specificity and Matthew's correlation coefficient (MCC). Performance was analyzed from different points of view. The first calculation was done on the whole dataset and VEGA and TOPKAT gave the best accuracy (88% and 87% respectively). Then we considered the compounds inside and outside the training set: BIOWIN 6 and 5 gave the best results for accuracy (81%) outside training set. Another analysis examined the applicability domain (AD). VEGA had the highest value for compounds inside the AD for all the parameters taken into account. Finally, compounds outside the training set and in the AD of the models were considered to assess predictive ability. VEGA gave the best accuracy results (99%) for this group of chemicals. Generally, START model gave poor results. Since BIOWIN, TOPKAT and VEGA models performed well, they may be used to predict ready biodegradability. Copyright © 2013 Elsevier B.V. All rights reserved.

Characterization of Volatile Compounds with HS-SPME from Oxidized n-3 PUFA Rich Oils via Rancimat Tests.

PubMed

Yang, Kai-Min; Cheng, Ming-Ching; Chen, Chih-Wei; Tseng, Chin-Yin; Lin, Li-Yun; Chiang, Po-Yuan

2017-02-01

Algae oil and fish oil are n-3 PUFA mainstream commercial products. The various sources for the stability of n-3 PUFA oxidation are influenced by the fatty acid composition, extraction and refined processing. In this study, the oil stability index (OSI) occurs within 2.3 to 7.6 hours with three different n-3 PUFA rich oil. To set the OSI in the Rancimat test as the oil stability limit and observed various degrees of oxidation (0, 25, 50, 75, 100 and 125%). The volatile oxidation compounds were analyzed via headspace-solid phase microextraction (HS-SPME) and GC/MS. We detected 51 volatile compound variations during the oxidation, which were composed of aldehydes, hydrocarbons, cyclic compounds, alcohols, benzene compounds, ketones, furans, ester and pyrrolidine. The off-flavor characteristics can be strongly influenced by the synergy effects of volatile oxidation compounds. Chemometric analysis (PCA and AHC) was applied to identify the sensitive oxidation marker compounds, which included a (E,E)-2,4-heptadienal appropriate marker, via lipid oxidation in the n-3 PUFA rich oil.

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.

A preliminary MTD-PLS study for androgen receptor binding of steroid compounds

NASA Astrophysics Data System (ADS)

Bora, Alina; Seclaman, E.; Kurunczi, L.; Funar-Timofei, Simona

The relative binding affinities (RBA) of a series of 30 steroids for Human Androgen Receptor (AR) were used to initiate a MTD-PLS study. The 3D structures of all the compounds were obtained through geometry optimization in the framework of AM1 semiempirical quantum chemical method. The MTD hypermolecule (HM) was constructed, superposing these structures on the AR-bonded dihydrotestosterone (DHT) skeleton obtained from PDB (AR complex, ID 1I37). The parameters characterizing the HM vertices were collected using: AM1 charges, XlogP fragmental values, calculated fragmental polarizabilities (from refractivities), volumes, and H-bond parameters (Raevsky's thermodynamic originated scale). The resulted QSAR data matrix was submitted to PCA (Principal Component Analysis) and PLS (Projections in Latent Structures) procedure (SIMCA P 9.0); five compounds were selected as test set, and the remaining 25 molecules were used as training set. In the PLS procedure supplementary chemical information was introduced, i.e. the steric effect was always considered detrimental, and the hydrophobic and van der Waals interactions were imposed to be beneficial. The initial PLS model using the entire training set has the following characteristics: R2Y = 0.584, Q2 = 0.344. Based on distances to the model criterions (DMODX and DMODY), five compounds were eliminated and the obtained final model had the following characteristics: R2Y D 0.891, Q2 D 0.591. For this the external predictivity on the test set was unsatisfactory. A tentative explanation for these behaviors is the weak information content of the input QSAR matrix for the present series comparatively with other successful MTD-PLS modeling published elsewhere.

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.

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.

Evidence-based selection of training compounds for use in the mechanism-based integrated prediction of drug-induced liver injury in man.

PubMed

Dragovic, Sanja; Vermeulen, Nico P E; Gerets, Helga H; Hewitt, Philip G; Ingelman-Sundberg, Magnus; Park, B Kevin; Juhila, Satu; Snoeys, Jan; Weaver, Richard J

2016-12-01

The current test systems employed by pharmaceutical industry are poorly predictive for drug-induced liver injury (DILI). The 'MIP-DILI' project addresses this situation by the development of innovative preclinical test systems which are both mechanism-based and of physiological, pharmacological and pathological relevance to DILI in humans. An iterative, tiered approach with respect to test compounds, test systems, bioanalysis and systems analysis is adopted to evaluate existing models and develop new models that can provide validated test systems with respect to the prediction of specific forms of DILI and further elucidation of mechanisms. An essential component of this effort is the choice of compound training set that will be used to inform refinement and/or development of new model systems that allow prediction based on knowledge of mechanisms, in a tiered fashion. In this review, we focus on the selection of MIP-DILI training compounds for mechanism-based evaluation of non-clinical prediction of DILI. The selected compounds address both hepatocellular and cholestatic DILI patterns in man, covering a broad range of pharmacologies and chemistries, and taking into account available data on potential DILI mechanisms (e.g. mitochondrial injury, reactive metabolites, biliary transport inhibition, and immune responses). Known mechanisms by which these compounds are believed to cause liver injury have been described, where many if not all drugs in this review appear to exhibit multiple toxicological mechanisms. Thus, the training compounds selection offered a valuable tool to profile DILI mechanisms and to interrogate existing and novel in vitro systems for the prediction of human DILI.

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.

Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data.

PubMed

Hettne, Kristina M; Boorsma, André; van Dartel, Dorien A M; Goeman, Jelle J; de Jong, Esther; Piersma, Aldert H; Stierum, Rob H; Kleinjans, Jos C; Kors, Jan A

2013-01-29

Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with gene set analysis (GSA) methods for chemical treatment identification, for pharmacological mechanism elucidation, and for comparing compound toxicity profiles. We created 30,211 chemical response-specific gene sets for human and mouse by next-gen TM, and derived 1,189 (human) and 588 (mouse) gene sets from the Comparative Toxicogenomics Database (CTD). We tested for significant differential expression (SDE) (false discovery rate -corrected p-values < 0.05) of the next-gen TM-derived gene sets and the CTD-derived gene sets in gene expression (GE) data sets of five chemicals (from experimental models). We tested for SDE of gene sets for six fibrates in a peroxisome proliferator-activated receptor alpha (PPARA) knock-out GE dataset and compared to results from the Connectivity Map. We tested for SDE of 319 next-gen TM-derived gene sets for environmental toxicants in three GE data sets of triazoles, and tested for SDE of 442 gene sets associated with embryonic structures. We compared the gene sets to triazole effects seen in the Whole Embryo Culture (WEC), and used principal component analysis (PCA) to discriminate triazoles from other chemicals. Next-gen TM-derived gene sets matching the chemical treatment were significantly altered in three GE data sets, and the corresponding CTD-derived gene sets were significantly altered in five GE data sets. Six next-gen TM-derived and four CTD-derived fibrate gene sets were significantly altered in the PPARA knock-out GE dataset. None of the fibrate signatures in cMap scored significant against the PPARA GE signature. 33 environmental toxicant gene sets were significantly altered in the triazole GE data sets. 21 of these toxicants had a similar toxicity pattern as the triazoles. We confirmed embryotoxic effects, and discriminated triazoles from other chemicals. Gene set analysis with next-gen TM-derived chemical response-specific gene sets is a scalable method for identifying similarities in gene responses to other chemicals, from which one may infer potential mode of action and/or toxic effect.

Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

PubMed Central

2013-01-01

Background Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with gene set analysis (GSA) methods for chemical treatment identification, for pharmacological mechanism elucidation, and for comparing compound toxicity profiles. Methods We created 30,211 chemical response-specific gene sets for human and mouse by next-gen TM, and derived 1,189 (human) and 588 (mouse) gene sets from the Comparative Toxicogenomics Database (CTD). We tested for significant differential expression (SDE) (false discovery rate -corrected p-values < 0.05) of the next-gen TM-derived gene sets and the CTD-derived gene sets in gene expression (GE) data sets of five chemicals (from experimental models). We tested for SDE of gene sets for six fibrates in a peroxisome proliferator-activated receptor alpha (PPARA) knock-out GE dataset and compared to results from the Connectivity Map. We tested for SDE of 319 next-gen TM-derived gene sets for environmental toxicants in three GE data sets of triazoles, and tested for SDE of 442 gene sets associated with embryonic structures. We compared the gene sets to triazole effects seen in the Whole Embryo Culture (WEC), and used principal component analysis (PCA) to discriminate triazoles from other chemicals. Results Next-gen TM-derived gene sets matching the chemical treatment were significantly altered in three GE data sets, and the corresponding CTD-derived gene sets were significantly altered in five GE data sets. Six next-gen TM-derived and four CTD-derived fibrate gene sets were significantly altered in the PPARA knock-out GE dataset. None of the fibrate signatures in cMap scored significant against the PPARA GE signature. 33 environmental toxicant gene sets were significantly altered in the triazole GE data sets. 21 of these toxicants had a similar toxicity pattern as the triazoles. We confirmed embryotoxic effects, and discriminated triazoles from other chemicals. Conclusions Gene set analysis with next-gen TM-derived chemical response-specific gene sets is a scalable method for identifying similarities in gene responses to other chemicals, from which one may infer potential mode of action and/or toxic effect. PMID:23356878

Spray CVD for Making Solar-Cell Absorber Layers

NASA Technical Reports Server (NTRS)

Banger, Kulbinder K.; Harris, Jerry; Jin, Michael H.; Hepp, Aloysius

2007-01-01

Spray chemical vapor deposition (spray CVD) processes of a special type have been investigated for use in making CuInS2 absorber layers of thin-film solar photovoltaic cells from either of two subclasses of precursor compounds: [(PBu3) 2Cu(SEt)2In(SEt)2] or [(PPh3)2Cu(SEt)2 In(SEt)2]. The CuInS2 films produced in the experiments have been characterized by x-ray diffraction, scanning electron microscopy, energy-dispersive spectroscopy, and four-point-probe electrical tests.

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.

Trace detection of organic compounds in complex sample matrixes by single photon ionization ion trap mass spectrometry: real-time detection of security-relevant compounds and online analysis of the coffee-roasting process.

PubMed

Schramm, Elisabeth; Kürten, Andreas; Hölzer, Jasper; Mitschke, Stefan; Mühlberger, Fabian; Sklorz, Martin; Wieser, Jochen; Ulrich, Andreas; Pütz, Michael; Schulte-Ladbeck, Rasmus; Schultze, Rainer; Curtius, Joachim; Borrmann, Stephan; Zimmermann, Ralf

2009-06-01

An in-house-built ion trap mass spectrometer combined with a soft ionization source has been set up and tested. As ionization source, an electron beam pumped vacuum UV (VUV) excimer lamp (EBEL) was used for single-photon ionization. It was shown that soft ionization allows the reduction of fragmentation of the target analytes and the suppression of most matrix components. Therefore, the combination of photon ionization with the tandem mass spectrometry (MS/MS) capability of an ion trap yields a powerful tool for molecular ion peak detection and identification of organic trace compounds in complex matrixes. This setup was successfully tested for two different applications. The first one is the detection of security-relevant substances like explosives, narcotics, and chemical warfare agents. One test substance from each of these groups was chosen and detected successfully with single photon ionization ion trap mass spectrometry (SPI-ITMS) MS/MS measurements. Additionally, first tests were performed, demonstrating that this method is not influenced by matrix compounds. The second field of application is the detection of process gases. Here, exhaust gas from coffee roasting was analyzed in real time, and some of its compounds were identified using MS/MS studies.

Binding site feature description of 2-substituted benzothiazoles as potential AcrAB-TolC efflux pump inhibitors in E. coli.

PubMed

Yilmaz, S; Altinkanat-Gelmez, G; Bolelli, K; Guneser-Merdan, D; Ufuk Over-Hasdemir, M; Aki-Yalcin, E; Yalcin, I

2015-01-01

The resistance-nodulation-division (RND) family efflux pumps are important in the antibiotic resistance of Gram-negative bacteria. However, although a number of bacterial RND efflux pump inhibitors have been developed, there has been no clinically available RND efflux pump inhibitor to date. A set of BSN-coded 2-substituted benzothiazoles were tested alone and in combinations with ciprofloxacin (CIP) against the AcrAB-TolC overexpressor Escherichia coli AG102 clinical strain. The results indicated that the BSN compounds did not show intrinsic antimicrobial activity when tested alone. However, when used in combinations with CIP, a reversal in the antibacterial activity of CIP with up to 10-fold better MIC values was observed. In order to describe the binding site features of these BSN compounds with AcrB, docking studies were performed using the CDocker method. The performed docking poses and the calculated binding energy scores revealed that the tested compounds BSN-006, BSN-023, and BSN-004 showed significant binding interactions with the phenylalanine-rich region in the distal binding site of the AcrB binding monomer. Moreover, the tested compounds BSN-006 and BSN-023 possessed stronger binding energies than CIP, verifying that BSN compounds are acting as the putative substrates of AcrB.

Experimental dermatophytosis in nude guinea pigs compared with infections in Pirbright White animals.

PubMed

Hänel, H; Braun, B; Löschhorn, K

1990-04-01

With this investigation we wanted to compare the suitability of two different strains of guinea pigs to evaluate topical antifungals after experimental Trichophyton mentagrophytes infection. The "hairless"-strain was compared with the hairy Pirbright White strain. The infection areas were treated with a skin retention test (application before infection) and two sets of therapy tests (application after infection). In the retention test the different antimycotic compounds led to better gradations. Also, in the two sets of therapy tests the gradations among the compounds were more clearly and more comparable to published results of clinical trials. In the histological investigations the infections in the "hairless" animals developed in a way which is known from dermatophytoses in human skin. In the Pirbright White strain, however, due to the adjacent hair roots, a marked inflammatory reaction of the tissue persisted for 3 weeks which is not observed on human skin of the trunk and extremities. We, therefore, consider the "hairless" strain of guinea pigs to be more suitable than hairy animals for the comparison of topical antimycotics.

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.

Extension of a dynamic headspace multi-volatile method to milliliter injection volumes with full sample evaporation: Application to green tea.

PubMed

Ochiai, Nobuo; Sasamoto, Kikuo; Tsunokawa, Jun; Hoffmann, Andreas; Okanoya, Kazunori; MacNamara, Kevin

2015-11-20

An extension of multi-volatile method (MVM) technology using the combination of a standard dynamic headspace (DHS) configuration, and a modified DHS configuration incorporating an additional vacuum module, was developed for milliliter injection volume of aqueous sample with full sample evaporation. A prior step involved investigation of water management by weighing of the water residue in the adsorbent trap. The extended MVM for 1 mL aqueous sample consists of five different DHS method parameter sets including choice of the replaceable adsorbent trap. An initial two DHS sampling sets at 25°C with the standard DHS configuration using a carbon-based adsorbent trap target very volatile solutes with high vapor pressure (>10 kPa) and volatile solutes with moderate vapor pressure (1-10 kPa). Subsequent three DHS sampling sets at 80°C with the modified DHS configuration using a Tenax TA trap target solutes with low vapor pressure (<1 kPa) and/or hydrophilic characteristics. After the five sequential DHS samplings using the same HS vial, the five traps are sequentially desorbed with thermal desorption in reverse order of the DHS sampling and the desorbed compounds are trapped and concentrated in a programmed temperature vaporizing (PTV) inlet and subsequently analyzed in a single GC-MS run. Recoveries of 21 test aroma compounds in 1 mL water for each separate DHS sampling and the combined MVM procedure were evaluated as a function of vapor pressure in the range of 0.000088-120 kPa. The MVM procedure provided high recoveries (>88%) for 17 test aroma compounds and moderate recoveries (44-71%) for 4 test compounds. The method showed good linearity (r(2)>0.9913) and high sensitivity (limit of detection: 0.1-0.5 ng mL(-1)) even with MS scan mode. The improved sensitivity of the method was demonstrated with analysis of a wide variety of aroma compounds in brewed green tea. Compared to the original 100 μL MVM procedure, this extension to 1 mL MVM allowed detection of nearly twice the number of aroma compounds, including 18 potent aroma compounds from top-note to base-note (e.g. 2,3-butanedione, coumarin, furaneol, guaiacol, cis-3-hexenol, linalool, maltol, methional, 3-methyl butanal, 2,3,5-trimethyl pyrazine, and vanillin). Sensitivity for 23 compounds improved by a factor of 3.4-15 under 1 mL MVM conditions. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

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.

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.

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.

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.

Quantitative structure-activity relationship of the curcumin-related compounds using various regression methods

NASA Astrophysics Data System (ADS)

Khazaei, Ardeshir; Sarmasti, Negin; Seyf, Jaber Yousefi

2016-03-01

Quantitative structure activity relationship were used to study a series of curcumin-related compounds with inhibitory effect on prostate cancer PC-3 cells, pancreas cancer Panc-1 cells, and colon cancer HT-29 cells. Sphere exclusion method was used to split data set in two categories of train and test set. Multiple linear regression, principal component regression and partial least squares were used as the regression methods. In other hand, to investigate the effect of feature selection methods, stepwise, Genetic algorithm, and simulated annealing were used. In two cases (PC-3 cells and Panc-1 cells), the best models were generated by a combination of multiple linear regression and stepwise (PC-3 cells: r2 = 0.86, q2 = 0.82, pred_r2 = 0.93, and r2m (test) = 0.43, Panc-1 cells: r2 = 0.85, q2 = 0.80, pred_r2 = 0.71, and r2m (test) = 0.68). For the HT-29 cells, principal component regression with stepwise (r2 = 0.69, q2 = 0.62, pred_r2 = 0.54, and r2m (test) = 0.41) is the best method. The QSAR study reveals descriptors which have crucial role in the inhibitory property of curcumin-like compounds. 6ChainCount, T_C_C_1, and T_O_O_7 are the most important descriptors that have the greatest effect. With a specific end goal to design and optimization of novel efficient curcumin-related compounds it is useful to introduce heteroatoms such as nitrogen, oxygen, and sulfur atoms in the chemical structure (reduce the contribution of T_C_C_1 descriptor) and increase the contribution of 6ChainCount and T_O_O_7 descriptors. Models can be useful in the better design of some novel curcumin-related compounds that can be used in the treatment of prostate, pancreas, and colon cancers.

Experimental and computational prediction of glass transition temperature of drugs.

PubMed

Alzghoul, Ahmad; Alhalaweh, Amjad; Mahlin, Denny; Bergström, Christel A S

2014-12-22

Glass transition temperature (Tg) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between Tg and melting temperature (Tm) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of Tg were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on Tm predicted Tg with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict Tg of drug-like molecules with high accuracy were developed. If Tm is available, a simple linear regression can be used to predict Tg. However, the results also suggest that support vector regression and calculated molecular descriptors can predict Tg with equal accuracy, already before compound synthesis.

A Low-Cost, Hands-on Module to Characterize Antimicrobial Compounds Using an Interdisciplinary, Biophysical Approach

PubMed Central

Kaushik, Karishma S.; Kessel, Ashley; Ratnayeke, Nalin; Gordon, Vernita D.

2015-01-01

We have developed a hands-on experimental module that combines biology experiments with a physics-based analytical model in order to characterize antimicrobial compounds. To understand antibiotic resistance, participants perform a disc diffusion assay to test the antimicrobial activity of different compounds and then apply a diffusion-based analytical model to gain insights into the behavior of the active antimicrobial component. In our experience, this module was robust, reproducible, and cost-effective, suggesting that it could be implemented in diverse settings such as undergraduate research, STEM (science, technology, engineering, and math) camps, school programs, and laboratory training workshops. By providing valuable interdisciplinary research experience in science outreach and education initiatives, this module addresses the paucity of structured training or education programs that integrate diverse scientific fields. Its low-cost requirements make it especially suitable for use in resource-limited settings. PMID:25602254

Predicting the activity of drugs for a group of imidazopyridine anticoccidial compounds.

PubMed

Si, Hongzong; Lian, Ning; Yuan, Shuping; Fu, Aiping; Duan, Yun-Bo; Zhang, Kejun; Yao, Xiaojun

2009-10-01

Gene expression programming (GEP) is a novel machine learning technique. The GEP is used to build nonlinear quantitative structure-activity relationship model for the prediction of the IC(50) for the imidazopyridine anticoccidial compounds. This model is based on descriptors which are calculated from the molecular structure. Four descriptors are selected from the descriptors' pool by heuristic method (HM) to build multivariable linear model. The GEP method produced a nonlinear quantitative model with a correlation coefficient and a mean error of 0.96 and 0.24 for the training set, 0.91 and 0.52 for the test set, respectively. It is shown that the GEP predicted results are in good agreement with experimental ones.

Relationship between soybean yield/quality and soil quality in a major soybean-producing area based on a 2D-QSAR model

NASA Astrophysics Data System (ADS)

Gao, Ming; Li, Shiwei

2017-05-01

Based on experimental data of the soybean yield and quality from 30 sampling points, a quantitative structure-activity relationship model (2D-QSAR) was established using the soil quality (elements, pH, organic matter content and cation exchange capacity) as independent variables and soybean yield or quality as the dependent variable, with SPSS software. During the modeling, the full data set (30 and 14 compounds) was divided into a training set (24 and 11 compounds) for model generation and a test set (6 and 3 compounds) for model validation. The R2 values of the resulting models and data were 0.826 and 0.808 for soybean yield and quality, respectively, and all regression coefficients were significant (P < 0.05). The correlation coefficient R2pred of observed values and predicted values of the soybean yield and soybean quality in the test set were 0.961 and 0.956, respectively, indicating that the models had a good predictive ability. Moreover, the Mo, Se, K, N and organic matter contents and the cation exchange capacity of soil had a positive effect on soybean production, and the B, Mo, Se, K and N contents and cation exchange coefficient had a positive effect on soybean quality. The results are instructive for enhancing soils to improve the yield and quality of soybean, and this method can also be used to study other crops or regions, providing a theoretical basis to improving the yield and quality of crops.

ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches.

PubMed

Wang, Shuangquan; Sun, Huiyong; Liu, Hui; Li, Dan; Li, Youyong; Hou, Tingjun

2016-08-01

Blockade of human ether-à-go-go related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia, and Torsades de Pointes (TdP), and therefore reliable prediction of hERG liability in the early stages of drug design is quite important to reduce the risk of cardiotoxicity-related attritions in the later development stages. In this study, pharmacophore modeling and machine learning approaches were combined to construct classification models to distinguish hERG active from inactive compounds based on a diverse data set. First, an optimal ensemble of pharmacophore hypotheses that had good capability to differentiate hERG active from inactive compounds was identified by the recursive partitioning (RP) approach. Then, the naive Bayesian classification (NBC) and support vector machine (SVM) approaches were employed to construct classification models by integrating multiple important pharmacophore hypotheses. The integrated classification models showed improved predictive capability over any single pharmacophore hypothesis, suggesting that the broad binding polyspecificity of hERG can only be well characterized by multiple pharmacophores. The best SVM model achieved the prediction accuracies of 84.7% for the training set and 82.1% for the external test set. Notably, the accuracies for the hERG blockers and nonblockers in the test set reached 83.6% and 78.2%, respectively. Analysis of significant pharmacophores helps to understand the multimechanisms of action of hERG blockers. We believe that the combination of pharmacophore modeling and SVM is a powerful strategy to develop reliable theoretical models for the prediction of potential hERG liability.

CoMFA and CoMSIA studies on C-aryl glucoside SGLT2 inhibitors as potential anti-diabetic agents.

PubMed

Vyas, V K; Bhatt, H G; Patel, P K; Jalu, J; Chintha, C; Gupta, N; Ghate, M

2013-01-01

SGLT2 has become a target of therapeutic interest in diabetes research. CoMFA and CoMSIA studies were performed on C-aryl glucoside SGLT2 inhibitors (180 analogues) as potential anti-diabetic agents. Three different alignment strategies were used for the compounds. The best CoMFA and CoMSIA models were obtained by means of Distill rigid body alignment of training and test sets, and found statistically significant with cross-validated coefficients (q²) of 0.602 and 0.618, respectively, and conventional coefficients (r²) of 0.905 and 0.902, respectively. Both models were validated by a test set of 36 compounds giving satisfactory predicted correlation coefficients (r² pred) of 0.622 and 0.584 for CoMFA and CoMSIA models, respectively. A comparison was made with earlier 3D QSAR study on SGLT2 inhibitors, which shows that our 3D QSAR models are better than earlier models to predict good inhibitory activity. CoMFA and CoMSIA models generated in this work can provide useful information to design new compounds and helped in prediction of activity prior to synthesis.

High-Throughput Screening of Na(V)1.7 Modulators Using a Giga-Seal Automated Patch Clamp Instrument.

PubMed

Chambers, Chris; Witton, Ian; Adams, Cathryn; Marrington, Luke; Kammonen, Juha

2016-03-01

Voltage-gated sodium (Na(V)) channels have an essential role in the initiation and propagation of action potentials in excitable cells, such as neurons. Of these channels, Na(V)1.7 has been indicated as a key channel for pain sensation. While extensive efforts have gone into discovering novel Na(V)1.7 modulating compounds for the treatment of pain, none has reached the market yet. In the last two years, new compound screening technologies have been introduced, which may speed up the discovery of such compounds. The Sophion Qube(®) is a next-generation 384-well giga-seal automated patch clamp (APC) screening instrument, capable of testing thousands of compounds per day. By combining high-throughput screening and follow-up compound testing on the same APC platform, it should be possible to accelerate the hit-to-lead stage of ion channel drug discovery and help identify the most interesting compounds faster. Following a period of instrument beta-testing, a Na(V)1.7 high-throughput screen was run with two Pfizer plate-based compound subsets. In total, data were generated for 158,000 compounds at a median success rate of 83%, which can be considered high in APC screening. In parallel, IC50 assay validation and protocol optimization was completed with a set of reference compounds to understand how the IC50 potencies generated on the Qube correlate with data generated on the more established Sophion QPatch(®) APC platform. In summary, the results presented here demonstrate that the Qube provides a comparable but much faster approach to study Na(V)1.7 in a robust and reliable APC assay for compound screening.

Which Method of Assigning Bond Orders in Lewis Structures Best Reflects Experimental Data? An Analysis of the Octet Rule and Formal Charge Systems for Period 2 and 3 Nonmetallic Compounds

ERIC Educational Resources Information Center

See, Ronald F.

2009-01-01

Two systems were evaluated for drawing Lewis structures of period 2 and 3 non-metallic compounds: the octet rule and minimization of formal charge. The test set of molecules consisted of the oxides, halides, oxohalides, oxoanions, and oxoacids of B, N, O, F, Al, P, S, and Cl. Bond orders were quantified using experimental data, including bond…

A reliable computational workflow for the selection of optimal screening libraries.

PubMed

Gilad, Yocheved; Nadassy, Katalin; Senderowitz, Hanoch

2015-01-01

The experimental screening of compound collections is a common starting point in many drug discovery projects. Successes of such screening campaigns critically depend on the quality of the screened library. Many libraries are currently available from different vendors yet the selection of the optimal screening library for a specific project is challenging. We have devised a novel workflow for the rational selection of project-specific screening libraries. The workflow accepts as input a set of virtual candidate libraries and applies the following steps to each library: (1) data curation; (2) assessment of ADME/T profile; (3) assessment of the number of promiscuous binders/frequent HTS hitters; (4) assessment of internal diversity; (5) assessment of similarity to known active compound(s) (optional); (6) assessment of similarity to in-house or otherwise accessible compound collections (optional). For ADME/T profiling, Lipinski's and Veber's rule-based filters were implemented and a new blood brain barrier permeation model was developed and validated (85 and 74 % success rate for training set and test set, respectively). Diversity and similarity descriptors which demonstrated best performances in terms of their ability to select either diverse or focused sets of compounds from three databases (Drug Bank, CMC and CHEMBL) were identified and used for diversity and similarity assessments. The workflow was used to analyze nine common screening libraries available from six vendors. The results of this analysis are reported for each library providing an assessment of its quality. Furthermore, a consensus approach was developed to combine the results of these analyses into a single score for selecting the optimal library under different scenarios. We have devised and tested a new workflow for the rational selection of screening libraries under different scenarios. The current workflow was implemented using the Pipeline Pilot software yet due to the usage of generic components, it can be easily adapted and reproduced by computational groups interested in rational selection of screening libraries. Furthermore, the workflow could be readily modified to include additional components. This workflow has been routinely used in our laboratory for the selection of libraries in multiple projects and consistently selects libraries which are well balanced across multiple parameters.Graphical abstract.

Testing chemical carcinogenicity by using a transcriptomics HepaRG-based model?

PubMed Central

Doktorova, T. Y.; Yildirimman, Reha; Ceelen, Liesbeth; Vilardell, Mireia; Vanhaecke, Tamara; Vinken, Mathieu; Ates, Gamze; Heymans, Anja; Gmuender, Hans; Bort, Roque; Corvi, Raffaella; Phrakonkham, Pascal; Li, Ruoya; Mouchet, Nicolas; Chesne, Christophe; van Delft, Joost; Kleinjans, Jos; Castell, Jose; Herwig, Ralf; Rogiers, Vera

2014-01-01

The EU FP6 project carcinoGENOMICS explored the combination of toxicogenomics and in vitro cell culture models for identifying organotypical genotoxic- and non-genotoxic carcinogen-specific gene signatures. Here the performance of its gene classifier, derived from exposure of metabolically competent human HepaRG cells to prototypical non-carcinogens (10 compounds) and hepatocarcinogens (20 compounds), is reported. Analysis of the data at the gene and the pathway level by using independent biostatistical approaches showed a distinct separation of genotoxic from non-genotoxic hepatocarcinogens and non-carcinogens (up to 88 % correct prediction). The most characteristic pathway responding to genotoxic exposure was DNA damage. Interlaboratory reproducibility was assessed by blindly testing of three compounds, from the set of 30 compounds, by three independent laboratories. Subsequent classification of these compounds resulted in correct prediction of the genotoxicants. As expected, results on the non-genotoxic carcinogens and the non-carcinogens were less predictive. In conclusion, the combination of transcriptomics with the HepaRG in vitro cell model provides a potential weight of evidence approach for the evaluation of the genotoxic potential of chemical substances. PMID:26417288

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.

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.

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.

Toward automated biochemotype annotation for large compound libraries.

PubMed

Chen, Xian; Liang, Yizeng; Xu, Jun

2006-08-01

Combinatorial chemistry allows scientists to probe large synthetically accessible chemical space. However, identifying the sub-space which is selectively associated with an interested biological target, is crucial to drug discovery and life sciences. This paper describes a process to automatically annotate biochemotypes of compounds in a library and thus to identify bioactivity related chemotypes (biochemotypes) from a large library of compounds. The process consists of two steps: (1) predicting all possible bioactivities for each compound in a library, and (2) deriving possible biochemotypes based on predictions. The Prediction of Activity Spectra for Substances program (PASS) was used in the first step. In second step, structural similarity and scaffold-hopping technologies are employed. These technologies are used to derive biochemotypes from bioactivity predictions and the corresponding annotated biochemotypes from MDL Drug Data Report (MDDR) database. About a one million (982,889) commercially available compound library (CACL) has been tested using this process. This paper demonstrates the feasibility of automatically annotating biochemotypes for large libraries of compounds. Nevertheless, some issues need to be considered in order to improve the process. First, the prediction accuracy of PASS program has no significant correlation with the number of compounds in a training set. Larger training sets do not necessarily increase the maximal error of prediction (MEP), nor do they increase the hit structural diversity. Smaller training sets do not necessarily decrease MEP, nor do they decrease the hit structural diversity. Second, the success of systematic bioactivity prediction relies on modeling, training data, and the definition of bioactivities (biochemotype ontology). Unfortunately, the biochemotype ontology was not well developed in the PASS program. Consequently, "ill-defined" bioactivities can reduce the quality of predictions. This paper suggests the ways in which the systematic bioactivities prediction program should be improved.

Hit Dexter: A Machine-Learning Model for the Prediction of Frequent Hitters.

PubMed

Stork, Conrad; Wagner, Johannes; Friedrich, Nils-Ole; de Bruyn Kops, Christina; Šícho, Martin; Kirchmair, Johannes

2018-03-20

False-positive assay readouts caused by badly behaving compounds-frequent hitters, pan-assay interference compounds (PAINS), aggregators, and others-continue to pose a major challenge to experimental screening. There are only a few in silico methods that allow the prediction of such problematic compounds. We report the development of Hit Dexter, two extremely randomized trees classifiers for the prediction of compounds likely to trigger positive assay readouts either by true promiscuity or by assay interference. The models were trained on a well-prepared dataset extracted from the PubChem Bioassay database, consisting of approximately 311 000 compounds tested for activity on at least 50 proteins. Hit Dexter reached MCC and AUC values of up to 0.67 and 0.96 on an independent test set, respectively. The models are expected to be of high value, in particular to medicinal chemists and biochemists who can use Hit Dexter to identify compounds for which extra caution should be exercised with positive assay readouts. Hit Dexter is available as a free web service at http://hitdexter.zbh. uni-hamburg.de. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Novel inhibitors to Taenia solium Cu/Zn superoxide dismutase identified by virtual screening

NASA Astrophysics Data System (ADS)

García-Gutiérrez, P.; Landa-Piedra, A.; Rodríguez-Romero, A.; Parra-Unda, R.; Rojo-Domínguez, A.

2011-12-01

We describe in this work a successful virtual screening and experimental testing aimed to the identification of novel inhibitors of superoxide dismutase of the worm Taenia solium ( TsCu/Zn-SOD), a human parasite. Conformers from LeadQuest® database of drug-like compounds were selected and then docked on the surface of TsCu/Zn-SOD. Results were screened looking for ligand contacts with receptor side-chains not conserved in the human homologue, with a subsequent development of a score optimization by a set of energy minimization steps, aimed to identify lead compounds for in vitro experiments. Six out of fifty experimentally tested compounds showed μM inhibitory activity toward TsCu/Zn-SOD. Two of them showed species selectivity since did not inhibit the homologous human enzyme when assayed in vitro.

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.

Weighted Feature Significance: A Simple, Interpretable Model of Compound Toxicity Based on the Statistical Enrichment of Structural Features

PubMed Central

Huang, Ruili; Southall, Noel; Xia, Menghang; Cho, Ming-Hsuang; Jadhav, Ajit; Nguyen, Dac-Trung; Inglese, James; Tice, Raymond R.; Austin, Christopher P.

2009-01-01

In support of the U.S. Tox21 program, we have developed a simple and chemically intuitive model we call weighted feature significance (WFS) to predict the toxicological activity of compounds, based on the statistical enrichment of structural features in toxic compounds. We trained and tested the model on the following: (1) data from quantitative high–throughput screening cytotoxicity and caspase activation assays conducted at the National Institutes of Health Chemical Genomics Center, (2) data from Salmonella typhimurium reverse mutagenicity assays conducted by the U.S. National Toxicology Program, and (3) hepatotoxicity data published in the Registry of Toxic Effects of Chemical Substances. Enrichments of structural features in toxic compounds are evaluated for their statistical significance and compiled into a simple additive model of toxicity and then used to score new compounds for potential toxicity. The predictive power of the model for cytotoxicity was validated using an independent set of compounds from the U.S. Environmental Protection Agency tested also at the National Institutes of Health Chemical Genomics Center. We compared the performance of our WFS approach with classical classification methods such as Naive Bayesian clustering and support vector machines. In most test cases, WFS showed similar or slightly better predictive power, especially in the prediction of hepatotoxic compounds, where WFS appeared to have the best performance among the three methods. The new algorithm has the important advantages of simplicity, power, interpretability, and ease of implementation. PMID:19805409

Chemical ecology of obligate pollination mutualisms: testing the 'private channel' hypothesis in the Breynia-Epicephala association.

PubMed

Svensson, Glenn P; Okamoto, Tomoko; Kawakita, Atsushi; Goto, Ryutaro; Kato, Makoto

2010-06-01

*Obligate mutualisms involving actively pollinating seed predators are among the most remarkable insect-plant relationships known, yet almost nothing is known about the chemistry of pollinator attraction in these systems. The extreme species specificity observed in these mutualisms may be maintained by specific chemical compounds through 'private channels'. Here, we tested this hypothesis using the monoecious Breynia vitis-idaea and its host-specific Epicephala pollinator as a model. *Headspace samples were collected from both male and female flowers of the host. Gas chromatography with electroantennographic detection (GC-EAD), coupled gas chromatography-mass spectrometry, and olfactometer bioassays were used to identify the floral compounds acting as the pollinator attractant. *Male and female flowers of B. vitis-idaea produced similar sets of general floral compounds, but in different ratios, and male flowers emitted significantly more scent than female flowers. A mixture of 2-phenylethyl alcohol and 2-phenylacetonitrile, the two most abundant compounds in male flowers, was as attractive to female moths as the male flower sample, although the individual compounds were slightly less attractive when tested separately. *Data on the floral scent signals mediating obligate mutualisms involving active pollination are still very limited. We show that system-specific chemistry is not necessary for efficient host location by exclusive pollinators in these tightly coevolved mutualisms.

How to Achieve Better Results Using Pass-Based Virtual Screening: Case Study for Kinase Inhibitors

NASA Astrophysics Data System (ADS)

Pogodin, Pavel V.; Lagunin, Alexey A.; Rudik, Anastasia V.; Filimonov, Dmitry A.; Druzhilovskiy, Dmitry S.; Nicklaus, Mark C.; Poroikov, Vladimir V.

2018-04-01

Discovery of new pharmaceutical substances is currently boosted by the possibility of utilization of the Synthetically Accessible Virtual Inventory (SAVI) library, which includes about 283 million molecules, each annotated with a proposed synthetic one-step route from commercially available starting materials. The SAVI database is well-suited for ligand-based methods of virtual screening to select molecules for experimental testing. In this study, we compare the performance of three approaches for the analysis of structure-activity relationships that differ in their criteria for selecting of “active” and “inactive” compounds included in the training sets. PASS (Prediction of Activity Spectra for Substances), which is based on a modified Naïve Bayes algorithm, was applied since it had been shown to be robust and to provide good predictions of many biological activities based on just the structural formula of a compound even if the information in the training set is incomplete. We used different subsets of kinase inhibitors for this case study because many data are currently available on this important class of drug-like molecules. Based on the subsets of kinase inhibitors extracted from the ChEMBL 20 database we performed the PASS training, and then applied the model to ChEMBL 23 compounds not yet present in ChEMBL 20 to identify novel kinase inhibitors. As one may expect, the best prediction accuracy was obtained if only the experimentally confirmed active and inactive compounds for distinct kinases in the training procedure were used. However, for some kinases, reasonable results were obtained even if we used merged training sets, in which we designated as inactives the compounds not tested against the particular kinase. Thus, depending on the availability of data for a particular biological activity, one may choose the first or the second approach for creating ligand-based computational tools to achieve the best possible results in virtual screening.

Quantitative structure-activity relationship of organosulphur compounds as soybean 15-lipoxygenase inhibitors using CoMFA and CoMSIA.

PubMed

Caballero, Julio; Fernández, Michael; Coll, Deysma

2010-12-01

Three-dimensional quantitative structure-activity relationship studies were carried out on a series of 28 organosulphur compounds as 15-lipoxygenase inhibitors using comparative molecular field analysis and comparative molecular similarity indices analysis. Quantitative information on structure-activity relationships is provided for further rational development and direction of selective synthesis. All models were carried out over a training set including 22 compounds. The best comparative molecular field analysis model only included steric field and had a good Q² = 0.789. Comparative molecular similarity indices analysis overcame the comparative molecular field analysis results: the best comparative molecular similarity indices analysis model also only included steric field and had a Q² = 0.894. In addition, this model predicted adequately the compounds contained in the test set. Furthermore, plots of steric comparative molecular similarity indices analysis field allowed conclusions to be drawn for the choice of suitable inhibitors. In this sense, our model should prove useful in future 15-lipoxygenase inhibitor design studies. © 2010 John Wiley & Sons A/S.

Quantitative structural modeling on the wavelength interval (Δλ) in synchronous fluorescence spectroscopy

NASA Astrophysics Data System (ADS)

Samari, Fayezeh; Yousefinejad, Saeed

2017-11-01

Emission fluorescence spectroscopy has an extremely restricted scope of application to analyze of complex mixtures since its selectivity is reduced by the extensive spectral overlap. Synchronous fluorescence spectroscopy (SFS) is a technique enables us to analyze complex mixtures with overlapped emission and/or excitation spectra. The difference of excitation and emission wavelength of compounds (interval wavelength or Δλ) is an important characteristic in SFS. Thus a multi-parameter model was constructed to predict Δλ in 63 fluorescent compounds and the regression coefficient in training set, cross validation and test set were 0.88, 0.85 and 0.91 respectively. Furthermore, the applicability and validity of model were evaluated using different statistical methods such as y-scrambling and applicability domain. It was concluded that increasing average valence connectivity, number of Al2-NH functional group and Geary autocorrelation (lag 4) with electronegative weights can lead to increasing Δλ in the fluorescent compounds. The current study obtained an insight into the structural properties of compounds effective on their Δλ as an important parameter in SFS.

Electronic tongue for nitro and peroxide explosive sensing.

PubMed

González-Calabuig, Andreu; Cetó, Xavier; Del Valle, Manel

2016-06-01

This work reports the application of a voltammetric electronic tongue (ET) towards the simultaneous determination of both nitro-containing and peroxide-based explosive compounds, two families that represent the vast majority of compounds employed either in commercial mixtures or in improvised explosive devices. The multielectrode array was formed by graphite, gold and platinum electrodes, which exhibited marked mix-responses towards the compounds examined; namely, 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX), pentaerythritol tetranitrate (PETN), 2,4,6-trinitrotoluene (TNT), N-methyl-N,2,4,6-tetranitroaniline (Tetryl) and triacetone triperoxide (TATP). Departure information was the set of voltammograms, which were first analyzed by means of principal component analysis (PCA) allowing the discrimination of the different individual compounds, while artificial neural networks (ANNs) were used for the resolution and individual quantification of some of their mixtures (total normalized root mean square error for the external test set of 0.108 and correlation of the obtained vs. expected concentrations comparison graphs r>0.929). Copyright © 2016 Elsevier B.V. All rights reserved.

Highly Efficient Design-of-Experiments Methods for Combining CFD Analysis and Experimental Data

NASA Technical Reports Server (NTRS)

Anderson, Bernhard H.; Haller, Harold S.

2009-01-01

It is the purpose of this study to examine the impact of "highly efficient" Design-of-Experiments (DOE) methods for combining sets of CFD generated analysis data with smaller sets of Experimental test data in order to accurately predict performance results where experimental test data were not obtained. The study examines the impact of micro-ramp flow control on the shock wave boundary layer (SWBL) interaction where a complete paired set of data exist from both CFD analysis and Experimental measurements By combining the complete set of CFD analysis data composed of fifteen (15) cases with a smaller subset of experimental test data containing four/five (4/5) cases, compound data sets (CFD/EXP) were generated which allows the prediction of the complete set of Experimental results No statistical difference were found to exist between the combined (CFD/EXP) generated data sets and the complete Experimental data set composed of fifteen (15) cases. The same optimal micro-ramp configuration was obtained using the (CFD/EXP) generated data as obtained with the complete set of Experimental data, and the DOE response surfaces generated by the two data sets were also not statistically different.

A novel genotoxin-specific qPCR array based on the metabolically competent human HepaRG™ cell line as a rapid and reliable tool for improved in vitro hazard assessment.

PubMed

Ates, Gamze; Mertens, Birgit; Heymans, Anja; Verschaeve, Luc; Milushev, Dimiter; Vanparys, Philippe; Roosens, Nancy H C; De Keersmaecker, Sigrid C J; Rogiers, Vera; Doktorova, Tatyana Y

2018-04-01

Although the value of the regulatory accepted batteries for in vitro genotoxicity testing is recognized, they result in a high number of false positives. This has a major impact on society and industries developing novel compounds for pharmaceutical, chemical, and consumer products, as afflicted compounds have to be (prematurely) abandoned or further tested on animals. Using the metabolically competent human HepaRG ™ cell line and toxicogenomics approaches, we have developed an upgraded, innovative, and proprietary gene classifier. This gene classifier is based on transcriptomic changes induced by 12 genotoxic and 12 non-genotoxic reference compounds tested at sub-cytotoxic concentrations, i.e., IC10 concentrations as determined by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. The resulting gene classifier was translated into an easy-to-handle qPCR array that, as shown by pathway analysis, covers several different cellular processes related to genotoxicity. To further assess the predictivity of the tool, a set of 5 known positive and 5 known negative test compounds for genotoxicity was evaluated. In addition, 2 compounds with debatable genotoxicity data were tested to explore how the qPCR array would classify these. With an accuracy of 100%, when equivocal results were considered positive, the results showed that combining HepaRG ™ cells with a genotoxin-specific qPCR array can improve (geno)toxicological hazard assessment. In addition, the developed qPCR array was able to provide additional information on compounds for which so far debatable genotoxicity data are available. The results indicate that the new in vitro tool can improve human safety assessment of chemicals in general by basing predictions on mechanistic toxicogenomics information.

Synthesis and Biological Evaluation of Some [1,2,4]Triazolo[4,3-a]quinoxaline Derivatives as Novel Anticonvulsant Agents

PubMed Central

Ghiaty, Adel; El-Morsy, Ahmed; El-Gamal, Kamal

2013-01-01

2-([1,2,4]Triazolo[4,3-a]quinoxalin-4-ylthio)acetic acid hydrazide (10) was used as a precursor for the syntheses of novel quinoxaline derivatives with potential anticonvulsant properties. The newly synthesized compounds have been characterized by IR, 1H NMR, and mass spectral data followed by elemental analysis. The anticonvulsant evaluation was carried out for eleven of the synthesized compounds using metrazol induced convulsions model and phenobarbitone sodium as a standard. Among this set of tested compounds, two of them (14, and 15b) showed the best anticonvulsant activities. PMID:24198971

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

A hierarchical clustering methodology for the estimation of toxicity.

PubMed

Martin, Todd M; Harten, Paul; Venkatapathy, Raghuraman; Das, Shashikala; Young, Douglas M

2008-01-01

ABSTRACT A quantitative structure-activity relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural similarity is defined in terms of 2-D physicochemical descriptors (such as connectivity and E-state indices). A genetic algorithm-based technique is used to generate statistically valid QSAR models for each cluster (using the pool of descriptors described above). The toxicity for a given query compound is estimated using the weighted average of the predictions from the closest cluster from each step in the hierarchical clustering assuming that the compound is within the domain of applicability of the cluster. The hierarchical clustering methodology was tested using a Tetrahymena pyriformis acute toxicity data set containing 644 chemicals in the training set and with two prediction sets containing 339 and 110 chemicals. The results from the hierarchical clustering methodology were compared to the results from several different QSAR methodologies.

Differentiation of AmpC beta-lactamase binders vs. decoys using classification kNN QSAR modeling and application of the QSAR classifier to virtual screening

NASA Astrophysics Data System (ADS)

Hsieh, Jui-Hua; Wang, Xiang S.; Teotico, Denise; Golbraikh, Alexander; Tropsha, Alexander

2008-09-01

The use of inaccurate scoring functions in docking algorithms may result in the selection of compounds with high predicted binding affinity that nevertheless are known experimentally not to bind to the target receptor. Such falsely predicted binders have been termed `binding decoys'. We posed a question as to whether true binders and decoys could be distinguished based only on their structural chemical descriptors using approaches commonly used in ligand based drug design. We have applied the k-Nearest Neighbor ( kNN) classification QSAR approach to a dataset of compounds characterized as binders or binding decoys of AmpC beta-lactamase. Models were subjected to rigorous internal and external validation as part of our standard workflow and a special QSAR modeling scheme was employed that took into account the imbalanced ratio of inhibitors to non-binders (1:4) in this dataset. 342 predictive models were obtained with correct classification rate (CCR) for both training and test sets as high as 0.90 or higher. The prediction accuracy was as high as 100% (CCR = 1.00) for the external validation set composed of 10 compounds (5 true binders and 5 decoys) selected randomly from the original dataset. For an additional external set of 50 known non-binders, we have achieved the CCR of 0.87 using very conservative model applicability domain threshold. The validated binary kNN QSAR models were further employed for mining the NCGC AmpC screening dataset (69653 compounds). The consensus prediction of 64 compounds identified as screening hits in the AmpC PubChem assay disagreed with their annotation in PubChem but was in agreement with the results of secondary assays. At the same time, 15 compounds were identified as potential binders contrary to their annotation in PubChem. Five of them were tested experimentally and showed inhibitory activities in millimolar range with the highest binding constant Ki of 135 μM. Our studies suggest that validated QSAR models could complement structure based docking and scoring approaches in identifying promising hits by virtual screening of molecular libraries.

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

Applicability of canisters for sample storage in the determination of hazardous air pollutants

NASA Astrophysics Data System (ADS)

Kelly, Thomas J.; Holdren, Michael W.

This paper evaluates the applicability of canisters for storage of air samples containing volatile organic compounds listed among the 189 hazardous air pollutants (HAPs) in the 1990 U.S. Clean Air Act Amendments. Nearly 100 HAPs have sufficient vapor pressure to be considered volatile compounds. Of those volatile organic HAPs, 52 have been tested previously for stability during storage in canisters. The published HAP stability studies are reviewed, illustrating that for most of the 52 HAPs tested, canisters are an effective sample storage approach. However, the published stability studies used a variety of canister types and test procedures, and generally considered only a few compounds in a very small set of canisters. A comparison of chemical and physical properties of the HAPs has also been conducted, to evaluate the applicability of canister sampling for other HAPs, for which canister stability testing has never been conducted. Of 45 volatile HAPs never tested in canisters, this comparison identifies nine for which canisters should be effective, and 17 for which canisters are not likely to be effective. For the other 19 HAPs, no clear decision can be reached on the likely applicability of air sample storage in canisters.

Prediction of human population responses to toxic compounds by a collaborative competition.

PubMed

Eduati, Federica; Mangravite, Lara M; Wang, Tao; Tang, Hao; Bare, J Christopher; Huang, Ruili; Norman, Thea; Kellen, Mike; Menden, Michael P; Yang, Jichen; Zhan, Xiaowei; Zhong, Rui; Xiao, Guanghua; Xia, Menghang; Abdo, Nour; Kosyk, Oksana; Friend, Stephen; Dearry, Allen; Simeonov, Anton; Tice, Raymond R; Rusyn, Ivan; Wright, Fred A; Stolovitzky, Gustavo; Xie, Yang; Saez-Rodriguez, Julio

2015-09-01

The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.

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

Wrappers for Performance Enhancement and Oblivious Decision Graphs

DTIC Science & Technology

1995-09-01

always select all relevant features. We test di erent search engines to search the space of feature subsets and introduce compound operators to speed...distinct instances from the original dataset appearing in the test set is thus 0:632m. The 0i accuracy estimate is derived by using bootstrap sample...i for training and the rest of the instances for testing . Given a number b, the number of bootstrap samples, let 0i be the accuracy estimate for

Spelling and Meaning of Compounds in the Early School Years through Classroom Games: An Intervention Study.

PubMed

Tsesmeli, Styliani N

2017-01-01

The study aimed to evaluate the intervention effects on spelling and meaning of compounds by Greek students via group board games in classroom settings. The sample consisted of 60 pupils, who were attending the first and second grade of two primary schools in Greece. Each grade-class was divided into an intervention ( N = 29 children) and a control group ( N = 31 children). Before intervention, groups were evaluated by standardized tests of reading words/pseudowords, spelling words, and vocabulary. Students were also assessed on compound knowledge by a word analogy task, a meaning task and a spelling task. The experimental design of the intervention included a pre-test, a training program, and a post-test. The pre- and post-assessments consisted of the spelling and the meaning tasks entailing equally morphologically transparent and opaque compounds. The training program was based on word families ( N = 10 word families, 56 trained items, 5 sessions) and aimed to offer instruction of morphological decomposition and meaning of words. The findings showed that training was effective in enhancing the spelling and most notably the meaning of compounds. A closer inspection of intervention data in terms of morphological transparency, revealed that training group of first graders improved significantly both on transparent and opaque compounds, while the degree of gains was larger on opaque items for the second graders. These findings are consistent with the experimental literature and particularly optimistic for the literacy enhancement of typically developing children in regular classrooms.

Spelling and Meaning of Compounds in the Early School Years through Classroom Games: An Intervention Study

PubMed Central

Tsesmeli, Styliani N.

2017-01-01

The study aimed to evaluate the intervention effects on spelling and meaning of compounds by Greek students via group board games in classroom settings. The sample consisted of 60 pupils, who were attending the first and second grade of two primary schools in Greece. Each grade-class was divided into an intervention (N = 29 children) and a control group (N = 31 children). Before intervention, groups were evaluated by standardized tests of reading words/pseudowords, spelling words, and vocabulary. Students were also assessed on compound knowledge by a word analogy task, a meaning task and a spelling task. The experimental design of the intervention included a pre-test, a training program, and a post-test. The pre- and post-assessments consisted of the spelling and the meaning tasks entailing equally morphologically transparent and opaque compounds. The training program was based on word families (N = 10 word families, 56 trained items, 5 sessions) and aimed to offer instruction of morphological decomposition and meaning of words. The findings showed that training was effective in enhancing the spelling and most notably the meaning of compounds. A closer inspection of intervention data in terms of morphological transparency, revealed that training group of first graders improved significantly both on transparent and opaque compounds, while the degree of gains was larger on opaque items for the second graders. These findings are consistent with the experimental literature and particularly optimistic for the literacy enhancement of typically developing children in regular classrooms. PMID:29238316

Antimicrobial activity of organometallic isonicotinyl and pyrazinyl ferrocenyl-derived complexes

USDA-ARS?s Scientific Manuscript database

The discovery of new drugs against microbial diseases is imperative to human and animal health. In this study, we synthesized a novel set of iron-based compounds and tested them against three widespread microbial diseases –tuberculosis, malaria, and trichomoniasis. Our results identified several lea...

Evaluation of a Conductive Elastomer Seal for Spacecraft

NASA Technical Reports Server (NTRS)

Daniels, C. C.; Mather, J. L.; Oravec, H. A.; Dunlap, P. H., Jr.

2016-01-01

An electrically conductive elastomer was evaluated as a material candidate for a spacecraft seal. The elastomer used electrically conductive constituents as a means to reduce the resistance between mating interfaces of a sealed joint to meet spacecraft electrical bonding requirements. The compound's outgassing levels were compared against published NASA requirements. The compound was formed into a hollow O-ring seal and its compression set was measured. The O-ring seal was placed into an interface and the electrical resistance and leak rate were quantified. The amount of force required to fully compress the test article in the sealing interface and the force needed to separate the joint were also measured. The outgassing and resistance measurements were below the maximum allowable levels. The room temperature compression set and leak rates were fairly high when compared against other typical spacecraft seal materials, but were not excessive. The compression and adhesion forces were desirably low. Overall, the performance of the elastomer compound was sufficient to be considered for future spacecraft seal applications.

QSAR analysis of nicotinamidic compounds and design of potential Bruton's tyrosine kinase (Btk) inhibitors.

PubMed

Santos-Garcia, Letícia; Assis, Letícia C; Silva, Daniela R; Ramalho, Teodorico C; da Cunha, Elaine F F

2016-07-01

Bruton's tyrosine kinase (Btk) is an important enzyme in B-lymphocyte development and differentiation. Furthermore, Btk expression is considered essential for the proliferation and survival of these cells. Btk inhibition has become an attractive strategy for treating autoimmune diseases, B-cell leukemia, and lymphomas. With the objective of proposing new candidates for Btk inhibitors, we applied receptor-dependent four-dimensional quantitative structure-activity relationship (QSAR) methodology to a series of 96 nicotinamide analogs useful as Btk modulators. The QSAR models were developed using 71 compounds, the training set, and externally validated using 25 compounds, the test set. The conformations obtained by molecular dynamics simulation were overlapped in a virtual three-dimensional cubic box comprised of 2 and 5 Å cells, according to the six trial alignments. The models were generated by combining genetic function approximation and partial least squares regression technique. The analyses suggest that Model 1a yields the best results. The best equation shows [Formula: see text], r(2) = .743, RMSEC = .831, RMSECV = .879. Given the importance of the Tyr551, this residue could become a strategic target for the design of novel Btk inhibitors with improved potency. In addition, the good potency predicted for the proposed M2 compound indicates this compound as a potential Btk inhibitor candidate.

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.

Solution-phase microwave assisted parallel synthesis, biological evaluation and in silico docking studies of 2-chlorobenzoyl thioureas derivatives

NASA Astrophysics Data System (ADS)

Khan, Muhammad Riaz; Zaib, Sumera; Rauf, Muhammad Khawar; Ebihara, Masahiro; Badshah, Amin; Zahid, Muhammad; Nadeem, Muhammad Arif; Iqbal, Jamshed

2018-07-01

An efficient and facile microwave-assisted solution phase parallel synthesis for a 38-member library of N-aroyl-N‧-aryl thioureas was accomplished successfully. These analogues (1-38) were synthesized under identical set of conditions. It has been observed that the reaction time was drastically reduced from 8 to 12 h for conventional methods to only 10-15 mins. Products obtained were more than 98% pure, as characterized by elemental analysis along with FT-IR and 1H, 13C NMR. The solid-phase structural analysis was accomplished by single crystal XRD analysis. The urease inhibitory potential of synthetic compounds was tested and compounds were found to inhibit urease in moderate to significant manner. Compound 17 was the most potent inhibitor of urease having an IC50 value of 0.17 ± 0.1 μM. To check the cytotoxic profile of the derivatives, lungs cancer cell lines were used. Cytotoxicity analysis revealed remarkable toxicity of the compounds against tested lungs carcinoma and compounds showed variation in inhibition activity due to the substituents attached. The molecular docking studies were carried out to identify the possible binding modes of potent inhibitors in the active site of enzyme. The results suggested that the compounds can be further investigated and used against different cancers.

New hydroxypyridinone iron-chelators as potential anti-neurodegenerative drugs.

PubMed

Arduino, Daniela; Silva, Daniel; Cardoso, Sandra M; Chaves, Silvia; Oliveira, Catarina R; Santos, M Amelia

2008-05-01

The neuroprotective action of a set of new hydroxypyridinone-based (3,4-HP) compounds (A, B and C), which are iron chelators extra-functionalized with a propargylamino group for potential MAO-B inhibition, was evaluated after cell treatment with MPP+ (an in vivo inducer of parkinsonism) and Abeta(1-40) and/or Abeta(1-42) peptides. Our results show that all these compounds improved cell viability in cells treated with MPP+ and Abeta(1-40) peptide or Abeta(1-42) peptide. In order to evaluate the cellular mechanisms underlying the activity of these compounds, we studied their protective role in caspase activation. All compounds tested were able to prevent MPP+ and Brefeldin A induced caspase-2 activation. They also showed quite effective in the inhibition of caspase-4 and caspase-3 activity, an effector caspase in the apoptotic process. Finally, detection of apoptotic-like cell death after cell exposure to MPP+ was also performed by TUNEL assay. Our results demonstrated that all tested compounds prevented DNA fragmentation by decreasing TUNEL positive cells. A, B and C were more effective than DFP (a 3,4-HP iron-chelating agent in clinical use) in MPP+ induced cell death. Therefore, these results evidenced a neuroprotective and antiapoptotic role for the compounds studied.

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.

The Mu.Ta.Lig. Chemotheca: A community-populated molecular database for multi-target ligands identification and compound-repurposing

NASA Astrophysics Data System (ADS)

Ortuso, Francesco; Bagetta, Donatella; Maruca, Annalisa; Talarico, Carmine; Bolognesi, Maria L.; Haider, Norbert; Borges, Fernanda; Bryant, Sharon; Langer, Thierry; Senderowitz, Hanoch; Alcaro, Stefano

2018-04-01

Abstract For every lead compound developed in medicinal chemistry research, numerous other inactive or less active candidates are synthetized/isolated and tested. The majority of these compounds will not be selected for further development due to a sub-optimal pharmacological profile. However, some poorly active or even inactive compounds could live a second life if tested against other targets. Thus, new therapeutic opportunities could emerge and synergistic activities could be identified and exploited for existing compounds by sharing information between researchers who are working on different targets. The Mu.Ta.Lig (Multi-Target Ligand) Chemotheca database aims to offer such opportunities by facilitating information exchange among researchers worldwide. After a preliminary registration, users can (a) virtually upload structures and activity data for their compounds with corresponding, and eventually known activity data, and (b) search for other available compounds uploaded by the users community. Each piece of information about given compounds is owned by the user who initially uploaded it and multiple ownership is possible (occurs if different users uploaded the same compounds or information pertaining to the same compounds). A web-based graphical user interface has been developed to assist compound uploading, compounds searching and data retrieval. Physico-chemical and ADME properties as well as substructure-based PAINS evaluations are computed on the fly for each uploaded compound. Samples of compounds that match a set of search criteria and additional data on these compounds could be requested directly from their owners with no mediation by the Mu.Ta.Lig Chemotheca team. Guest access provides a simplified search interface to retrieve only basic information such as compound IDs and related 2D or 3D chemical structures. Moreover, some compounds can be hidden from Guest users according to an owner’s decision. In contrast, registered users have full access to all of the Chemotheca data including the permission to upload new compounds and/or update experimental/theoretical data (e.g., activities against new targets tested) related to already stored compounds. In order to facilitate scientific collaborations, all available data are connected to the corresponding owner’s email address (available for registered users only). The Chemotheca web site is accessible at http://chemotheca.unicz.it.

The Mu.Ta.Lig. Chemotheca: A Community-Populated Molecular Database for Multi-Target Ligands Identification and Compound-Repurposing.

PubMed

Ortuso, Francesco; Bagetta, Donatella; Maruca, Annalisa; Talarico, Carmine; Bolognesi, Maria L; Haider, Norbert; Borges, Fernanda; Bryant, Sharon; Langer, Thierry; Senderowitz, Hanoch; Alcaro, Stefano

2018-01-01

For every lead compound developed in medicinal chemistry research, numerous other inactive or less active candidates are synthetized/isolated and tested. The majority of these compounds will not be selected for further development due to a sub-optimal pharmacological profile. However, some poorly active or even inactive compounds could live a second life if tested against other targets. Thus, new therapeutic opportunities could emerge and synergistic activities could be identified and exploited for existing compounds by sharing information between researchers who are working on different targets. The Mu.Ta.Lig (Multi-Target Ligand) Chemotheca database aims to offer such opportunities by facilitating information exchange among researchers worldwide. After a preliminary registration, users can (a) virtually upload structures and activity data for their compounds with corresponding, and eventually known activity data, and (b) search for other available compounds uploaded by the users community. Each piece of information about given compounds is owned by the user who initially uploaded it and multiple ownership is possible (this occurs if different users uploaded the same compounds or information pertaining to the same compounds). A web-based graphical user interface has been developed to assist compound uploading, compounds searching and data retrieval. Physico-chemical and ADME properties as well as substructure-based PAINS evaluations are computed on the fly for each uploaded compound. Samples of compounds that match a set of search criteria and additional data on these compounds could be requested directly from their owners with no mediation by the Mu.Ta.Lig Chemotheca team. Guest access provides a simplified search interface to retrieve only basic information such as compound IDs and related 2D or 3D chemical structures. Moreover, some compounds can be hidden to Guest users according to an owner's decision. In contrast, registered users have full access to all of the Chemotheca data including the permission to upload new compounds and/or update experimental/theoretical data (e.g., activities against new targets tested) related to already stored compounds. In order to facilitate scientific collaborations, all available data are connected to the corresponding owner's email address (available for registered users only). The Chemotheca web site is accessible at http://chemotheca.unicz.it.

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

Design new P-glycoprotein modulators based on molecular docking and CoMFA study of α, β-unsaturated carbonyl-based compounds and oxime analogs as anticancer agents

NASA Astrophysics Data System (ADS)

Sepehri, Bakhtyar; Ghavami, Raouf

2017-02-01

In this research, molecular docking and CoMFA were used to determine interactions of α, β-unsaturated carbonyl-based compounds and oxime analogs with P-glycoprotein and prediction of their activity. Molecular docking study shown these molecules establish strong Van der Waals interactions with side chain of PHE-332, PHE-728 and PHE-974. Based on the effect of component numbers on squared correlation coefficient for cross validation tests (including leave-one-out and leave-many-out), CoMFA models with five components were built to predict pIC50 of molecules in seven cancer cell lines (including Panc-1 (pancreas cancer cell line), PaCa-2 (pancreatic carcinoma cell line), MCF-7 (breast cancer cell line), A-549 (epithelial), HT-29 (colon cancer cell line), H-460 (lung cancer cell line), PC-3 (prostate cancer cell line)). R2 values for training and test sets were in the range of 0.94-0.97 and 0.84 to 0.92, respectively, and for LOO and LMO cross validation test, q2 values were in the range of 0.75-0.82 and 0.65 to 0.73, respectively. Based on molecular docking results and extracted steric and electrostatic contour maps for CoMFA models, four new molecules with higher activity with respect to the most active compound in data set were designed.

Urinary arsenic, pesticides, heavy metals, phthalates, polyaromatic hydrocarbons, and polyfluoroalkyl compounds are associated with sleep troubles in adults: USA NHANES, 2005-2006.

PubMed

Shiue, Ivy

2017-01-01

Links between environmental chemicals and human health have emerged, but the effects on sleep health were less studied. Therefore, the aim of the present study was to investigate the relationships of different sets of environmental chemicals and common sleep troubles in a national and population-based setting. Data were retrieved from the United States National Health and Nutrition Examination Surveys, 2005-2006 including demographics, serum measurements, lifestyle factors, self-reported sleep troubles, and urinary environmental chemical concentrations. Statistical analyses including descriptive statistics, t-test, chi-square test, and survey-weighted logistic regression models were performed. Of all 5563 Americans aged 18-85, 2331 (42.0%) had wake-up at night, 2914 (52.5%) felt unrested during the day, 740 (13.4%) had leg jerks while sleeping, and 1059 (19.1%) had leg cramps for 2+ times a month. Higher levels of urinary arsenic, phthalates, and polyfluoroalkyl compounds were associated with wake-up at night. Higher levels of urinary 4-tert-octylphenol and polyfluoroalkyl compounds were associated with being unrested during the day. Higher levels of urinary arsenic, polyaromatic hydrocarbons, and polyfluoroalkyl compounds were associated with leg jerks while sleeping. Higher levels of urinary pesticides, heavy metals, phthalates, and polyaromatic hydrocarbons were associated with leg cramps while sleeping. However, there were no significant associations with other environmental chemicals such as parabens, bisphenol A, benzophenone-3, triclosan, perchlorate, nitrate, or thiocyanate. Eliminating arsenic, heavy metals, phthalate, pesticides, polyaromatic hydrocarbons, and polyfluoroalkyl compounds to improve sleep health might be considered while understanding the biological pathway with a longitudinal or experimental approach in future research would be suggested.

Breath Tests in Respiratory and Critical Care Medicine: From Research to Practice in Current Perspectives

PubMed Central

Cheepsattayakorn, Attapon; Cheepsattayakorn, Ruangrong

2013-01-01

Today, exhaled nitric oxide has been studied the most, and most researches have now focusd on asthma. More than a thousand different volatile organic compounds have been observed in low concentrations in normal human breath. Alkanes and methylalkanes, the majority of breath volatile organic compounds, have been increasingly used by physicians as a novel method to diagnose many diseases without discomforts of invasive procedures. None of the individual exhaled volatile organic compound alone is specific for disease. Exhaled breath analysis techniques may be available to diagnose and monitor the diseases in home setting when their sensitivity and specificity are improved in the future. PMID:24151617

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.

Development of Personalized Cancer Therapy for Men with Advanced Prostate Cancer

DTIC Science & Technology

2015-10-01

BGJ398; Novartis Pharmaceuticals ), is the lead compound being tested as anticancer therapy by Novartis. In addition, in an agreement with Janssen... Pharmaceutical Companies of Johnson & Johnson we obtained a pan-FGFR inhibitor from (JNJS 42756493) to test in a preclinical setting. For this...10ml/kg x BID) according to Janssen Pharmaceutical instructions. Treatment started 10 days after cell injection. After 3 weeks of treatment, we

Prediction of gas chromatographic retention indices by the use of radial basis function neural networks.

PubMed

Yao, Xiaojun; Zhang, Xiaoyun; Zhang, Ruisheng; Liu, Mancang; Hu, Zhide; Fan, Botao

2002-05-16

A new method for the prediction of retention indices for a diverse set of compounds from their physicochemical parameters has been proposed. The two used input parameters for representing molecular properties are boiling point and molar volume. Models relating relationships between physicochemical parameters and retention indices of compounds are constructed by means of radial basis function neural networks. To get the best prediction results, some strategies are also employed to optimize the topology and learning parameters of the RBFNNs. For the test set, a predictive correlation coefficient R=0.9910 and root mean squared error of 14.1 are obtained. Results show that radial basis function networks can give satisfactory prediction ability and its optimization is less-time consuming and easy to implement.

QSAR models to predict mutagenicity of acrylates, methacrylates and alpha,beta-unsaturated carbonyl compounds.

PubMed

Pérez-Garrido, Alfonso; Helguera, Aliuska Morales; Rodríguez, Francisco Girón; Cordeiro, M Natália D S

2010-05-01

The purpose of this study is to develop a quantitative structure-activity relationship (QSAR) model that can distinguish mutagenic from non-mutagenic species with alpha,beta-unsaturated carbonyl moiety using two endpoints for this activity - Ames test and mammalian cell gene mutation test - and also to gather information about the molecular features that most contribute to eliminate the mutagenic effects of these chemicals. Two data sets were used for modeling the two mutagenicity endpoints: (1) Ames test and (2) mammalian cells mutagenesis. The first one comprised 220 molecules, while the second one 48 substances, ranging from acrylates, methacrylates to alpha,beta-unsaturated carbonyl compounds. The QSAR models were developed by applying linear discriminant analysis (LDA) along with different sets of descriptors computed using the DRAGON software. For both endpoints, there was a concordance of 89% in the prediction and 97% confidentiality by combining the three models for the Ames test mutagenicity. We have also identified several structural alerts to assist the design of new monomers. These individual models and especially their combination are attractive from the point of view of molecular modeling and could be used for the prediction and design of new monomers that do not pose a human health risk. 2010 Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

Global structure–activity relationship model for nonmutagenic carcinogens using virtual ligand-protein interactions as model descriptors

PubMed Central

Cunningham, Albert R.; Trent, John O.

2012-01-01

Structure–activity relationship (SAR) models are powerful tools to investigate the mechanisms of action of chemical carcinogens and to predict the potential carcinogenicity of untested compounds. We describe the use of a traditional fragment-based SAR approach along with a new virtual ligand-protein interaction-based approach for modeling of nonmutagenic carcinogens. The ligand-based SAR models used descriptors derived from computationally calculated ligand-binding affinities for learning set agents to 5495 proteins. Two learning sets were developed. One set was from the Carcinogenic Potency Database, where chemicals tested for rat carcinogenesis along with Salmonella mutagenicity data were provided. The second was from Malacarne et al. who developed a learning set of nonalerting compounds based on rodent cancer bioassay data and Ashby’s structural alerts. When the rat cancer models were categorized based on mutagenicity, the traditional fragment model outperformed the ligand-based model. However, when the learning sets were composed solely of nonmutagenic or nonalerting carcinogens and noncarcinogens, the fragment model demonstrated a concordance of near 50%, whereas the ligand-based models demonstrated a concordance of 71% for nonmutagenic carcinogens and 74% for nonalerting carcinogens. Overall, these findings suggest that expert system analysis of virtual chemical protein interactions may be useful for developing predictive SAR models for nonmutagenic carcinogens. Moreover, a more practical approach for developing SAR models for carcinogenesis may include fragment-based models for chemicals testing positive for mutagenicity and ligand-based models for chemicals devoid of DNA reactivity. PMID:22678118

Global structure-activity relationship model for nonmutagenic carcinogens using virtual ligand-protein interactions as model descriptors.

PubMed

Cunningham, Albert R; Carrasquer, C Alex; Qamar, Shahid; Maguire, Jon M; Cunningham, Suzanne L; Trent, John O

2012-10-01

Structure-activity relationship (SAR) models are powerful tools to investigate the mechanisms of action of chemical carcinogens and to predict the potential carcinogenicity of untested compounds. We describe the use of a traditional fragment-based SAR approach along with a new virtual ligand-protein interaction-based approach for modeling of nonmutagenic carcinogens. The ligand-based SAR models used descriptors derived from computationally calculated ligand-binding affinities for learning set agents to 5495 proteins. Two learning sets were developed. One set was from the Carcinogenic Potency Database, where chemicals tested for rat carcinogenesis along with Salmonella mutagenicity data were provided. The second was from Malacarne et al. who developed a learning set of nonalerting compounds based on rodent cancer bioassay data and Ashby's structural alerts. When the rat cancer models were categorized based on mutagenicity, the traditional fragment model outperformed the ligand-based model. However, when the learning sets were composed solely of nonmutagenic or nonalerting carcinogens and noncarcinogens, the fragment model demonstrated a concordance of near 50%, whereas the ligand-based models demonstrated a concordance of 71% for nonmutagenic carcinogens and 74% for nonalerting carcinogens. Overall, these findings suggest that expert system analysis of virtual chemical protein interactions may be useful for developing predictive SAR models for nonmutagenic carcinogens. Moreover, a more practical approach for developing SAR models for carcinogenesis may include fragment-based models for chemicals testing positive for mutagenicity and ligand-based models for chemicals devoid of DNA reactivity.

AN EVALUATION OF THREE EMPIRICAL AIR-TO-LEAF MODELS FOR POLYCHLORINATED DIBENZO-P-DIOXINS AND DIBENZOFURANS

EPA Science Inventory

Three empirical air-to-leaf models for estimating grass concentrations of polychlorinated dibenzo-p-dioxins and dibenzofurans (abbreviated dioxins and furans) from air concentrations of these compounds are described and tested against two field data sets. All are empirical in th...

Design and implementation of an automated compound management system in support of lead optimization.

PubMed

Quintero, Catherine; Kariv, Ilona

2009-06-01

To meet the needs of the increasingly rapid and parallelized lead optimization process, a fully integrated local compound storage and liquid handling system was designed and implemented to automate the generation of assay-ready plates directly from newly submitted and cherry-picked compounds. A key feature of the system is the ability to create project- or assay-specific compound-handling methods, which provide flexibility for any combination of plate types, layouts, and plate bar-codes. Project-specific workflows can be created by linking methods for processing new and cherry-picked compounds and control additions to produce a complete compound set for both biological testing and local storage in one uninterrupted workflow. A flexible cherry-pick approach allows for multiple, user-defined strategies to select the most appropriate replicate of a compound for retesting. Examples of custom selection parameters include available volume, compound batch, and number of freeze/thaw cycles. This adaptable and integrated combination of software and hardware provides a basis for reducing cycle time, fully automating compound processing, and ultimately increasing the rate at which accurate, biologically relevant results can be produced for compounds of interest in the lead optimization process.

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.

Occurrence of Pharmaceuticals in Shallow Ground-Water of Suffolk County, New York, 2002-05

USGS Publications Warehouse

Benotti, Mark J.; Fisher, Shawn; Terracciano, Stephen

2006-01-01

Seventy (70) water samples were collected from 61 wells in the upper glacial and Magothy aquifers (9 wells were sampled twice) during 2002-05 and analyzed for 24 pharmaceuticals. Wells were selected for their proximity to known wastewater-treatment facilities that discharge to the shallow upper glacial aquifer. Pharmaceuticals were detected in 28 of the 70 samples, 19 of which contained one compound, and 9 of which contained two or more compounds. Concentrations of detected compounds were extremely low; most ranged from 0.001 to 0.1 microgram per liter (part per billion). The two most commonly detected compounds were carbamazepine (an antiepileptic drug) and sulfamethoxazole (an antibiotic). Occurrence of pharmaceutical compounds in Suffolk County ground-water is less prevalent than in susceptible streams of the United States that were tested in 1998-2000, but the similarity of median concentrations of the detected compounds of the two data sets indicates that current wastewater practices can serve to introduce pharmaceuticals to this shallow aquifer.

Nonvolatile, semivolatile, or volatile: redefining volatile for volatile organic compounds.

PubMed

Võ, Uyên-Uyén T; Morris, Michael P

2014-06-01

Although widely used in air quality regulatory frameworks, the term "volatile organic compound" (VOC) is poorly defined. Numerous standardized tests are currently used in regulations to determine VOC content (and thus volatility), but in many cases the tests do not agree with each other, nor do they always accurately represent actual evaporation rates under ambient conditions. The parameters (time, temperature, reference material, column polarity, etc.) used in the definitions and the associated test methods were created without a significant evaluation of volatilization characteristics in real world settings. Not only do these differences lead to varying VOC content results, but occasionally they conflict with one another. An ambient evaporation study of selected compounds and a few formulated products was conducted and the results were compared to several current VOC test methodologies: SCAQMD Method 313 (M313), ASTM Standard Test Method E 1868-10 (E1868), and US. EPA Reference Method 24 (M24). The ambient evaporation study showed a definite distinction between nonvolatile, semivolatile, and volatile compounds. Some low vapor pressure (LVP) solvents, currently considered exempt as VOCs by some methods, volatilize at ambient conditions nearly as rapidly as the traditional high-volatility solvents they are meant to replace. Conversely, bio-based and heavy hydrocarbons did not readily volatilize, though they often are calculated as VOCs in some traditional test methods. The study suggests that regulatory standards should be reevaluated to more accurately reflect real-world emission from the use of VOC containing products. The definition of VOC in current test methods may lead to regulations that exclude otherwise viable alternatives or allow substitutions of chemicals that may limit the environmental benefits sought in the regulation. A study was conducted to examine volatility of several compounds and a few formulated products under several current VOC test methodologies and ambient evaporation. This paper provides ample evidence to warrant a reevaluation of regulatory standards and provides a framework for progressive developments based on reasonable and scientifically justifiable definitions of VOCs.

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.

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.

Molecule kernels: a descriptor- and alignment-free quantitative structure-activity relationship approach.

PubMed

Mohr, Johannes A; Jain, Brijnesh J; Obermayer, Klaus

2008-09-01

Quantitative structure activity relationship (QSAR) analysis is traditionally based on extracting a set of molecular descriptors and using them to build a predictive model. In this work, we propose a QSAR approach based directly on the similarity between the 3D structures of a set of molecules measured by a so-called molecule kernel, which is independent of the spatial prealignment of the compounds. Predictors can be build using the molecule kernel in conjunction with the potential support vector machine (P-SVM), a recently proposed machine learning method for dyadic data. The resulting models make direct use of the structural similarities between the compounds in the test set and a subset of the training set and do not require an explicit descriptor construction. We evaluated the predictive performance of the proposed method on one classification and four regression QSAR datasets and compared its results to the results reported in the literature for several state-of-the-art descriptor-based and 3D QSAR approaches. In this comparison, the proposed molecule kernel method performed better than the other QSAR methods.

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.

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.

Binary classification of aqueous solubility using support vector machines with reduction and recombination feature selection.

PubMed

Cheng, Tiejun; Li, Qingliang; Wang, Yanli; Bryant, Stephen H

2011-02-28

Aqueous solubility is recognized as a critical parameter in both the early- and late-stage drug discovery. Therefore, in silico modeling of solubility has attracted extensive interests in recent years. Most previous studies have been limited in using relatively small data sets with limited diversity, which in turn limits the predictability of derived models. In this work, we present a support vector machines model for the binary classification of solubility by taking advantage of the largest known public data set that contains over 46 000 compounds with experimental solubility. Our model was optimized in combination with a reduction and recombination feature selection strategy. The best model demonstrated robust performance in both cross-validation and prediction of two independent test sets, indicating it could be a practical tool to select soluble compounds for screening, purchasing, and synthesizing. Moreover, our work may be used for comparative evaluation of solubility classification studies ascribe to the use of completely public resources.

Anticancer Properties of Distinct Antimalarial Drug Classes

PubMed Central

Hooft van Huijsduijnen, Rob; Guy, R. Kiplin; Chibale, Kelly; Haynes, Richard K.; Peitz, Ingmar; Kelter, Gerhard; Phillips, Margaret A.; Vennerstrom, Jonathan L.; Yuthavong, Yongyuth; Wells, Timothy N. C.

2013-01-01

We have tested five distinct classes of established and experimental antimalarial drugs for their anticancer potential, using a panel of 91 human cancer lines. Three classes of drugs: artemisinins, synthetic peroxides and DHFR (dihydrofolate reductase) inhibitors effected potent inhibition of proliferation with IC50s in the nM- low µM range, whereas a DHODH (dihydroorotate dehydrogenase) and a putative kinase inhibitor displayed no activity. Furthermore, significant synergies were identified with erlotinib, imatinib, cisplatin, dasatinib and vincristine. Cluster analysis of the antimalarials based on their differential inhibition of the various cancer lines clearly segregated the synthetic peroxides OZ277 and OZ439 from the artemisinin cluster that included artesunate, dihydroartemisinin and artemisone, and from the DHFR inhibitors pyrimethamine and P218 (a parasite DHFR inhibitor), emphasizing their shared mode of action. In order to further understand the basis of the selectivity of these compounds against different cancers, microarray-based gene expression data for 85 of the used cell lines were generated. For each compound, distinct sets of genes were identified whose expression significantly correlated with compound sensitivity. Several of the antimalarials tested in this study have well-established and excellent safety profiles with a plasma exposure, when conservatively used in malaria, that is well above the IC50s that we identified in this study. Given their unique mode of action and potential for unique synergies with established anticancer drugs, our results provide a strong basis to further explore the potential application of these compounds in cancer in pre-clinical or and clinical settings. PMID:24391728

40 CFR 265.1034 - Test methods and procedures.

Code of Federal Regulations, 2014 CFR

2014-07-01

... or n-hexane and air at a concentration of approximately, but less than, 10,000 ppm methane or n-hexane. (5) The background level shall be determined as set forth in Reference Method 21. (6) The... or exiting control device, as determined by Method 2, dscm/h; n = Number of organic compounds in the...

40 CFR 265.1034 - Test methods and procedures.

Code of Federal Regulations, 2011 CFR

2011-07-01

... or n-hexane and air at a concentration of approximately, but less than, 10,000 ppm methane or n-hexane. (5) The background level shall be determined as set forth in Reference Method 21. (6) The... or exiting control device, as determined by Method 2, dscm/h; n = Number of organic compounds in the...

40 CFR 265.1034 - Test methods and procedures.

Code of Federal Regulations, 2013 CFR

2013-07-01

... or n-hexane and air at a concentration of approximately, but less than, 10,000 ppm methane or n-hexane. (5) The background level shall be determined as set forth in Reference Method 21. (6) The... or exiting control device, as determined by Method 2, dscm/h; n = Number of organic compounds in the...

40 CFR 265.1034 - Test methods and procedures.

Code of Federal Regulations, 2012 CFR

2012-07-01

... or n-hexane and air at a concentration of approximately, but less than, 10,000 ppm methane or n-hexane. (5) The background level shall be determined as set forth in Reference Method 21. (6) The... or exiting control device, as determined by Method 2, dscm/h; n = Number of organic compounds in the...

40 CFR 265.1034 - Test methods and procedures.

Code of Federal Regulations, 2010 CFR

2010-07-01

... or n-hexane and air at a concentration of approximately, but less than, 10,000 ppm methane or n-hexane. (5) The background level shall be determined as set forth in Reference Method 21. (6) The... or exiting control device, as determined by Method 2, dscm/h; n = Number of organic compounds in the...

Neural network pattern recognition of thermal-signature spectra for chemical defense

NASA Astrophysics Data System (ADS)

Carrieri, Arthur H.; Lim, Pascal I.

1995-05-01

We treat infrared patterns of absorption or emission by nerve and blister agent compounds (and simulants of this chemical group) as features for the training of neural networks to detect the compounds' liquid layers on the ground or their vapor plumes during evaporation by external heating. Training of a four-layer network architecture is composed of a backward-error-propagation algorithm and a gradient-descent paradigm. We conduct testing by feed-forwarding preprocessed spectra through the network in a scaled format consistent with the structure of the training-data-set representation. The best-performance weight matrix (spectral filter) evolved from final network training and testing with software simulation trials is electronically transferred to a set of eight artificial intelligence integrated circuits (ICs') in specific modular form (splitting of weight matrices). This form makes full use of all input-output IC nodes. This neural network computer serves an important real-time detection function when it is integrated into pre-and postprocessing data-handling units of a tactical prototype thermoluminescence sensor now under development at the Edgewood Research, Development, and Engineering Center.

Discriminating Drug-Like Compounds by Partition Trees with Quantum Similarity Indices and Graph Invariants.

PubMed

Julián-Ortiz, Jesus V de; Gozalbes, Rafael; Besalú, Emili

2016-01-01

The search for new drug candidates in databases is of paramount importance in pharmaceutical chemistry. The selection of molecular subsets is greatly optimized and much more promising when potential drug-like molecules are detected a priori. In this work, about one hundred thousand molecules are ranked following a new methodology: a drug/non-drug classifier constructed by a consensual set of classification trees. The classification trees arise from the stochastic generation of training sets, which in turn are used to estimate probability factors of test molecules to be drug-like compounds. Molecules were represented by Topological Quantum Similarity Indices and their Graph Theoretical counterparts. The contribution of the present paper consists of presenting an effective ranking method able to improve the probability of finding drug-like substances by using these types of molecular descriptors.

Total Biosynthesis and Diverse Applications of the Nonribosomal Peptide-Polyketide Siderophore Yersiniabactin

PubMed Central

Ahmadi, Mahmoud Kamal; Fawaz, Samar; Jones, Charles H.; Zhang, Guojian

2015-01-01

Yersiniabactin (Ybt) is a mixed nonribosomal peptide-polyketide natural product natively produced by the pathogen Yersinia pestis. The compound enables iron scavenging capabilities upon host infection and is biosynthesized by a nonribosomal peptide synthetase featuring a polyketide synthase module. This pathway has been engineered for expression and biosynthesis using Escherichia coli as a heterologous host. In the current work, the biosynthetic process for Ybt formation was improved through the incorporation of a dedicated step to eliminate the need for exogenous salicylate provision. When this improvement was made, the compound was tested in parallel applications that highlight the metal-chelating nature of the compound. In the first application, Ybt was assessed as a rust remover, demonstrating a capacity of ∼40% compared to a commercial removal agent and ∼20% relative to total removal capacity. The second application tested Ybt in removing copper from a variety of nonbiological and biological solution mixtures. Success across a variety of media indicates potential utility in diverse scenarios that include environmental and biomedical settings. PMID:26025901

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.

Efficient enumeration of monocyclic chemical graphs with given path frequencies

PubMed Central

2014-01-01

Background The enumeration of chemical graphs (molecular graphs) satisfying given constraints is one of the fundamental problems in chemoinformatics and bioinformatics because it leads to a variety of useful applications including structure determination and development of novel chemical compounds. Results We consider the problem of enumerating chemical graphs with monocyclic structure (a graph structure that contains exactly one cycle) from a given set of feature vectors, where a feature vector represents the frequency of the prescribed paths in a chemical compound to be constructed and the set is specified by a pair of upper and lower feature vectors. To enumerate all tree-like (acyclic) chemical graphs from a given set of feature vectors, Shimizu et al. and Suzuki et al. proposed efficient branch-and-bound algorithms based on a fast tree enumeration algorithm. In this study, we devise a novel method for extending these algorithms to enumeration of chemical graphs with monocyclic structure by designing a fast algorithm for testing uniqueness. The results of computational experiments reveal that the computational efficiency of the new algorithm is as good as those for enumeration of tree-like chemical compounds. Conclusions We succeed in expanding the class of chemical graphs that are able to be enumerated efficiently. PMID:24955135

Comprehensive and Automated Linear Interaction Energy Based Binding-Affinity Prediction for Multifarious Cytochrome P450 Aromatase Inhibitors

PubMed Central

2017-01-01

Cytochrome P450 aromatase (CYP19A1) plays a key role in the development of estrogen dependent breast cancer, and aromatase inhibitors have been at the front line of treatment for the past three decades. The development of potent, selective and safer inhibitors is ongoing with in silico screening methods playing a more prominent role in the search for promising lead compounds in bioactivity-relevant chemical space. Here we present a set of comprehensive binding affinity prediction models for CYP19A1 using our automated Linear Interaction Energy (LIE) based workflow on a set of 132 putative and structurally diverse aromatase inhibitors obtained from a typical industrial screening study. We extended the workflow with machine learning methods to automatically cluster training and test compounds in order to maximize the number of explained compounds in one or more predictive LIE models. The method uses protein–ligand interaction profiles obtained from Molecular Dynamics (MD) trajectories to help model search and define the applicability domain of the resolved models. Our method was successful in accounting for 86% of the data set in 3 robust models that show high correlation between calculated and observed values for ligand-binding free energies (RMSE < 2.5 kJ mol–1), with good cross-validation statistics. PMID:28776988

Neutronic experiments with fluorine rich compounds at LR-0 reactor

DOE PAGES

Losa, Evzen; Kostal, Michal; Czakoj, T.; ...

2018-06-06

Here, research on molten salt reactor (MSR) neutronics continues in Research Centre Rez (Czech Republic) with experimental work being conducted using fluoride salt that was originally used in the Molten Salt Reactor Experiment (MSRE). Previous results identified significant variations in the neutron spectrum measured in LiF-NaF salt. These variations could originate from the fluorine description in current nuclear data sets. Subsequent experiments were performed to try to confirm this phenomenon. Therefore, another fluorine-rich compound, Teflon, was used for testing. Critical experiments showed slight discrepancies in C/E-1 for both compounds, Teflon and FLIBE, and systematic overestimation of criticality was observed inmore » calculations. Different nuclear data libraries were used for data set testing. For Teflon, the overestimation is higher when using JENDL-3.3, JENDL-4, and RUSFOND-2010 libraries, all three of which share the same inelastic-to-elastic scattering cross section ratio. Calculations using other libraries (ENDF/B-VII.1, ENDF/B-VII.0, JEFF-3.2, JEFF-3.1, and CENDL-3.1) tend to be closer to the experimental value. Neutron spectrum measurement in both substances revealed structure similar to that seen in previous measurements using LiF-NaF salt, which indicates that the neutron spectrum seems to be strongly shaped by fluorine. Discrepancies between experimental and calculational results seem to be larger in the neutron energy range of 100–1300 keV than in higher energies. In the case of neutron spectrum calculation, none of the tested libraries gives overall better results than the others.« less

Chemical function based pharmacophore generation of endothelin-A selective receptor antagonists.

PubMed

Funk, Oliver F; Kettmann, Viktor; Drimal, Jan; Langer, Thierry

2004-05-20

Both quantitative and qualitative chemical function based pharmacophore models of endothelin-A (ET(A)) selective receptor antagonists were generated by using the two algorithms HypoGen and HipHop, respectively, which are implemented in the Catalyst molecular modeling software. The input for HypoGen is a training set of 18 ET(A) antagonists exhibiting IC(50) values ranging between 0.19 nM and 67 microM. The best output hypothesis consists of five features: two hydrophobic (HY), one ring aromatic (RA), one hydrogen bond acceptor (HBA), and one negative ionizable (NI) function. The highest scoring Hip Hop model consists of six features: three hydrophobic (HY), one ring aromatic (RA), one hydrogen bond acceptor (HBA), and one negative ionizable (NI). It is the result of an input of three highly active, selective, and structurally diverse ET(A) antagonists. The predictive power of the quantitative model could be approved by using a test set of 30 compounds, whose activity values spread over 6 orders of magnitude. The two pharmacophores were tested according to their ability to extract known endothelin antagonists from the 3D molecular structure database of Derwent's World Drug Index. Thereby the main part of selective ET(A) antagonistic entries was detected by the two hypotheses. Furthermore, the pharmacophores were used to screen the Maybridge database. Six compounds were chosen from the output hit lists for in vitro testing of their ability to displace endothelin-1 from its receptor. Two of these are new potential lead compounds because they are structurally novel and exhibit satisfactory activity in the binding assay.

Neutronic experiments with fluorine rich compounds at LR-0 reactor

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

Losa, Evzen; Kostal, Michal; Czakoj, T.

Here, research on molten salt reactor (MSR) neutronics continues in Research Centre Rez (Czech Republic) with experimental work being conducted using fluoride salt that was originally used in the Molten Salt Reactor Experiment (MSRE). Previous results identified significant variations in the neutron spectrum measured in LiF-NaF salt. These variations could originate from the fluorine description in current nuclear data sets. Subsequent experiments were performed to try to confirm this phenomenon. Therefore, another fluorine-rich compound, Teflon, was used for testing. Critical experiments showed slight discrepancies in C/E-1 for both compounds, Teflon and FLIBE, and systematic overestimation of criticality was observed inmore » calculations. Different nuclear data libraries were used for data set testing. For Teflon, the overestimation is higher when using JENDL-3.3, JENDL-4, and RUSFOND-2010 libraries, all three of which share the same inelastic-to-elastic scattering cross section ratio. Calculations using other libraries (ENDF/B-VII.1, ENDF/B-VII.0, JEFF-3.2, JEFF-3.1, and CENDL-3.1) tend to be closer to the experimental value. Neutron spectrum measurement in both substances revealed structure similar to that seen in previous measurements using LiF-NaF salt, which indicates that the neutron spectrum seems to be strongly shaped by fluorine. Discrepancies between experimental and calculational results seem to be larger in the neutron energy range of 100–1300 keV than in higher energies. In the case of neutron spectrum calculation, none of the tested libraries gives overall better results than the others.« less

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

Modeling the Biodegradability of Chemical Compounds Using the Online CHEmical Modeling Environment (OCHEM)

PubMed Central

Vorberg, Susann

2013-01-01

Abstract Biodegradability describes the capacity of substances to be mineralized by free‐living bacteria. It is a crucial property in estimating a compound’s long‐term impact on the environment. The ability to reliably predict biodegradability would reduce the need for laborious experimental testing. However, this endpoint is difficult to model due to unavailability or inconsistency of experimental data. Our approach makes use of the Online Chemical Modeling Environment (OCHEM) and its rich supply of machine learning methods and descriptor sets to build classification models for ready biodegradability. These models were analyzed to determine the relationship between characteristic structural properties and biodegradation activity. The distinguishing feature of the developed models is their ability to estimate the accuracy of prediction for each individual compound. The models developed using seven individual descriptor sets were combined in a consensus model, which provided the highest accuracy. The identified overrepresented structural fragments can be used by chemists to improve the biodegradability of new chemical compounds. The consensus model, the datasets used, and the calculated structural fragments are publicly available at http://ochem.eu/article/31660. PMID:27485201

Anti AIDS drug design with the help of neural networks

NASA Astrophysics Data System (ADS)

Tetko, I. V.; Tanchuk, V. Yu.; Luik, A. I.

1995-04-01

Artificial neural networks were used to analyze and predict the human immunodefiency virus type 1 reverse transcriptase inhibitors. Training and control set included 44 molecules (most of them are well-known substances such as AZT, TIBO, dde, etc.) The biological activities of molecules were taken from literature and rated for two classes: active and inactive compounds according to their values. We used topological indices as molecular parameters. Four most informative parameters (out of 46) were chosen using cluster analysis and original input parameters' estimation procedure and were used to predict activities of both control and new (synthesized in our institute) molecules. We applied pruning network algorithm and network ensembles to obtain the final classifier and avoid chance correlation. The increasing of neural network generalization of the data from the control set was observed, when using the aforementioned methods. The prognosis of new molecules revealed one molecule as possibly active. It was confirmed by further biological tests. The compound was as active as AZT and in order less toxic. The active compound is currently being evaluated in pre clinical trials as possible drug for anti-AIDS therapy.

Evaluating Zeolite-Modified Sensors: towards a faster set of chemical sensors

NASA Astrophysics Data System (ADS)

Berna, A. Z.; Vergara, A.; Trincavelli, M.; Huerta, R.; Afonja, A.; Parkin, I. P.; Binions, R.; Trowell, S.

2011-09-01

The responses of zeolite-modified sensors, prepared by screen printing layers of chromium titanium oxide (CTO), were compared to unmodified tin oxide sensors using amplitude and transient responses. For transient responses we used a family of features, derived from the exponential moving average (EMA), to characterize chemo-resistive responses. All sensors were tested simultaneously against 20 individual volatile compounds from four chemical groups. The responses of the two types of sensors showed some independence. The zeolite-modified CTO sensors discriminated compounds better using either amplitude response or EMA features and CTO-modified sensors also responded three times faster.

Identification of biochemical features of defective Coffea arabica L. beans.

PubMed

Casas, María I; Vaughan, Michael J; Bonello, Pierluigi; McSpadden Gardener, Brian; Grotewold, Erich; Alonso, Ana P

2017-05-01

Coffee organoleptic properties are based in part on the quality and chemical composition of coffee beans. The presence of defective beans during processing and roasting contribute to off flavors and reduce overall cup quality. A multipronged approach was undertaken to identify specific biochemical markers for defective beans. To this end, beans were split into defective and non-defective fractions and biochemically profiled in both green and roasted states. A set of 17 compounds in green beans, including organic acids, amino acids and reducing sugars; and 35 compounds in roasted beans, dominated by volatile compounds, organic acids, sugars and sugar alcohols, were sufficient to separate the defective and non-defective fractions. Unsorted coffee was examined for the presence of the biochemical markers to test their utility in detecting defective beans. Although the green coffee marker compounds were found in all fractions, three of the roasted coffee marker compounds (1-methylpyrrole, 5-methyl- 2-furfurylfuran, and 2-methylfuran) were uniquely present in defective fractions. Published by Elsevier Ltd.

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do.

PubMed

Zhao, Linlin; Wang, Wenyi; Sedykh, Alexander; Zhu, Hao

2017-06-30

Numerous chemical data sets have become available for quantitative structure-activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting.

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do

PubMed Central

2017-01-01

Numerous chemical data sets have become available for quantitative structure–activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting. PMID:28691113

Linear Discriminant Analysis for the in Silico Discovery of Mechanism-Based Reversible Covalent Inhibitors of a Serine Protease: Application of Hydration Thermodynamics Analysis and Semi-empirical Molecular Orbital Calculation.

PubMed

Masuda, Yosuke; Yoshida, Tomoki; Yamaotsu, Noriyuki; Hirono, Shuichi

2018-01-01

We recently reported that the Gibbs free energy of hydrolytic water molecules (ΔG wat ) in acyl-trypsin intermediates calculated by hydration thermodynamics analysis could be a useful metric for estimating the catalytic rate constants (k cat ) of mechanism-based reversible covalent inhibitors. For thorough evaluation, the proposed method was tested with an increased number of covalent ligands that have no corresponding crystal structures. After modeling acyl-trypsin intermediate structures using flexible molecular superposition, ΔG wat values were calculated according to the proposed method. The orbital energies of antibonding π* molecular orbitals (MOs) of carbonyl C=O in covalently modified catalytic serine (E orb ) were also calculated by semi-empirical MO calculations. Then, linear discriminant analysis (LDA) was performed to build a model that can discriminate covalent inhibitor candidates from substrate-like ligands using ΔG wat and E orb . The model was built using a training set (10 compounds) and then validated by a test set (4 compounds). As a result, the training set and test set ligands were perfectly discriminated by the model. Hydrolysis was slower when (1) the hydrolytic water molecule has lower ΔG wat ; (2) the covalent ligand presents higher E orb (higher reaction barrier). Results also showed that the entropic term of hydrolytic water molecule (-TΔS wat ) could be used for estimating k cat and for covalent inhibitor optimization; when the rotational freedom of the hydrolytic water molecule is limited, the chance for favorable interaction with the electrophilic acyl group would also be limited. The method proposed in this study would be useful for screening and optimizing the mechanism-based reversible covalent inhibitors.

Screening of antifungal azole drugs and agrochemicals with an adapted alamarBlue-based assay demonstrates antibacterial activity of croconazole against Mycobacterium ulcerans.

PubMed

Scherr, Nicole; Röltgen, Katharina; Witschel, Matthias; Pluschke, Gerd

2012-12-01

An alamarBlue-based growth inhibition assay has been adapted for the thermosensitive and slow-growing pathogen Mycobacterium ulcerans. The standardized test procedure enables medium-throughput screening of preselected compound libraries. Testing of a set of 48 azoles with known antifungal activity led to the identification of an imidazole antifungal displaying an inhibitory dose (ID) of 9 μM for M. ulcerans.

Screening of Antifungal Azole Drugs and Agrochemicals with an Adapted alamarBlue-Based Assay Demonstrates Antibacterial Activity of Croconazole against Mycobacterium ulcerans

PubMed Central

Röltgen, Katharina; Witschel, Matthias; Pluschke, Gerd

2012-01-01

An alamarBlue-based growth inhibition assay has been adapted for the thermosensitive and slow-growing pathogen Mycobacterium ulcerans. The standardized test procedure enables medium-throughput screening of preselected compound libraries. Testing of a set of 48 azoles with known antifungal activity led to the identification of an imidazole antifungal displaying an inhibitory dose (ID) of 9 μM for M. ulcerans. PMID:23006761

From bird's eye views to molecular communities: two-layered visualization of structure-activity relationships in large compound data sets

NASA Astrophysics Data System (ADS)

Kayastha, Shilva; Kunimoto, Ryo; Horvath, Dragos; Varnek, Alexandre; Bajorath, Jürgen

2017-11-01

The analysis of structure-activity relationships (SARs) becomes rather challenging when large and heterogeneous compound data sets are studied. In such cases, many different compounds and their activities need to be compared, which quickly goes beyond the capacity of subjective assessments. For a comprehensive large-scale exploration of SARs, computational analysis and visualization methods are required. Herein, we introduce a two-layered SAR visualization scheme specifically designed for increasingly large compound data sets. The approach combines a new compound pair-based variant of generative topographic mapping (GTM), a machine learning approach for nonlinear mapping, with chemical space networks (CSNs). The GTM component provides a global view of the activity landscapes of large compound data sets, in which informative local SAR environments are identified, augmented by a numerical SAR scoring scheme. Prioritized local SAR regions are then projected into CSNs that resolve these regions at the level of individual compounds and their relationships. Analysis of CSNs makes it possible to distinguish between regions having different SAR characteristics and select compound subsets that are rich in SAR information.

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.

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.

Investigation of antimicrobial activities, DNA interaction, structural and spectroscopic properties of 2-chloro-6-(trifluoromethyl)pyridine

NASA Astrophysics Data System (ADS)

Evecen, Meryem; Kara, Mehmet; Idil, Onder; Tanak, Hasan

2017-06-01

2-Chloro-6-(trifluoromethyl)pyridine has been characterized by FT-IR, 1H and 13C NMR experiment. FT-IR spectra of the molecule has been recorded in the 4000-400 cm-1 region. The molecular structural parameters and vibrational frequencies were computed using the HF and DFT (B3LYP, B3PW91) methods with the 6-31+G(d,p) and 6-311++G(d,p) basis sets. 1H and 13C NMR Gauge Including Atomic Orbital (GIAO) chemical shifts of the compound were calculated using the density functional method (B3LYP) with the 6-311++G(d,p) basis set. The vibrational wavenumbers and chemical shifts were compared with the experimental data of the compound. Using the TD-DFT methodology, electronic absorption spectra of the compound have been computed. Besides, solvent effects on the excitation energies and chemical shifts were carried out using the integral equation formalism of the polarisable continuum model (IEF-PCM). DFT calculations of the compound, Mulliken's charges, molecular electrostatic potential (MEP), natural bond orbital (NBO) and thermodynamic properties were also obtained theoretically. In addition, the antimicrobial activities were tested by using minimal inhibitory concentration method (MIC) and also the effect of the molecule on pBR322 plasmid DNA was monitored byagarose gel electrophoresis experiments.

Pharmacophore Modelling and 4D-QSAR Study of Ruthenium(II) Arene Complexes as Anticancer Agents (Inhibitors) by Electron Conformational- Genetic Algorithm Method.

PubMed

Yavuz, Sevtap Caglar; Sabanci, Nazmiye; Saripinar, Emin

2018-01-01

The EC-GA method was employed in this study as a 4D-QSAR method, for the identification of the pharmacophore (Pha) of ruthenium(II) arene complex derivatives and quantitative prediction of activity. The arrangement of the computed geometric and electronic parameters for atoms and bonds of each compound occurring in a matrix is known as the electron-conformational matrix of congruity (ECMC). It contains the data from HF/3-21G level calculations. Compounds were represented by a group of conformers for each compound rather than a single conformation, known as fourth dimension to generate the model. ECMCs were compared within a certain range of tolerance values by using the EMRE program and the responsible pharmacophore group for ruthenium(II) arene complex derivatives was found. For selecting the sub-parameter which had the most effect on activity in the series and the calculation of theoretical activity values, the non-linear least square method and genetic algorithm which are included in the EMRE program were used. In addition, compounds were classified as the training and test set and the accuracy of the models was tested by cross-validation statistically. The model for training and test sets attained by the optimum 10 parameters gave highly satisfactory results with R2 training= 0.817, q 2=0.718 and SEtraining=0.066, q2 ext1 = 0.867, q2 ext2 = 0.849, q2 ext3 =0.895, ccctr = 0.895, ccctest = 0.930 and cccall = 0.905. Since there is no 4D-QSAR research on metal based organic complexes in the literature, this study is original and gives a powerful tool to the design of novel and selective ruthenium(II) arene complexes. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Design and synthesis of new potent PTP1B inhibitors with the skeleton of 2-substituted imino-3-substituted-5-heteroarylidene-1,3-thiazolidine-4-one: Part I.

PubMed

Meng, Ge; Zheng, Meilin; Wang, Mei; Tong, Jing; Ge, Weijuan; Zhang, Jiehe; Zheng, Aqun; Li, Jingya; Gao, Lixin; Li, Jia

2016-10-21

A new series of 2-substituted imino-3-substituted-5- heteroarylidene-1,3-thiazolidine-4-ones as the potent bidentate PTP1B inhibitors were designed and synthesized in this paper. All of the new compounds were characterized and identified by spectra analysis. The biological screening test against PTP1B showed that some of these compounds have the positive inhibitory activity against PTP1B. The activity of the compounds with 5-substituted pyrrole on 5-postion of 1,3-thiazolidine-4-one are more potent than that of those compounds with 5-substituted pyridine group. Compound 14b, 14h and 14i showed IC50 values of 8.66 μM, 6.83 μM and 6.09 μM against PTP1B, respectively. Docking analysis of these active compounds with PTP1B showed the possible interaction modes of these biheterocyclic compounds with the active sites of PTP1B. The inhibition tests against oncogenetic CDC25B were also conducted on this set of compounds to evaluate the selectivity and possible anti-neoplastic activity. Compound 14b also showed the lowest IC50 of 1.66 μM against CDC25B among all the possible inhibitors, including 14g, 14h, 14i and 15c. Some pharmacological parameters including VolSurf, steric and electric descriptors of all the compounds were calculated to give some hints about the relative relationship with the biological activity. The result of this study might give some light on designing the possible anti-cancer drugs targeting at phosphatases. The most active compound 14i might be used as the lead compound for further structure modification of the new low molecular weight PTP1B inhibitors with the N-containing heterocyclic skeleton. Copyright © 2016. Published by Elsevier Masson SAS.

75 FR 79320 - Animal Drugs, Feeds, and Related Products; Regulation of Carcinogenic Compounds in Food-Producing...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-20

... is calculated from tumor data of the cancer bioassays using a statistical extrapolation procedure... carcinogenic concern currently set forth in Sec. 500.84 utilizes a statistical extrapolation procedure that... procedures did not rely on a statistical extrapolation of the data to a 1 in 1 million risk of cancer to test...

Exploring the structural diversity in inhibitors of α-synuclein amyloidogenic folding, aggregation and neurotoxicity

NASA Astrophysics Data System (ADS)

Das, Sukanya; Pukala, Tara L.; Smid, Scott D.

2018-05-01

Aggregation of α-Synuclein (αS) protein to amyloid fibrils is a neuropathological hallmark of Parkinson’s disease (PD). Growing evidence suggests that extracellular αS aggregation plays a pivotal role in neurodegeneration found in PD in addition to the intracellular αS aggregates in Lewy bodies (LB). Here, we identified and compared a diverse set of molecules capable of mitigating protein aggregation and exogenous toxicity of αSA53T, a more aggregation-prone αS mutant found in familial PD. For the first time, we investigated the αS anti-amyloid activity of semi-synthetic flavonoid 2', 3', 4' trihydroxyflavone or 2-D08, which was compared with natural flavones myricetin and transilitin, as well as such structurally diverse polyphenols as honokiol and punicalagin. Additionally, two novel synthetic compounds with a dibenzyl imidazolidine scaffold, Compound 1 and Compound 2, were also investigated as they exhibited favourable binding with αSA53T. All seven compounds inhibited αSA53T aggregation as demonstrated by Thioflavin T fluorescence assays, with modified fibril morphology observed by transmission electron microscopy. Ion mobility-mass spectrometry (IM-MS) was used to monitor the structural conversion of native αSA53T into amyloidogenic conformations and all seven compounds preserved the native unfolded conformations of αSA53T following 48 hrs incubation. The presence of each test compound in a 1:2 molar ratio was also shown to inhibit the neurotoxicity of preincubated αSA53T using phaeochromocytoma (PC12) cell viability assays. Among the seven tested compounds 2-D08, honokiol and the synthetic Compound 2 demonstrated the highest inhibition of aggregation, coupled with neuroprotection from preincubated αSA53T in vitro. Molecular docking predicted that all compounds bound near the lysine-rich region of the N-terminus of αSA53T, where the flavonoids and honokiol predominantly interacted with Lys 23. Overall, these findings highlight that i) restricted vicinal trihydroxylation in the flavone B-ring is more effective in stabilizing the native αS conformations, thus blocking amyloidogenic aggregation, than dihydroxylation in both A and B-ring, and ii) honokiol, punicalagin and the synthetic imidazolidine Compound 2 also inhibit αS amyloidogenic aggregation by stabilizing its native conformations. This diverse set of molecules acting on a singular pathological target with predicted binding to αSA53T in the folding-prone N-terminal region may contribute towards novel drug-design for PD.

Exploring the Structural Diversity in Inhibitors of α-Synuclein Amyloidogenic Folding, Aggregation, and Neurotoxicity

PubMed Central

Das, Sukanya; Pukala, Tara L.; Smid, Scott D.

2018-01-01

Aggregation of α-Synuclein (αS) protein to amyloid fibrils is a neuropathological hallmark of Parkinson's disease (PD). Growing evidence suggests that extracellular αS aggregation plays a pivotal role in neurodegeneration found in PD in addition to the intracellular αS aggregates in Lewy bodies (LB). Here, we identified and compared a diverse set of molecules capable of mitigating protein aggregation and exogenous toxicity of αSA53T, a more aggregation-prone αS mutant found in familial PD. For the first time, we investigated the αS anti-amyloid activity of semi-synthetic flavonoid 2′, 3′, 4′ trihydroxyflavone or 2-D08, which was compared with natural flavones myricetin and transilitin, as well as such structurally diverse polyphenols as honokiol and punicalagin. Additionally, two novel synthetic compounds with a dibenzyl imidazolidine scaffold, Compound 1 and Compound 2, were also investigated as they exhibited favorable binding with αSA53T. All seven compounds inhibited αSA53T aggregation as demonstrated by Thioflavin T fluorescence assays, with modified fibril morphology observed by transmission electron microscopy. Ion mobility-mass spectrometry (IM-MS) was used to monitor the structural conversion of native αSA53T into amyloidogenic conformations and all seven compounds preserved the native unfolded conformations of αSA53T following 48 h incubation. The presence of each test compound in a 1:2 molar ratio was also shown to inhibit the neurotoxicity of preincubated αSA53T using phaeochromocytoma (PC12) cell viability assays. Among the seven tested compounds 2-D08, honokiol, and the synthetic Compound 2 demonstrated the highest inhibition of aggregation, coupled with neuroprotection from preincubated αSA53T in vitro. Molecular docking predicted that all compounds bound near the lysine-rich region of the N-terminus of αSA53T, where the flavonoids and honokiol predominantly interacted with Lys 23. Overall, these findings highlight that (i) restricted vicinal trihydroxylation in the flavone B-ring is more effective in stabilizing the native αS conformations, thus blocking amyloidogenic aggregation, than dihydroxylation aggregation in both A and B-ring, and (ii) honokiol, punicalagin, and the synthetic imidazolidine Compound 2 also inhibit αS amyloidogenic aggregation by stabilizing its native conformations. This diverse set of molecules acting on a singular pathological target with predicted binding to αSA53T in the folding-prone N-terminal region may contribute toward novel drug-design for PD. PMID:29888220

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.

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

Predicting hydration free energies of amphetamine-type stimulants with a customized molecular model

NASA Astrophysics Data System (ADS)

Li, Jipeng; Fu, Jia; Huang, Xing; Lu, Diannan; Wu, Jianzhong

2016-09-01

Amphetamine-type stimulants (ATS) are a group of incitation and psychedelic drugs affecting the central nervous system. Physicochemical data for these compounds are essential for understanding the stimulating mechanism, for assessing their environmental impacts, and for developing new drug detection methods. However, experimental data are scarce due to tight regulation of such illicit drugs, yet conventional methods to estimate their properties are often unreliable. Here we introduce a tailor-made multiscale procedure for predicting the hydration free energies and the solvation structures of ATS molecules by a combination of first principles calculations and the classical density functional theory. We demonstrate that the multiscale procedure performs well for a training set with similar molecular characteristics and yields good agreement with a testing set not used in the training. The theoretical predictions serve as a benchmark for the missing experimental data and, importantly, provide microscopic insights into manipulating the hydrophobicity of ATS compounds by chemical modifications.

4-Aminoquinolines Active against Chloroquine-Resistant Plasmodium falciparum: Basis of Antiparasite Activity and Quantitative Structure-Activity Relationship Analyses▿

PubMed Central

Hocart, Simon J.; Liu, Huayin; Deng, Haiyan; De, Dibyendu; Krogstad, Frances M.; Krogstad, Donald J.

2011-01-01

Chloroquine (CQ) is a safe and economical 4-aminoquinoline (AQ) antimalarial. However, its value has been severely compromised by the increasing prevalence of CQ resistance. This study examined 108 AQs, including 68 newly synthesized compounds. Of these 108 AQs, 32 (30%) were active only against CQ-susceptible Plasmodium falciparum strains and 59 (55%) were active against both CQ-susceptible and CQ-resistant P. falciparum strains (50% inhibitory concentrations [IC50s], ≤25 nM). All AQs active against both CQ-susceptible and CQ-resistant P. falciparum strains shared four structural features: (i) an AQ ring without alkyl substitution, (ii) a halogen at position 7 (Cl, Br, or I but not F), (iii) a protonatable nitrogen at position 1, and (iv) a second protonatable nitrogen at the end of the side chain distal from the point of attachment to the AQ ring via the nitrogen at position 4. For activity against CQ-resistant parasites, side chain lengths of ≤3 or ≥10 carbons were necessary but not sufficient; they were identified as essential factors by visual comparison of 2-dimensional (2-D) structures in relation to the antiparasite activities of the AQs and were confirmed by computer-based 3-D comparisons and differential contour plots of activity against P. falciparum. The advantage of the method reported here (refinement of quantitative structure-activity relationship [QSAR] descriptors by random assignment of compounds to multiple training and test sets) is that it retains QSAR descriptors according to their abilities to predict the activities of unknown test compounds rather than according to how well they fit the activities of the compounds in the training sets. PMID:21383099

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.

Design, synthesis and screening studies of potent thiazol-2-amine derivatives as fibroblast growth factor receptor 1 inhibitors.

PubMed

Kumar, B V S Suneel; Lakshmi, Narasu; Kumar, M Ravi; Rambabu, Gundla; Manjashetty, Thimmappa H; Arunasree, Kalle M; Sriram, Dharmarajan; Ramkumar, Kavya; Neamati, Nouri; Dayam, Raveendra; Sarma, J A R P

2014-01-01

Fibroblast growth factor receptor 1 (FGFR1) a tyrosine kinase receptor, plays important roles in angiogenesis, embryonic development, cell proliferation, cell differentiation, and wound healing. The FGFR isoforms and their receptors (FGFRs) considered as a potential targets and under intense research to design potential anticancer agents. Fibroblast growth factors (FGF's) and its growth factor receptors (FGFR) plays vital role in one of the critical pathway in monitoring angiogenesis. In the current study, quantitative pharmacophore models were generated and validated using known FGFR1 inhibitors. The pharmacophore models were generated using a set of 28 compounds (training). The top pharmacophore model was selected and validated using a set of 126 compounds (test set) and also using external validation. The validated pharmacophore was considered as a virtual screening query to screen a database of 400,000 virtual molecules and pharmacophore model retrieved 2800 hits. The retrieved hits were subsequently filtered based on the fit value. The selected hits were subjected for docking studies to observe the binding modes of the retrieved hits and also to reduce the false positives. One of the potential hits (thiazole-2-amine derivative) was selected based the pharmacophore fit value, dock score, and synthetic feasibility. A few analogues of the thiazole-2-amine derivative were synthesized. These compounds were screened for FGFR1 activity and anti-proliferative studies. The top active compound showed 56.87% inhibition of FGFR1 activity at 50 µM and also showed good cellular activity. Further optimization of thiazole-2-amine derivatives is in progress.

A successful virtual screening application: prediction of anticonvulsant activity in MES test of widely used pharmaceutical and food preservatives methylparaben and propylparaben.

PubMed

Talevi, Alan; Bellera, Carolina L; Castro, Eduardo A; Bruno-Blanch, Luis E

2007-09-01

A discriminant function based on topological descriptors was derived from a training set composed by anticonvulsants of clinical use or in clinical phase of development and compounds with other therapeutic uses. This model was internally and externally validated and applied in the virtual screening of chemical compounds from the Merck Index 13th. Methylparaben (Nipagin), a preservative widely used in food, cosmetics and pharmaceutics, was signaled as active by the discriminant function and tested in mice in the Maximal Electroshock (MES) test (i.p. administration), according to the NIH Program for Anticonvulsant Drug Development. Based on the results of Methylparaben, Propylparaben (Nipasol), another preservative usually used in association with the former, was also tested. Both methyl and propylparaben were found active in mice at doses of 30, 100, and 300 mg/kg. The discovery of the anticonvulsant activities in the MES test of methylparaben and propylparaben might be useful for the development of new anticonvulsant medications, specially considering the well-known toxicological profile of these drugs.

A successful virtual screening application: prediction of anticonvulsant activity in MES test of widely used pharmaceutical and food preservatives methylparaben and propylparaben

NASA Astrophysics Data System (ADS)

Talevi, Alan; Bellera, Carolina L.; Castro, Eduardo A.; Bruno-Blanch, Luis E.

2007-09-01

A discriminant function based on topological descriptors was derived from a training set composed by anticonvulsants of clinical use or in clinical phase of development and compounds with other therapeutic uses. This model was internally and externally validated and applied in the virtual screening of chemical compounds from the Merck Index 13th. Methylparaben (Nipagin), a preservative widely used in food, cosmetics and pharmaceutics, was signaled as active by the discriminant function and tested in mice in the Maximal Electroshock (MES) test (i.p. administration), according to the NIH Program for Anticonvulsant Drug Development. Based on the results of Methylparaben, Propylparaben (Nipasol), another preservative usually used in association with the former, was also tested. Both methyl and propylparaben were found active in mice at doses of 30, 100, and 300 mg/kg. The discovery of the anticonvulsant activities in the MES test of methylparaben and propylparaben might be useful for the development of new anticonvulsant medications, specially considering the well-known toxicological profile of these drugs.

Prediction of luciferase inhibitors by the high-performance MIEC-GBDT approach based on interaction energetic patterns.

PubMed

Chen, Fu; Sun, Huiyong; Liu, Hui; Li, Dan; Li, Youyong; Hou, Tingjun

2017-04-12

High-throughput screening (HTS) is widely applied in many fields ranging from drug discovery to clinical diagnostics and toxicity assessment. Firefly luciferase is commonly used as a reporter to monitor the effect of chemical compounds on the activity of a specific target or pathway in HTS. However, the false positive rate of luciferase-based HTS is relatively high because many artifacts or promiscuous compounds that have direct interaction with the luciferase reporter enzyme are usually identified as active compounds (hits). Therefore, it is necessary to develop a rapid screening method to identify these compounds that can inhibit the luciferase activity directly. In this study, a virtual screening (VS) classification model called MIEC-GBDT (MIEC: Molecular Interaction Energy Components; GBDT: Gradient Boosting Decision Tree) was developed to distinguish luciferase inhibitors from non-inhibitors. The MIECs calculated by Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) free energy decomposition were used to energetically characterize the binding pattern of each small molecule at the active site of luciferase, and then the GBDT algorithm was employed to construct the classifiers based on MIECs. The predictions to the test set show that the optimized MIEC-GBDT model outperformed molecular docking and MM/GBSA rescoring. The best MIEC-GBDT model based on the MIECs with the energy terms of ΔG ele , ΔG vdW , ΔG GB , and ΔG SA achieves the prediction accuracies of 87.2% and 90.3% for the inhibitors and non-inhibitors in the test sets, respectively. Moreover, the energetic analysis of the vital residues suggests that the energetic contributions of the vital residues to the binding of inhibitors are quite different from those to the binding of non-inhibitors. These results suggest that the MIEC-GBDT model is reliable and can be used as a powerful tool to identify potential interference compounds in luciferase-based HTS experiments.

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.

Inverse sequential detection of parameter changes in developing time series

NASA Technical Reports Server (NTRS)

Radok, Uwe; Brown, Timothy J.

1992-01-01

Progressive values of two probabilities are obtained for parameter estimates derived from an existing set of values and from the same set enlarged by one or more new values, respectively. One probability is that of erroneously preferring the second of these estimates for the existing data ('type 1 error'), while the second probability is that of erroneously accepting their estimates for the enlarged test ('type 2 error'). A more stable combined 'no change' probability which always falls between 0.5 and 0 is derived from the (logarithmic) width of the uncertainty region of an equivalent 'inverted' sequential probability ratio test (SPRT, Wald 1945) in which the error probabilities are calculated rather than prescribed. A parameter change is indicated when the compound probability undergoes a progressive decrease. The test is explicitly formulated and exemplified for Gaussian samples.

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...

Enantioselective potential of polysaccharide-based chiral stationary phases in supercritical fluid chromatography.

PubMed

Kucerova, Gabriela; Kalikova, Kveta; Tesarova, Eva

2017-06-01

The enantioselective potential of two polysaccharide-based chiral stationary phases for analysis of chiral structurally diverse biologically active compounds was evaluated in supercritical fluid chromatography using a set of 52 analytes. The chiral selectors immobilized on 2.5 μm silica particles were tris-(3,5-dimethylphenylcarmabate) derivatives of cellulose or amylose. The influence of the polysaccharide backbone, different organic modifiers, and different mobile phase additives on retention and enantioseparation was monitored. Conditions for fast baseline enantioseparation were found for the majority of the compounds. The success rate of baseline and partial enantioseparation with cellulose-based chiral stationary phase was 51.9% and 15.4%, respectively. Using amylose-based chiral stationary phase we obtained 76.9% of baseline enantioseparations and 9.6% of partial enantioseparations of the tested compounds. The best results on cellulose-based chiral stationary phase were achieved particularly with propane-2-ol and a mixture of isopropylamine and trifluoroacetic acid as organic modifier and additive to CO 2 , respectively. Methanol and basic additive isopropylamine were preferred on amylose-based chiral stationary phase. The complementary enantioselectivity of the cellulose- and amylose-based chiral stationary phases allows separation of the majority of the tested structurally different compounds. Separation systems were found to be directly applicable for analyses of biologically active compounds of interest. © 2017 Wiley Periodicals, Inc.

Design, discovery, modelling, synthesis, and biological evaluation of novel and small, low toxicity s-triazine derivatives as HIV-1 non-nucleoside reverse transcriptase inhibitors.

PubMed

Viira, Birgit; Selyutina, Anastasia; García-Sosa, Alfonso T; Karonen, Maarit; Sinkkonen, Jari; Merits, Andres; Maran, Uko

2016-06-01

A set of top-ranked compounds from a multi-objective in silico screen was experimentally tested for toxicity and the ability to inhibit the activity of HIV-1 reverse transcriptase (RT) in cell-free assay and in cell-based assay using HIV-1 based virus-like particles. Detailed analysis of a commercial sample that indicated specific inhibition of HIV-1 reverse transcription revealed that a minor component that was structurally similar to that of the main compound was responsible for the strongest inhibition. As a result, novel s-triazine derivatives were proposed, modelled, discovered, and synthesised, and their antiviral activity and cellular toxicity were tested. Compounds 18a and 18b were found to be efficient HIV-1 RT inhibitors, with an IC50 of 5.6±1.1μM and 0.16±0.05μM in a cell-based assay using infectious HIV-1, respectively. Compound 18b also had no detectable toxicity for different human cell lines. Their binding mode and interactions with the RT suggest that there was strong and adaptable binding in a tight (NNRTI) hydrophobic pocket. In summary, this iterative study produced structural clues and led to a group of non-toxic, novel compounds to inhibit HIV-RT with up to nanomolar potency. Copyright © 2016 Elsevier Ltd. All rights reserved.

Bioturbo similarity searching: combining chemical and biological similarity to discover structurally diverse bioactive molecules.

PubMed

Wassermann, Anne Mai; Lounkine, Eugen; Glick, Meir

2013-03-25

Virtual screening using bioactivity profiles has become an integral part of currently applied hit finding methods in pharmaceutical industry. However, a significant drawback of this approach is that it is only applicable to compounds that have been biologically tested in the past and have sufficient activity annotations for meaningful profile comparisons. Although bioactivity data generated in pharmaceutical institutions are growing on an unprecedented scale, the number of biologically annotated compounds still covers only a minuscule fraction of chemical space. For a newly synthesized compound or an isolated natural product to be biologically characterized across multiple assays, it may take a considerable amount of time. Consequently, this chemical matter will not be included in virtual screening campaigns based on bioactivity profiles. To overcome this problem, we herein introduce bioturbo similarity searching that uses chemical similarity to map molecules without biological annotations into bioactivity space and then searches for biologically similar compounds in this reference system. In benchmark calculations on primary screening data, we demonstrate that our approach generally achieves higher hit rates and identifies structurally more diverse compounds than approaches using chemical information only. Furthermore, our method is able to discover hits with novel modes of inhibition that traditional 2D and 3D similarity approaches are unlikely to discover. Test calculations on a set of natural products reveal the practical utility of the approach for identifying novel and synthetically more accessible chemical matter.

Experimental Investigation of Elastomer Docking Seal Compression Set, Adhesion, and Leakage

NASA Technical Reports Server (NTRS)

Daniels, Christopher C.; Oswald, Jay J.; Bastrzyk, Marta B.; Smith, Ian; Dunlap, Patrick H., Jr.; Steinetz, Bruce M.

2008-01-01

A universal docking and berthing system is being developed by the National Aeronautics and Space Administration (NASA) to support all future space exploration missions to low-Earth orbit (LEO), to the Moon, and to Mars. An investigation of the compression set of two seals mated in a seal-on-seal configuration and the force required to separate the two seals after periods of mating was conducted. The leakage rates of seals made from two silicone elastomer compounds, S0383-70 and S0899-50, configured in seal-on-seal mating were quantified. The test specimens were sub-scale seals with representative cross-sections and a 12 inch outside diameter. The leakage rate of the seals manufactured from S0899-50 was higher than that of the seals made from S0383-70 by a factor of 1.8. Similarly, the adhesion of the 50 durometer elastomer was significantly higher than that of the 70 durometer compound. However, the compression set values of the S0899-50 material were observed to be significantly lower than those for the S0383-70.

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

Major Source of Error in QSPR Prediction of Intrinsic Thermodynamic Solubility of Drugs: Solid vs Nonsolid State Contributions?

PubMed

Abramov, Yuriy A

2015-06-01

The main purpose of this study is to define the major limiting factor in the accuracy of the quantitative structure-property relationship (QSPR) models of the thermodynamic intrinsic aqueous solubility of the drug-like compounds. For doing this, the thermodynamic intrinsic aqueous solubility property was suggested to be indirectly "measured" from the contributions of solid state, ΔGfus, and nonsolid state, ΔGmix, properties, which are estimated by the corresponding QSPR models. The QSPR models of ΔGfus and ΔGmix properties were built based on a set of drug-like compounds with available accurate measurements of fusion and thermodynamic solubility properties. For consistency ΔGfus and ΔGmix models were developed using similar algorithms and descriptor sets, and validated against the similar test compounds. Analysis of the relative performances of these two QSPR models clearly demonstrates that it is the solid state contribution which is the limiting factor in the accuracy and predictive power of the QSPR models of the thermodynamic intrinsic solubility. The performed analysis outlines a necessity of development of new descriptor sets for an accurate description of the long-range order (periodicity) phenomenon in the crystalline state. The proposed approach to the analysis of limitations and suggestions for improvement of QSPR-type models may be generalized to other applications in the pharmaceutical industry.

Stereochemical analysis of (+)-limonene using theoretical and experimental NMR and chiroptical data

NASA Astrophysics Data System (ADS)

Reinscheid, F.; Reinscheid, U. M.

2016-02-01

Using limonene as test molecule, the success and the limitations of three chiroptical methods (optical rotatory dispersion (ORD), electronic and vibrational circular dichroism, ECD and VCD) could be demonstrated. At quite low levels of theory (mpw1pw91/cc-pvdz, IEFPCM (integral equation formalism polarizable continuum model)) the experimental ORD values differ by less than 10 units from the calculated values. The modelling in the condensed phase still represents a challenge so that experimental NMR data were used to test for aggregation and solvent-solute interactions. After establishing a reasonable structural model, only the ECD spectra prediction showed a decisive dependence on the basis set: only augmented (in the case of Dunning's basis sets) or diffuse (in the case of Pople's basis sets) basis sets predicted the position and shape of the ECD bands correctly. Based on these result we propose a procedure to assign the absolute configuration (AC) of an unknown compound using the comparison between experimental and calculated chiroptical data.

Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization.

PubMed

Turk, Samo; Merget, Benjamin; Rippmann, Friedrich; Fulle, Simone

2017-12-26

Matched molecular pair (MMP) analyses are widely used in compound optimization projects to gain insights into structure-activity relationships (SAR). The analysis is traditionally done via statistical methods but can also be employed together with machine learning (ML) approaches to extrapolate to novel compounds. The here introduced MMP/ML method combines a fragment-based MMP implementation with different machine learning methods to obtain automated SAR decomposition and prediction. To test the prediction capabilities and model transferability, two different compound optimization scenarios were designed: (1) "new fragments" which occurs when exploring new fragments for a defined compound series and (2) "new static core and transformations" which resembles for instance the identification of a new compound series. Very good results were achieved by all employed machine learning methods especially for the new fragments case, but overall deep neural network models performed best, allowing reliable predictions also for the new static core and transformations scenario, where comprehensive SAR knowledge of the compound series is missing. Furthermore, we show that models trained on all available data have a higher generalizability compared to models trained on focused series and can extend beyond chemical space covered in the training data. Thus, coupling MMP with deep neural networks provides a promising approach to make high quality predictions on various data sets and in different compound optimization scenarios.

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

metAlignID: a high-throughput software tool set for automated detection of trace level contaminants in comprehensive LECO two-dimensional gas chromatography time-of-flight mass spectrometry data.

PubMed

Lommen, Arjen; van der Kamp, Henk J; Kools, Harrie J; van der Lee, Martijn K; van der Weg, Guido; Mol, Hans G J

2012-11-09

A new alternative data processing tool set, metAlignID, is developed for automated pre-processing and library-based identification and concentration estimation of target compounds after analysis by comprehensive two-dimensional gas chromatography with mass spectrometric detection. The tool set has been developed for and tested on LECO data. The software is developed to run multi-threaded (one thread per processor core) on a standard PC (personal computer) under different operating systems and is as such capable of processing multiple data sets simultaneously. Raw data files are converted into netCDF (network Common Data Form) format using a fast conversion tool. They are then preprocessed using previously developed algorithms originating from metAlign software. Next, the resulting reduced data files are searched against a user-composed library (derived from user or commercial NIST-compatible libraries) (NIST=National Institute of Standards and Technology) and the identified compounds, including an indicative concentration, are reported in Excel format. Data can be processed batch wise. The overall time needed for conversion together with processing and searching of 30 raw data sets for 560 compounds is routinely within an hour. The screening performance is evaluated for detection of pesticides and contaminants in raw data obtained after analysis of soil and plant samples. Results are compared to the existing data-handling routine based on proprietary software (LECO, ChromaTOF). The developed software tool set, which is freely downloadable at www.metalign.nl, greatly accelerates data-analysis and offers more options for fine-tuning automated identification toward specific application needs. The quality of the results obtained is slightly better than the standard processing and also adds a quantitative estimate. The software tool set in combination with two-dimensional gas chromatography coupled to time-of-flight mass spectrometry shows great potential as a highly-automated and fast multi-residue instrumental screening method. Copyright © 2012 Elsevier B.V. All rights reserved.

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.

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.

A prototypic small molecule database for bronchoalveolar lavage-based metabolomics

NASA Astrophysics Data System (ADS)

Walmsley, Scott; Cruickshank-Quinn, Charmion; Quinn, Kevin; Zhang, Xing; Petrache, Irina; Bowler, Russell P.; Reisdorph, Richard; Reisdorph, Nichole

2018-04-01

The analysis of bronchoalveolar lavage fluid (BALF) using mass spectrometry-based metabolomics can provide insight into lung diseases, such as asthma. However, the important step of compound identification is hindered by the lack of a small molecule database that is specific for BALF. Here we describe prototypic, small molecule databases derived from human BALF samples (n=117). Human BALF was extracted into lipid and aqueous fractions and analyzed using liquid chromatography mass spectrometry. Following filtering to reduce contaminants and artifacts, the resulting BALF databases (BALF-DBs) contain 11,736 lipid and 658 aqueous compounds. Over 10% of these were found in 100% of samples. Testing the BALF-DBs using nested test sets produced a 99% match rate for lipids and 47% match rate for aqueous molecules. Searching an independent dataset resulted in 45% matching to the lipid BALF-DB compared to<25% when general databases are searched. The BALF-DBs are available for download from MetaboLights. Overall, the BALF-DBs can reduce false positives and improve confidence in compound identification compared to when general databases are used.

4D-Fingerprint Categorical QSAR Models for Skin Sensitization Based on Classification Local Lymph Node Assay Measures

PubMed Central

Li, Yi; Tseng, Yufeng J.; Pan, Dahua; Liu, Jianzhong; Kern, Petra S.; Gerberick, G. Frank; Hopfinger, Anton J.

2008-01-01

Currently, the only validated methods to identify skin sensitization effects are in vivo models, such as the Local Lymph Node Assay (LLNA) and guinea pig studies. There is a tremendous need, in particular due to novel legislation, to develop animal alternatives, eg. Quantitative Structure-Activity Relationship (QSAR) models. Here, QSAR models for skin sensitization using LLNA data have been constructed. The descriptors used to generate these models are derived from the 4D-molecular similarity paradigm and are referred to as universal 4D-fingerprints. A training set of 132 structurally diverse compounds and a test set of 15 structurally diverse compounds were used in this study. The statistical methodologies used to build the models are logistic regression (LR), and partial least square coupled logistic regression (PLS-LR), which prove to be effective tools for studying skin sensitization measures expressed in the two categorical terms of sensitizer and non-sensitizer. QSAR models with low values of the Hosmer-Lemeshow goodness-of-fit statistic, χHL2, are significant and predictive. For the training set, the cross-validated prediction accuracy of the logistic regression models ranges from 77.3% to 78.0%, while that of PLS-logistic regression models ranges from 87.1% to 89.4%. For the test set, the prediction accuracy of logistic regression models ranges from 80.0%-86.7%, while that of PLS-logistic regression models ranges from 73.3%-80.0%. The QSAR models are made up of 4D-fingerprints related to aromatic atoms, hydrogen bond acceptors and negatively partially charged atoms. PMID:17226934

Quantitative structure-property relationship analysis for the retention index of fragrance-like compounds on a polar stationary phase.

PubMed

Rojas, Cristian; Duchowicz, Pablo R; Tripaldi, Piercosimo; Pis Diez, Reinaldo

2015-11-27

A quantitative structure-property relationship (QSPR) was developed for modeling the retention index of 1184 flavor and fragrance compounds measured using a Carbowax 20M glass capillary gas chromatography column. The 4885 molecular descriptors were calculated using Dragon software, and then were simultaneously analyzed through multivariable linear regression analysis using the replacement method (RM) variable subset selection technique. We proceeded in three steps, the first one by considering all descriptor blocks, the second one by excluding conformational descriptor blocks, and the last one by analyzing only 3D-descriptor families. The models were validated through an external test set of compounds. Cross-validation methods such as leave-one-out and leave-many-out were applied, together with Y-randomization and applicability domain analysis. The developed model was used to estimate the I of a set of 22 molecules. The results clearly suggest that 3D-descriptors do not offer relevant information for modeling the retention index, while a topological index such as the Randić-like index from reciprocal squared distance matrix has a high relevance for this purpose. Copyright © 2015 Elsevier B.V. All rights reserved.

Classification and virtual screening of androgen receptor antagonists.

PubMed

Li, Jiazhong; Gramatica, Paola

2010-05-24

Computational tools, such as quantitative structure-activity relationship (QSAR), are highly useful as screening support for prioritization of substances of very high concern (SVHC). From the practical point of view, QSAR models should be effective to pick out more active rather than inactive compounds, expressed as sensitivity in classification works. This research investigates the classification of a big data set of endocrine-disrupting chemicals (EDCs)-androgen receptor (AR) antagonists, mainly aiming to improve the external sensitivity and to screen for potential AR binders. The kNN, lazy IB1, and ADTree methods and the consensus approach were used to build different models, which improve the sensitivity on external chemicals from 57.1% (literature) to 76.4%. Additionally, the models' predictive abilities were further validated on a blind collected data set (sensitivity: 85.7%). Then the proposed classifiers were used: (i) to distinguish a set of AR binders into antagonists and agonists; (ii) to screen a combined estrogen receptor binder database to find out possible chemicals that can bind to both AR and ER; and (iii) to virtually screen our in-house environmental chemical database. The in silico screening results suggest: (i) that some compounds can affect the normal endocrine system through a complex mechanism binding both to ER and AR; (ii) new EDCs, which are nonER binders, but can in silico bind to AR, are recognized; and (iii) about 20% of compounds in a big data set of environmental chemicals are predicted as new AR antagonists. The priority should be given to them to experimentally test the binding activities with AR.

Predicting the partitioning of biological compounds between room-temperature ionic liquids and water by means of the solvation-parameter model.

PubMed

Padró, Juan M; Ponzinibbio, Agustín; Mesa, Leidy B Agudelo; Reta, Mario

2011-03-01

The partition coefficients, P(IL/w), for different probe molecules as well as for compounds of biological interest between the room-temperature ionic liquids (RTILs) 1-butyl-3-methylimidazolium hexafluorophosphate, [BMIM][PF(6)], 1-hexyl-3-methylimidazolium hexafluorophosphate, [HMIM][PF(6)], 1-octyl-3-methylimidazolium tetrafluoroborate, [OMIM][BF(4)] and water were accurately measured. [BMIM][PF(6)] and [OMIM][BF(4)] were synthesized by adapting a procedure from the literature to a simpler, single-vessel and faster methodology, with a much lesser consumption of organic solvent. We employed the solvation-parameter model to elucidate the general chemical interactions involved in RTIL/water partitioning. With this purpose, we have selected different solute descriptor parameters that measure polarity, polarizability, hydrogen-bond-donor and hydrogen-bond-acceptor interactions, and cavity formation for a set of specifically selected probe molecules (the training set). The obtained multiparametric equations were used to predict the partition coefficients for compounds not present in the training set (the test set), most being of biological interest. Partial solubility of the ionic liquid in water (and water into the ionic liquid) was taken into account to explain the obtained results. This fact has not been deeply considered up to date. Solute descriptors were obtained from the literature, when available, or else calculated through commercial software. An excellent agreement between calculated and experimental log P(IL/w) values was obtained, which demonstrated that the resulting multiparametric equations are robust and allow predicting partitioning for any organic molecule in the biphasic systems studied.

A Structure-Activity Study with Aryl Acylamidases

PubMed Central

Villarreal, David T.; Turco, Ronald F.; Konopka, Allan

1994-01-01

We examined the relationship between chemical structure and biodegradability of acylanilide herbicides by using a set of model compounds. Four bacterial isolates (one gram-negative and three gram-positive) that grew on acetanilide were used. These soil isolates cleaved the amide bond of acetanilide via an aryl acylamidase reaction, producing aniline and the organic acid acetate. A series of acetanilide analogs with alkyl substitutions on the nitrogen atom or the aromatic ring were tested for their ability to induce aryl acylamidase activity and act as substrates for the enzyme. The substrate range, in general, was limited to those analogs not disubstituted in the ortho position of the benzene ring or which did not contain an alkyl group on the nitrogen atom. These same N-substituted compounds did not induce enzyme activity either, whereas the ortho-substituted compounds could in some cases. PMID:16349428

Shifting from the single- to the multitarget paradigm in drug discovery

PubMed Central

Medina-Franco, José L.; Giulianotti, Marc A.; Welmaker, Gregory S.; Houghten, Richard A.

2013-01-01

Increasing evidence that several drug compounds exert their effects through interactions with multiple targets is boosting the development of research fields that challenge the data reductionism approach. In this article, we review and discuss the concepts of drug repurposing, polypharmacology, chemogenomics, phenotypic screening and highthroughput in vivo testing of mixture-based libraries in an integrated manner. These research fields offer alternatives to the current paradigm of drug discovery, from a one target–one drug model to a multiple-target approach. Furthermore, the goals of lead identification are being expanded accordingly to identify not only ‘key’ compounds that fit with a single-target ‘lock’, but also ‘master key’ compounds that favorably interact with multiple targets (i.e. operate a set of desired locks to gain access to the expected clinical effects). PMID:23340113

High-Throughput Screening Using iPSC-Derived Neuronal Progenitors to Identify Compounds Counteracting Epigenetic Gene Silencing in Fragile X Syndrome.

PubMed

Kaufmann, Markus; Schuffenhauer, Ansgar; Fruh, Isabelle; Klein, Jessica; Thiemeyer, Anke; Rigo, Pierre; Gomez-Mancilla, Baltazar; Heidinger-Millot, Valerie; Bouwmeester, Tewis; Schopfer, Ulrich; Mueller, Matthias; Fodor, Barna D; Cobos-Correa, Amanda

2015-10-01

Fragile X syndrome (FXS) is the most common form of inherited mental retardation, and it is caused in most of cases by epigenetic silencing of the Fmr1 gene. Today, no specific therapy exists for FXS, and current treatments are only directed to improve behavioral symptoms. Neuronal progenitors derived from FXS patient induced pluripotent stem cells (iPSCs) represent a unique model to study the disease and develop assays for large-scale drug discovery screens since they conserve the Fmr1 gene silenced within the disease context. We have established a high-content imaging assay to run a large-scale phenotypic screen aimed to identify compounds that reactivate the silenced Fmr1 gene. A set of 50,000 compounds was tested, including modulators of several epigenetic targets. We describe an integrated drug discovery model comprising iPSC generation, culture scale-up, and quality control and screening with a very sensitive high-content imaging assay assisted by single-cell image analysis and multiparametric data analysis based on machine learning algorithms. The screening identified several compounds that induced a weak expression of fragile X mental retardation protein (FMRP) and thus sets the basis for further large-scale screens to find candidate drugs or targets tackling the underlying mechanism of FXS with potential for therapeutic intervention. © 2015 Society for Laboratory Automation and Screening.

Modeling the Biodegradability of Chemical Compounds Using the Online CHEmical Modeling Environment (OCHEM).

PubMed

Vorberg, Susann; Tetko, Igor V

2014-01-01

Biodegradability describes the capacity of substances to be mineralized by free-living bacteria. It is a crucial property in estimating a compound's long-term impact on the environment. The ability to reliably predict biodegradability would reduce the need for laborious experimental testing. However, this endpoint is difficult to model due to unavailability or inconsistency of experimental data. Our approach makes use of the Online Chemical Modeling Environment (OCHEM) and its rich supply of machine learning methods and descriptor sets to build classification models for ready biodegradability. These models were analyzed to determine the relationship between characteristic structural properties and biodegradation activity. The distinguishing feature of the developed models is their ability to estimate the accuracy of prediction for each individual compound. The models developed using seven individual descriptor sets were combined in a consensus model, which provided the highest accuracy. The identified overrepresented structural fragments can be used by chemists to improve the biodegradability of new chemical compounds. The consensus model, the datasets used, and the calculated structural fragments are publicly available at http://ochem.eu/article/31660. © 2014 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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.

Novel pyrrole derivatives bearing sulfonamide groups: Synthesis in vitro cytotoxicity evaluation, molecular docking and DFT study

NASA Astrophysics Data System (ADS)

Bavadi, Masoumeh; Niknam, Khodabakhsh; Shahraki, Omolbanin

2017-10-01

The synthesis of new derivatives of pyrrole substituted sulfonamide groups is described. The in vitro anticancer activity of these pyrroles was evaluated against MCF7, MOLT-4 and HL-60 cells using MTT assay. The target compounds showed inhibitory activity against tested cell lines. Among the compounds, compound 1a exhibited good cytotoxic activity. The potential of this analog to induce apoptosis was confirmed in a nuclear morphological assay by Hoechst 33258 staining in the PC-12 cells. Finally, molecular docking was performed to determine the probable binding mode of the designed pyrrole derivatives into the active site of FGFR1 protein. DFT calculations were carried out at the B3LYP levels of theory with 6-31+G (d,p) basis set for compound 1a. The point group (C1) of it was obtained based on the optimized structures; the calculation of the FT-IR vibrational frequencies, 1H NMR and 13C NMR chemical shifts of the compound were carried out and compared with those obtained experimentally.

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.

Pesticides in ground water in selected agricultural land-use areas and hydrogeologic settings in Pennsylvania, 2003-07

USGS Publications Warehouse

Loper, Connie A.; Breen, Kevin J.; Zimmerman, Tammy M.; Clune, John W.

2009-01-01

This report was prepared by the U.S. Geological Survey (USGS) in cooperation with the Pennsylvania Department of Agriculture (PDA) as part of the Pennsylvania Pesticides and Ground Water Strategy (PPGWS). Monitoring data and extensive quality-assurance data on the occurrence of pesticides in ground water during 2003–07 are presented and evaluated; decreases in the land area used for agriculture and corresponding changes in the use of pesticides also are documented. In the Pennsylvania ground waters assessed since 2003, concentrations of pesticides did not exceed any maximum contaminant or health advisory levels established by the U.S. Environmental Protection Agency; PPGWS actions are invoked by the PDA at fractions of these levels and were needed only in areas designated by the PDA for special ground-water protection. Previous investigations through 1998 of pesticides in Pennsylvania ground water identified land use, as a surrogate for pesticide use, and rock type of the aquifer combined with physiography as key hydrogeologic setting variables for understanding aquifer vulnerability to contamination and the common occurrence of atrazine and metolachlor in ground water. Of 20 major hydrogeologic settings in a framework established in 1999 for pesticide monitoring in Pennsylvania, 9 were identified as priorities for data collection in order to change the monitoring status from "inadequate" to "adequate" for the PPGWS. Agricultural and forested land-use areas are decreasing because of urban and suburban growth. In the nine hydrogeologic settings evaluated using 1992 and 2001 data, decreases of up to 12 percent for agricultural land and 10 percent for forested land corresponded to increases of up to 11 percent for urban land. Changes in agricultural pesticide use were computed from crop data. For example, from 1996 to 2004–05, atrazine use declined by about 15 percent to 1,314,000 lb/yr (pounds per year) and metolachlor use increased by about 20 percent to 895,000 lb/yr; these compounds are the two most-used agricultural pesticides statewide. In 2003–07, a baseline assessment of pesticides was conducted in five of nine hydrogeologic settings with inadequate monitoring data—the Blue Ridge crystalline and Triassic Lowland siliciclastic, Eastern Lake surficial, Devonian-Silurian carbonate, Great Valley siliciclastic, and Northeastern Glaciated surficial settings. Between 20 and 30 wells in each setting were monitored. Of the 126 wells sampled, 96 well-water samples were analyzed for at least 52 pesticide compounds at the USGS National Water Quality Laboratory (NWQL) using a method with a minimum reporting level (MRL) at or above 0.002 µg/L (micrograms per liter). Of the 96 well waters analyzed by NWQL, 43 had measureable concentrations of one or more pesticides. Atrazine and (or) deethylatrazine (CIAT), a degradation product of atrazine, were reported at or above the MRL in 39 of the 43 well waters. Neither atrazine nor CIAT were reported at concentrations exceeding 0.10 µg/L; all measured concentrations in these five settings were below PPGWS action levels. Metolachlor was present in 7 of the 43 well waters with measureable concentrations of 1 or more pesticides; however, concentrations were below the MRL. The other 30 samples (10 of 20 wells in the Blue Ridge crystalline and Triassic Lowland siliciclastic setting and all 20 wells in the Eastern Lake surficial setting) were analyzed for at least 19 pesticide compounds at the Pennsylvania Department of Environmental Protection Laboratory (PADEPL); the PADEPL reported no concentrations of pesticides at or above an MRL of 0.10 µg/L. Statistical tests using the NWQL analytical results showed correlations between pesticide occurrence and two indicators of water-quality degradation—the occurrence of total coliform bacteria and nitrate concentration. A 2 × 2 contingency-table test indicated a relation between presence or absence of atrazine or metolachlor and presence or absence of bacteria only for the 10 wells representing the Blue Ridge crystalline and Triassic Lowland siliciclastic setting. Results of Spearman’s rank test showed strong positive correlations in the Devonian-Silurian carbonate setting between 1) the number of pesticides above the MRLs and nitrate concentration, and 2) concentrations of atrazine and nitrate. Atrazine concentration and nitrate concentration also showed a statistically significant positive correlation in the Great Valley siliciclastic setting. An additional component of baseline monitoring was to evaluate changes in pesticide concentration in water from wells representing hydrogeologic settings most vulnerable to contamination from pesticides. In 2003, 16 wells originally sampled in the 1990s were resampled—4 each in the Appalachian Mountain carbonate, Triassic Lowland siliciclastic, Great Valley carbonate, and Piedmont carbonate settings. Nine of these wells, where pesticide concentrations from 1993 and 2003 were analyzed at the NWQL, were chosen for a paired-sample analysis using concentrations of atrazine and metolachlor. A statistically significant decrease in atrazine concentration was identified using the Wilcoxon signed-rank test (p = 0.004); significant temporal changes in metolachlor concentrations were not observed (p = 0.625). Monitoring in three areas of special ground-water protection, where selected pesticide concentrations in well water were at or above the PPGWS action levels, was done at wells BE 1370 (Berks County, Oley Township), BA 437 (Blair County, North Woodbury Township), and LN 1842 (Lancaster County, Earl Township). Co-occurrence of pesticide-degradation products with parent compounds was documented for the first time in ground-water samples collected from these three wells. Degradation products of atrazine, cyanazine, acetochlor, alachlor, and metolachlor were commonly at larger concentrations than the parent compound in the same water sample. Pesticide occurrence in water from wells neighboring the hot-spot wells was highly variable; however, the same sets of pesticide compounds that were present in wells BA 437, BE 1370, and LN 1842 were present to some degree in water from neighboring wells. To evaluate temporal changes in concentration, nonparametric statistical tests were used to determine overall and seasonal monotonic trends. Concentrations of alachlor, atrazine, metolachlor, and nitrate were examined using the 5-year (2003–07) and the long-term data from wells BA 437 and LN 1842 (1996–2007 and 1995–2007, respectively), and the long-term data for well BE 1370 (1998–2007); results showed either downward trends or no trends. Trends in acetochlor concentrations were tested only at well LN 1842 using the 5-year data; no trends were observed. Homogeneity of trend tests indicated statistically significant downward concentration trends in the long-term data were due to seasonal trends as follows: BA 437—alachlor and atrazine (summer); BE 1370—atrazine and metolachlor (winter) and alachlor (winter and spring); LN 1842—alachlor (summer and fall) and atrazine (spring and fall).

Spectroscopic characteristic (FT-IR, FT-Raman, UV, 1H and 13C NMR), theoretical calculations and biological activity of alkali metal homovanillates

NASA Astrophysics Data System (ADS)

Samsonowicz, M.; Kowczyk-Sadowy, M.; Piekut, J.; Regulska, E.; Lewandowski, W.

2016-04-01

The structural and vibrational properties of lithium, sodium, potassium, rubidium and cesium homovanillates were investigated in this paper. Supplementary molecular spectroscopic methods such as: FT-IR, FT-Raman in the solid phase, UV and NMR were applied. The geometrical parameters and energies were obtained from density functional theory (DFT) B3LYP method with 6-311++G** basis set calculations. The geometry of the molecule was fully optimized, vibrational spectra were calculated and fundamental vibrations were assigned. Geometric and magnetic aromaticity indices, atomic charges, dipole moments, HOMO and LUMO energies were also calculated. The microbial activity of investigated compounds was tested against Bacillus subtilis (BS), Pseudomonas aeruginosa (PA), Escherichia coli (EC), Staphylococcus aureus (SA) and Candida albicans (CA). The relationship between the molecular structure of tested compounds and their antimicrobial activity was studied. The principal component analysis (PCA) was applied in order to attempt to distinguish the biological activities of these compounds according to selected band wavenumbers. Obtained data show that the FT-IR spectra can be a rapid and reliable analytical tool and a good source of information for the quantitative analysis of the relationship between the molecular structure of the compound and its biological activity.

Enhancing of chemical compound and drug name recognition using representative tag scheme and fine-grained tokenization.

PubMed

Dai, Hong-Jie; Lai, Po-Ting; Chang, Yung-Chun; Tsai, Richard Tzong-Han

2015-01-01

The functions of chemical compounds and drugs that affect biological processes and their particular effect on the onset and treatment of diseases have attracted increasing interest with the advancement of research in the life sciences. To extract knowledge from the extensive literatures on such compounds and drugs, the organizers of BioCreative IV administered the CHEMical Compound and Drug Named Entity Recognition (CHEMDNER) task to establish a standard dataset for evaluating state-of-the-art chemical entity recognition methods. This study introduces the approach of our CHEMDNER system. Instead of emphasizing the development of novel feature sets for machine learning, this study investigates the effect of various tag schemes on the recognition of the names of chemicals and drugs by using conditional random fields. Experiments were conducted using combinations of different tokenization strategies and tag schemes to investigate the effects of tag set selection and tokenization method on the CHEMDNER task. This study presents the performance of CHEMDNER of three more representative tag schemes-IOBE, IOBES, and IOB12E-when applied to a widely utilized IOB tag set and combined with the coarse-/fine-grained tokenization methods. The experimental results thus reveal that the fine-grained tokenization strategy performance best in terms of precision, recall and F-scores when the IOBES tag set was utilized. The IOBES model with fine-grained tokenization yielded the best-F-scores in the six chemical entity categories other than the "Multiple" entity category. Nonetheless, no significant improvement was observed when a more representative tag schemes was used with the coarse or fine-grained tokenization rules. The best F-scores that were achieved using the developed system on the test dataset of the CHEMDNER task were 0.833 and 0.815 for the chemical documents indexing and the chemical entity mention recognition tasks, respectively. The results herein highlight the importance of tag set selection and the use of different tokenization strategies. Fine-grained tokenization combined with the tag set IOBES most effectively recognizes chemical and drug names. To the best of the authors' knowledge, this investigation is the first comprehensive investigation use of various tag set schemes combined with different tokenization strategies for the recognition of chemical entities.

Behavior of platinum(iv) complexes in models of tumor hypoxia: cytotoxicity, compound distribution and accumulation.

PubMed

Schreiber-Brynzak, Ekaterina; Pichler, Verena; Heffeter, Petra; Hanson, Buck; Theiner, Sarah; Lichtscheidl-Schultz, Irene; Kornauth, Christoph; Bamonti, Luca; Dhery, Vineet; Groza, Diana; Berry, David; Berger, Walter; Galanski, Markus; Jakupec, Michael A; Keppler, Bernhard K

2016-04-01

Hypoxia in solid tumors remains a challenge for conventional cancer therapeutics. As a source for resistance, metastasis development and drug bioprocessing, it influences treatment results and disease outcome. Bioreductive platinum(iv) prodrugs might be advantageous over conventional metal-based therapeutics, as biotransformation in a reductive milieu, such as under hypoxia, is required for drug activation. This study deals with a two-step screening of experimental platinum(iv) prodrugs with different rates of reduction and lipophilicity with the aim of identifying the most appropriate compounds for further investigations. In the first step, the cytotoxicity of all compounds was compared in hypoxic multicellular spheroids and monolayer culture using a set of cancer cell lines with different sensitivities to platinum(ii) compounds. Secondly, two selected compounds were tested in hypoxic xenografts in SCID mouse models in comparison to satraplatin, and, additionally, (LA)-ICP-MS-based accumulation and distribution studies were performed for these compounds in hypoxic spheroids and xenografts. Our findings suggest that, while cellular uptake and cytotoxicity strongly correlate with lipophilicity, cytotoxicity under hypoxia compared to non-hypoxic conditions and antitumor activity of platinum(iv) prodrugs are dependent on their rate of reduction.

Performance Evaluation of Frequency Transform Based Block Classification of Compound Image Segmentation Techniques

NASA Astrophysics Data System (ADS)

Selwyn, Ebenezer Juliet; Florinabel, D. Jemi

2018-04-01

Compound image segmentation plays a vital role in the compression of computer screen images. Computer screen images are images which are mixed with textual, graphical, or pictorial contents. In this paper, we present a comparison of two transform based block classification of compound images based on metrics like speed of classification, precision and recall rate. Block based classification approaches normally divide the compound images into fixed size blocks of non-overlapping in nature. Then frequency transform like Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are applied over each block. Mean and standard deviation are computed for each 8 × 8 block and are used as features set to classify the compound images into text/graphics and picture/background block. The classification accuracy of block classification based segmentation techniques are measured by evaluation metrics like precision and recall rate. Compound images of smooth background and complex background images containing text of varying size, colour and orientation are considered for testing. Experimental evidence shows that the DWT based segmentation provides significant improvement in recall rate and precision rate approximately 2.3% than DCT based segmentation with an increase in block classification time for both smooth and complex background images.

Development of an in Silico Model of DPPH• Free Radical Scavenging Capacity: Prediction of Antioxidant Activity of Coumarin Type Compounds.

PubMed

Goya Jorge, Elizabeth; Rayar, Anita Maria; Barigye, Stephen J; Jorge Rodríguez, María Elisa; Sylla-Iyarreta Veitía, Maité

2016-06-07

A quantitative structure-activity relationship (QSAR) study of the 2,2-diphenyl-l-picrylhydrazyl (DPPH•) radical scavenging ability of 1373 chemical compounds, using DRAGON molecular descriptors (MD) and the neural network technique, a technique based on the multilayer multilayer perceptron (MLP), was developed. The built model demonstrated a satisfactory performance for the training ( R 2 = 0.713 ) and test set ( Q ext 2 = 0.654 ) , respectively. To gain greater insight on the relevance of the MD contained in the MLP model, sensitivity and principal component analyses were performed. Moreover, structural and mechanistic interpretation was carried out to comprehend the relationship of the variables in the model with the modeled property. The constructed MLP model was employed to predict the radical scavenging ability for a group of coumarin-type compounds. Finally, in order to validate the model's predictions, an in vitro assay for one of the compounds (4-hydroxycoumarin) was performed, showing a satisfactory proximity between the experimental and predicted pIC50 values.

The preclinical testing strategy for the development of novel chemical entities for the treatment of asthma.

PubMed

Hahn, Christian; Erb, Klaus Joseph

2008-06-01

Identifying and developing novel chemical entities (NCE) for the treatment of asthma is a time-consuming process and liabilities that endanger the successful progression of a compound from research into the patient are found throughout all phases of drug discovery. In particular the failure of advanced compounds in clinical studies due to lack of efficacy and/or safety concerns is tremendously costly. Therefore, in order to try and reduce the failure rate in clinical trials various in vitro and in vivo tests are performed during preclinical development, to rapidly identify liabilities, eliminate high risk compounds and promote promising potential drug candidates. To achieve this objective, numerous prerequisites have to be met regarding the physico-chemical properties of the compound, and bioactivity or model systems are needed to rate the therapeutic potential of new compounds. Drug liabilities such as target and species specificity, formulation issues, pharmacokinetics as well as pharmacodynamics and the toxic potential of the compound have to be analyzed in great detail before a compound can enter a clinical trial. A particularly challenging aspect of developing novel NCEs for the treatment of asthma is choosing and setting up in vivo models believed to be predictive for human disease. Numerous companies have in the past and are currently developing NCEs targeting many different pathways and cells with the aim to treat asthma. However, currently the only NCE having a significant market share are long-acting beta-agonists (LABA), inhaled and orally active steroids and leukotriene receptor antagonists. In the past many novel NCE for the treatment of asthma were effective in animal models but failed in the clinic. In this review we outline the prerequisites of novel NCE needed for clinical development.

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

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.

Ligand-based virtual screening and in silico design of new antimalarial compounds using nonstochastic and stochastic total and atom-type quadratic maps.

PubMed

Marrero-Ponce, Yovani; Iyarreta-Veitía, Maité; Montero-Torres, Alina; Romero-Zaldivar, Carlos; Brandt, Carlos A; Avila, Priscilla E; Kirchgatter, Karin; Machado, Yanetsy

2005-01-01

Malaria has been one of the most significant public health problems for centuries. It affects many tropical and subtropical regions of the world. The increasing resistance of Plasmodium spp. to existing therapies has heightened alarms about malaria in the international health community. Nowadays, there is a pressing need for identifying and developing new drug-based antimalarial therapies. In an effort to overcome this problem, the main purpose of this study is to develop simple linear discriminant-based quantitative structure-activity relationship (QSAR) models for the classification and prediction of antimalarial activity using some of the TOMOCOMD-CARDD (TOpological MOlecular COMputer Design-Computer Aided "Rational" Drug Design) fingerprints, so as to enable computational screening from virtual combinatorial datasets. In this sense, a database of 1562 organic chemicals having great structural variability, 597 of them antimalarial agents and 965 compounds having other clinical uses, was analyzed and presented as a helpful tool, not only for theoretical chemists but also for other researchers in this area. This series of compounds was processed by a k-means cluster analysis in order to design training and predicting sets. Afterward, two linear classification functions were derived in order to discriminate between antimalarial and nonantimalarial compounds. The models (including nonstochastic and stochastic indices) correctly classify more than 93% of the compound set, in both training and external prediction datasets. They showed high Matthews' correlation coefficients, 0.889 and 0.866 for the training set and 0.855 and 0.857 for the test one. The models' predictivity was also assessed and validated by the random removal of 10% of the compounds to form a new test set, for which predictions were made using the models. The overall means of the correct classification for this process (leave group 10% full-out cross validation) using the equations with nonstochastic and stochastic atom-based quadratic fingerprints were 93.93% and 92.77%, respectively. The quadratic maps-based TOMOCOMD-CARDD approach implemented in this work was successfully compared with four of the most useful models for antimalarials selection reported to date. The developed models were then used in a simulation of a virtual search for Ras FTase (FTase = farnesyltransferase) inhibitors with antimalarial activity; 70% and 100% of the 10 inhibitors used in this virtual search were correctly classified, showing the ability of the models to identify new lead antimalarials. Finally, these two QSAR models were used in the identification of previously unknown antimalarials. In this sense, three synthetic intermediaries of quinolinic compounds were evaluated as active/inactive ones using the developed models. The synthesis and biological evaluation of these chemicals against two malaria strains, using chloroquine as a reference, was performed. An accuracy of 100% with the theoretical predictions was observed. Compound 3 showed antimalarial activity, being the first report of an arylaminomethylenemalonate having such behavior. This result opens a door to a virtual study considering a higher variability of the structural core already evaluated, as well as of other chemicals not included in this study. We conclude that the approach described here seems to be a promising QSAR tool for the molecular discovery of novel classes of antimalarial drugs, which may meet the dual challenges posed by drug-resistant parasites and the rapid progression of malaria illnesses.

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.

Studies at the Ionizable Position of Cephalosporins and Penicillins: Hydroxamates as Substitutes for the Traditional Carboxylate Group

PubMed Central

Majewski, Mark W.; Miller, Patricia A.; Miller, Marvin J.

2016-01-01

Classically, β-lactams need an ionizable group to potentiate antibacterial activity. Sets of cephalosporins and penicillins featuring different substituted hydroxamates in place of the traditional carboxylate group have been synthesized and tested for antibiotic activity. Many of the compounds exhibited anti-bacterial activities with notable MIC values in the range of 6-0.2 μM. PMID:27999444

Three-dimensional quantitative structure-activity relationship study on anti-cancer activity of 3,4-dihydroquinazoline derivatives against human lung cancer A549 cells

NASA Astrophysics Data System (ADS)

Cho, Sehyeon; Choi, Min Ji; Kim, Minju; Lee, Sunhoe; Lee, Jinsung; Lee, Seok Joon; Cho, Haelim; Lee, Kyung-Tae; Lee, Jae Yeol

2015-03-01

A series of 3,4-dihydroquinazoline derivatives with anti-cancer activities against human lung cancer A549 cells were subjected to three-dimensional quantitative structure-activity relationship (3D-QSAR) studies using the comparative molecular similarity indices analysis (CoMSIA) approaches. The most potent compound, 1 was used to align the molecules. As a result, the best prediction was obtained with CoMSIA combined the steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor fields (q2 = 0.720, r2 = 0.897). This model was validated by an external test set of 6 compounds giving satisfactory predictive r2 value of 0.923 as well as the scrambling stability test. This model would guide the design of potent 3,4-dihydroquinazoline derivatives as anti-cancer agent for the treatment of human lung cancer.

Investigation of non-hydroxamate scaffolds against HDAC6 inhibition: A pharmacophore modeling, molecular docking, and molecular dynamics simulation approach.

PubMed

Zeb, Amir; Park, Chanin; Son, Minky; Rampogu, Shailima; Alam, Syed Ibrar; Park, Seok Ju; Lee, Keun Woo

2018-06-01

Proteins deacetylation by Histone deacetylase 6 (HDAC6) has been shown in various human chronic diseases like neurodegenerative diseases and cancer, and hence is an important therapeutic target. Since, the existing inhibitors have hydroxamate group, and are not HDAC6-selective, therefore, this study has designed to investigate non-hydroxamate HDAC6 inhibitors. Ligand-based pharmacophore was generated from 26 training set compounds of HDAC6 inhibitors. The statistical parameters of pharmacophore (Hypo1) included lowest total cost of 115.63, highest cost difference of 135.00, lowest RMSD of 0.70 and the highest correlation of 0.98. The pharmacophore was validated by Fischer's Randomization and Test Set validation, and used as screening tool for chemical databases. The screened compounds were filtered by fit value ([Formula: see text]), estimated Inhibitory Concentration (IC[Formula: see text]) ([Formula: see text]), Lipinski's Rule of Five and Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Descriptors to identify drug-like compounds. Furthermore, the drug-like compounds were docked into the active site of HDAC6. The best docked compounds were selected having goldfitness score [Formula: see text] and [Formula: see text], and hydrogen bond interaction with catalytic active residues. Finally, three inhibitors having sulfamoyl group were selected by Molecular Dynamic (MD) simulation, which showed stable root mean square deviation (RMSD) (1.6-1.9[Formula: see text]Å), lowest potential energy ([Formula: see text][Formula: see text]kJ/mol), and hydrogen bonding with catalytic active residues of HDAC6.

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

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.

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.

The efficiency of macroporous polystyrene ion-exchange resins in natural organic matter removal from surface water

NASA Astrophysics Data System (ADS)

Urbanowska, Agnieszka; Kabsch-Korbutowicz, Małgorzata

2017-11-01

Natural water sources used for water treatment contains various organic and inorganic compounds. Surface waters are commonly contaminated with natural organic matter (NOM). NOM removal from water is important e.g. due to lowering the risk of disinfection by-product formation during chlorination. Ion exchange with the use of synthetic ion-exchange resins is an alternative process to typical NOM removal approach (e.g. coagulation, adsorption or oxidation) as most NOM compounds have anionic character. Moreover, neutral fraction could be removed from water due to its adsorption on resin surface. In this study, applicability of two macroporous, polystyrene ion exchange resins (BD400FD and A100) in NOM removal from water was assessed including comparison of treatment efficiency in various process set-ups and conditions. Moreover, resin regeneration effectivity was determined. Obtained results shown that examined resins could be applied in NOM removal and it should be noticed that column set-up yielded better results (contrary to batch set-up). Among the examined resins A100 one possessed better properties. It was determined that increase of solution pH resulted in a slight decrease in treatment efficiency while higher temperature improved it. It was also observed that regeneration efficiency was comparable in both tested methods but batch set-up required less reagents.

Informing the Human Plasma Protein Binding of ...

EPA Pesticide Factsheets

The free fraction of a xenobiotic in plasma (Fub) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data is scarce for environmentally relevant chemicals. The presented work explores the merit of utilizing available pharmaceutical data to predict Fub for environmentally relevant chemicals via machine learning techniques. Quantitative structure-activity relationship (QSAR) models were constructed with k nearest neighbors (kNN), support vector machines (SVM), and random forest (RF) machine learning algorithms from a training set of 1045 pharmaceuticals. The models were then evaluated with independent test sets of pharmaceuticals (200 compounds) and environmentally relevant ToxCast chemicals (406 total, in two groups of 238 and 168 compounds). The selection of a minimal feature set of 10-15 2D molecular descriptors allowed for both informative feature interpretation and practical applicability domain assessment via a bounded box of descriptor ranges and principal component analysis. The diverse pharmaceutical and environmental chemical sets exhibit similarities in terms of chemical space (99-82% overlap), as well as comparable bias and variance in constructed learning curves. All the models exhibit significant predictability with mean absolute errors (MAE) in the range of 0.10-0.18 Fub. The models performed best for highly bound chemicals (MAE 0.07-0.12), neutrals (MAE 0

2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors

PubMed Central

Zhao, Manman; Zheng, Linfeng; Qiu, Chun

2017-01-01

Epidermal growth factor receptor (EGFR) is an important target for cancer therapy. In this study, EGFR inhibitors were investigated to build a two-dimensional quantitative structure-activity relationship (2D-QSAR) model and a three-dimensional quantitative structure-activity relationship (3D-QSAR) model. In the 2D-QSAR model, the support vector machine (SVM) classifier combined with the feature selection method was applied to predict whether a compound was an EGFR inhibitor. As a result, the prediction accuracy of the 2D-QSAR model was 98.99% by using tenfold cross-validation test and 97.67% by using independent set test. Then, in the 3D-QSAR model, the model with q2 = 0.565 (cross-validated correlation coefficient) and r2 = 0.888 (non-cross-validated correlation coefficient) was built to predict the activity of EGFR inhibitors. The mean absolute error (MAE) of the training set and test set was 0.308 log units and 0.526 log units, respectively. In addition, molecular docking was also employed to investigate the interaction between EGFR inhibitors and EGFR. PMID:28630865

A specific pharmacophore model of sodium-dependent glucose co-transporter 2 (SGLT2) inhibitors.

PubMed

Tang, Chunlei; Zhu, Xiaoyun; Huang, Dandan; Zan, Xin; Yang, Baowei; Li, Ying; Du, Xiaoyong; Qian, Hai; Huang, Wenlong

2012-06-01

Sodium-dependent glucose co-transporter 2 (SGLT2) plays a pivotal role in maintaining glucose equilibrium in the human body, emerging as one of the most promising targets for the treatment of diabetes mellitus type 2. Pharmacophore models of SGLT2 inhibitors have been generated with a training set of 25 SGLT2 inhibitors using Discovery Studio V2.1. The best hypothesis (Hypo1(SGLT2)) contains one hydrogen bond donor, five excluded volumes, one ring aromatic and three hydrophobic features, and has a correlation coefficient of 0.955, cost difference of 68.76, RMSD of 0.85. This model was validated by test set, Fischer randomization test and decoy set methods. The specificity of Hypo1(SGLT2) was evaluated. The pharmacophore features of Hypo1(SGLT2) were different from the best pharmacophore model (Hypo1(SGLT1)) of SGLT1 inhibitors we developed. Moreover, Hypo1(SGLT2) could effectively distinguish selective inhibitors of SGLT2 from those of SGLT1. These results indicate that a highly predictive and specific pharmacophore model of SGLT2 inhibitors has been successfully obtained. Then Hypo1(SGLT2) was used as a 3D query to screen databases including NCI and Maybridge for identifying new inhibitors of SGLT2. The hit compounds were subsequently subjected to filtering by Lipinski's rule of five. And several compounds selected from the top ranked hits have been suggested for further experimental assay studies.

Morphosemantic parsing of medical compound words: transferring a French analyzer to English.

PubMed

Deléger, Louise; Namer, Fiammetta; Zweigenbaum, Pierre

2009-04-01

Medical language, as many technical languages, is rich with morphologically complex words, many of which take their roots in Greek and Latin--in which case they are called neoclassical compounds. Morphosemantic analysis can help generate definitions of such words. The similarity of structure of those compounds in several European languages has also been observed, which seems to indicate that a same linguistic analysis could be applied to neo-classical compounds from different languages with minor modifications. This paper reports work on the adaptation of a morphosemantic analyzer dedicated to French (DériF) to analyze English medical neo-classical compounds. It presents the principles of this transposition and its current performance. The analyzer was tested on a set of 1299 compounds extracted from the WHO-ART terminology. 859 could be decomposed and defined, 675 of which successfully. An advantage of this process is that complex linguistic analyses designed for French could be successfully transposed to the analysis of English medical neoclassical compounds, which confirmed our hypothesis of transferability. The fact that the method was successfully applied to a Germanic language such as English suggests that performances would be at least as high if experimenting with Romance languages such as Spanish. Finally, the resulting system can produce more complete analyses of English medical compounds than existing systems, including a hierarchical decomposition and semantic gloss of each word.

Application of computational methods for the design of BACE-1 inhibitors: validation of in silico modelling.

PubMed

Bajda, Marek; Jończyk, Jakub; Malawska, Barbara; Filipek, Sławomir

2014-03-24

β-Secretase (BACE-1) constitutes an important target for search of anti-Alzheimer's drugs. The first inhibitors of this enzyme were peptidic compounds with high molecular weight and low bioavailability. Therefore, the search for new efficient non-peptidic inhibitors has been undertaken by many scientific groups. We started our work from the development of in silico methodology for the design of novel BACE-1 ligands. It was validated on the basis of crystal structures of complexes with inhibitors, redocking, cross-docking and training/test sets of reference ligands. The presented procedure of assessment of the novel compounds as β-secretase inhibitors could be widely used in the design process.

Modelling of ceramide interactions with porous graphite carbon in non-aqueous liquid chromatography.

PubMed

West, C; Cilpa, G; Gaudin, K; Chaminade, P; Lesellier, E

2005-09-16

Interactions of solutes on porous graphitic carbon (PGC) with non-aqueous mobile phases are studied by the linear solvation energy relationship (LSER). Studies have been carried out with eight binary mixtures composed of a weak solvent (acetonitrile or methanol) and a strong solvent (tetrahydrofuran, n-butanol, CH2Cl2, 1,1,2-trichloro-2,2,1-trifluoroethane). The systematic analysis of a set of test compounds was performed for each solvent mixture in isocratic mode (50:50). The results were compared to those obtained on PGC with hydro-organic liquids and supercritical fluids. They were then correlated with the observed retention behaviour of lipid compounds, more particularly ceramides.

Versatility of the mouse reversal/set-shifting test: effects of topiramate and sex

PubMed Central

Bissonette, Gregory B.; Lande, Michelle D.; Martins, Gabriela J.; Powell, Elizabeth M.

2012-01-01

The ability to learn a rule to guide behavior is crucial for cognition and executive function. However, in a constantly changing environment, flexibility in terms of learning and changing rules is paramount. Research suggests there may be common underlying causes for the similar rule learning impairments observed in many psychiatric disorders. One of these common anatomical manifestations involves deficits to the GABAergic system, particularly in the frontal cerebral cortical regions. Many common anti-epileptic drugs and mood stabilizers activate the GABA system with the reported adverse side effects of cognitive dysfunction. The mouse reversal/set-shifting test was used to evaluate effects in mice given topiramate, which is reported to impair attention in humans. Here we report that in mice topiramate prevents formation of the attentional set, but does not alter reversal learning. Differences in the GABA system are also found in many neuropsychiatric disorders that are more common in males, including schizophrenia and autism. Initial findings with the reversal/set-shifting task excluded female subjects. In this study, female mice tested on the standard reversal/set-shifting task showed similar reversal learning, but were not able to form the attentional set. The behavioral paradigm was modified and when presented with sufficient discrimination tasks, female mice performed the same as male mice, requiring the same number of trials to reach criterion and form the attentional set. The notable difference was that female mice had an extended latency to complete the trials for all discriminations. In summary, the reversal/set-shifting test can be used to screen for cognitive effects of potential therapeutic compounds in both male and female mice. PMID:22677721

Quantitative on-line analysis of sulfur compounds in complex hydrocarbon matrices.

PubMed

Djokic, Marko R; Ristic, Nenad D; Olahova, Natalia; Marin, Guy B; Van Geem, Kevin M

2017-08-04

An improved method for on-line measurement of sulfur containing compounds in complex matrices is presented. The on-line system consists of a specifically designed sampling system connected to a comprehensive two-dimensional gas chromatograph (GC×GC) equipped with two capillary columns (Rtx ® -1 PONA×SGE BPX50), a flame ionization detector (FID) and a sulfur chemiluminescence detector (SCD). The result is an unprecedented sensitivity down to ppm level (1 ppm-w) for various sulfur containing compounds in very complex hydrocarbon matrices. In addition to the GC×GC-SCD, the low molecular weight sulfur containing compounds such as hydrogen sulfide (H 2 S) and carbonyl sulfide (COS) can be analyzed using a thermal conductivity detector of a so-called refinery gas analyzer (RGA). The methodology was extensively tested on a continuous flow pilot plant for steam cracking, in which quantification of sulfur containing compounds in the reactor effluent was carried out using 3-chlorothiophene as internal standard. The GC×GC-FID/-SCD settings were optimized for ppm analysis of sulfur compounds in olefin-rich (ethylene- and propylene-rich) hydrocarbon matrices produced by steam cracking of petroleum feedstocks. Besides that is primarily used for analysis of the hydrocarbon matrix, FID of the GC×GC-FID/-SCD set-up serves to double check the amount of added sulfur internal standard which is crucial for a proper quantification of sulfur compounds. When vacuum gas oil containing 780 ppm-w of elemental sulfur in the form of benzothiophenes and dibenzothiophenes is subjected to steam cracking, the sulfur balance was closed, with 75% of the sulfur contained in the feed is converted to hydrogen sulfide, 13% to alkyl homologues of thiophene while the remaining 12% is present in the form of alkyl homologues of benzothiophenes. The methodology can be applied for many other conversion processes which use sulfur containing feeds such as hydrocracking, catalytic cracking, kerogen evolution, bio-waste pyrolysis, supercritical water treatment, etc. Copyright © 2017 Elsevier B.V. All rights reserved.

A New In Vivo Screening Paradigm to Accelerate Antimalarial Drug Discovery

PubMed Central

Jiménez-Díaz, María Belén; Viera, Sara; Ibáñez, Javier; Mulet, Teresa; Magán-Marchal, Noemí; Garuti, Helen; Gómez, Vanessa; Cortés-Gil, Lorena; Martínez, Antonio; Ferrer, Santiago; Fraile, María Teresa; Calderón, Félix; Fernández, Esther; Shultz, Leonard D.; Leroy, Didier; Wilson, David M.; García-Bustos, José Francisco; Gamo, Francisco Javier; Angulo-Barturen, Iñigo

2013-01-01

The emergence of resistance to available antimalarials requires the urgent development of new medicines. The recent disclosure of several thousand compounds active in vitro against the erythrocyte stage of Plasmodium falciparum has been a major breakthrough, though converting these hits into new medicines challenges current strategies. A new in vivo screening concept was evaluated as a strategy to increase the speed and efficiency of drug discovery projects in malaria. The new in vivo screening concept was developed based on human disease parameters, i.e. parasitemia in the peripheral blood of patients on hospital admission and parasite reduction ratio (PRR), which were allometrically down-scaled into P. berghei-infected mice. Mice with an initial parasitemia (P0) of 1.5% were treated orally for two consecutive days and parasitemia measured 24 h after the second dose. The assay was optimized for detection of compounds able to stop parasite replication (PRR = 1) or induce parasite clearance (PRR >1) with statistical power >99% using only two mice per experimental group. In the P. berghei in vivo screening assay, the PRR of a set of eleven antimalarials with different mechanisms of action correlated with human-equivalent data. Subsequently, 590 compounds from the Tres Cantos Antimalarial Set with activity in vitro against P. falciparum were tested at 50 mg/kg (orally) in an assay format that allowed the evaluation of hundreds of compounds per month. The rate of compounds with detectable efficacy was 11.2% and about one third of active compounds showed in vivo efficacy comparable with the most potent antimalarials used clinically. High-throughput, high-content in vivo screening could rapidly select new compounds, dramatically speeding up the discovery of new antimalarial medicines. A global multilateral collaborative project aimed at screening the significant chemical diversity within the antimalarial in vitro hits described in the literature is a feasible task. PMID:23825598

QSAR and molecular modelling studies on B-DNA recognition of minor groove binders.

PubMed

de Oliveira, André Mauricio; Custódio, Flávia Beatriz; Donnici, Cláudio Luis; Montanari, Carlos Alberto

2003-02-01

Aromatic bisamidines have been proved to be efficient compounds against Leishmania spp. and Pneumocystis carinii. Although the mode of action is still not known, these molecules are supposed to be DNA minor groove binders (MGBs). This paper describes a molecular modelling study for a set of MGBs in order to rank them through their complementarity to the Dickerson Drew Dodecamer (DDD) according to their interaction energies with B-DNA. A comparative molecular field analysis (CoMFA) has shown the importance of relatively bulky positively charged groups attached to the MGB aromatic rings, and small and negatively charged substituents into the middle chain. Models were obtained for DNA denaturation related to H-bonding processes of binding modes. Validation of the model demonstrated the robustness of CoMFA in terms of independent test set of similar MGBs. GRID results allotted bioisosteric substitution of z.sbnd;Oz.sbnd; by z.sbnd;NHz.sbnd; in furan ring of furamidine and related compounds as being capable to enhance the binding to DDD.

From Molecular Docking to 3D-Quantitative Structure-Activity Relationships (3D-QSAR): Insights into the Binding Mode of 5-Lipoxygenase Inhibitors.

PubMed

Eren, Gokcen; Macchiarulo, Antonio; Banoglu, Erden

2012-02-01

Pharmacological intervention with 5-Lipoxygenase (5-LO) is a promising strategy for treatment of inflammatory and allergic ailments, including asthma. With the aim of developing predictive models of 5-LO affinity and gaining insights into the molecular basis of ligand-target interaction, we herein describe QSAR studies of 59 diverse nonredox-competitive 5-LO inhibitors based on the use of molecular shape descriptors and docking experiments. These studies have successfully yielded a predictive model able to explain much of the variance in the activity of the training set compounds while predicting satisfactorily the 5-LO inhibitory activity of an external test set of compounds. The inspection of the selected variables in the QSAR equation unveils the importance of specific interactions which are observed from docking experiments. Collectively, these results may be used to design novel potent and selective nonredox 5-LO inhibitors. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

DFT calculations, spectroscopy and antioxidant activity studies on (E)-2-nitro-4-[(phenylimino)methyl]phenol

NASA Astrophysics Data System (ADS)

Temel, Ersin; Alaşalvar, Can; Gökçe, Halil; Güder, Aytaç; Albayrak, Çiğdem; Alpaslan, Yelda Bingöl; Alpaslan, Gökhan; Dilek, Nefise

2015-02-01

We have reported synthesis and characterization of (E)-2-nitro-4-[(phenylimino)methyl]phenol by using X-ray crystallographic method, FT-IR and UV-vis spectroscopies and density functional theory (DFT). Optimized geometry and vibrational frequencies of the title compound in the ground state have been computed by using B3LYP with the 6-311G+(d,p) basis set. HOMO-LUMO energy gap, Non-linear optical properties and NBO analysis of the compound are performed at B3LYP/6-311G+(d,p) level. Additionally, as remarkable properties, antioxidant activity of the title compound (CMPD) has been determined by using different antioxidant test methods i.e. ferric reducing antioxidant power (FRAP), hydrogen peroxide scavenging (HPSA), free radical scavenging (FRSA) and ferrous ion chelating activities (FICA). When compared with standards (BHA, BHT, and α-tocopherol), we have concluded that CPMD has effective FRAP, HPSA, FRSA and FICA.

Identification of hydroxycinnamoylquinic acids of arnica flowers and burdock roots using a standardized LC-DAD-ESI/MS profiling method.

PubMed

Lin, Long-Ze; Harnly, James M

2008-11-12

A screening method using LC-DAD-ESI/MS was developed for the identification of common hydroxycinnamoylquinic acids based on direct comparison with standards. A complete standard set for mono-, di-, and tricaffeoylquinic isomers was assembled from commercially available standards, positively identified compounds in common plants (artichokes, asparagus, coffee bean, honeysuckle flowers, sweet potato, and Vernonia amygdalina leaves) and chemically modified standards. Four C18 reversed phase columns were tested using the standardized profiling method (based on LC-DAD-ESI/MS) for 30 phenolic compounds, and their elution order and retention times were evaluated. Using only two columns under standardized LC condition and the collected phenolic compound database, it was possible to separate all of the hydroxycinnamoylquinic acid conjugates and to identify 28 and 18 hydroxycinnamoylquinic acids in arnica flowers (Arnica montana L.) and burdock roots (Arctium lappa L.), respectively. Of these, 22 are reported for the first time.

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

Rapid Diagnosis of Tuberculosis from Analysis of Urine Volatile Organic Compounds

PubMed Central

Lim, Sung H.; Martino, Raymond; Anikst, Victoria; Xu, Zeyu; Mix, Samantha; Benjamin, Robert; Schub, Herbert; Eiden, Michael; Rhodes, Paul A.; Banaei, Niaz

2017-01-01

The World Health Organization has called for simple, sensitive, and non-sputum diagnostics for tuberculosis. We report development of a urine tuberculosis test using a colorimetric sensor array (CSA). The sensor comprised of 73 different indicators captures high-dimensional, spatiotemporal signatures of volatile chemicals emitted by human urine samples. The sensor responses to 63 urine samples collected from 22 tuberculosis cases and 41 symptomatic controls were measured under five different urine test conditions. Basified testing condition yielded the best accuracy with 85.5% sensitivity and 79.5% specificity. The CSA urine assay offers desired features needed for tuberculosis diagnosis in endemic settings. PMID:29057329

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.

Prediction of octanol-air partition coefficients for polychlorinated biphenyls (PCBs) using 3D-QSAR models.

PubMed

Chen, Ying; Cai, Xiaoyu; Jiang, Long; Li, Yu

2016-02-01

Based on the experimental data of octanol-air partition coefficients (KOA) for 19 polychlorinated biphenyl (PCB) congeners, two types of QSAR methods, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), are used to establish 3D-QSAR models using the structural parameters as independent variables and using logKOA values as the dependent variable with the Sybyl software to predict the KOA values of the remaining 190 PCB congeners. The whole data set (19 compounds) was divided into a training set (15 compounds) for model generation and a test set (4 compounds) for model validation. As a result, the cross-validation correlation coefficient (q(2)) obtained by the CoMFA and CoMSIA models (shuffled 12 times) was in the range of 0.825-0.969 (>0.5), the correlation coefficient (r(2)) obtained was in the range of 0.957-1.000 (>0.9), and the SEP (standard error of prediction) of test set was within the range of 0.070-0.617, indicating that the models were robust and predictive. Randomly selected from a set of models, CoMFA analysis revealed that the corresponding percentages of the variance explained by steric and electrostatic fields were 23.9% and 76.1%, respectively, while CoMSIA analysis by steric, electrostatic and hydrophobic fields were 0.6%, 92.6%, and 6.8%, respectively. The electrostatic field was determined as a primary factor governing the logKOA. The correlation analysis of the relationship between the number of Cl atoms and the average logKOA values of PCBs indicated that logKOA values gradually increased as the number of Cl atoms increased. Simultaneously, related studies on PCB detection in the Arctic and Antarctic areas revealed that higher logKOA values indicate a stronger PCB migration ability. From CoMFA and CoMSIA contour maps, logKOA decreased when substituents possessed electropositive groups at the 2-, 3-, 3'-, 5- and 6- positions, which could reduce the PCB migration ability. These results are expected to be beneficial in predicting logKOA values of PCB homologues and derivatives and in providing a theoretical foundation for further elucidation of the global migration behaviour of PCBs. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

Quantitative structure-retention relationship studies using immobilized artificial membrane chromatography I: amended linear solvation energy relationships with the introduction of a molecular electronic factor.

PubMed

Li, Jie; Sun, Jin; Cui, Shengmiao; He, Zhonggui

2006-11-03

Linear solvation energy relationships (LSERs) amended by the introduction of a molecular electronic factor were employed to establish quantitative structure-retention relationships using immobilized artificial membrane (IAM) chromatography, in particular ionizable solutes. The chromatographic indices, log k(IAM), were determined by HPLC on an IAM.PC.DD2 column for 53 structurally diverse compounds, including neutral, acidic and basic compounds. Unlike neutral compounds, the IAM chromatographic retention of ionizable compounds was affected by their molecular charge state. When the mean net charge per molecule (delta) was introduced into the amended LSER as the sixth variable, the LSER regression coefficient was significantly improved for the test set including ionizable solutes. The delta coefficients of acidic and basic compounds were quite different indicating that the molecular electronic factor had a markedly different impact on the retention of acidic and basic compounds on IAM column. Ionization of acidic compounds containing a carboxylic group tended to impair their retention on IAM, while the ionization of basic compounds did not have such a marked effect. In addition, the extra-interaction with the polar head of phospholipids might cause a certain change in the retention of basic compounds. A comparison of calculated and experimental retention indices suggested that the semi-empirical LSER amended by the addition of a molecular electronic factor was able to reproduce adequately the experimental retention factors of the structurally diverse solutes investigated.

Analytic Methods Used in Quality Control in a Compounding Pharmacy.

PubMed

Allen, Loyd V

2017-01-01

Analytical testing will no doubt become a more important part of pharmaceutical compounding as the public and regulatory agencies demand increasing documentation of the quality of compounded preparations. Compounding pharmacists must decide what types of testing and what amount of testing to include in their quality-control programs, and whether testing should be done in-house or outsourced. Like pharmaceutical compounding, analytical testing should be performed only by those who are appropriately trained and qualified. This article discusses the analytical methods that are used in quality control in a compounding pharmacy. Copyright© by International Journal of Pharmaceutical Compounding, Inc.

Pareto fronts for multiobjective optimization design on materials data

NASA Astrophysics Data System (ADS)

Gopakumar, Abhijith; Balachandran, Prasanna; Gubernatis, James E.; Lookman, Turab

Optimizing multiple properties simultaneously is vital in materials design. Here we apply infor- mation driven, statistical optimization strategies blended with machine learning methods, to address multi-objective optimization tasks on materials data. These strategies aim to find the Pareto front consisting of non-dominated data points from a set of candidate compounds with known character- istics. The objective is to find the pareto front in as few additional measurements or calculations as possible. We show how exploration of the data space to find the front is achieved by using uncer- tainties in predictions from regression models. We test our proposed design strategies on multiple, independent data sets including those from computations as well as experiments. These include data sets for Max phases, piezoelectrics and multicomponent alloys.

Fragment-based prediction of skin sensitization using recursive partitioning

NASA Astrophysics Data System (ADS)

Lu, Jing; Zheng, Mingyue; Wang, Yong; Shen, Qiancheng; Luo, Xiaomin; Jiang, Hualiang; Chen, Kaixian

2011-09-01

Skin sensitization is an important toxic endpoint in the risk assessment of chemicals. In this paper, structure-activity relationships analysis was performed on the skin sensitization potential of 357 compounds with local lymph node assay data. Structural fragments were extracted by GASTON (GrAph/Sequence/Tree extractiON) from the training set. Eight fragments with accuracy significantly higher than 0.73 ( p < 0.1) were retained to make up an indicator descriptor fragment. The fragment descriptor and eight other physicochemical descriptors closely related to the endpoint were calculated to construct the recursive partitioning tree (RP tree) for classification. The balanced accuracy of the training set, test set I, and test set II in the leave-one-out model were 0.846, 0.800, and 0.809, respectively. The results highlight that fragment-based RP tree is a preferable method for identifying skin sensitizers. Moreover, the selected fragments provide useful structural information for exploring sensitization mechanisms, and RP tree creates a graphic tree to identify the most important properties associated with skin sensitization. They can provide some guidance for designing of drugs with lower sensitization level.

Application of the criteria for classification of existing chemicals as dangerous for the environment.

PubMed

Knacker, T; Schallnaß, H J; Klaschka, U; Ahlers, J

1995-11-01

The criteria for classification and labelling of substances as "dangerous for the environment" agreed upon within the European Union (EU) were applied to two sets of existing chemicals. One set (sample A) consisted of 41 randomly selected compounds listed in the European Inventory of Existing Chemical Substances (EINECS). The other set (sample B) comprised 115 substances listed in Annex I of Directive 67/548/EEC which were classified by the EU Working Group on Classification and Labelling of Existing Chemicals. The aquatic toxicity (fish mortality,Daphnia immobilisation, algal growth inhibition), ready biodegradability and n-octanol/water partition coefficient were measured for sample A by one and the same laboratory. For sample B, the available ecotoxicological data originated from many different sources and therefore was rather heterogeneous. In both samples, algal toxicity was the most sensitive effect parameter for most substances. Furthermore, it was found that, classification based on a single aquatic test result differs in many cases from classification based on a complete data set, although a correlation exists between the biological end-points of the aquatic toxicity test systems.

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

An infrastructure to mine molecular descriptors for ligand selection on virtual screening.

PubMed

Seus, Vinicius Rosa; Perazzo, Giovanni Xavier; Winck, Ana T; Werhli, Adriano V; Machado, Karina S

2014-01-01

The receptor-ligand interaction evaluation is one important step in rational drug design. The databases that provide the structures of the ligands are growing on a daily basis. This makes it impossible to test all the ligands for a target receptor. Hence, a ligand selection before testing the ligands is needed. One possible approach is to evaluate a set of molecular descriptors. With the aim of describing the characteristics of promising compounds for a specific receptor we introduce a data warehouse-based infrastructure to mine molecular descriptors for virtual screening (VS). We performed experiments that consider as target the receptor HIV-1 protease and different compounds for this protein. A set of 9 molecular descriptors are taken as the predictive attributes and the free energy of binding is taken as a target attribute. By applying the J48 algorithm over the data we obtain decision tree models that achieved up to 84% of accuracy. The models indicate which molecular descriptors and their respective values are relevant to influence good FEB results. Using their rules we performed ligand selection on ZINC database. Our results show important reduction in ligands selection to be applied in VS experiments; for instance, the best selection model picked only 0.21% of the total amount of drug-like ligands.

Proposed phase 2/ step 2 in-vitro test on basis of EN 14561 for standardised testing of the wound antiseptics PVP-iodine, chlorhexidine digluconate, polihexanide and octenidine dihydrochloride.

PubMed

Schedler, Kathrin; Assadian, Ojan; Brautferger, Uta; Müller, Gerald; Koburger, Torsten; Classen, Simon; Kramer, Axel

2017-02-13

Currently, there is no agreed standard for exploring the antimicrobial activity of wound antiseptics in a phase 2/ step 2 test protocol. In the present study, a standardised in-vitro test is proposed, which allows to test potential antiseptics in a more realistically simulation of conditions found in wounds as in a suspension test. Furthermore, factors potentially influencing test results such as type of materials used as test carrier or various compositions of organic soil challenge were investigated in detail. This proposed phase 2/ step 2 test method was modified on basis of the EN 14561 by drying the microbial test suspension on a metal carrier for 1 h, overlaying the test wound antiseptic, washing-off, neutralization, and dispersion at serial dilutions at the end of the required exposure time yielded reproducible, consistent test results. The difference between the rapid onset of the antiseptic effect of PVP-I and the delayed onset especially of polihexanide was apparent. Among surface-active antimicrobial compounds, octenidine was more effective than chlorhexidine digluconate and polihexanide, with some differences depending on the test organisms. However, octenidine and PVP-I were approximately equivalent in efficiency and microbial spectrum, while polihexanide required longer exposure times or higher concentrations for a comparable antimicrobial efficacy. Overall, this method allowed testing and comparing differ liquid and gel based antimicrobial compounds in a standardised setting.

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

PubMed Central

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

2013-01-01

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

Application of Titration-Based Screening for the Rapid Pilot Testing of High-Throughput Assays.

PubMed

Zhang, Ji-Hu; Kang, Zhao B; Ardayfio, Ophelia; Ho, Pei-i; Smith, Thomas; Wallace, Iain; Bowes, Scott; Hill, W Adam; Auld, Douglas S

2014-06-01

Pilot testing of an assay intended for high-throughput screening (HTS) with small compound sets is a necessary but often time-consuming step in the validation of an assay protocol. When the initial testing concentration is less than optimal, this can involve iterative testing at different concentrations to further evaluate the pilot outcome, which can be even more time-consuming. Quantitative HTS (qHTS) enables flexible and rapid collection of assay performance statistics, hits at different concentrations, and concentration-response curves in a single experiment. Here we describe the qHTS process for pilot testing in which eight-point concentration-response curves are produced using an interplate asymmetric dilution protocol in which the first four concentrations are used to represent the range of typical HTS screening concentrations and the last four concentrations are added for robust curve fitting to determine potency/efficacy values. We also describe how these data can be analyzed to predict the frequency of false-positives, false-negatives, hit rates, and confirmation rates for the HTS process as a function of screening concentration. By taking into account the compound pharmacology, this pilot-testing paradigm enables rapid assessment of the assay performance and choosing the optimal concentration for the large-scale HTS in one experiment. © 2013 Society for Laboratory Automation and Screening.

Incorporation of in silico biodegradability screening in early drug development--a feasible approach?

PubMed

Steger-Hartmann, Thomas; Länge, Reinhard; Heuck, Klaus

2011-05-01

The concentration of a pharmaceutical found in the environment is determined by the amount used by the patient, the excretion and metabolism pattern, and eventually by its persistence. Biological degradation or persistence of a pharmaceutical is experimentally tested rather late in the development of a pharmaceutical, often shortly before submission of the dossier to regulatory authorities. To investigate whether the aspect of persistence of a compound could be assessed early during drug development, we investigated whether biodegradation of pharmaceuticals could be predicted with the help of in silico tools. To assess the value of in silico prediction, we collected results for the OECD 301 degradation test ("ready biodegradability") of 42 drugs or drug synthesis intermediates and compared them to the prediction of the in silico tool BIOWIN. Of these compounds, 38 were predictable with BIOWIN, which is a module of the Estimation Programs Interface (EPI) Suite™ provided by the US EPA. The program failed to predict the two drugs which proved to be readily biodegradable in the degradation tests. On the other hand, BIOWIN predicted two compounds to be readily biodegradable which, however, proved to be persistent in the test setting. The comparison of experimental data with the predicted one resulted in a specificity of 94% and a sensitivity of 0%. The results of this study do not indicate that application of the biodegradation prediction tool BIOWIN is a feasible approach to assess the ready biodegradability during early drug development.

Approved oncology drugs lack in vivo activity against Trichuris muris despite in vitro activity.

PubMed

Cowan, Noemi; Raimondo, Alessia; Keiser, Jennifer

2016-11-01

Infections with soil-transmitted helminths (STHs) are considered among the most persistent global health problems. The few available drugs have limitations including low efficacy against Trichuris trichiura infections. As a starting point toward drug repositioning, we studied a set of FDA-approved oncology drugs for activity against Trichuris muris since targets relevant to cancer therapy might have a function in helminth biology. Drugs were tested in vitro on the larval and adult stage of T. muris. Compounds active in vitro were tested in the T. muris mouse model at single oral dosages of 200-400 mg/kg. Of the 114 drugs tested in vitro, 12 showed activity against T. muris larvae (>80 % drug effect at 50 μM). Ten of these drugs were also active on the adult worm stage (>80 % drug effect at 50 μM), of which six revealed IC 50 values between 1.8 and 5.0 μM. Except for tamoxifen citrate, all in vitro active drugs were protein kinase inhibitors. None of the drugs tested in vivo showed efficacy, revealing worm burden reductions of 0-24 % and worm expulsion rates of 0-7.9 %. The promising in vitro activities of protein kinases could not be confirmed in vivo. Drug discovery against STH should be strengthened including the definition of compound progression criteria. Follow-up structure-activity relationship studies with modified compounds might be considered.

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.

Experimental investigation of the effects of compound angle holes on film cooling effectiveness and heat transfer performance using a transient liquid crystal thermometry technique

NASA Astrophysics Data System (ADS)

Seager, David J.; Liburdy, James A.

1997-11-01

To further understand the effect of both compound angle holes and hole shaping on film cooling, detailed heat transfer measurements were obtained using hue based thermochromic liquid crystal method. The data were analyzed to measure both the full surface adiabatic effectiveness and heat transfer coefficient. The compound angles that were evaluated consist of holes that were aligned 0 degrees, 45 degrees, 60 degrees and 90 degrees to the main cross flow direction. Hole shaping variations from the traditional cylindrical shaped hole include forward diffused and laterally diffused hole geometries. Geometric parameters that were selected were the length to diameter ratio of 3.0, and the inclination angle 35 degrees. A density ratio of 1.55 was obtained for all teste. For each set of conditions the blowing ratio was varied to be 0.88, 1.25, and 1.88. Adiabatic effectiveness was obtained using a steady state test, while an active heating surface was used to determine the heat transfer coefficient using a transient method. The experimental method provides a unique method of analyzing a three-temperature heat transfer problem by providing detailed surface transport properties. Based on these results for the different hole geometries at each blowing ratio conclusions are drawn relative to the effects of compound angle holes on the overall film cooling performance.

The multidimensional perturbation value: a single metric to measure similarity and activity of treatments in high-throughput multidimensional screens.

PubMed

Hutz, Janna E; Nelson, Thomas; Wu, Hua; McAllister, Gregory; Moutsatsos, Ioannis; Jaeger, Savina A; Bandyopadhyay, Somnath; Nigsch, Florian; Cornett, Ben; Jenkins, Jeremy L; Selinger, Douglas W

2013-04-01

Screens using high-throughput, information-rich technologies such as microarrays, high-content screening (HCS), and next-generation sequencing (NGS) have become increasingly widespread. Compared with single-readout assays, these methods produce a more comprehensive picture of the effects of screened treatments. However, interpreting such multidimensional readouts is challenging. Univariate statistics such as t-tests and Z-factors cannot easily be applied to multidimensional profiles, leaving no obvious way to answer common screening questions such as "Is treatment X active in this assay?" and "Is treatment X different from (or equivalent to) treatment Y?" We have developed a simple, straightforward metric, the multidimensional perturbation value (mp-value), which can be used to answer these questions. Here, we demonstrate application of the mp-value to three data sets: a multiplexed gene expression screen of compounds and genomic reagents, a microarray-based gene expression screen of compounds, and an HCS compound screen. In all data sets, active treatments were successfully identified using the mp-value, and simulations and follow-up analyses supported the mp-value's statistical and biological validity. We believe the mp-value represents a promising way to simplify the analysis of multidimensional data while taking full advantage of its richness.

Synthesis and biological evaluation of 3-thiazolocoumarinyl Schiff-base derivatives as cholinesterase inhibitors.

PubMed

Raza, Rabia; Saeed, Aamer; Arif, Mubeen; Mahmood, Shamsul; Muddassar, Muhammad; Raza, Ahsan; Iqbal, Jamshed

2012-10-01

On the basis of the observed biological activity of the coumarins, a new set of 3-thiazolocoumarinyl Schiff-base derivatives with chlorine, hydroxy and methoxy functional group substitutions were designed and synthesized. These compounds were tested against acetylcholinesterase from Electrophorus electricus and butyrylcholinesterase from horse serum and their structure-activity relationship was established. Studies revealed them as the potential inhibitors of cholinesterase (acetylcholinesterase and butyrylcholinesterase). The 3f was found to be most potent against acetylcholinesterase with K(i) value of 1.05 ± 0.3 μM and 3l showed excellent inhibitory action against butyrylcholinesterase with K(i) value of 0.041 ± 0.002 μM. The synthesized compounds were also docked into the active sites of the homology models of acetylcholinesterase and butyrylcholinesterase to predict the binding modes of these compounds. It was predicted that most of the compounds have similar binding modes with reasonable binding affinities. Our docking studies have also shown that these synthesized compounds have better interaction patterns with butyrylcholinesterase over acetylcholinesterase. The main objective of the study was to develop new potent and selective compounds, which might be further optimized to prevent the progression of the Alzheimer's disease and could provide symptomatic treatment. © 2012 John Wiley & Sons A/S.

Characterization of anticancer agents by their growth inhibitory activity and relationships to mechanism of action and structure.

PubMed

Keskin, O; Bahar, I; Jernigan, R L; Beutler, J A; Shoemaker, R H; Sausville, E A; Covell, D G

2000-04-01

An analysis of the growth inhibitory potency of 122 anticancer agents available from the National Cancer Institute anticancer drug screen is presented. Methods of singular value decomposition (SVD) were applied to determine the matrix of distances between all compounds. These SVD-derived dissimilarity distances were used to cluster compounds that exhibit similar tumor growth inhibitory activity patterns against 60 human cancer cell lines. Cluster analysis divides the 122 standard agents into 25 statistically distinct groups. The first eight groups include structurally diverse compounds with reactive functionalities that act as DNA-damaging agents while the remaining 17 groups include compounds that inhibit nucleic acid biosynthesis and mitosis. Examination of the average activity patterns across the 60 tumor cell lines reveals unique 'fingerprints' associated with each group. A diverse set of structural features are observed for compounds within these groups, with frequent occurrences of strong within-group structural similarities. Clustering of cell types by their response to the 122 anticancer agents divides the 60 cell types into 21 groups. The strongest within-panel groupings were found for the renal, leukemia and ovarian cell panels. These results contribute to the basis for comparisons between log(GI(50)) screening patterns of the 122 anticancer agents and additional tested compounds.

Nanoparticles and Controlled Delivery for Bioactive Compounds: Outlining Challenges for New “Smart-Foods” for Health

PubMed Central

Martínez-Ballesta, MCarment; García-Viguera, Cristina

2018-01-01

Nanotechnology is a field of research that has been stressed as a very valuable approach for the prevention and treatment of different human health disorders. This has been stressed as a delivery system for the therapeutic fight against an array of pathophysiological situations. Actually, industry has applied this technology in the search for new oral delivery alternatives obtained upon the modification of the solubility properties of bioactive compounds. Significant works have been made in the last years for testing the input that nanomaterials and nanoparticles provide for an array of pathophysiological situations. In this frame, this review addresses general questions concerning the extent to which nanoparticles offer alternatives that improve therapeutic value, while avoid toxicity, by releasing bioactive compounds specifically to target tissues affected by specific chemical and pathophysiological settings. In this regard, to date, the contribution of nanoparticles to protect encapsulated bioactive compounds from degradation as a result of gastrointestinal digestion and cellular metabolism, to enable their release in a controlled manner, enhancing biodistribution of bioactive compounds, and to allow them to target those tissues affected by biological disturbances has been demonstrated. PMID:29735897

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.

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.

Proline-Based Carbamates as Cholinesterase Inhibitors.

PubMed

Pizova, Hana; Havelkova, Marketa; Stepankova, Sarka; Bak, Andrzej; Kauerova, Tereza; Kozik, Violetta; Oravec, Michal; Imramovsky, Ales; Kollar, Peter; Bobal, Pavel; Jampilek, Josef

2017-11-14

Series of twenty-five benzyl (2S)-2-(arylcarbamoyl)pyrrolidine-1-carboxylates was prepared and completely characterized. All the compounds were tested for their in vitro ability to inhibit acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), and the selectivity of compounds to individual cholinesterases was determined. Screening of the cytotoxicity of all the compounds was performed using a human monocytic leukaemia THP-1 cell line, and the compounds demonstrated insignificant toxicity. All the compounds showed rather moderate inhibitory effect against AChE; benzyl (2 S )-2-[(2-chlorophenyl)carbamoyl]pyrrolidine-1-carboxylate (IC 50 = 46.35 μM) was the most potent agent. On the other hand, benzyl (2 S )-2-[(4-bromophenyl)-] and benzyl (2 S )-2-[(2-bromophenyl)carbamoyl]pyrrolidine-1-carboxylates expressed anti-BChE activity (IC 50 = 28.21 and 27.38 μM, respectively) comparable with that of rivastigmine. The ortho -brominated compound as well as benzyl (2 S )-2-[(2-hydroxyphenyl)carbamoyl]pyrrolidine-1-carboxylate demonstrated greater selectivity to BChE. The in silico characterization of the structure-inhibitory potency for the set of proline-based carbamates considering electronic, steric and lipophilic properties was provided using comparative molecular surface analysis (CoMSA) and principal component analysis (PCA). Moreover, the systematic space inspection with splitting data into the training/test subset was performed to monitor the statistical estimators performance in the effort to map the probability-guided pharmacophore pattern. The comprehensive screening of the AChE/BChE profile revealed potentially relevant structural and physicochemical features that might be essential for mapping of the carbamates inhibition efficiency indicating qualitative variations exerted on the reaction site by the substituent in the 3'-/4'-position of the phenyl ring. In addition, the investigation was completed by a molecular docking study of recombinant human AChE.

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.

Analysis of Pfizer compounds in EPA's ToxCast chemicals-assay space.

PubMed

Shah, Falgun; Greene, Nigel

2014-01-21

The U.S. Environmental Protection Agency (EPA) launched the ToxCast program in 2007 with the goal of evaluating high-throughput in vitro assays to prioritize chemicals that need toxicity testing. Their goal was to develop predictive bioactivity signatures for toxic compounds using a set of in vitro assays and/or in silico properties. In 2009, Pfizer joined the ToxCast initiative by contributing 52 compounds with preclinical and clinical data for profiling across the multiple assay platforms available. Here, we describe the initial analysis of the Pfizer subset of compounds within the ToxCast chemical (n = 1814) and in vitro assay (n = 486) space. An analysis of the hit rate of Pfizer compounds in the ToxCast assay panel allowed us to focus our mining of assays potentially most relevant to the attrition of our compounds. We compared the bioactivity profile of Pfizer compounds to other compounds in the ToxCast chemical space to gain insights into common toxicity pathways. Additionally, we explored the similarity in the chemical and biological spaces between drug-like compounds and environmental chemicals in ToxCast and compared the in vivo profiles of a subset of failed pharmaceuticals having high similarity in both spaces. We found differences in the chemical and biological spaces of pharmaceuticals compared to environmental chemicals, which may question the applicability of bioactivity signatures developed exclusively based on the latter to drug-like compounds if used without prior validation with the ToxCast Phase-II chemicals. Finally, our analysis has allowed us to identify novel interactions for our compounds in particular with multiple nuclear receptors that were previously not known. This insight may help us to identify potential liabilities with future novel compounds.

Crystal structure, phytochemical study and enzyme inhibition activity of Ajaconine and Delectinine

NASA Astrophysics Data System (ADS)

Ahmad, Shujaat; Ahmad, Hanif; Khan, Hidayat Ullah; Shahzad, Adnan; Khan, Ezzat; Ali Shah, Syed Adnan; Ali, Mumtaz; Wadud, Abdul; Ghufran, Mehreen; Naz, Humera; Ahmad, Manzoor

2016-11-01

The Crystal structure, comparative DFT study and phytochemical investigation of atisine type C-20 diterpenoid alkaloid ajaconine (1) and lycoctonine type C-19 diterpenoid alkaloid delectinine (2) is reported here. These compounds were isolated from Delphinium chitralense. Both the natural products 1 and 2 crystallize in orthorhombic crystal system with identical space group of P212121. The geometric parameters of both compounds were calculated with the help of DFT using B3LYP/6-31+G (p) basis set and HOMO-LUMO energies, optimized band gaps, global hardness, ionization potential, electron affinity and global electrophilicity are calculated. The compounds 1 and 2 were screened for acetyl cholinesterase and butyryl cholinesterase inhibition activities in a dose dependent manner followed by molecular docking to explore the possible inhibitory mechanism of ajaconine (1) and delectinine (2). The IC50 values of tested compounds against AChE were observed as 12.61 μM (compound 1) and 5.04 μM (compound 2). The same experiments were performed for inhibition of BChE and IC50 was observed to be 10.18 μM (1) and 9.21 μM (2). Promising inhibition activity was shown by both the compounds against AChE and BChE in comparison with standard drugs available in the market such as allanzanthane and galanthamine. The inhibition efficiency of both the natural products was determined in a dose dependent manner.

DNA-polyfluorophore Chemosensors for Environmental Remediation: Vapor-phase Identification of Petroleum Products in Contaminated Soil†

PubMed Central

Jiang, Wei; Wang, Shenliang; Yuen, Lik Hang; Kwon, Hyukin; Ono, Toshikazu

2013-01-01

Contamination of soil and groundwater by petroleum-based products is an extremely widespread and important environmental problem. Here we have tested a simple optical approach for detecting and identifying such industrial contaminants in soil samples, using a set of fluorescent DNA-based chemosensors in pattern-based sensing. We used a set of diverse industrial volatile chemicals to screen and identify a set of five short oligomeric DNA fluorophores on PEG-polystyrene microbeads that could differentiate the entire set after exposure to their vapors in air. We then tested this set of five fluorescent chemosensor compounds for their ability to respond with fluorescence changes when exposed to headgas over soil samples contaminated with one of ten different samples of crude oil, petroleum distillates, fuels, lubricants and additives. Statistical analysis of the quantitative fluorescence change data (as Δ(R,G,B) emission intensities) revealed that these five chemosensors on beads could differentiate all ten product mixtures at 1000 ppm in soil within 30 minutes. Tests of sensitivity with three of the contaminant mixtures showed that they could be detected and differentiated in amounts at least as low as one part per million in soil. The results establish that DNA-polyfluorophores may have practical utility in monitoring the extent and identity of environmental spills and leaks, while they occur and during their remediation. PMID:23878719

Rickettsia prowazekii methionine aminopeptidase as a promising target for the development of antibacterial agents

DOE PAGES

Helgren, Travis R.; Chen, Congling; Wangtrakuldee, Phumvadee; ...

2016-11-10

Methionine aminopeptidase (MetAP) is a class of ubiquitous enzymes essential for the survival of numerous bacterial species. These enzymes are responsible for the cleavage of N-terminal formyl-methionine initiators from nascent proteins to initiate post-translational modifications that are often essential to proper protein function. Thus, inhibition of MetAP activity has been implicated as a novel antibacterial target. In this study, we tested this idea in the present study by targeting the MetAP enzyme in the obligate intracellular pathogen Rickettsia prowazekii. We first identified potent RpMetAP inhibitory species by employing an in vitro enzymatic activity assay. The molecular docking program AutoDock wasmore » then utilized to compare published crystal structures of inhibited MetAP species to docked poses of RpMetAP. Based on these in silico and in vitro screens, a subset of 17 compounds was tested for inhibition of R. prowazekii growth in a pulmonary vascular endothelial cell (EC) culture infection model system. All compounds were tested over concentration ranges that were determined to be non-toxic to the ECs and 8 of the 17 compounds displayed substantial inhibition of R. prowazekii growth. Lastly, these data highlight the therapeutic potential for inhibiting RpMetAP as a novel antimicrobial strategy and set the stage for future studies in pre-clinical animal models of infection.« less

Rickettsia prowazekii methionine aminopeptidase as a promising target for the development of antibacterial agents

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

Helgren, Travis R.; Chen, Congling; Wangtrakuldee, Phumvadee

Methionine aminopeptidase (MetAP) is a class of ubiquitous enzymes essential for the survival of numerous bacterial species. These enzymes are responsible for the cleavage of N-terminal formyl-methionine initiators from nascent proteins to initiate post-translational modifications that are often essential to proper protein function. Thus, inhibition of MetAP activity has been implicated as a novel antibacterial target. In this study, we tested this idea in the present study by targeting the MetAP enzyme in the obligate intracellular pathogen Rickettsia prowazekii. We first identified potent RpMetAP inhibitory species by employing an in vitro enzymatic activity assay. The molecular docking program AutoDock wasmore » then utilized to compare published crystal structures of inhibited MetAP species to docked poses of RpMetAP. Based on these in silico and in vitro screens, a subset of 17 compounds was tested for inhibition of R. prowazekii growth in a pulmonary vascular endothelial cell (EC) culture infection model system. All compounds were tested over concentration ranges that were determined to be non-toxic to the ECs and 8 of the 17 compounds displayed substantial inhibition of R. prowazekii growth. Lastly, these data highlight the therapeutic potential for inhibiting RpMetAP as a novel antimicrobial strategy and set the stage for future studies in pre-clinical animal models of infection.« less

Differentiating true androgen receptor inhibition from cytotoxicity-mediated reduction of reporter-gene transactivation in-vitro.

PubMed

Marin-Kuan, Maricel; Fussell, Karma C; Riederer, Nicolas; Latado, Helia; Serrant, Patrick; Mollergues, Julie; Coulet, Myriam; Schilter, Benoit

2017-12-01

In vitro effect-based reporter assays are applied as biodetection tools designed to address nuclear receptor mediated-modulation. While such assays detect receptor modulating potential, cell viability needs to be addressed, preferably in the same well. Some assays circumvent this by co-transfecting a second constitutively-expressed marker gene or by multiplexing a cytotoxicity assay. Some assays, such as the CALUX®, lack this feature. The cytotoxic effects of unknown substances can confound in vitro assays, making the interpretation of results difficult and uncertain, particularly when assessing antagonistic activity. It's necessary to determine whether the cause of the reporter signal decrease is an antagonistic effect or a non-specific cytotoxic effect. To remedy this, we assessed the suitability of multiplexing a cell viability assay within the CALUX® transcriptional activation test for anti-androgenicity. Tests of both well-characterized anti-androgens and cytotoxic compounds demonstrated the suitability of this approach for discerning between the molecular mechanisms of action without altering the nuclear receptor assay; though some compounds were both cytotoxic and anti-androgenic. The optimized multiplexed assay was then applied to an uncharacterized set of polycyclic aromatic compounds. These results better characterized the mode of action and the classification of effects. Overall, the multiplexed protocol added value to CALUX test performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Selection, application, and validation of a set of molecular descriptors for nuclear receptor ligands.

PubMed

Stewart, Eugene L; Brown, Peter J; Bentley, James A; Willson, Timothy M

2004-08-01

A methodology for the selection and validation of nuclear receptor ligand chemical descriptors is described. After descriptors for a targeted chemical space were selected, a virtual screening methodology utilizing this space was formulated for the identification of potential NR ligands from our corporate collection. Using simple descriptors and our virtual screening method, we are able to quickly identify potential NR ligands from a large collection of compounds. As validation of the virtual screening procedure, an 8, 000-membered NR targeted set and a 24, 000-membered diverse control set of compounds were selected from our in-house general screening collection and screened in parallel across a number of orphan NR FRET assays. For the two assays that provided at least one hit per set by the established minimum pEC(50) for activity, the results showed a 2-fold increase in the hit-rate of the targeted compound set over the diverse set.

Multifunctional Hybrid Compounds Derived from 2-(2,5-Dioxopyrrolidin-1-yl)-3-methoxypropanamides with Anticonvulsant and Antinociceptive Properties.

PubMed

Abram, Michał; Zagaja, Mirosław; Mogilski, Szczepan; Andres-Mach, Marta; Latacz, Gniewomir; Baś, Sebastian; Łuszczki, Jarogniew J; Kieć-Kononowicz, Katarzyna; Kamiński, Krzysztof

2017-10-26

The focused set of new pyrrolidine-2,5-diones as potential broad-spectrum hybrid anticonvulsants was described. These derivatives integrate on the common structural scaffold the chemical fragments of well-known antiepileptic drugs such as ethosuximide, levetiracetam, and lacosamide. Such hybrids demonstrated effectiveness in two of the most widely used animal seizure models, namely, the maximal electroshock (MES) test and the psychomotor 6 Hz (32 mA) seizure models. Compound 33 showed the highest anticonvulsant activity in these models (ED 50 MES = 79.5 mg/kg, ED 50 6 Hz = 22.4 mg/kg). Compound 33 was also found to be effective in pentylenetetrazole-induced seizure model (ED 50 PTZ = 123.2 mg/kg). In addition, 33 demonstrated effectiveness by decreasing pain responses in formalin-induced tonic pain, in capsaicin-induced neurogenic pain, and notably in oxaliplatin-induced neuropathic pain in mice. The pharmacological data of stereoisomers of compound 33 revealed greater anticonvulsant activity by R(+)-33 enantiomer in both MES and 6 Hz seizure models.

Fatty acid synthase inhibitors from plants: isolation, structure elucidation, and SAR studies.

PubMed

Li, Xing-Cong; Joshi, Alpana S; ElSohly, Hala N; Khan, Shabana I; Jacob, Melissa R; Zhang, Zhizheng; Khan, Ikhlas A; Ferreira, Daneel; Walker, Larry A; Broedel, Sheldon E; Raulli, Robert E; Cihlar, Ronald L

2002-12-01

Fatty acid synthase (FAS) has been identified as a potential antifungal target. FAS prepared from Saccharomyces cerevisiae was employed for bioactivity-guided fractionation of Chlorophora tinctoria,Paspalum conjugatum, Symphonia globulifera, Buchenavia parviflora, and Miconia pilgeriana. Thirteen compounds (1-13), including three new natural products (1, 4, 12), were isolated and their structures identified by spectroscopic interpretation. They represented five chemotypes, namely, isoflavones, flavones, biflavonoids, hydrolyzable tannin-related derivatives, and triterpenoids. 3'-Formylgenistein (1) and ellagic acid 4-O-alpha-l-rhamnopyranoside (9) were the most potent compounds against FAS, with IC(50) values of 2.3 and 7.5 microg/mL, respectively. Furthermore, 43 (14-56) analogues of the five chemotypes from our natural product repository and commercial sources were tested for their FAS inhibitory activity. Structure-activity relationships for some chemotypes were investigated. All these compounds were further evaluated for antifungal activity against Candida albicans and Cryptococcus neoformans. Although there were several antifungal compounds in the set, correlation between the FAS inhibitory activity and antifungal activity could not be defined.

1-[(2-arylthiazol-4-yl)methyl]azoles as a new class of anticonvulsants: design, synthesis, in vivo screening, and in silico drug-like properties.

PubMed

Ahangar, Nematollah; Ayati, Adile; Alipour, Eskandar; Pashapour, Arsalan; Foroumadi, Alireza; Emami, Saeed

2011-11-01

A series of novel thiazole incorporated (arylalkyl)azoles were synthesized and screened for their anticonvulsant properties using maximal electroshock and pentylenetetrazole models in mice. Among target compounds, 1-[(2-(4-chlorophenyl)thiazol-4-yl)methyl]-1H-imidazole (compound 4b), 1-[(2-phenylthiazol-4-yl)methyl]-1H-1,2,4-tria-zole (8a), and its 4-chlorophenyl analog (compound 8b) were able to display noticeable anticonvulsant activity in both pentylenetetrazole and maximal electroshock tests with percentage protection range of 33-100%. A computational study was carried out for prediction of pharmacokinetics properties and drug-likeness. The structure-activity relationship and in silico drug relevant properties (molecular weight, topological polar surface area, clog P, hydrogen bond donors, hydrogen bond acceptors, and log BB) confirmed that the compounds were within the range set by Lipinski's rule-of-five, and possessing favorable physicochemical properties for acting as CNS-drugs, making them potentially promising agents for epilepsy therapy. © 2011 John Wiley & Sons A/S.

The Design and Development of a Potent and Selective Novel Diprolyl Derivative That Binds to the N-Domain of Angiotensin-I Converting Enzyme.

PubMed

Fienberg, Stephen; Cozier, Gyles E; Acharya, K Ravi; Chibale, Kelly; Sturrock, Edward D

2018-01-11

Angiotensin-I converting enzyme (ACE) is a zinc metalloprotease consisting of two catalytic domains (N- and C-). Most clinical ACE inhibitor(s) (ACEi) have been shown to inhibit both domains nonselectively, resulting in adverse effects such as cough and angioedema. Selectively inhibiting the individual domains is likely to reduce these effects and potentially treat fibrosis in addition to hypertension. ACEi from the GVK Biosciences database were inspected for possible N-domain selective binding patterns. From this set, a diprolyl chemical series was modeled using docking simulations. The series was expanded based on key target interactions involving residues known to impart N-domain selectivity. In total, seven diprolyl compounds were synthesized and tested for N-domain selective ACE inhibition. One compound with an aspartic acid in the P 2 position (compound 16) displayed potent inhibition (K i = 11.45 nM) and was 84-fold more selective toward the N-domain. A high-resolution crystal structure of compound 16 in complex with the N-domain revealed the molecular basis for the observed selectivity.

Informing the Human Plasma Protein Binding of Environmental Chemicals by Machine Learning in the Pharmaceutical Space: Applicability Domain and Limits of Predictability.

PubMed

Ingle, Brandall L; Veber, Brandon C; Nichols, John W; Tornero-Velez, Rogelio

2016-11-28

The free fraction of a xenobiotic in plasma (F ub ) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data are scarce for environmentally relevant chemicals. The presented work explores the merit of utilizing available pharmaceutical data to predict F ub for environmentally relevant chemicals via machine learning techniques. Quantitative structure-activity relationship (QSAR) models were constructed with k nearest neighbors (kNN), support vector machines (SVM), and random forest (RF) machine learning algorithms from a training set of 1045 pharmaceuticals. The models were then evaluated with independent test sets of pharmaceuticals (200 compounds) and environmentally relevant ToxCast chemicals (406 total, in two groups of 238 and 168 compounds). The selection of a minimal feature set of 10-15 2D molecular descriptors allowed for both informative feature interpretation and practical applicability domain assessment via a bounded box of descriptor ranges and principal component analysis. The diverse pharmaceutical and environmental chemical sets exhibit similarities in terms of chemical space (99-82% overlap), as well as comparable bias and variance in constructed learning curves. All the models exhibit significant predictability with mean absolute errors (MAE) in the range of 0.10-0.18F ub . The models performed best for highly bound chemicals (MAE 0.07-0.12), neutrals (MAE 0.11-0.14), and acids (MAE 0.14-0.17). A consensus model had the highest accuracy across both pharmaceuticals (MAE 0.151-0.155) and environmentally relevant chemicals (MAE 0.110-0.131). The inclusion of the majority of the ToxCast test sets within the AD of the consensus model, coupled with high prediction accuracy for these chemicals, indicates the model provides a QSAR for F ub that is broadly applicable to both pharmaceuticals and environmentally relevant chemicals.

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.

Measurement of volatile organic compounds inside automobiles.

PubMed

Fedoruk, Marion J; Kerger, Brent D

2003-01-01

The objective of the current study was to evaluate the types and concentrations of volatile organic compounds (VOCs) in the passenger cabin of selected sedan automobiles under static (parked, unventilated) and specified conditions of operation (i.e., driving the vehicle using air conditioning alone, vent mode alone, or driver's window half open). Data were collected on five different passenger sedan vehicles from three major automobile manufacturers. Airborne concentrations were assessed using 90-min time-weighted average (TWA) samples under U.S. Environmental Protection Agency (USEPA) Method IP-1B to assess individual VOC compounds and total VOCs (TVOCs) calibrated to toluene. Static vehicle testing demonstrated TVOC levels of approximately 400-800 microg/m(3) at warm interior vehicle temperatures (approximately 80 degrees F), whereas TVOCs at least fivefold higher were observed under extreme heat conditions (e.g., up to 145 degrees F). The profile of most prevalent individual VOC compounds varied considerably according to vehicle brand, age, and interior temperature tested, with predominant compounds including styrene, toluene, and 8- to 12-carbon VOCs. TVOC levels under varied operating conditions (and ventilation) were generally four- to eightfold lower (at approximately 50-160 microg/m(3)) than the static vehicle measurements under warm conditions, with the lowest measured levels generally observed in the trials with the driver's window half open. These data indicate that while relatively high concentrations of certain VOCs can be measured inside static vehicles under extreme heat conditions, normal modes of operation rapidly reduce the inside-vehicle VOC concentrations even when the air conditioning is set on recirculation mode.

Pederin-type pathways of uncultivated bacterial symbionts: analysis of o-methyltransferases and generation of a biosynthetic hybrid.

PubMed

Zimmermann, Katrin; Engeser, Marianne; Blunt, John W; Munro, Murray H G; Piel, Jörn

2009-03-04

The complex polyketide pederin is a potent antitumor agent isolated from Paederus spp. rove beetles. We have previously isolated a set of genes from a bacterial endosymbiont that are good candidates for pederin biosynthesis. To biochemically study this pathway, we expressed three methyltransferases from the putative pederin pathway and used the partially unmethylated analogue mycalamide A from the marine sponge Mycale hentscheli as test substrate. Analysis by high-resolution MS/MS and NMR revealed that PedO regiospecifically methylates the marine compound to generate the nonnatural hybrid compound 18-O-methylmycalamide A with increased cytotoxicity. To our knowledge, this is the first biochemical evidence that invertebrates can obtain defensive complex polyketides from bacterial symbionts.

The quantitative structure-insecticidal activity relationships from plant derived compounds against chikungunya and zika Aedes aegypti (Diptera:Culicidae) vector.

PubMed

Saavedra, Laura M; Romanelli, Gustavo P; Rozo, Ciro E; Duchowicz, Pablo R

2018-01-01

The insecticidal activity of a series of 62 plant derived molecules against the chikungunya, dengue and zika vector, the Aedes aegypti (Diptera:Culicidae) mosquito, is subjected to a Quantitative Structure-Activity Relationships (QSAR) analysis. The Replacement Method (RM) variable subset selection technique based on Multivariable Linear Regression (MLR) proves to be successful for exploring 4885 molecular descriptors calculated with Dragon 6. The predictive capability of the obtained models is confirmed through an external test set of compounds, Leave-One-Out (LOO) cross-validation and Y-Randomization. The present study constitutes a first necessary computational step for designing less toxic insecticides. Copyright © 2017 Elsevier B.V. All rights reserved.

An Automated High-Throughput Metabolic Stability Assay Using an Integrated High-Resolution Accurate Mass Method and Automated Data Analysis Software.

PubMed

Shah, Pranav; Kerns, Edward; Nguyen, Dac-Trung; Obach, R Scott; Wang, Amy Q; Zakharov, Alexey; McKew, John; Simeonov, Anton; Hop, Cornelis E C A; Xu, Xin

2016-10-01

Advancement of in silico tools would be enabled by the availability of data for metabolic reaction rates and intrinsic clearance (CLint) of a diverse compound structure data set by specific metabolic enzymes. Our goal is to measure CLint for a large set of compounds with each major human cytochrome P450 (P450) isozyme. To achieve our goal, it is of utmost importance to develop an automated, robust, sensitive, high-throughput metabolic stability assay that can efficiently handle a large volume of compound sets. The substrate depletion method [in vitro half-life (t1/2) method] was chosen to determine CLint The assay (384-well format) consisted of three parts: 1) a robotic system for incubation and sample cleanup; 2) two different integrated, ultraperformance liquid chromatography/mass spectrometry (UPLC/MS) platforms to determine the percent remaining of parent compound, and 3) an automated data analysis system. The CYP3A4 assay was evaluated using two long t1/2 compounds, carbamazepine and antipyrine (t1/2 > 30 minutes); one moderate t1/2 compound, ketoconazole (10 < t1/2 < 30 minutes); and two short t1/2 compounds, loperamide and buspirone (t½ < 10 minutes). Interday and intraday precision and accuracy of the assay were within acceptable range (∼12%) for the linear range observed. Using this assay, CYP3A4 CLint and t1/2 values for more than 3000 compounds were measured. This high-throughput, automated, and robust assay allows for rapid metabolic stability screening of large compound sets and enables advanced computational modeling for individual human P450 isozymes. U.S. Government work not protected by U.S. copyright.

Selection of representative emerging micropollutants for drinking water treatment studies: a systematic approach.

PubMed

Jin, Xiaohui; Peldszus, Sigrid

2012-01-01

Micropollutants remain of concern in drinking water, and there is a broad interest in the ability of different treatment processes to remove these compounds. To gain a better understanding of treatment effectiveness for structurally diverse compounds and to be cost effective, it is necessary to select a small set of representative micropollutants for experimental studies. Unlike other approaches to-date, in this research micropollutants were systematically selected based solely on their physico-chemical and structural properties that are important in individual water treatment processes. This was accomplished by linking underlying principles of treatment processes such as coagulation/flocculation, oxidation, activated carbon adsorption, and membrane filtration to compound characteristics and corresponding molecular descriptors. A systematic statistical approach not commonly used in water treatment was then applied to a compound pool of 182 micropollutants (identified from the literature) and their relevant calculated molecular descriptors. Principal component analysis (PCA) was used to summarize the information residing in this large dataset. D-optimal onion design was then applied to the PCA results to select structurally representative compounds that could be used in experimental treatment studies. To demonstrate the applicability and flexibility of this selection approach, two sets of 22 representative micropollutants are presented. Compounds in the first set are representative when studying a range of water treatment processes (coagulation/flocculation, oxidation, activated carbon adsorption, and membrane filtration), whereas the second set shows representative compounds for ozonation and advanced oxidation studies. Overall, selected micropollutants in both lists are structurally diverse, have wide-ranging physico-chemical properties and cover a large spectrum of applications. The systematic compound selection approach presented here can also be adjusted to fit individual research needs with respect to type of micropollutants, treatment processes and number of compounds selected. Copyright © 2011 Elsevier B.V. All rights reserved.

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.

Population pharmacokinetic model of THC integrates oral, intravenous, and pulmonary dosing and characterizes short- and long-term pharmacokinetics.

PubMed

Heuberger, Jules A A C; Guan, Zheng; Oyetayo, Olubukayo-Opeyemi; Klumpers, Linda; Morrison, Paul D; Beumer, Tim L; van Gerven, Joop M A; Cohen, Adam F; Freijer, Jan

2015-02-01

Δ(9)-Tetrahydrocannobinol (THC), the main psychoactive compound of Cannabis, is known to have a long terminal half-life. However, this characteristic is often ignored in pharmacokinetic (PK) studies of THC, which may affect the accuracy of predictions in different pharmacologic areas. For therapeutic use for example, it is important to accurately describe the terminal phase of THC to describe accumulation of the drug. In early clinical research, the THC challenge test can be optimized through more accurate predictions of the dosing sequence and the wash-out between occasions in a crossover setting, which is mainly determined by the terminal half-life of the compound. The purpose of this study is to better quantify the long-term pharmacokinetics of THC. A population-based PK model for THC was developed describing the profile up to 48 h after an oral, intravenous, and pulmonary dose of THC in humans. In contrast to earlier models, the current model integrates all three major administration routes and covers the long terminal phase of THC. Results show that THC has a fast initial and intermediate half-life, while the apparent terminal half-life is long (21.5 h), with a clearance of 38.8 L/h. Because the current model characterizes the long-term pharmacokinetics, it can be used to assess the accumulation of THC in a multiple-dose setting and to forecast concentration profiles of the drug under many different dosing regimens or administration routes. Additionally, this model could provide helpful insights into the THC challenge test used for the development of (novel) compounds targeting the cannabinoid system for different therapeutic applications and could improve decision making in future clinical trials.

How soil organic matter composition controls hexachlorobenzene-soil-interactions: adsorption isotherms and quantum chemical modeling.

PubMed

Ahmed, Ashour A; Kühn, Oliver; Aziz, Saadullah G; Hilal, Rifaat H; Leinweber, Peter

2014-04-01

Hazardous persistent organic pollutants (POPs) interact in soil with the soil organic matter (SOM) but this interaction is insufficiently understood at the molecular level. We investigated the adsorption of hexachlorobenzene (HCB) on soil samples with systematically modified SOM. These samples included the original soil, the soil modified by adding a hot water extract (HWE) fraction (soil+3 HWE and soil+6 HWE), and the pyrolyzed soil. The SOM contents increased in the order pyrolyzed soil

In silico approaches to identify novel myeloid cell leukemia-1 (Mcl-1) inhibitors for treatment of cancer.

PubMed

Ren, Ji-Xia; Li, Cheng-Ping; Zhou, Xiu-Ling; Cao, Xue-Song; Xie, Yong

2017-08-22

Myeloid cell leukemia-1 (Mcl-1) has been a validated and attractive target for cancer therapy. Over-expression of Mcl-1 in many cancers allows cancer cells to evade apoptosis and contributes to the resistance to current chemotherapeutics. Here, we identified new Mcl-1 inhibitors using a multi-step virtual screening approach. First, based on two different ligand-receptor complexes, 20 pharmacophore models were established by simultaneously using 'Receptor-Ligand Pharmacophore Generation' method and manual build feature method, and then carefully validated by a test database. Then, pharmacophore-based virtual screening (PB-VS) could be performed by using the 20 pharmacophore models. In addition, docking study was used to predict the possible binding poses of compounds, and the docking parameters were optimized before performing docking-based virtual screening (DB-VS). Moreover, a 3D QSAR model was established by applying the 55 aligned Mcl-1 inhibitors. The 55 inhibitors sharing the same scaffold were docked into the Mcl-1 active site before alignment, then the inhibitors with possible binding conformations were aligned. For the training set, the 3D QSAR model gave a correlation coefficient r 2 of 0.996; for the test set, the correlation coefficient r 2 was 0.812. Therefore, the developed 3D QSAR model was a good model, which could be applied for carrying out 3D QSAR-based virtual screening (QSARD-VS). After the above three virtual screening methods orderly filtering, 23 potential inhibitors with novel scaffolds were identified. Furthermore, we have discussed in detail the mapping results of two potent compounds onto pharmacophore models, 3D QSAR model, and the interactions between the compounds and active site residues.

Stability of Dexmedetomidine in 0.9% Sodium Chloride in Two Types of Intravenous Infusion Bags.

PubMed

Marquis, Kathleen; Hohlfelder, Benjamin; Szumita, Paul M

2017-01-01

Dexmedetomidine is a frequently used sedative in the critical care setting. It is commercially available as a 4-mg/mL premixed compound or as 200-mcg/2-mL vials that must be further diluted prior to administration. However, limited data exist regarding the stability of dexmedetomidine admixtures compounded from the 200-mcg/2-mL vials, particularly for durations greater than 48 hours. Therefore, we performed stability testing on compounded dexmedetomidine prepared in two types of intravenous infusion bags for 14 days. Dexmedetomidine is available as 200-mcg/2-mL vials for dilution, 80-mcg/20-mL single-dose vials, and as 200-mcg/50-mL and 400-mcg/100-mL glass bottles. The stability of dexmedetomidine admixtures has previously been tested for 48 hours. The purpose of this analysis was to test the stability of dexmedetomidine admixtures for 14 days. Six dexmedetomidine admixtures of 200 mcg/50 mL were compounded in polyvinyl chloride and non-polyvinyl chloride bags, three of which were stored under refrigeration and three of which were kept at room temperature. High-performance liquid chromatography testing was performed to determine the concentration at Days 1 through 14. Stability was determined by taking the mean concentration of samples taken from each bag. All samples were tested in duplicate. A sample was considered stable if the concentration was greater than 90% of the original concentration. All samples retained over 90% of the drug under their respective storage conditions for the duration of the study. At time 0, the concentration of dexmedetomidine was between 3.99 mcg/mL and 4.01 mcg/mL. On Day 14, the mean concentration was between 95.8% and 98.9%, depending on the bag type and storage condition. The pH remained between 4.7 and 5.8 during the study period as has previously been reported in the literature. Dexmedetomidine admixtures of 200 mcg/50 mL were stable in both polyvinyl chloride bags and non-polyvinyl chloride bags for 14 days under refrigeration and 48 hours at room temperature. This represents the longest time allowable under United States Pharmacopeia Chapter <797> without the need for sterility testing. Copyright© by International Journal of Pharmaceutical Compounding, Inc.

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.

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.

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

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....

Ultrasonic Monitoring of Setting and Strength Development of Ultra-High-Performance Concrete.

PubMed

Yoo, Doo-Yeol; Shin, Hyun-Oh; Yoon, Young-Soo

2016-04-19

In this study, the setting and tensile strength development of ultra-high-performance concrete (UHPC) at a very early age was investigated by performing the penetration resistance test (ASTM C403), as well as the direct tensile test using the newly developed test apparatus, and taking ultrasonic pulse velocity (UPV) measurements. In order to determine the optimum surface treatment method for preventing rapid surface drying of UHPC, four different methods were examined: plastic sheet, curing cover, membrane-forming compound, and paraffin oil. Based on the test results, the use of paraffin oil was found to be the best choice for measuring the penetration resistance and the UPV, and attaching the plastic sheet to the exposed surface was considered to be a simple method for preventing the rapid surface drying of UHPC elements. An S-shaped tensile strength development at a very early age (before 24 h) was experimentally obtained, and it was predicted by a power function of UPV. Lastly, the addition of shrinkage-reducing and expansive admixtures resulted in more rapid development of penetration resistance and UPV of UHPC.

Sensitivity of species to chemicals: dose-response characteristics for various test types (LC(50), LR(50) and LD(50)) and modes of action.

PubMed

Hendriks, A Jan; Awkerman, Jill A; de Zwart, Dick; Huijbregts, Mark A J

2013-11-01

While variable sensitivity of model species to common toxicants has been addressed in previous studies, a systematic analysis of inter-species variability for different test types, modes of action and species is as of yet lacking. Hence, the aim of the present study was to identify similarities and differences in contaminant levels affecting cold-blooded and warm-blooded species administered via different routes. To that end, data on lethal water concentrations LC50, tissue residues LR50 and oral doses LD50 were collected from databases, each representing the largest of its kind. LC50 data were multiplied by a bioconcentration factor (BCF) to convert them to internal concentrations that allow for comparison among species. For each endpoint data set, we calculated the mean and standard deviation of species' lethal level per compound. Next, the means and standard deviations were averaged by mode of action. Both the means and standard deviations calculated depended on the number of species tested, which is at odds with quality standard setting procedures. Means calculated from (BCF) LC50, LR50 and LD50 were largely similar, suggesting that different administration routes roughly yield similar internal levels. Levels for compounds interfering biochemically with elementary life processes were about one order of magnitude below that of narcotics disturbing membranes, and neurotoxic pesticides and dioxins induced death in even lower amounts. Standard deviations for LD50 data were similar across modes of action, while variability of LC50 values was lower for narcotics than for substances with a specific mode of action. The study indicates several directions to go for efficient use of available data in risk assessment and reduction of species testing. Copyright © 2013 Elsevier Inc. All rights reserved.

Simultaneous Determination of Four Compounds, Campesterol, Emodin8-O-β-D-Glucopyranoside, Quercetin, and Isoquercitrin in Reynoutria sachalinensis by High-performance Liquid Chromatography-Diode Array Detector

PubMed Central

Eom, Min Rye; Weon, Jin Bae; Jung, Youn Sik; Ryu, Ga Hee; Yang, Woo Seung; Ma, Choong Je

2017-01-01

Background: Reynoutria sachalinensis is a well-known and used herbal medicine to treatment of arthralgia, jaundice, amenorrhea, coughs, carbuncles, and sores. Objective: We have developed high-performance liquid chromatography analysis method for simultaneous determination of isolated four compounds, campesterol, emodin8-O-β-D-glucopyranoside, quercetin, and isoquercitrin from R. sachalinensis is. Materials and Methods: The four compounds were separated on Shiseido C18 column (S-5 μm, 4.6 mm I.D. ×250 mm) at a column temperature of 25°C. The mobile phase composed of water and methanol with gradient elution system, and flow rate is 1.0 ml/min. The detection wavelength was set at 205 nm. Results: Validation of this analytical method was evaluated by linearity, precision, and accuracy test. This established method had good linearity (R2 > 0.997). The relative standard deviation values of intra- and inter-day testing were indicated that <2%, and accuracy is 91.66%–103.31% at intraday and 91.69%–103.31% at intraday. The results of recovery test were 92.60%–108.99%. Conclusion: In these results, developed method was accurate and reliable to the quality evaluation of campesterol, emodin 8-O-β-D-glucopyranoside, quercetin, and isoquercitrin isolated from R. sachalinensis. SUMMARY We have developed high-performance liquid analysis method for simultaneous determination of 4 compounds of Reynoutria sachalinensis. Abbreviations used: HPLC: High-performance liquid chromatography, DAD: Diode array detector, LOD: Limit of detection, LOQ: Limit of quantitation, ICH: International Conference on Harmonisation. PMID:28808389

Genomic Models of Short-Term Exposure Accurately Predict Long-Term Chemical Carcinogenicity and Identify Putative Mechanisms of Action

PubMed Central

Gusenleitner, Daniel; Auerbach, Scott S.; Melia, Tisha; Gómez, Harold F.; Sherr, David H.; Monti, Stefano

2014-01-01

Background Despite an overall decrease in incidence of and mortality from cancer, about 40% of Americans will be diagnosed with the disease in their lifetime, and around 20% will die of it. Current approaches to test carcinogenic chemicals adopt the 2-year rodent bioassay, which is costly and time-consuming. As a result, fewer than 2% of the chemicals on the market have actually been tested. However, evidence accumulated to date suggests that gene expression profiles from model organisms exposed to chemical compounds reflect underlying mechanisms of action, and that these toxicogenomic models could be used in the prediction of chemical carcinogenicity. Results In this study, we used a rat-based microarray dataset from the NTP DrugMatrix Database to test the ability of toxicogenomics to model carcinogenicity. We analyzed 1,221 gene-expression profiles obtained from rats treated with 127 well-characterized compounds, including genotoxic and non-genotoxic carcinogens. We built a classifier that predicts a chemical's carcinogenic potential with an AUC of 0.78, and validated it on an independent dataset from the Japanese Toxicogenomics Project consisting of 2,065 profiles from 72 compounds. Finally, we identified differentially expressed genes associated with chemical carcinogenesis, and developed novel data-driven approaches for the molecular characterization of the response to chemical stressors. Conclusion Here, we validate a toxicogenomic approach to predict carcinogenicity and provide strong evidence that, with a larger set of compounds, we should be able to improve the sensitivity and specificity of the predictions. We found that the prediction of carcinogenicity is tissue-dependent and that the results also confirm and expand upon previous studies implicating DNA damage, the peroxisome proliferator-activated receptor, the aryl hydrocarbon receptor, and regenerative pathology in the response to carcinogen exposure. PMID:25058030

Physicochemical Parameters Affecting the Electrospray Ionization Efficiency of Amino Acids after Acylation

PubMed Central

2017-01-01

Electrospray ionization (ESI) is widely used in liquid chromatography coupled to mass spectrometry (LC–MS) for the analysis of biomolecules. However, the ESI process is still not completely understood, and it is often a matter of trial and error to enhance ESI efficiency and, hence, the response of a given set of compounds. In this work we performed a systematic study of the ESI response of 14 amino acids that were acylated with organic acid anhydrides of increasing chain length and with poly(ethylene glycol) (PEG) changing certain physicochemical properties in a predictable manner. By comparing the ESI response of 70 derivatives, we found that there was a strong correlation between the calculated molecular volume and the ESI response, while correlation with hydrophobicity (log P values), pKa, and the inverse calculated surface tension was significantly lower although still present, especially for individual derivatized amino acids with increasing acyl chain lengths. Acylation with PEG containing five ethylene glycol units led to the largest gain in ESI response. This response was maximal independent of the calculated physicochemical properties or the type of amino acid. Since no actual physicochemical data is available for most derivatized compounds, the responses were also used as input for a quantitative structure–property relationship (QSPR) model to find the best physicochemical descriptors relating to the ESI response from molecular structures using the amino acids and their derivatives as a reference set. A topological descriptor related to molecular size (SPAN) was isolated next to a descriptor related to the atomic composition and structural groups (BIC0). The validity of the model was checked with a test set of 43 additional compounds that were unrelated to amino acids. While prediction was generally good (R2 > 0.9), compounds containing halogen atoms or nitro groups gave a lower predicted ESI response. PMID:28737384

SAR matrices: automated extraction of information-rich SAR tables from large compound data sets.

PubMed

Wassermann, Anne Mai; Haebel, Peter; Weskamp, Nils; Bajorath, Jürgen

2012-07-23

We introduce the SAR matrix data structure that is designed to elucidate SAR patterns produced by groups of structurally related active compounds, which are extracted from large data sets. SAR matrices are systematically generated and sorted on the basis of SAR information content. Matrix generation is computationally efficient and enables processing of large compound sets. The matrix format is reminiscent of SAR tables, and SAR patterns revealed by different categories of matrices are easily interpretable. The structural organization underlying matrix formation is more flexible than standard R-group decomposition schemes. Hence, the resulting matrices capture SAR information in a comprehensive manner.

[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

Spectral imaging of chemical compounds using multivariate optically enhanced filters integrated with InGaAs VGA cameras

NASA Astrophysics Data System (ADS)

Priore, Ryan J.; Jacksen, Niels

2016-05-01

Infrared hyperspectral imagers (HSI) have been fielded for the detection of hazardous chemical and biological compounds, tag detection (friend versus foe detection) and other defense critical sensing missions over the last two decades. Low Size/Weight/Power/Cost (SWaPc) methods of identification of chemical compounds spectroscopy has been a long term goal for hand held applications. We describe a new HSI concept for low cost / high performance InGaAs SWIR camera chemical identification for military, security, industrial and commercial end user applications. Multivariate Optical Elements (MOEs) are thin-film devices that encode a broadband, spectroscopic pattern allowing a simple broadband detector to generate a highly sensitive and specific detection for a target analyte. MOEs can be matched 1:1 to a discrete analyte or class prediction. Additionally, MOE filter sets are capable of sensing an orthogonal projection of the original sparse spectroscopic space enabling a small set of MOEs to discriminate a multitude of target analytes. This paper identifies algorithms and broadband optical filter designs that have been demonstrated to identify chemical compounds using high performance InGaAs VGA detectors. It shows how some of the initial models have been reduced to simple spectral designs and tested to produce positive identification of such chemicals. We also are developing pixilated MOE compressed detection sensors for the detection of a multitude of chemical targets in challenging backgrounds/environments for both commercial and defense/security applications. This MOE based, real-time HSI sensor will exhibit superior sensitivity and specificity as compared to currently fielded HSI systems.

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...

Benefits of statistical molecular design, covariance analysis, and reference models in QSAR: a case study on acetylcholinesterase

NASA Astrophysics Data System (ADS)

Andersson, C. David; Hillgren, J. Mikael; Lindgren, Cecilia; Qian, Weixing; Akfur, Christine; Berg, Lotta; Ekström, Fredrik; Linusson, Anna

2015-03-01

Scientific disciplines such as medicinal- and environmental chemistry, pharmacology, and toxicology deal with the questions related to the effects small organic compounds exhort on biological targets and the compounds' physicochemical properties responsible for these effects. A common strategy in this endeavor is to establish structure-activity relationships (SARs). The aim of this work was to illustrate benefits of performing a statistical molecular design (SMD) and proper statistical analysis of the molecules' properties before SAR and quantitative structure-activity relationship (QSAR) analysis. Our SMD followed by synthesis yielded a set of inhibitors of the enzyme acetylcholinesterase (AChE) that had very few inherent dependencies between the substructures in the molecules. If such dependencies exist, they cause severe errors in SAR interpretation and predictions by QSAR-models, and leave a set of molecules less suitable for future decision-making. In our study, SAR- and QSAR models could show which molecular sub-structures and physicochemical features that were advantageous for the AChE inhibition. Finally, the QSAR model was used for the prediction of the inhibition of AChE by an external prediction set of molecules. The accuracy of these predictions was asserted by statistical significance tests and by comparisons to simple but relevant reference models.

Beyond the scope of Free-Wilson analysis: building interpretable QSAR models with machine learning algorithms.

PubMed

Chen, Hongming; Carlsson, Lars; Eriksson, Mats; Varkonyi, Peter; Norinder, Ulf; Nilsson, Ingemar

2013-06-24

A novel methodology was developed to build Free-Wilson like local QSAR models by combining R-group signatures and the SVM algorithm. Unlike Free-Wilson analysis this method is able to make predictions for compounds with R-groups not present in a training set. Eleven public data sets were chosen as test cases for comparing the performance of our new method with several other traditional modeling strategies, including Free-Wilson analysis. Our results show that the R-group signature SVM models achieve better prediction accuracy compared with Free-Wilson analysis in general. Moreover, the predictions of R-group signature models are also comparable to the models using ECFP6 fingerprints and signatures for the whole compound. Most importantly, R-group contributions to the SVM model can be obtained by calculating the gradient for R-group signatures. For most of the studied data sets, a significant correlation with that of a corresponding Free-Wilson analysis is shown. These results suggest that the R-group contribution can be used to interpret bioactivity data and highlight that the R-group signature based SVM modeling method is as interpretable as Free-Wilson analysis. Hence the signature SVM model can be a useful modeling tool for any drug discovery project.

D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions

NASA Astrophysics Data System (ADS)

Gathiaka, Symon; Liu, Shuai; Chiu, Michael; Yang, Huanwang; Stuckey, Jeanne A.; Kang, You Na; Delproposto, Jim; Kubish, Ginger; Dunbar, James B.; Carlson, Heather A.; Burley, Stephen K.; Walters, W. Patrick; Amaro, Rommie E.; Feher, Victoria A.; Gilson, Michael K.

2016-09-01

The Drug Design Data Resource (D3R) ran Grand Challenge 2015 between September 2015 and February 2016. Two targets served as the framework to test community docking and scoring methods: (1) HSP90, donated by AbbVie and the Community Structure Activity Resource (CSAR), and (2) MAP4K4, donated by Genentech. The challenges for both target datasets were conducted in two stages, with the first stage testing pose predictions and the capacity to rank compounds by affinity with minimal structural data; and the second stage testing methods for ranking compounds with knowledge of at least a subset of the ligand-protein poses. An additional sub-challenge provided small groups of chemically similar HSP90 compounds amenable to alchemical calculations of relative binding free energy. Unlike previous blinded Challenges, we did not provide cognate receptors or receptors prepared with hydrogens and likewise did not require a specified crystal structure to be used for pose or affinity prediction in Stage 1. Given the freedom to select from over 200 crystal structures of HSP90 in the PDB, participants employed workflows that tested not only core docking and scoring technologies, but also methods for addressing water-mediated ligand-protein interactions, binding pocket flexibility, and the optimal selection of protein structures for use in docking calculations. Nearly 40 participating groups submitted over 350 prediction sets for Grand Challenge 2015. This overview describes the datasets and the organization of the challenge components, summarizes the results across all submitted predictions, and considers broad conclusions that may be drawn from this collaborative community endeavor.

D3R Grand Challenge 2015: Evaluation of Protein-Ligand Pose and Affinity Predictions

PubMed Central

Gathiaka, Symon; Liu, Shuai; Chiu, Michael; Yang, Huanwang; Stuckey, Jeanne A; Kang, You Na; Delproposto, Jim; Kubish, Ginger; Dunbar, James B.; Carlson, Heather A.; Burley, Stephen K.; Walters, W. Patrick; Amaro, Rommie E.; Feher, Victoria A.; Gilson, Michael K.

2017-01-01

The Drug Design Data Resource (D3R) ran Grand Challenge 2015 between September 2015 and February 2016. Two targets served as the framework to test community docking and scoring methods: (i) HSP90, donated by AbbVie and the Community Structure Activity Resource (CSAR), and (ii) MAP4K4, donated by Genentech. The challenges for both target datasets were conducted in two stages, with the first stage testing pose predictions and the capacity to rank compounds by affinity with minimal structural data; and the second stage testing methods for ranking compounds with knowledge of at least a subset of the ligand-protein poses. An additional sub-challenge provided small groups of chemically similar HSP90 compounds amenable to alchemical calculations of relative binding free energy. Unlike previous blinded Challenges, we did not provide cognate receptors or receptors prepared with hydrogens and likewise did not require a specified crystal structure to be used for pose or affinity prediction in Stage 1. Given the freedom to select from over 200 crystal structures of HSP90 in the PDB, participants employed workflows that tested not only core docking and scoring technologies, but also methods for addressing water-mediated ligand-protein interactions, binding pocket flexibility, and the optimal selection of protein structures for use in docking calculations. Nearly 40 participating groups submitted over 350 prediction sets for Grand Challenge 2015. This overview describes the datasets and the organization of the challenge components, summarizes the results across all submitted predictions, and considers broad conclusions that may be drawn from this collaborative community endeavor. PMID:27696240

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.

Atmospheric pressure chemical ionisation mass spectrometry analysis linked with chemometrics for food classification - a case study: geographical provenance and cultivar classification of monovarietal clarified apple juices.

PubMed

Gan, Heng-Hui; Soukoulis, Christos; Fisk, Ian

2014-03-01

In the present work, we have evaluated for first time the feasibility of APCI-MS volatile compound fingerprinting in conjunction with chemometrics (PLS-DA) as a new strategy for rapid and non-destructive food classification. For this purpose 202 clarified monovarietal juices extracted from apples differing in their botanical and geographical origin were used for evaluation of the performance of APCI-MS as a classification tool. For an independent test set PLS-DA analyses of pre-treated spectral data gave 100% and 94.2% correct classification rate for the classification by cultivar and geographical origin, respectively. Moreover, PLS-DA analysis of APCI-MS in conjunction with GC-MS data revealed that masses within the spectral ACPI-MS data set were related with parent ions or fragments of alkyesters, carbonyl compounds (hexanal, trans-2-hexenal) and alcohols (1-hexanol, 1-butanol, cis-3-hexenol) and had significant discriminating power both in terms of cultivar and geographical origin. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

MTBE; to what extent will past releases contaminate community water supply wells?(Brief Article)

USGS Publications Warehouse

Johnson, Richard; Pankow, James; Bender, David A.; Price, Curtis; Zogorski, John S.

2000-01-01

The increasing frequency of detection of the widely used gasoline additive methyl tertbutyl ether (MTBE) in both ground- and surface waters is receiving much attention from the media, environmental scientists, state environmental agencies, and federal agencies. At the national level, the September 15,1999, Report of the Blue Ribbon Panel on Oxygenates in Gasoline (i) )tates that between 5 and 10% of community drinking water supplies in high MTBE use areas show at least detectable concentrations of MTBE, and about 1% of those systems are characterized by levels of this compound that are above 20 pg/L. In Maine, a desire to determine the extent of MTBE contamination led to a 1998 study (2) that revealed that this compound is found at levels above 0.1 pg/L in 16% of 951 randomly selected household wells and in 16% of the 793 community water systems tested in that state (37 wells were not tested). The study also suggested that between 1400 and 5200 household wells may have levels above 35 pg/L, although no community water supplies were found to be above that concentration. For comparison, Maryland, New Hampshire, New York, and California have set MTBE remediation "action levels" at or below 20 pg/L, and EPA has set its advisory level for taste and odor at 20-40 pg/L (3).

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.

In silico design, chemical synthesis and toxicological evaluation of 1,3-thiazolidine-2,4-dione derivatives as PPARγ agonists.

PubMed

Alemán-González-Duhart, Diana; Tamay-Cach, Feliciano; Correa-Basurto, José; Padilla-Martínez, Itzia Irene; Álvarez-Almazán, Samuel; Mendieta-Wejebe, Jessica Elena

2017-06-01

Peroxisome proliferator-activated receptors (PPARs) are nuclear receptors involved in the metabolism of lipids and carbohydrates. The exogenous ligands of these receptors are thiazolidinediones (TZDs), which are used to treat type 2 diabetes mellitus (DM2). However, drugs from this group produce adverse effects such as hepatic steatosis. Hence, the aim of this work was to design a set of small molecules that can activate the γ isoform of PPARs while minimizing the adverse effects. The derivatives were designed containing the polar head of TZD and an aromatic body, serving simultaneously as the body and tail. Two ligands were selected out of 130 tested. These compounds were synthesized in a solvent-free reaction and their physicochemical properties and toxicity were examined. Acute oral toxicity was determined by administering these compounds to female Wistar rats in increasing doses (as per the OECD protocol 425). The median lethal dose (LD50) of the compound substituted with a hydroxyl heteroatom was above 2000 mg/kg, and that of the compound substituted with halogens was 700-1400 mg/kg. The results suggest that the compounds can interact with PPARγ and elicit biological responses similar to other TZDs, but without showing adverse effects. The compounds will be subsequently evaluated in a DM2 animal model. Copyright © 2017. Published by Elsevier Inc.

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 ).

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.

Cytotoxicity assays with fish cells as an alternative to the acute lethality test with fish.

PubMed

Segner, Helmut

2004-10-01

In ecotoxicology, in vitro assays with fish cells are currently applied for mechanistic studies, bioanalytical purposes and toxicity screening. This paper discusses the potential of cytotoxicity assays with fish cells to reduce, refine or replace acute lethality tests using fish. Basal cytotoxicity data obtained with fish cell lines or fish primary cell cultures show a reasonable to good correlation with lethality data from acute toxicity tests, with the exception of compounds that exert a specific mode of toxic action. Basal cytotoxicity data from fish cell lines also correlate well with cytotoxicity data from mammalian cell lines. However, both the piscine and mammalian in vitro assays are clearly less sensitive than the fish test. Therefore, in vivo LC50 values (concentrations of the test compounds that are lethal to 50% of the fish in the experiment within 96 hours) currently cannot be predicted from in vitro values. This in vitro-in vivo difference in sensitivity appears to be true for both fish cell lines and mammalian cell lines. Given the good in vitro-in vivo correlation in toxicity ranking, together with the clear-cut difference in sensitivity, the role of cytotoxicity assays in a tiered alternative testing strategy could be in priority setting in relation to toxic hazard and in the toxicity classification of chemicals and environmental samples.

Toxicity evaluation of 2-hydroxybiphenyl and other compounds involved in studies of fossil fuels biodesulphurisation.

PubMed

Alves, L; Paixão, S M

2011-10-01

The acute toxicity of some compounds used in fossil fuels biodesulphurisation studies, on the respiration activity, was evaluated by Gordonia alkanivorans and Rhodococcus erythropolis. Moreover, the effect of 2-hydroxybiphenyl on cell growth of both strains was also determined, using batch (chronic bioassays) and continuous cultures. The IC₅₀ values obtained showed the toxicity of all the compounds tested to both strains, specially the high toxicity of 2-HBP. These results were confirmed by the chronic toxicity data. The toxicity data sets highlight for a higher sensitivity to the toxicant by the strain presenting a lower growth rate, due to a lower cells number in contact with the toxicant. Thus, microorganisms exhibiting faster generation times could be more resistant to 2-HBP accumulation during a BDS process. The physiological response of both strains to 2-HBP pulse in a steady-state continuous culture shows their potential to be used in a future fossil fuel BDS process. Copyright © 2011 Elsevier Ltd. All rights reserved.

Pharmacophore modeling, virtual screening and molecular docking of ATPase inhibitors of HSP70.

PubMed

Sangeetha, K; Sasikala, R P; Meena, K S

2017-10-01

Heat shock protein 70 is an effective anticancer target as it influences many signaling pathways. Hence the study investigated the important pharmacophore feature required for ATPase inhibitors of HSP70 by generating a ligand based pharmacophore model followed by virtual based screening and subsequent validation by molecular docking in Discovery studio V4.0. The most extrapolative pharmacophore model (hypotheses 8) consisted of four hydrogen bond acceptors. Further validation by external test set prediction identified 200 hits from Mini Maybridge, Drug Diverse, SCPDB compounds and Phytochemicals. Consequently, the screened compounds were refined by rule of five, ADMET and molecular docking to retain the best competitive hits. Finally Phytochemical compounds Muricatetrocin B, Diacetylphiladelphicalactone C, Eleutheroside B and 5-(3-{[1-(benzylsulfonyl)piperidin-4-yl]amino}phenyl)- 4-bromo-3-(carboxymethoxy)thiophene-2-carboxylic acid were obtained as leads to inhibit the ATPase activity of HSP70 in our findings and thus can be proposed for further in vitro and in vivo evaluation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Structure-based prediction of free energy changes of binding of PTP1B inhibitors

NASA Astrophysics Data System (ADS)

Wang, Jing; Ling Chan, Shek; Ramnarayan, Kal

2003-08-01

The goals were (1) to understand the driving forces in the binding of small molecule inhibitors to the active site of PTP1B and (2) to develop a molecular mechanics-based empirical free energy function for compound potency prediction. A set of compounds with known activities was docked onto the active site. The related energy components and molecular surface areas were calculated. The bridging water molecules were identified and their contributions were considered. Linear relationships were explored between the above terms and the binding free energies of compounds derived based on experimental inhibition constants. We found that minimally three terms are required to give rise to a good correlation (0.86) with predictive power in five-group cross-validation test (q2 = 0.70). The dominant terms are the electrostatic energy and non-electrostatic energy stemming from the intra- and intermolecular interactions of solutes and from those of bridging water molecules in complexes.

DFT calculations, spectroscopy and antioxidant activity studies on (E)-2-nitro-4-[(phenylimino)methyl]phenol.

PubMed

Temel, Ersin; Alaşalvar, Can; Gökçe, Halil; Güder, Aytaç; Albayrak, Çiğdem; Alpaslan, Yelda Bingöl; Alpaslan, Gökhan; Dilek, Nefise

2015-02-05

We have reported synthesis and characterization of (E)-2-nitro-4-[(phenylimino)methyl]phenol by using X-ray crystallographic method, FT-IR and UV-vis spectroscopies and density functional theory (DFT). Optimized geometry and vibrational frequencies of the title compound in the ground state have been computed by using B3LYP with the 6-311G+(d,p) basis set. HOMO-LUMO energy gap, Non-linear optical properties and NBO analysis of the compound are performed at B3LYP/6-311G+(d,p) level. Additionally, as remarkable properties, antioxidant activity of the title compound (CMPD) has been determined by using different antioxidant test methods i.e. ferric reducing antioxidant power (FRAP), hydrogen peroxide scavenging (HPSA), free radical scavenging (FRSA) and ferrous ion chelating activities (FICA). When compared with standards (BHA, BHT, and α-tocopherol), we have concluded that CPMD has effective FRAP, HPSA, FRSA and FICA. Copyright © 2014 Elsevier B.V. All rights reserved.

Arylimidamide-Azole Combinations against Leishmaniasis

DTIC Science & Technology

2016-09-01

This compound will be selected for further in vivo testing in Q1 of Year 3. 3. Accomplishments: The efficacy of 3 arylimidamide compounds was...of this compound will take place in Q1 of Year 3. 4. IMPACT: The search for an orally bioavailable arylimidamide analogue with efficacy against...macrophage assay2 against L. major. One of the 4 compounds tested, AA2- 160, showed potency in this assay. This compound will be selected for toxicity testing and in vivo efficacy testing in Q1 of Year 3.

Screening of pharmacokinetic properties of fifty dihydropyrimidin(thi)one derivatives using a combo of in vitro and in silico assays.

PubMed

Matias, Mariana; Fortuna, Ana; Bicker, Joana; Silvestre, Samuel; Falcão, Amílcar; Alves, Gilberto

2017-11-15

The heterocycles dihydropyrimidin(thi)ones have been under intensive pharmacological research, but their pharmacokinetic properties remain almost unknown. Herein, fifty dihydropyrimidin(thi)ones were submitted to in vitro screening tests using parallel artificial membrane permeability assays (PAMPA) to evaluate their apparent permeability (Papp) through intestinal membrane and blood-brain barrier models, and cell-based assays to assess their interference on the efflux transporter P-glycoprotein (P-gp). Moreover, a set of kinetic and toxicological parameters was also estimated employing a new computational tool, the pkCSM. The in vitro results suggested that 82% of the test compounds have good intestinal permeability (Papp>1.1×10 -6 cm/s), and 66% of these are also expected to exhibit good permeability through blood-brain barrier (Papp>2.0×10 -6 cm/s); these findings are consistent with a high transport rate by passive transcellular pathway. In both PAMPA models, thiourea derivatives presented higher Papp values than the respective urea analogues, which were further corroborated by in silico predictions. The in vitro results also suggested a low extent of plasma protein binding for all compounds (Papp<1.0×10 -5 cm/s), and these findings were also supported by in silico data (unbound fraction ranging from 0.13 to 0.59). In addition, although approximately half of the compounds did not modulate P-gp at the tested concentrations (10 and 50μM), nine of them presented a trend to induce P-gp and particularly the chlorinated compounds exhibited a marked P-gp inhibition at 50μM. Furthermore, the in silico predictions suggested that half of the compounds have hepatotoxic potential. Overall, within this group of compounds, the thiourea derivatives containing an unsubstituted or a monosubstituted (NO 2 , CH 3 , OCH 3 ) phenyl ring attached to the position 4 of the dihydropyrimidine ring represented the most promising structures and should be considered in the subsequent studies of the development of new structurally related drug candidates. Copyright © 2017 Elsevier B.V. All rights reserved.

Solid-phase extraction versus matrix solid-phase dispersion: Application to white grapes.

PubMed

Dopico-García, M S; Valentão, P; Jagodziñska, A; Klepczyñska, J; Guerra, L; Andrade, P B; Seabra, R M

2007-11-15

The use of matrix solid-phase dispersion (MSPD) was tested to, separately, extract phenolic compounds and organic acids from white grapes. This method was compared with a more conventional analytical method previously developed that combines solid liquid extraction (SL) to simultaneously extract phenolic compounds and organic acids followed by a solid-phase extraction (SPE) to separate the two types of compounds. Although the results were qualitatively similar for both techniques, the levels of extracted compounds were in general quite lower on using MSPD, especially for organic acids. Therefore, SL-SPE method was preferred to analyse white "Vinho Verde" grapes. Twenty samples of 10 different varieties (Alvarinho, Avesso, Asal-Branco, Batoca, Douradinha, Esganoso de Castelo Paiva, Loureiro, Pedernã, Rabigato and Trajadura) from four different locations in Minho (Portugal) were analysed in order to study the effects of variety and origin on the profile of the above mentioned compounds. Principal component analysis (PCA) was applied separately to establish the main sources of variability present in the data sets for phenolic compounds, organic acids and for the global data. PCA of phenolic compounds accounted for the highest variability (77.9%) with two PCs, enabling characterization of the varieties of samples according to their higher content in flavonol derivatives or epicatechin. Additionally, a strong effect of sample origin was observed. Stepwise linear discriminant analysis (SLDA) was used for differentiation of grapes according to the origin and variety, resulting in a correct classification of 100 and 70%, respectively.

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

PubMed

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

2015-06-15

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

A combined application of thermal desorber and gas chromatography to the analysis of gaseous carbonyls with the aid of two internal standards.

PubMed

Kim, Ki-Hyun; Anthwal, A; Pandey, Sudhir Kumar; Kabir, Ehsanul; Sohn, Jong Ryeul

2010-11-01

In this study, a series of GC calibration experiments were conducted to examine the feasibility of the thermal desorption approach for the quantification of five carbonyl compounds (acetaldehyde, propionaldehyde, butyraldehyde, isovaleraldehyde, and valeraldehyde) in conjunction with two internal standard compounds. The gaseous working standards of carbonyls were calibrated with the aid of thermal desorption as a function of standard concentration and of loading volume. The detection properties were then compared against two types of external calibration data sets derived by fixed standard volume and fixed standard concentration approach. According to this comparison, the fixed standard volume-based calibration of carbonyls should be more sensitive and reliable than its fixed standard concentration counterpart. Moreover, the use of internal standard can improve the analytical reliability of aromatics and some carbonyls to a considerable extent. Our preliminary test on real samples, however, indicates that the performance of internal calibration, when tested using samples of varying dilution ranges, can be moderately different from that derivable from standard gases. It thus suggests that the reliability of calibration approaches should be examined carefully with the considerations on the interactive relationships between the compound-specific properties and the operation conditions of the instrumental setups.

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

A Quantitative Model for the Prediction of Sooting Tendency from Molecular Structure

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

St. John, Peter C.; Kairys, Paul; Das, Dhrubajyoti D.

Particulate matter emissions negatively affect public health and global climate, yet newer fuel-efficient gasoline direct injection engines tend to produce more soot than their port-fuel injection counterparts. Fortunately, the search for sustainable biomass-based fuel blendstocks provides an opportunity to develop fuels that suppress soot formation in more efficient engine designs. However, as emissions tests are experimentally cumbersome and the search space for potential bioblendstocks is vast, new techniques are needed to estimate the sooting tendency of a diverse range of compounds. In this study, we develop a quantitative structure-activity relationship (QSAR) model of sooting tendency based on the experimental yieldmore » sooting index (YSI), which ranks molecules on a scale from n-hexane, 0, to benzene, 100. The model includes a rigorously defined applicability domain, and the predictive performance is checked using both internal and external validation. Model predictions for compounds in the external test set had a median absolute error of ~3 YSI units. An investigation of compounds that are poorly predicted by the model lends new insight into the complex mechanisms governing soot formation. Predictive models of soot formation can therefore be expected to play an increasingly important role in the screening and development of next-generation biofuels.« less

A Quantitative Model for the Prediction of Sooting Tendency from Molecular Structure

DOE PAGES

St. John, Peter C.; Kairys, Paul; Das, Dhrubajyoti D.; ...

2017-07-24

Particulate matter emissions negatively affect public health and global climate, yet newer fuel-efficient gasoline direct injection engines tend to produce more soot than their port-fuel injection counterparts. Fortunately, the search for sustainable biomass-based fuel blendstocks provides an opportunity to develop fuels that suppress soot formation in more efficient engine designs. However, as emissions tests are experimentally cumbersome and the search space for potential bioblendstocks is vast, new techniques are needed to estimate the sooting tendency of a diverse range of compounds. In this study, we develop a quantitative structure-activity relationship (QSAR) model of sooting tendency based on the experimental yieldmore » sooting index (YSI), which ranks molecules on a scale from n-hexane, 0, to benzene, 100. The model includes a rigorously defined applicability domain, and the predictive performance is checked using both internal and external validation. Model predictions for compounds in the external test set had a median absolute error of ~3 YSI units. An investigation of compounds that are poorly predicted by the model lends new insight into the complex mechanisms governing soot formation. Predictive models of soot formation can therefore be expected to play an increasingly important role in the screening and development of next-generation biofuels.« less

Small-Chamber Measurements of Chemical-Specific Emission Factors for Drywall

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

Maddalena, Randy; Russell, Marion; Apte, Michael G.

2010-06-01

Imported drywall installed in U.S. homes is suspected of being a source of odorous and potentially corrosive indoor pollutants. To support an investigation of those building materials by the Consumer Products Safety Commission (CPSC), Lawrence Berkeley National Laboratory (LBNL) measured chemical-specific emission factors for 30 samples of drywall materials. Emission factors are reported for 75 chemicals and 30 different drywall samples encompassing both domestic and imported stock and incorporating natural, synthetic, or mixed gypsum core material. CPSC supplied all drywall materials. First the drywall samples were isolated and conditioned in dedicated chambers, then they were transferred to small chambers wheremore » emission testing was performed. Four sampling and analysis methods were utilized to assess (1) volatile organic compounds, (2) low molecular weight carbonyls, (3) volatile sulfur compounds, and (4) reactive sulfur gases. LBNL developed a new method that combines the use of solid phase microextraction (SPME) with small emission chambers to measure the reactive sulfur gases, then extended that technique to measure the full suite of volatile sulfur compounds. The testing procedure and analysis methods are described in detail herein. Emission factors were measured under a single set of controlled environmental conditions. The results are compared graphically for each method and in detailed tables for use in estimating indoor exposure concentrations.« less

DEVELOPMENT AND VALIDATION OF AN AIR-TO-BEEF ...

EPA Pesticide Factsheets

A model for predicting concentrations of dioxin-like compounds in beef is developed and tested. The key premise of the model is that concentrations of these compounds in air are the source term, or starting point, for estimating beef concentrations. Vapor-phase concentrations transfer to vegetations cattle consume, and particle-bound concentrations deposit onto soils and these vegetations as well. Congener-specific bioconcentration parameters, coupled with assumptions on cattle diet, transform soil and vegetative concentrations into beef fat concentrations. The premise of the validation exercise is that a profile of typical air concentrations of dioxin-like compounds in a United States rural environment is an appropriate observed independent data set, and that a representative profile of United States beef concentrations of dioxin-like compounds is an appropriate observed dependent result. These data were developed for the validation exercise. An observed concentration of dioxin toxic equivalents in whole beef of 0.48 ng/kg is compared with a predicted 0.36 ng/kg. Principal uncertainties in the approach are identified and discussed. A major finding of this exercise was that vapor phase transfers of dioxin-like compounds to vegetations that cattle consume dominate the estimation of final beef concentrations: over 80% of the modeled beef concentration was attributed to such transfers. journal article

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

Composite multi-parameter ranking of real and virtual compounds for design of MC4R agonists: renaissance of the Free-Wilson methodology.

PubMed

Nilsson, Ingemar; Polla, Magnus O

2012-10-01

Drug design is a multi-parameter task present in the analysis of experimental data for synthesized compounds and in the prediction of new compounds with desired properties. This article describes the implementation of a binned scoring and composite ranking scheme for 11 experimental parameters that were identified as key drivers in the MC4R project. The composite ranking scheme was implemented in an AstraZeneca tool for analysis of project data, thereby providing an immediate re-ranking as new experimental data was added. The automated ranking also highlighted compounds overlooked by the project team. The successful implementation of a composite ranking on experimental data led to the development of an equivalent virtual score, which was based on Free-Wilson models of the parameters from the experimental ranking. The individual Free-Wilson models showed good to high predictive power with a correlation coefficient between 0.45 and 0.97 based on the external test set. The virtual ranking adds value to the selection of compounds for synthesis but error propagation must be controlled. The experimental ranking approach adds significant value, is parameter independent and can be tuned and applied to any drug discovery project.

An Evaluation of ToxCast Angiogenic Disruptors for Effects on ...

EPA Pesticide Factsheets

Angiogenesis is a critical developmental process and a potential target for chemical teratogenesis. Over one-tenth of the Tox21 library of 10,000 compounds have been shown to disrupt mitochondrial function [Attene-Ramos et al., 2015]. Previous studies utilizing ToxCast chemicals have shown a correlation between vascular disruption in Tg(kdrl:EGFP)mitfab692 zebrafish embryos and mitochondrial disruption reported in literature [McCollum et al., submitted]. To more closely examine this correlation, we culled ToxCast data for mitochondrial translocator protein (TSPO; NovaScreen) and mitochondrial membrane potential (MMP) and biomass (Tox21 and Apredica) for a total of 192 chemicals tested for adverse effects on vascular development in transgenic zebrafish embryos [McCollum et al., submitted; Tal et al., submitted]. This set included 40 compounds that disrupted vascular development in zebrafish embryos (zVDC) and 152 compounds that did not. The zVDC set displayed consistent in vitro bioactivity on mitochondrial membrane potential (with a Pearson Chi-Square value of 16.92, p < 0.0001), but did not have consistent effects on mitochondrial biomass (0.4; p = 0.527) or translocator protein ligand binding (0.05; p = 0.823). The effect on MMP is consistent with the hypothesis that disruption of the mitochondrial respiratory complexes is a potential mode of action of angiogenic disruptors (complex I for pyridaben, fenpyroxymate, tebufenpyrad, and rotenone; complex III for py

Linear and nonlinear methods in modeling the aqueous solubility of organic compounds.

PubMed

Catana, Cornel; Gao, Hua; Orrenius, Christian; Stouten, Pieter F W

2005-01-01

Solubility data for 930 diverse compounds have been analyzed using linear Partial Least Square (PLS) and nonlinear PLS methods, Continuum Regression (CR), and Neural Networks (NN). 1D and 2D descriptors from MOE package in combination with E-state or ISIS keys have been used. The best model was obtained using linear PLS for a combination between 22 MOE descriptors and 65 ISIS keys. It has a correlation coefficient (r2) of 0.935 and a root-mean-square error (RMSE) of 0.468 log molar solubility (log S(w)). The model validated on a test set of 177 compounds not included in the training set has r2 0.911 and RMSE 0.475 log S(w). The descriptors were ranked according to their importance, and at the top of the list have been found the 22 MOE descriptors. The CR model produced results as good as PLS, and because of the way in which cross-validation has been done it is expected to be a valuable tool in prediction besides PLS model. The statistics obtained using nonlinear methods did not surpass those got with linear ones. The good statistic obtained for linear PLS and CR recommends these models to be used in prediction when it is difficult or impossible to make experimental measurements, for virtual screening, combinatorial library design, and efficient leads optimization.

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.

Identification of novel thiazolo[5,4-d]pyrimidine derivatives as human A1 and A2A adenosine receptor antagonists/inverse agonists.

PubMed

Varano, Flavia; Catarzi, Daniela; Falsini, Matteo; Vincenzi, Fabrizio; Pasquini, Silvia; Varani, Katia; Colotta, Vittoria

2018-07-23

In this study a new set of thiazolo[5,4-d]pyrimidine derivatives was synthesized. These derivatives bear different substituents at positions 2 and 5 of the thiazolopyrimidine core while maintaining a free amino group at position-7. The new compounds were tested for their affinity and potency at human (h) A 1 , A 2A , A 2B and A 3 adenosine receptors expressed in CHO cells. The results reveal that the higher affinity of these new set of thiazolopyrimidines is toward the hA 1 and hA 2A adenosine receptors subtypes and is tuned by the substitution pattern at both the 2 and 5 positions of the thiazolopyrimidine nucleus. Functional studies evidenced that the compounds behaved as dual A 1 /A 2A antagonists/inverse agonists. Compound 3, bearing a 5-((2-methoxyphenyl) methylamino) group and a phenyl moiety at position 2, displayed the highest affinity (hA 1 K i  = 10.2 nM; hA 2A K i  = 4.72 nM) and behaved as a potent A 1 /A 2A antagonist/inverse agonist (hA 1 IC 50  = 13.4 nM; hA 2A IC 50  = 5.34 nM). Copyright © 2018 Elsevier Ltd. All rights reserved.

Estimation of reliability of predictions and model applicability domain evaluation in the analysis of acute toxicity (LD50).

PubMed

Sazonovas, A; Japertas, P; Didziapetris, R

2010-01-01

This study presents a new type of acute toxicity (LD(50)) prediction that enables automated assessment of the reliability of predictions (which is synonymous with the assessment of the Model Applicability Domain as defined by the Organization for Economic Cooperation and Development). Analysis involved nearly 75,000 compounds from six animal systems (acute rat toxicity after oral and intraperitoneal administration; acute mouse toxicity after oral, intraperitoneal, intravenous, and subcutaneous administration). Fragmental Partial Least Squares (PLS) with 100 bootstraps yielded baseline predictions that were automatically corrected for non-linear effects in local chemical spaces--a combination called Global, Adjusted Locally According to Similarity (GALAS) modelling methodology. Each prediction obtained in this manner is provided with a reliability index value that depends on both compound's similarity to the training set (that accounts for similar trends in LD(50) variations within multiple bootstraps) and consistency of experimental results with regard to the baseline model in the local chemical environment. The actual performance of the Reliability Index (RI) was proven by its good (and uniform) correlations with Root Mean Square Error (RMSE) in all validation sets, thus providing quantitative assessment of the Model Applicability Domain. The obtained models can be used for compound screening in the early stages of drug development and prioritization for experimental in vitro testing or later in vivo animal acute toxicity studies.

Novel naïve Bayes classification models for predicting the chemical Ames mutagenicity.

PubMed

Zhang, Hui; Kang, Yan-Li; Zhu, Yuan-Yuan; Zhao, Kai-Xia; Liang, Jun-Yu; Ding, Lan; Zhang, Teng-Guo; Zhang, Ji

2017-06-01

Prediction of drug candidates for mutagenicity is a regulatory requirement since mutagenic compounds could pose a toxic risk to humans. The aim of this investigation was to develop a novel prediction model of mutagenicity by using a naïve Bayes classifier. The established model was validated by the internal 5-fold cross validation and external test sets. For comparison, the recursive partitioning classifier prediction model was also established and other various reported prediction models of mutagenicity were collected. Among these methods, the prediction performance of naïve Bayes classifier established here displayed very well and stable, which yielded average overall prediction accuracies for the internal 5-fold cross validation of the training set and external test set I set were 89.1±0.4% and 77.3±1.5%, respectively. The concordance of the external test set II with 446 marketed drugs was 90.9±0.3%. In addition, four simple molecular descriptors (e.g., Apol, No. of H donors, Num-Rings and Wiener) related to mutagenicity and five representative substructures of mutagens (e.g., aromatic nitro, hydroxyl amine, nitroso, aromatic amine and N-methyl-N-methylenemethanaminum) produced by ECFP_14 fingerprints were identified. We hope the established naïve Bayes prediction model can be applied to risk assessment processes; and the obtained important information of mutagenic chemicals can guide the design of chemical libraries for hit and lead optimization. Copyright © 2017 Elsevier B.V. All rights reserved.

Unconscious biases in task choices depend on conscious expectations.

PubMed

González-García, Carlos; Tudela, Pío; Ruz, María

2015-12-01

Recent studies highlight the influence of non-conscious information on task-set selection. However, it has not yet been tested whether this influence depends on conscious settings, as some theoretical models propose. In a series of three experiments, we explored whether non-conscious abstract cues could bias choices between a semantic and a perceptual task. In Experiment 1, we observed a non-conscious influence on task-set selection even when perceptual priming and cue-target compound confounds did not apply. Experiments 2 and 3 showed that, under restrictive conditions of visibility, cues only biased task selection when the conscious task-setting mindset led participants to search for information during the time period of the cue. However, this conscious strategy did not modulate the effect found when a subjective measure of consciousness was used. Altogether, our results show that the configuration of the conscious mindset determines the potential bias of non-conscious information on task-set selection. Copyright © 2015 Elsevier Inc. All rights reserved.

PBT assessment under REACH: Screening for low aquatic bioaccumulation with QSAR classifications based on physicochemical properties to replace BCF in vivo testing on fish.

PubMed

Nendza, Monika; Kühne, Ralph; Lombardo, Anna; Strempel, Sebastian; Schüürmann, Gerrit

2018-03-01

Aquatic bioconcentration factors (BCFs) are critical in PBT (persistent, bioaccumulative, toxic) and risk assessment of chemicals. High costs and use of more than 100 fish per standard BCF study (OECD 305) call for alternative methods to replace as much in vivo testing as possible. The BCF waiving scheme is a screening tool combining QSAR classifications based on physicochemical properties related to the distribution (hydrophobicity, ionisation), persistence (biodegradability, hydrolysis), solubility and volatility (Henry's law constant) of substances in water bodies and aquatic biota to predict substances with low aquatic bioaccumulation (nonB, BCF<2000). The BCF waiving scheme was developed with a dataset of reliable BCFs for 998 compounds and externally validated with another 181 substances. It performs with 100% sensitivity (no false negatives), >50% efficacy (waiving potential), and complies with the OECD principles for valid QSARs. The chemical applicability domain of the BCF waiving scheme is given by the structures of the training set, with some compound classes explicitly excluded like organometallics, poly- and perfluorinated compounds, aromatic triphenylphosphates, surfactants. The prediction confidence of the BCF waiving scheme is based on applicability domain compliance, consensus modelling, and the structural similarity with known nonB and B/vB substances. Compounds classified as nonB by the BCF waiving scheme are candidates for waiving of BCF in vivo testing on fish due to low concern with regard to the B criterion. The BCF waiving scheme supports the 3Rs with a possible reduction of >50% of BCF in vivo testing on fish. If the target chemical is outside the applicability domain of the BCF waiving scheme or not classified as nonB, further assessments with in silico, in vitro or in vivo methods are necessary to either confirm or reject bioaccumulative behaviour. Copyright © 2017 Elsevier B.V. All rights reserved.

Effect of some nitrogen compounds thermal stability of jet A

NASA Technical Reports Server (NTRS)

Antoine, A. C.

1982-01-01

The effect of known concentrations of some nitrogen containing compounds on the thermal stability of a conventional fuel, namely, Jet A was investigated. The concentration range from 0.01 to 0.1 wt% nitrogen was examined. Solutions were made containing, individually, pyrrole, indole, quinoline, pyridine, and 4 ethylpyridine at 0.01, 0.03, 0.06, and 0.1 wt% nitrogen concentrations in Jet A. The measurements were all made by using a standard ASTM test for evaluating fuel thermal oxidation behavior, namely, ASTM D3241, 'thermal oxidation stability of turbine fuels (JFTOT procedure).' Measurements were made at two temperature settings, and 'breakpoint temperatures' were determined. The results show that the pyrrole and indole solutions have breakpoint temperatures substantially lower than those of the Jet A used.

Probability Based hERG Blocker Classifiers.

PubMed

Wang, Zhi; Mussa, Hamse Y; Lowe, Robert; Glen, Robert C; Yan, Aixia

2012-09-01

The US Food and Drug Administration (FDA) require in vitro human ether-a-go-go related (hERG) ion channel affinity tests for all drug candidates prior to clinical trials. In this study, probabilistic-based methods were employed to develop prediction models on hERG inhibition prediction, which are different from traditional QSAR models that are mainly based on supervised 'hard point' (HP) classification approaches giving 'yes/no' answers. The obtained models can 'ascertain' whether or not a given set of compounds can block hERG ion channels. The results presented indicate that the proposed probabilistic-based method can be a valuable tool for ranking compounds with respect to their potential cardio-toxicity and will be promising for other toxic property predictions. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Pharmacophore modeling of diverse classes of p38 MAP kinase inhibitors.

PubMed

Sarma, Rituparna; Sinha, Sharat; Ravikumar, Muttineni; Kishore Kumar, Madala; Mahmood, S K

2008-12-01

Mitogen-activated protein (MAP) p38 kinase is a serine-threonine protein kinase and its inhibitors are useful in the treatment of inflammatory diseases. Pharmacophore models were developed using HypoGen program of Catalyst with diverse classes of p38 MAP kinase inhibitors. The best pharmacophore hypothesis (Hypo1) with hydrogen-bond acceptor (HBA), hydrophobic (HY), hydrogen-bond donor (HBD), and ring aromatic (RA) as features has correlation coefficient of 0.959, root mean square deviation (RMSD) of 1.069 and configuration cost of 14.536. The model was validated using test set containing 119 compounds and had high correlation coefficient of 0.851. The results demonstrate that results obtained in this study can be considered to be useful and reliable tools in identifying structurally diverse compounds with desired biological activity.

Further evaluation of quantitative structure--activity relationship models for the prediction of the skin sensitization potency of selected fragrance allergens.

PubMed

Patlewicz, Grace Y; Basketter, David A; Pease, Camilla K Smith; Wilson, Karen; Wright, Zoe M; Roberts, David W; Bernard, Guillaume; Arnau, Elena Giménez; Lepoittevin, Jean-Pierre

2004-02-01

Fragrance substances represent a very diverse group of chemicals; a proportion of them are associated with the ability to cause allergic reactions in the skin. Efforts to find substitute materials are hindered by the need to undertake animal testing for determining both skin sensitization hazard and potency. One strategy to avoid such testing is through an understanding of the relationships between chemical structure and skin sensitization, so-called structure-activity relationships. In recent work, we evaluated 2 groups of fragrance chemicals -- saturated aldehydes and alpha,beta-unsaturated aldehydes. Simple quantitative structure-activity relationship (QSAR) models relating the EC3 values [derived from the local lymph node assay (LLNA)] to physicochemical properties were developed for both sets of aldehydes. In the current study, we evaluated an additional group of carbonyl-containing compounds to test the predictive power of the developed QSARs and to extend their scope. The QSAR models were used to predict EC3 values of 10 newly selected compounds. Local lymph node assay data generated for these compounds demonstrated that the original QSARs were fairly accurate, but still required improvement. Development of these QSAR models has provided us with a better understanding of the potential mechanisms of action for aldehydes, and hence how to avoid or limit allergy. Knowledge generated from this work is being incorporated into new/improved rules for sensitization in the expert toxicity prediction system, deductive estimation of risk from existing knowledge (DEREK).

Targeting natural compounds against HER2 kinase domain as potential anticancer drugs applying pharmacophore based molecular modelling approaches.

PubMed

Rampogu, Shailima; Son, Minky; Baek, Ayoung; Park, Chanin; Rana, Rabia Mukthar; Zeb, Amir; Parameswaran, Saravanan; Lee, Keun Woo

2018-04-20

Human epidermal growth factor receptors are implicated in several types of cancers characterized by aberrant signal transduction. This family comprises of EGFR (ErbB1), HER2 (ErbB2, HER2/neu), HER3 (ErbB3), and HER4 (ErbB4). Amongst them, HER2 is associated with breast cancer and is one of the most valuable targets in addressing the breast cancer incidences. For the current investigation, we have performed 3D-QSAR based pharmacophore search for the identification of potential inhibitors against the kinase domain of HER2 protein. Correspondingly, a pharmacophore model, Hypo1, with four features was generated and was validated employing Fischer's randomization, test set method and the decoy test method. The validated pharmacophore was allowed to screen the colossal natural compounds database (UNPD). Subsequently, the identified 33 compounds were docked into the proteins active site along with the reference after subjecting them to ADMET and Lipinski's Rule of Five (RoF) employing the CDOCKER implemented on the Discovery Studio. The compounds that have displayed higher dock scores than the reference compound were scrutinized for interactions with the key residues and were escalated to MD simulations. Additionally, molecular dynamics simulations performed by GROMACS have rendered stable root mean square deviation values, radius of gyration and potential energy values. Eventually, based upon the molecular dock score, interactions between the ligands and the active site residues and the stable MD results, the number of Hits was culled to two identifying Hit1 and Hit2 has potential leads against HER2 breast cancers. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Effectiveness of a Closed-System Transfer Device in Reducing Surface Contamination in a New Antineoplastic Drug-Compounding Unit: A Prospective, Controlled, Parallel Study

PubMed Central

Pinturaud, Marine; Soichot, Marion; Richeval, Camille; Humbert, Luc; Lebecque, Michèle; Sidikou, Ousseini; Barthelemy, Christine; Bonnabry, Pascal; Allorge, Delphine; Décaudin, Bertrand; Odou, Pascal

2016-01-01

Background The objective of this randomized, prospective and controlled study was to investigate the ability of a closed-system transfer device (CSTD; BD-Phaseal) to reduce the occupational exposure of two isolators to 10 cytotoxic drugs and compare to standard compounding devices. Methods and Findings The 6-month study started with the opening of a new compounding unit. Two isolators were set up with 2 workstations each, one to compound with standard devices (needles and spikes) and the other using the Phaseal system. Drugs were alternatively compounded in each isolator. Sampling involved wiping three surfaces (gloves, window, worktop), before and after a cleaning process. Exposure to ten antineoplastic drugs (cyclophosphamide, ifosfamide, dacarbazine, 5-FU, methotrexate, gemcitabine, cytarabine, irinotecan, doxorubicine and ganciclovir) was assessed on wipes by LC-MS/MS analysis. Contamination rates were compared using a Chi2 test and drug amounts by a Mann-Whitney test. Significance was defined for p<0.05. Overall contamination was lower in the “Phaseal” isolator than in the “Standard” isolator (12.24% vs. 26.39%; p < 0.0001) although it differed according to drug. Indeed, the contamination rates of gemcitabine were 49.3 and 43.4% (NS) for the Standard and Phaseal isolators, respectively, whereas for ganciclovir, they were 54.2 and 2.8% (p<0.0001). Gemcitabine amounts were 220.6 and 283.6 ng for the Standard and Phaseal isolators (NS), and ganciclovir amounts were 179.9 and 2.4 ng (p<0.0001). Conclusion This study confirms that using a CSTD may significantly decrease the chemical contamination of barrier isolators compared to standard devices for some drugs, although it does not eliminate contamination totally. PMID:27391697

Fluorescence-based recombination assay for sensitive and specific detection of genotoxic carcinogens in human cells.

PubMed

Ireno, Ivanildce C; Baumann, Cindy; Stöber, Regina; Hengstler, Jan G; Wiesmüller, Lisa

2014-05-01

In vitro genotoxicity tests are known to suffer from several shortcomings, mammalian cell-based assays, in particular, from low specificities. Following a novel concept of genotoxicity detection, we developed a fluorescence-based method in living human cells. The assay quantifies DNA recombination events triggered by DNA double-strand breaks and damage-induced replication fork stalling predicted to detect a broad spectrum of genotoxic modes of action. To maximize sensitivities, we engineered a DNA substrate encompassing a chemoresponsive element from the human genome. Using this substrate, we screened various human tumor and non-transformed cell types differing in the DNA damage response, which revealed that detection of genotoxic carcinogens was independent of the p53 status but abrogated by apoptosis. Cell types enabling robust and sensitive genotoxicity detection were selected for the generation of reporter clones with chromosomally integrated DNA recombination substrate. Reporter cell lines were scrutinized with 21 compounds, stratified into five sets according to the established categories for identification of carcinogenic compounds: genotoxic carcinogens ("true positives"), non-genotoxic carcinogens, compounds without genotoxic or carcinogenic effect ("true negatives") and non-carcinogenic compounds, which have been reported to induce chromosomal aberrations or mutations in mammalian cell-based assays ("false positives"). Our results document detection of genotoxic carcinogens in independent cell clones and at levels of cellular toxicities <60 % with a sensitivity of >85 %, specificity of ≥90 % and detection of false-positive compounds <17 %. Importantly, through testing cyclophosphamide in combination with primary hepatocyte cultures, we additionally provide proof-of-concept for the identification of carcinogens requiring metabolic activation using this novel assay system.

Synthesis, crystal structures and theoretical calculations of new 1-[2-(5-chloro-2-benzoxazolinone-3-yl)acetyl]-3,5-diphenyl-4,5-dihydro-(1H)-pyrazoles

NASA Astrophysics Data System (ADS)

Gökşen, Umut Salgın; Alpaslan, Yelda Bingöl; Kelekçi, Nesrin Gökhan; Işık, Şamil; Ekizoğlu, Melike

2013-05-01

1-[2-(5-Chloro-2-benzoxazolinone-3-yl)acetyl]-3-phenyl-5-(3-methoxyphenyl)-4,5-dihydro-(1H)-pyrazole (5a), 1-[2-(5-chloro-2-benzoxazolinone-3-yl)acetyl]-3-phenyl-5-(3,4-dimethoxyphenyl)-4,5-dihydro-(1H)-pyrazole (5b) and 1-[2-(5-chloro-2-benzoxazolinone-3-yl)acetyl]-3-(4-methylphenyl)-5-(2,3-dimethoxyphenyl)-4,5-dihydro-(1H)-pyrazole (5c) were synthesized. The crystal and molecular structures of the compounds 5a, 5b and 5c were determined by elemental analyses, IR, 1H NMR, ESI-MS and single-crystal X-ray diffraction. DFT method with 6-31G(d,p) basis set was used to calculate the optimized geometrical parameters, vibrational frequencies and chemical shift values. The calculated vibrational frequencies and chemical shift values were compared with experimental IR and 1H NMR values. The results represented that there was a good agreement between experimental and calculated values of the compounds 5a-5c. In addition, DFT calculations of the compounds, molecular electrostatic potentials (MEPs) and frontier molecular orbitals were performed at B3LYP/6-31G(d,p) level of theory. Furthermore, compounds were tested against three Gram-positive bacteria: Staphylococcus aureus ATCC 29213 (American Type Culture Collection), methicillin resistant S. aureus (MRSA) ATCC 43300 and Enterococcus faecalis ATCC 29212; two Gram negative bacteria: Escherichia coli ATCC 25922 and Pseudomonas aeruginosa ATCC 27853; and three fungi: Candida albicans ATCC 90028, Candida krusei ATCC 6258 and Candida parapsilosis ATCC 90018. In general, all of the compounds were found to be slightly active against tested microorganisms.

Noscapinoids with anti-cancer activity against human acute lymphoblastic leukemia cells (CEM): a three dimensional chemical space pharmacophore modeling and electronic feature analysis.

PubMed

Naik, Pradeep K; Santoshi, Seneha; Joshi, Harish C

2012-01-01

We have identified a new class of microtubule-binding compounds-noscapinoids-that alter microtubule dynamics at stoichiometric concentrations without affecting tubulin polymer mass. Noscapinoids show great promise as chemotherapeutic agents for the treatment of human cancers. To investigate the structural determinants of noscapinoids responsible for anti-cancer activity, we tested 36 structurally diverse noscapinoids in human acute lymphoblastic leukemia cells (CEM). The IC(50) values of these noscapinoids vary from 1.2 to 56.0 μM. Pharmacophore models of anti-cancer activity were generated that identify two hydrogen bond acceptors, two aromatic rings, two hydrophobic groups, and one positively charged group as essential structural features. Additionally, an atom-based quantitative structure-activity relationship (QSAR) model was developed that gave a statistically satisfying result (R(2) = 0.912, Q(2) = 0.908, Pearson R = 0.951) and effectively predicts the anti-cancer activity of training and test set compounds. The pharmacophore model presented here is well supported by electronic property analysis using density functional theory at B3LYP/3-21*G level. Molecular electrostatic potential, particularly localization of negative potential near oxygen atoms of the dimethoxy isobenzofuranone ring of active compounds, matched the hydrogen bond acceptor feature of the generated pharmacophore. Our results further reveal that all active compounds have smaller lowest unoccupied molecular orbital (LUMO) energies concentrated over the dimethoxy isobenzofuranone ring, azido group, and nitro group, which is indicative of the electron acceptor capacity of the compounds. Results obtained from this study will be useful in the efficient design and development of more active noscapinoids.

QSARpy: A new flexible algorithm to generate QSAR models based on dissimilarities. The log Kow case study.

PubMed

Ferrari, Thomas; Lombardo, Anna; Benfenati, Emilio

2018-05-14

Several methods exist to develop QSAR models automatically. Some are based on indices of the presence of atoms, other on the most similar compounds, other on molecular descriptors. Here we introduce QSARpy v1.0, a new QSAR modeling tool based on a different approach: the dissimilarity. This tool fragments the molecules of the training set to extract fragments that can be associated to a difference in the property/activity value, called modulators. If the target molecule share part of the structure with a molecule of the training set and differences can be explained with one or more modulators, the property/activity value of the molecule of the training set is adjusted using the value associated to the modulator(s). This tool is tested here on the n-octanol/water partition coefficient (Kow, usually expressed in logarithmic units as log Kow). It is a key parameter in risk assessment since it is a measure of hydrophobicity. Its wide spread use makes these estimation methods very useful to reduce testing costs. Using QSARpy v1.0, we obtained a new model to predict log Kow with accurate performance (RMSE 0.43 and R 2 0.94 for the external test set), comparing favorably with other programs. QSARpy is freely available on request. Copyright © 2018 Elsevier B.V. All rights reserved.

Quantitative structure-activity relationships for organophosphates binding to acetylcholinesterase.

PubMed

Ruark, Christopher D; Hack, C Eric; Robinson, Peter J; Anderson, Paul E; Gearhart, Jeffery M

2013-02-01

Organophosphates are a group of pesticides and chemical warfare nerve agents that inhibit acetylcholinesterase, the enzyme responsible for hydrolysis of the excitatory neurotransmitter acetylcholine. Numerous structural variants exist for this chemical class, and data regarding their toxicity can be difficult to obtain in a timely fashion. At the same time, their use as pesticides and military weapons is widespread, which presents a major concern and challenge in evaluating human toxicity. To address this concern, a quantitative structure-activity relationship (QSAR) was developed to predict pentavalent organophosphate oxon human acetylcholinesterase bimolecular rate constants. A database of 278 three-dimensional structures and their bimolecular rates was developed from 15 peer-reviewed publications. A database of simplified molecular input line entry notations and their respective acetylcholinesterase bimolecular rate constants are listed in Supplementary Material, Table I. The database was quite diverse, spanning 7 log units of activity. In order to describe their structure, 675 molecular descriptors were calculated using AMPAC 8.0 and CODESSA 2.7.10. Orthogonal projection to latent structures regression, bootstrap leave-random-many-out cross-validation and y-randomization were used to develop an externally validated consensus QSAR model. The domain of applicability was assessed by the William's plot. Six external compounds were outside the warning leverage indicating potential model extrapolation. A number of compounds had residuals >2 or <-2, indicating potential outliers or activity cliffs. The results show that the HOMO-LUMO energy gap contributed most significantly to the binding affinity. A mean training R (2) of 0.80, a mean test set R (2) of 0.76 and a consensus external test set R (2) of 0.66 were achieved using the QSAR. The training and external test set RMSE values were found to be 0.76 and 0.88. The results suggest that this QSAR model can be used in physiologically based pharmacokinetic/pharmacodynamic models of organophosphate toxicity to determine the rate of acetylcholinesterase inhibition.

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.

Using information from historical high-throughput screens to predict active compounds.

PubMed

Riniker, Sereina; Wang, Yuan; Jenkins, Jeremy L; Landrum, Gregory A

2014-07-28

Modern high-throughput screening (HTS) is a well-established approach for hit finding in drug discovery that is routinely employed in the pharmaceutical industry to screen more than a million compounds within a few weeks. However, as the industry shifts to more disease-relevant but more complex phenotypic screens, the focus has moved to piloting smaller but smarter chemically/biologically diverse subsets followed by an expansion around hit compounds. One standard method for doing this is to train a machine-learning (ML) model with the chemical fingerprints of the tested subset of molecules and then select the next compounds based on the predictions of this model. An alternative approach would be to take advantage of the wealth of bioactivity information contained in older (full-deck) screens using so-called HTS fingerprints, where each element of the fingerprint corresponds to the outcome of a particular assay, as input to machine-learning algorithms. We constructed HTS fingerprints using two collections of data: 93 in-house assays and 95 publicly available assays from PubChem. For each source, an additional set of 51 and 46 assays, respectively, was collected for testing. Three different ML methods, random forest (RF), logistic regression (LR), and naïve Bayes (NB), were investigated for both the HTS fingerprint and a chemical fingerprint, Morgan2. RF was found to be best suited for learning from HTS fingerprints yielding area under the receiver operating characteristic curve (AUC) values >0.8 for 78% of the internal assays and enrichment factors at 5% (EF(5%)) >10 for 55% of the assays. The RF(HTS-fp) generally outperformed the LR trained with Morgan2, which was the best ML method for the chemical fingerprint, for the majority of assays. In addition, HTS fingerprints were found to retrieve more diverse chemotypes. Combining the two models through heterogeneous classifier fusion led to a similar or better performance than the best individual model for all assays. Further validation using a pair of in-house assays and data from a confirmatory screen--including a prospective set of around 2000 compounds selected based on our approach--confirmed the good performance. Thus, the combination of machine-learning with HTS fingerprints and chemical fingerprints utilizes information from both domains and presents a very promising approach for hit expansion, leading to more hits. The source code used with the public data is provided.

Molecular Docking and Screening Studies of New Natural Sortase A Inhibitors

PubMed Central

Nitulescu, Georgiana; Nicorescu, Isabela Madalina; Olaru, Octavian Tudorel; Ungurianu, Anca; Mihai, Dragos Paul; Zanfirescu, Anca; Nitulescu, George Mihai; Margina, Denisa

2017-01-01

To date, multi-drug resistant bacteria represent an increasing health threat, with a high impact on mortality, morbidity, and health costs on a global scale. The ability of bacteria to rapidly and permanently acquire new virulence factors and drug-resistance elements requires the development of new antimicrobial agents and selection of new proper targets, such as sortase A. This specific bacterial target plays an important role in the virulence of many Gram-positive pathogens, and its inhibition should produce a mild evolutionary pressure which will not favor the development of resistance. A primary screening using a fluorescence resonance energy transfer assay was used to experimentally evaluate the inhibitory activity of several compounds on sortase A. Using molecular docking and structure-activity relationship analyses, several lead inhibitors were identified, which were further tested for antimicrobial activity using the well diffusion test and minimum inhibitory concentration. The toxicity was assessed using the Daphnia magna test and used as a future screening filter. Three natural compounds were identified in this study as promising candidates for further development into therapeutically useful anti-infective agents that could be used to treat infections caused by multi-drug resistant bacterial pathogens which include sortase A in their enzymatic set. PMID:29065551

Structure based classification for bile salt export pump (BSEP) inhibitors using comparative structural modeling of human BSEP

NASA Astrophysics Data System (ADS)

Jain, Sankalp; Grandits, Melanie; Richter, Lars; Ecker, Gerhard F.

2017-06-01

The bile salt export pump (BSEP) actively transports conjugated monovalent bile acids from the hepatocytes into the bile. This facilitates the formation of micelles and promotes digestion and absorption of dietary fat. Inhibition of BSEP leads to decreased bile flow and accumulation of cytotoxic bile salts in the liver. A number of compounds have been identified to interact with BSEP, which results in drug-induced cholestasis or liver injury. Therefore, in silico approaches for flagging compounds as potential BSEP inhibitors would be of high value in the early stage of the drug discovery pipeline. Up to now, due to the lack of a high-resolution X-ray structure of BSEP, in silico based identification of BSEP inhibitors focused on ligand-based approaches. In this study, we provide a homology model for BSEP, developed using the corrected mouse P-glycoprotein structure (PDB ID: 4M1M). Subsequently, the model was used for docking-based classification of a set of 1212 compounds (405 BSEP inhibitors, 807 non-inhibitors). Using the scoring function ChemScore, a prediction accuracy of 81% on the training set and 73% on two external test sets could be obtained. In addition, the applicability domain of the models was assessed based on Euclidean distance. Further, analysis of the protein-ligand interaction fingerprints revealed certain functional group-amino acid residue interactions that could play a key role for ligand binding. Though ligand-based models, due to their high speed and accuracy, remain the method of choice for classification of BSEP inhibitors, structure-assisted docking models demonstrate reasonably good prediction accuracies while additionally providing information about putative protein-ligand interactions.

Virtual screening of selective multitarget kinase inhibitors by combinatorial support vector machines.

PubMed

Ma, X H; Wang, R; Tan, C Y; Jiang, Y Y; Lu, T; Rao, H B; Li, X Y; Go, M L; Low, B C; Chen, Y Z

2010-10-04

Multitarget agents have been increasingly explored for enhancing efficacy and reducing countertarget activities and toxicities. Efficient virtual screening (VS) tools for searching selective multitarget agents are desired. Combinatorial support vector machines (C-SVM) were tested as VS tools for searching dual-inhibitors of 11 combinations of 9 anticancer kinase targets (EGFR, VEGFR, PDGFR, Src, FGFR, Lck, CDK1, CDK2, GSK3). C-SVM trained on 233-1,316 non-dual-inhibitors correctly identified 26.8%-57.3% (majority >36%) of the 56-230 intra-kinase-group dual-inhibitors (equivalent to the 50-70% yields of two independent individual target VS tools), and 12.2% of the 41 inter-kinase-group dual-inhibitors. C-SVM were fairly selective in misidentifying as dual-inhibitors 3.7%-48.1% (majority <20%) of the 233-1,316 non-dual-inhibitors of the same kinase pairs and 0.98%-4.77% of the 3,971-5,180 inhibitors of other kinases. C-SVM produced low false-hit rates in misidentifying as dual-inhibitors 1,746-4,817 (0.013%-0.036%) of the 13.56 M PubChem compounds, 12-175 (0.007%-0.104%) of the 168 K MDDR compounds, and 0-84 (0.0%-2.9%) of the 19,495-38,483 MDDR compounds similar to the known dual-inhibitors. C-SVM was compared to other VS methods Surflex-Dock, DOCK Blaster, kNN and PNN against the same sets of kinase inhibitors and the full set or subset of the 1.02 M Zinc clean-leads data set. C-SVM produced comparable dual-inhibitor yields, slightly better false-hit rates for kinase inhibitors, and significantly lower false-hit rates for the Zinc clean-leads data set. Combinatorial SVM showed promising potential for searching selective multitarget agents against intra-kinase-group kinases without explicit knowledge of multitarget agents.

Promotion of anagen, increased hair density and reduction of hair fall in a clinical setting following identification of FGF5-inhibiting compounds via a novel 2-stage process

PubMed Central

Burg, Dominic; Yamamoto, Masakuni; Namekata, Masato; Haklani, Joseph; Koike, Koichiro; Halasz, Maria

2017-01-01

Background There are very few effective, scientifically validated treatments with known mechanisms of action for treatment of hair loss in both men and women. Fibroblast growth factor 5 (FGF5) is an important factor in the irreversible transition from anagen to catagen, and inhibition of FGF5 prolongs anagen phase and reduces hair loss. Objective We aimed to screen botanically derived molecules for FGF5 inhibitory activity in vitro and assess efficacy in a clinical setting. Methods We screened for FGF5 inhibitory efficacy via a novel 2-step in vitro pipeline consisting of an engineered FGF5 responsive cell line, followed by an activated dermal papillae (DP) cell method. Efficacy in a clinical setting was assessed in a randomized, single-blind, placebo-controlled trial against early- to mid-stage pattern hair loss in men and women. Results We observed FGF5 inhibitory activity for a number of compounds from the monoterpenoid family, many showing greater inhibitory efficacy than our previously reported crude plant extracts. Evaluation of a lead candidate in a clinical study over 112 days showed a significant improvement in anagen:telogen (AT) ratio (p = 0.002), reduced hair fall (p = 0.007) and improved visual grading (p = 0.004). Scientifically matched photography on a subgroup of randomly chosen participants highlighted significant improvement in hair density, with increases evident in all tested participants compared to baseline. Conclusion Isolates from the monoterpenoid family displayed efficacy in FGF5 inhibition in vitro. A topical formulation containing a leading isolate significantly improved AT ratio, reduced hair fall and increased apparent hair density in the tested population of men and women. PMID:28280377

Preparation and Testing of Impedance-based Fluidic Biochips with RTgill-W1 Cells for Rapid Evaluation of Drinking Water Samples for Toxicity

DTIC Science & Technology

2016-03-07

and rapid response to a broad spectrum of inorganic and organic chemicals at concentrations that are relevant to human health concerns, as well as the...broad spectrum of toxic industrial compounds rapidly (within an hour) at concentrations relevant to human health , that the device be field-portable...laboratory setting and was able to detect potential water contaminants at concentrations that are relevant to human health . The portability and

Identifying New Chemical Entities that Treat and Prevent Relapsing Vivax and Drug-Resistant Falciparum Malaria in U.S. Military Personnel

DTIC Science & Technology

2016-10-01

Malaria in U.S. Military Personnel PRINCIPAL INVESTIGATOR: David A. Fidock CONTRACTING ORGANIZATION: Trustees of Columbia University New York NY 10032...Relapsing Vivax and Drug-Resistant Falciparum Malaria in U.S. Military Personnel 5b. GRANT NUMBER W81XWH-15-2-0033 5c. PROGRAM ELEMENT NUMBER 6...concentrations Nearly 600 of our prioritized hit compounds have been tested against rodent malaria liver stages and we have a set of 43 active

Estimation of octanol/water partition coefficients using LSER parameters

USGS Publications Warehouse

Luehrs, Dean C.; Hickey, James P.; Godbole, Kalpana A.; Rogers, Tony N.

1998-01-01

The logarithms of octanol/water partition coefficients, logKow, were regressed against the linear solvation energy relationship (LSER) parameters for a training set of 981 diverse organic chemicals. The standard deviation for logKow was 0.49. The regression equation was then used to estimate logKow for a test of 146 chemicals which included pesticides and other diverse polyfunctional compounds. Thus the octanol/water partition coefficient may be estimated by LSER parameters without elaborate software but only moderate accuracy should be expected.

Terrestrial Microcosm Evaluation of Two Army Smoke-Producing Compounds.

DTIC Science & Technology

1988-01-29

a greenhouse under natural or controlled photoperiods (depending on the time of year) with rainfall input simulated. Parameters monitored S ’a. ’ ’a...Sixty intact soil-core microcosms that had been extracted from an undisturbed (for m. iy years) field site were set up in a greenhouse under strict...tests. The 60 cures were divided equally between two greenhouse bays, 30 cores for exposure to RP/BR and 30 cores for exposure to WP. Within each group

Characterization and Fate of Gun and Rocket Propellant Residues on Testing and Training Ranges: Interim Report 2

DTIC Science & Technology

2010-12-01

compounds and stabiliz- ers in more conventional energetic formulations such as nitrocellulose (NC)-based propellants. Assessing the deposition...were opened and spread out to dry on aluminum foil covered trays to dry at room temperature. The dried material was then sieved under a hood with a...filtrate generated were tracked. The filters were placed in a labeled jar and set out to dry . After drying , the jars were sealed and refrigerated

Improved therapy-success prediction with GSS estimated from clinical HIV-1 sequences.

PubMed

Pironti, Alejandro; Pfeifer, Nico; Kaiser, Rolf; Walter, Hauke; Lengauer, Thomas

2014-01-01

Rules-based HIV-1 drug-resistance interpretation (DRI) systems disregard many amino-acid positions of the drug's target protein. The aims of this study are (1) the development of a drug-resistance interpretation system that is based on HIV-1 sequences from clinical practice rather than hard-to-get phenotypes, and (2) the assessment of the benefit of taking all available amino-acid positions into account for DRI. A dataset containing 34,934 therapy-naïve and 30,520 drug-exposed HIV-1 pol sequences with treatment history was extracted from the EuResist database and the Los Alamos National Laboratory database. 2,550 therapy-change-episode baseline sequences (TCEB) were assigned to test set A. Test set B contains 1,084 TCEB from the HIVdb TCE repository. Sequences from patients absent in the test sets were used to train three linear support vector machines to produce scores that predict drug exposure pertaining to each of 20 antiretrovirals: the first one uses the full amino-acid sequences (DEfull), the second one only considers IAS drug-resistance positions (DEonlyIAS), and the third one disregards IAS drug-resistance positions (DEnoIAS). For performance comparison, test sets A and B were evaluated with DEfull, DEnoIAS, DEonlyIAS, geno2pheno[resistance], HIVdb, ANRS, HIV-GRADE, and REGA. Clinically-validated cut-offs were used to convert the continuous output of the first four methods into susceptible-intermediate-resistant (SIR) predictions. With each method, a genetic susceptibility score (GSS) was calculated for each therapy episode in each test set by converting the SIR prediction for its compounds to integer: S=2, I=1, and R=0. The GSS were used to predict therapy success as defined by the EuResist standard datum definition. Statistical significance was assessed using a Wilcoxon signed-rank test. A comparison of the therapy-success prediction performances among the different interpretation systems for test set A can be found in Table 1, while those for test set B are found in Figure 1. Therapy-success prediction of first-line therapies with DEnoIAS performed better than DEonlyIAS (p<10-16). Therapy success prediction benefits from the consideration of all available mutations. The increase in performance was largest in first-line therapies with transmitted drug-resistance mutations.

Laboratory considerations of United States Pharmacopeia Chapter <71> sterility tests and its application to pharmaceutical compounding.

PubMed

Hyde, Tiffany D

2014-01-01

The purpose of this article is to describe United States Pharmacopeia Chapter <71> Sterility Tests from the perspective of Current Good Manufacturing Practices in order to aid compounding pharmacists in understanding the details and complexities that are required. Compounding pharmacists face a unique challenge in the industry today, with their compounding practice and the U.S. Food and Drug Administration trying to impose Current Good Manufacturing Practices guidelines. Naturally, this becomes a challenge to contract testing laboratories as well, as they are caught between the testing for non-Current Good Manufacturing Practices compounding standards and Current Good Manufacturing Practices manufacturing. It is important that the compounding pharmacist and their partner testing laboratory work closely together to ensure appropriate requirements are being met.

Multigeneration effects of insect growth regulators on the springtail Folsomia candida.

PubMed

Campiche, Sophie; L'Ambert, Grégory; Tarradellas, Joseph; Becker-van Slooten, Kristin

2007-06-01

Multigeneration tests are very useful for the assessment of long term toxicity of pollutants such as endocrine disruptor compounds. In this study, multigeneration reproduction tests adapted from the ISO standard 11267 were conducted with the Collembola Folsomia candida. Springtails were exposed to artificial soil contaminated with four insect growth regulators (methoprene, fenoxycarb, teflubenzuron, and precocene II) according to two different experimental set-ups. In the first set-up, the parental generation (F(0)) of Collembola was exposed to a pollutant for 28 days. Juveniles from the F(1) generation were transferred to uncontaminated soil for another 28-day period to generate the F(2) generation. In the second set-up, the F(0) generation was exposed to a pollutant for 10 days before being transferred to uncontaminated soil to reproduce. After 18-28 days, juveniles from the F(1) were transferred to clean soil to generate the F(2) generation. An effect on the number of hatched juveniles of the F(2) generation was observed for methoprene after exposure of the F(0) for 28 days and hatching of F(1) in contaminated soil. For methoprene and teflubenzuron, significant effects were even observed on the F(2) generation with the second experimental set-up, when only the F(0) generation was exposed for 10 days. This shows that the impact of these substances is transgenerational, which can have important consequences for the population of these or other organisms. No effect on the F(2) generation was observed with fenoxycarb and precocene II with the 10-day exposure experiment. Our results show that the developed experimental procedures are appropriate to assess the long term effects of endocrine disrupting compounds on the reproduction of the non-target species F. candida. Another important finding is that two substances with the same predicted mode of action (i.e., the two juvenile hormone analogues fenoxycarb and methoprene) do not necessarily affect the same endpoints in F. candida.

Drug2Gene: an exhaustive resource to explore effectively the drug-target relation network.

PubMed

Roider, Helge G; Pavlova, Nadia; Kirov, Ivaylo; Slavov, Stoyan; Slavov, Todor; Uzunov, Zlatyo; Weiss, Bertram

2014-03-11

Information about drug-target relations is at the heart of drug discovery. There are now dozens of databases providing drug-target interaction data with varying scope, and focus. Therefore, and due to the large chemical space, the overlap of the different data sets is surprisingly small. As searching through these sources manually is cumbersome, time-consuming and error-prone, integrating all the data is highly desirable. Despite a few attempts, integration has been hampered by the diversity of descriptions of compounds, and by the fact that the reported activity values, coming from different data sets, are not always directly comparable due to usage of different metrics or data formats. We have built Drug2Gene, a knowledge base, which combines the compound/drug-gene/protein information from 19 publicly available databases. A key feature is our rigorous unification and standardization process which makes the data truly comparable on a large scale, allowing for the first time effective data mining in such a large knowledge corpus. As of version 3.2, Drug2Gene contains 4,372,290 unified relations between compounds and their targets most of which include reported bioactivity data. We extend this set with putative (i.e. homology-inferred) relations where sufficient sequence homology between proteins suggests they may bind to similar compounds. Drug2Gene provides powerful search functionalities, very flexible export procedures, and a user-friendly web interface. Drug2Gene v3.2 has become a mature and comprehensive knowledge base providing unified, standardized drug-target related information gathered from publicly available data sources. It can be used to integrate proprietary data sets with publicly available data sets. Its main goal is to be a 'one-stop shop' to identify tool compounds targeting a given gene product or for finding all known targets of a drug. Drug2Gene with its integrated data set of public compound-target relations is freely accessible without restrictions at http://www.drug2gene.com.

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.

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.

Validation of an in vitro contractility assay using canine ventricular myocytes

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

Harmer, A.R., E-mail: alex.harmer@astrazeneca.com; Abi-Gerges, N.; Morton, M.J.

Measurement of cardiac contractility is a logical part of pre-clinical safety assessment in a drug discovery project, particularly if a risk has been identified or is suspected based on the primary- or non-target pharmacology. However, there are limited validated assays available that can be used to screen several compounds in order to identify and eliminate inotropic liability from a chemical series. We have therefore sought to develop an in vitro model with sufficient throughput for this purpose. Dog ventricular myocytes were isolated using a collagenase perfusion technique and placed in a perfused recording chamber on the stage of a microscopemore » at ∼ 36 °C. Myocytes were stimulated to contract at a pacing frequency of 1 Hz and a digital, cell geometry measurement system (IonOptix™) was used to measure sarcomere shortening in single myocytes. After perfusion with vehicle (0.1% DMSO), concentration–effect curves were constructed for each compound in 4–30 myocytes taken from 1 or 2 dog hearts. The validation test-set was 22 negative and 8 positive inotropes, and 21 inactive compounds, as defined by their effect in dog, cynolomolgous monkey or humans. By comparing the outcome of the assay to the known in vivo contractility effects, the assay sensitivity was 81%, specificity was 75%, and accuracy was 78%. With a throughput of 6–8 compounds/week from 1 cell isolation, this assay may be of value to drug discovery projects to screen for direct contractility effects and, if a hazard is identified, help identify inactive compounds. -- Highlights: ► Cardiac contractility is an important physiological function of the heart. ► Assessment of contractility is a logical part of pre-clinical drug safety testing. ► There are limited validated assays that predict effects of compounds on contractility. ► Using dog myocytes, we have developed an in vitro cardiac contractility assay. ► The assay predicted the in vivo contractility with a good level of accuracy.« less

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.

An Artificial Neural Network Based Analysis of Factors Controlling Particle Size in a Virgin Coconut Oil-Based Nanoemulsion System Containing Copper Peptide

PubMed Central

Samson, Shazwani; Basri, Mahiran; Fard Masoumi, Hamid Reza; Abdul Malek, Emilia; Abedi Karjiban, Roghayeh

2016-01-01

A predictive model of a virgin coconut oil (VCO) nanoemulsion system for the topical delivery of copper peptide (an anti-aging compound) was developed using an artificial neural network (ANN) to investigate the factors that influence particle size. Four independent variables including the amount of VCO, Tween 80: Pluronic F68 (T80:PF68), xanthan gum and water were the inputs whereas particle size was taken as the response for the trained network. Genetic algorithms (GA) were used to model the data which were divided into training sets, testing sets and validation sets. The model obtained indicated the high quality performance of the neural network and its capability to identify the critical composition factors for the VCO nanoemulsion. The main factor controlling the particle size was found out to be xanthan gum (28.56%) followed by T80:PF68 (26.9%), VCO (22.8%) and water (21.74%). The formulation containing copper peptide was then successfully prepared using optimum conditions and particle sizes of 120.7 nm were obtained. The final formulation exhibited a zeta potential lower than -25 mV and showed good physical stability towards centrifugation test, freeze-thaw cycle test and storage at temperature 25°C and 45°C. PMID:27383135

An Artificial Neural Network Based Analysis of Factors Controlling Particle Size in a Virgin Coconut Oil-Based Nanoemulsion System Containing Copper Peptide.

PubMed

Samson, Shazwani; Basri, Mahiran; Fard Masoumi, Hamid Reza; Abdul Malek, Emilia; Abedi Karjiban, Roghayeh

2016-01-01

A predictive model of a virgin coconut oil (VCO) nanoemulsion system for the topical delivery of copper peptide (an anti-aging compound) was developed using an artificial neural network (ANN) to investigate the factors that influence particle size. Four independent variables including the amount of VCO, Tween 80: Pluronic F68 (T80:PF68), xanthan gum and water were the inputs whereas particle size was taken as the response for the trained network. Genetic algorithms (GA) were used to model the data which were divided into training sets, testing sets and validation sets. The model obtained indicated the high quality performance of the neural network and its capability to identify the critical composition factors for the VCO nanoemulsion. The main factor controlling the particle size was found out to be xanthan gum (28.56%) followed by T80:PF68 (26.9%), VCO (22.8%) and water (21.74%). The formulation containing copper peptide was then successfully prepared using optimum conditions and particle sizes of 120.7 nm were obtained. The final formulation exhibited a zeta potential lower than -25 mV and showed good physical stability towards centrifugation test, freeze-thaw cycle test and storage at temperature 25°C and 45°C.

CERAPP: Collaborative Estrogen Receptor Activity Prediction ...

EPA Pesticide Factsheets

Humans potentially are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Many of these chemicals never have been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for assessment in costly in vivo tests, for instance, within the EPA Endocrine Disruptor Screening Program. Here, we describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) demonstrating the efficacy of using predictive computational models on high-throughput screening data to screen thousands of chemicals against the ER. CERAPP combined multiple models developed in collaboration among 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1677 compounds provided by EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were tested using an evaluation set of 7522 chemicals collected from the literature. To overcome the limitations of single models, a consensus was built weighting models using a scoring function (0 to 1) based on their accuracies. Individual model scores ranged from 0.69 to 0.85, showing

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.

Toxicity profiles and solvent-toxicant interference in the planarian Schmidtea mediterranea after dimethylsulfoxide (DMSO) exposure.

PubMed

Stevens, An-Sofie; Pirotte, Nicky; Plusquin, Michelle; Willems, Maxime; Neyens, Thomas; Artois, Tom; Smeets, Karen

2015-03-01

To investigate hydrophobic test compounds in toxicological studies, solvents like dimethylsulfoxide (DMSO) are inevitable. However, using these solvents, the interpretation of test compound-induced responses can be biased. DMSO concentration guidelines are available, but are mostly based on acute exposures involving one specific toxicity endpoint. Hence, to avoid solvent-toxicant interference, we use multiple chronic test endpoints for additional interpretation of DMSO concentrations and propose a statistical model to assess possible synergistic, antagonistic or additive effects of test compounds and their solvents. In this study, the effects of both short- (1 day) and long-term (2 weeks) exposures to low DMSO concentrations (up to 1000 µl l(-1) ) were studied in the planarian Schmidtea mediterranea. We measured different biological levels in both fully developed and developing animals. In a long-term exposure set-up, a concentration of 500 µl l(-1) DMSO interfered with processes on different biological levels, e.g. behaviour, stem cell proliferation and gene expression profiles. After short exposure times, 500 µl l(-1) DMSO only affected motility, whereas the most significant changes on different parameters were observed at a concentration of 1000 µl l(-1) DMSO. As small sensitivity differences exist between biological levels and developmental stages, we advise the use of this solvent in concentrations below 500 µl l(-1) in this organism. In the second part of our study, we propose a statistical approach to account for solvent-toxicant interactions and discuss full-scale solvent toxicity studies. In conclusion, we reassessed DMSO concentration limits for different experimental endpoints in the planarian S. mediterranea. Copyright © 2014 John Wiley & Sons, Ltd.

Analysis of 27 antibiotic residues in raw cow's milk and milk-based products--validation of Delvotest® T.

PubMed

Bion, Cindy; Beck Henzelin, Andrea; Qu, Yajuan; Pizzocri, Giuseppe; Bolzoni, Giuseppe; Buffoli, Elena

2016-01-01

Delvotest® T was evaluated for its capability at detecting residues of 27 antibiotics in raw cow's milk and in some dairy ingredients (skimmed and full-cream milk powders). The kit was used as a screening tool for the qualitative determination of antibiotics from different families in a single test. Results delivered by such a method are expressed as 'positive' or 'negative', referring to the claimed screening target concentration (STC). Validation was conducted according to the European Community Reference Laboratories' (CRLs) residues guidelines of 20 January 2010 and performed by two laboratories, one located in Europe and the other in Asia. Five criteria were evaluated including detection capability at STC, false-positive (FP) rate, false-negative (FN) rate, robustness and cross-reactivity using visual reading and Delvoscan®. STCs were set at or below the corresponding maximum residue limit (MRL), as fixed by European Regulation EC No. 37/2010. Four antibiotics (nafcillin, oxytetracycline, tetracycline and rifaximin) out of 27 had a false-negative rate ranging from 1.7% to 4.9%; however, it was still compliant with the CRLs' requirements. Globally, Delvotest T can be recommended for the analysis of the surveyed antibiotics in raw cow's milk, skimmed and full-cream milk powders. Additional compounds were tested such as sulfamethazine, spiramycin and erythromycin; however, detection at the corresponding MRL was not achievable and these compounds were removed from the validation. Other drugs from the sulfonamide, aminoglycoside or macrolide families not detected by the test at the MRL were not evaluated in this study. Regarding the reliability of this rapid test to milk-based preparations, additional experiments should be performed on a larger range of compounds and samples to validate the Delvotest T in such matrices.

Practical aspects of mutagenicity testing strategy: an industrial perspective.

PubMed

Gollapudi, B B; Krishna, G

2000-11-20

Genetic toxicology studies play a central role in the development and marketing of new chemicals for pharmaceutical, agricultural, industrial, and consumer use. During the discovery phase of product development, rapid screening tests that require minimal amounts of test materials are used to assist in the design and prioritization of new molecules. At this stage, a modified Salmonella reverse mutation assay and an in vitro micronucleus test with mammalian cell culture are frequently used for screening. Regulatory genetic toxicology studies are conducted with a short list of compounds using protocols that conform to various international guidelines. A set of four assays usually constitutes the minimum test battery that satisfies global requirements. This set includes a bacterial reverse mutation assay, an in vitro cytogenetic test with mammalian cell culture, an in vitro gene mutation assay in mammalian cell cultures, and an in vivo rodent bone marrow micronucleus test. Supplementary studies are conducted in certain instances either as a follow-up to the findings from this initial testing battery and/or to satisfy a regulatory requirement. Currently available genetic toxicology assays have helped the scientific and industrial community over the past several decades in evaluating the mutagenic potential of chemical agents. The emerging field of toxicogenomics has the potential to redefine our ability to study the response of cells to genetic damage and hence our ability to study threshold phenomenon.

Acute oral toxicity test of chemical compounds in silkworms.

PubMed

Usui, Kimihito; Nishida, Satoshi; Sugita, Takuya; Ueki, Takuro; Matsumoto, Yasuhiko; Okumura, Hidenobu; Sekimizu, Kazuhisa

2016-02-01

This study performed an acute oral toxicity test of 59 compounds in silkworms. These compounds are listed in OECD guidelines as standard substances for a cytotoxicity test, and median lethal dose (LD(50)) werecalculated for each compound. Acute oral LD(50) values in mammals are listed in OECD guidelines and acute oral LD(50) values in silkworms were determined in this study. R(2) for the correlation between LD(50) values in mammals and LD(50) values in silkworms was 0.66. In addition, the acute oral toxicity test in silkworms was performed by two different facilities, and test results from the facilities were highly reproducible. These findings suggest that an acute oral toxicity test in silkworms is a useful way to evaluate the toxicity of compounds in mammals.

Repositioning FDA Drugs as Potential Cruzain Inhibitors from Trypanosoma cruzi: Virtual Screening, In Vitro and In Vivo Studies.

PubMed

Palos, Isidro; Lara-Ramirez, Edgar E; Lopez-Cedillo, Julio Cesar; Garcia-Perez, Carlos; Kashif, Muhammad; Bocanegra-Garcia, Virgilio; Nogueda-Torres, Benjamin; Rivera, Gildardo

2017-06-18

Chagas disease (CD) is a neglected disease caused by the parasite Trypanosoma cruzi , which affects underdeveloped countries. The current drugs of choice are nifurtimox and benznidazole, but both have severe adverse effects and less effectivity in chronic infections; therefore, the need to discover new drugs is essential. A computer-guided drug repositioning method was applied to identify potential FDA drugs (approved and withdrawn) as cruzain (Cz) inhibitors and trypanocidal effects were confirmed by in vitro and in vivo studies. 3180 FDA drugs were virtually screened using a structure-based approach. From a first molecular docking analysis, a set of 33 compounds with the best binding energies were selected. Subsequent consensus affinity binding, ligand amino acid contact clustering analysis, and ranked position were used to choose four known pharmacological compounds to be tested in vitro. Mouse blood samples infected with trypomastigotes from INC-5 and NINOA strains were used to test the trypanocidal effect of four selected compounds. Among these drugs, one fibrate antilipemic (etofyllin clofibrate) and three β-lactam antibiotics (piperacillin, cefoperazone, and flucloxacillin) showed better trypanocidal effects (LC 50 range 15.8-26.1 μg/mL) in comparison with benznidazole and nifurtimox (LC 50 range 33.1-46.7 μg/mL). A short-term in vivo evaluation of these compounds showed a reduction of parasitemia in infected mice (range 90-60%) at 6 h, but this was low compared to benznidazole (50%). This work suggests that four known FDA drugs could be used to design and obtain new trypanocidal agents.

Predicting Dural Tear in Compound Depressed Skull Fractures: A Prospective Multicenter Correlational Study.

PubMed

Salia, Shemsedin Musefa; Mersha, Hagos Biluts; Aklilu, Abenezer Tirsit; Baleh, Abat Sahlu; Lund-Johansen, Morten

2018-06-01

Compound depressed skull fracture (DSF) is a neurosurgical emergency. Preoperative knowledge of dural status is indispensable for treatment decision making. This study aimed to determine predictors of dural tear from clinical and imaging characteristics in patients with compound DSF. This prospective, multicenter correlational study in neurosurgical hospitals in Addis Ababa, Ethiopia, included 128 patients operated on from January 1, 2016, to October 31, 2016. Clinical, imaging, and intraoperative findings were evaluated. Univariate and multivariate analyses were used to establish predictors of dural tear. A logistic regression model was developed to predict probability of dural tear. Model validation was done using the receiver operating characteristic curve. Dural tear was seen in 55.5% of 128 patients. Demographics, injury mechanism, clinical presentation, and site of DSF had no significant correlation with dural tear. In univariate and multivariate analyses, depth of fracture depression (odds ratio 1.3, P < 0.001), pneumocephalus (odds ratio 2.8, P = 0.005), and brain contusions/intracerebral hematoma (odds ratio 5.5, P < 0.001) were significantly correlated with dural tear. We developed a logistic regression model (diagnostic test) to calculate probability of dural tear. Using the receiver operating characteristic curve, we determined the cutoff value for a positive test giving the highest accuracy to be 30% with a corresponding sensitivity of 93.0% and specificity of 43.9%. Dural tear in compound DSF can be predicted with 93.0% sensitivity using preoperative findings and may guide treatment decision making in resource-limited settings where risk of extensive cranial surgery outweighs the benefit. Copyright © 2018 Elsevier Inc. All rights reserved.

3D QSAR studies on protein tyrosine phosphatase 1B inhibitors: comparison of the quality and predictivity among 3D QSAR models obtained from different conformer-based alignments.

PubMed

Pandey, Gyanendra; Saxena, Anil K

2006-01-01

A set of 65 flexible peptidomimetic competitive inhibitors (52 in the training set and 13 in the test set) of protein tyrosine phosphatase 1B (PTP1B) has been used to compare the quality and predictive power of 3D quantitative structure-activity relationship (QSAR) comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models for the three most commonly used conformer-based alignments, namely, cocrystallized conformer-based alignment (CCBA), docked conformer-based alignment (DCBA), and global minima energy conformer-based alignment (GMCBA). These three conformers of 5-[(2S)-2-({(2S)-2-[(tert-butoxycarbonyl)amino]-3-phenylpropanoyl}amino)3-oxo-3-pentylamino)propyl]-2-(carboxymethoxy)benzoic acid (compound number 66) were obtained from the X-ray structure of its cocrystallized complex with PTP1B (PDB ID: 1JF7), its docking studies, and its global minima by simulated annealing. Among the 3D QSAR models developed using the above three alignments, the CCBA provided the optimal predictive CoMFA model for the training set with cross-validated r2 (q2)=0.708, non-cross-validated r2=0.902, standard error of estimate (s)=0.165, and F=202.553 and the optimal CoMSIA model with q2=0.440, r2=0.799, s=0.192, and F=117.782. These models also showed the best test set prediction for the 13 compounds with predictive r2 values of 0.706 and 0.683, respectively. Though the QSAR models derived using the other two alignments also produced statistically acceptable models in the order DCBA>GMCBA in terms of the values of q2, r2, and predictive r2, they were inferior to the corresponding models derived using CCBA. Thus, the order of preference for the alignment selection for 3D QSAR model development may be CCBA>DCBA>GMCBA, and the information obtained from the CoMFA and CoMSIA contour maps may be useful in designing specific PTP1B inhibitors.

Preliminary Results on the Surface of a New Fe-Based Metallic Material after “In Vivo” Maintaining

NASA Astrophysics Data System (ADS)

Săndulache, F.; Stanciu, S.; Cimpoeşu, N.; Stanciu, T.; Cimpoeșu, R.; Enache, A.; Baciu, R.

2017-06-01

Abstract A new Fe-based alloy was obtained using UltraCast melting equipment. The alloy, after mechanical processing, was implanted in five rabbit specimens (with respect for the “in-bone” procedure). After 30 days of implantation the samples were recovered and analyzed by weight and surface state meanings. Scanning electron microscopy technique was used to determine the new compounds morphology from the metallic surface and X-ray dispersive energy spectroscopy for chemical analyze results. A bond between the metallic material and biological material of the bone was observed through increasing of sample weight and by SEM images. After the first set of tests, as the samples were extracted and biologically cleaned, the samples were ultrasonically cleaned and re-analyzed in order to establish the stability of the chemical compounds.

Molecular Docking Study on Galantamine Derivatives as Cholinesterase Inhibitors.

PubMed

Atanasova, Mariyana; Yordanov, Nikola; Dimitrov, Ivan; Berkov, Strahil; Doytchinova, Irini

2015-06-01

A training set of 22 synthetic galantamine derivatives binding to acetylcholinesterase was docked by GOLD and the protocol was optimized in terms of scoring function, rigidity/flexibility of the binding site, presence/absence of a water molecule inside and radius of the binding site. A moderate correlation was found between the affinities of compounds expressed as pIC50 values and their docking scores. The optimized docking protocol was validated by an external test set of 11 natural galantamine derivatives and the correlation coefficient between the docking scores and the pIC50 values was 0.800. The derived relationship was used to analyze the interactions between galantamine derivatives and AChE. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Inclusion complex of novel curcumin analogue CDF and β-cyclodextrin (1:2) and its enhanced in vivo anticancer activity against pancreatic cancer.

PubMed

Dandawate, Prasad R; Vyas, Alok; Ahmad, Aamir; Banerjee, Sanjeev; Deshpande, Jyoti; Swamy, K Venkateswara; Jamadar, Abeda; Dumhe-Klaire, Anne Catherine; Padhye, Subhash; Sarkar, Fazlul H

2012-07-01

Several formulations have been proposed to improve the systemic delivery of novel cancer therapeutic compounds, including cyclodextrin derivatives. We aimed to synthesize and characterize of CDF-β-cyclodextrin inclusion complex (1:2) (CDFCD). The compound was characterized by Fourier transform infrared, differential scanning calorimetry, powder X-ray diffraction studies, H1 & C13 NMR studies and scanning electron microscopic analysis. Its activity was tested against multiple cancer cell lines, and in vivo bioavailability was checked. CDF-β-cyclodextrin was found to lower IC(50) value by half when tested against multiple cancer cell lines. It preferentially accumulated in the pancreas, where levels of CDF-β-cyclodextrin in mice were 10 times higher than in serum, following intravenous administration of an aqueous CDF-β-cyclodextrin preparation. Novel curcumin analog CDF preferentially accumulates in the pancreas, leading to its potent anticancer activity against pancreatic cancer cells. Synthesis of such CDF-β-cyclodextrin self-assembly is an effective strategy to enhance its bioavailability and tissue distribution, warranting further evaluation for CDF delivery in clinical settings for treatment of human malignancies.

Structure-based virtual screening efforts against HIV-1 reverse transcriptase to introduce the new potent non-nucleoside reverse transcriptase inhibitor

NASA Astrophysics Data System (ADS)

Hosseini, Yaser; Mollica, Adriano; Mirzaie, Sako

2016-12-01

The human immunodeficiency virus (HIV) which is strictly related to the development of AIDS, is treated by a cocktail of drugs, but due its high propensity gain drug resistance, the rational development of new medicine is highly desired. Among the different mechanism of action we selected the reverse transcriptase (RT) inhibition, for our studies. With the aim to identify new chemical entities to be used for further rational drug design, a set of 3000 molecules from the Zinc Database have been screened by docking experiments using AutoDock Vina software. The best ranked compounds with respect of the crystallographic inhibitor MK-4965 resulted to be five compounds, and the best among them was further tested by molecular dynamics (MD) simulation. Our results indicate that comp1 has a stronger interaction with the subsite p66 of RT than MK-4965 and that both are able to stabilize specific conformational changes of the RT 3D structure, which may explain their activity as inhibitors. Therefore comp1 could be a good candidate for biological tests and further development.

Disruption of ion homeostasis by verrucosin and a related compound.

PubMed

Akiyama, Koichi; Tone, Junichi; Yamauchi, Satoshi; Sugahara, Takuya; Maruyama, Masafumi; Kakinuma, Yoshimi

2011-01-01

We have found that (-)-virgatusin and related compounds have antimicrobial and antifungal activity. To identify further biological activities of these compounds, we tested the activity of acridine orange efflux, which shows ionophore-like disruption of cellular ion homeostasis activity. After testing 31 compounds, we found that verrucosin and a related compound had disruption activity.

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...

QSAR, docking, dynamic simulation and quantum mechanics studies to explore the recognition properties of cholinesterase binding sites.

PubMed

Correa-Basurto, J; Bello, M; Rosales-Hernández, M C; Hernández-Rodríguez, M; Nicolás-Vázquez, I; Rojo-Domínguez, A; Trujillo-Ferrara, J G; Miranda, René; Flores-Sandoval, C A

2014-02-25

A set of 84 known N-aryl-monosubstituted derivatives (42 amides: series 1 and 2, and 42 imides: series 3 an 4, from maleic and succinic anhydrides, respectively) that display inhibitory activity toward both acetylcholinesterase and butyrylcholinesterase (ChEs) was considered for Quantitative structure-activity relationship (QSAR) studies. These QSAR studies employed docking data from both ChEs that were previously submitted to molecular dynamics (MD) simulations. Donepezil and galanthamine stereoisomers were included to analyze their quantum mechanics properties and for validating the docking procedure. Quantum parameters such as frontier orbital energies, dipole moment, molecular volume, atomic charges, bond length and reactivity parameters were measured, as well as partition coefficients, molar refractivity and polarizability were also analyzed. In order to evaluate the obtained equations, four compounds: 1a (4-oxo-4-(phenylamino)butanoic acid), 2a ((2Z)-4-oxo-4-(phenylamino)but-2-enoic acid), 3a (2-phenylcyclopentane-1,3-dione) and 4a (2-phenylcyclopent-4-ene-1,3-dione) were employed as independent data set, using only equations with r(m(test))²>0.5. It was observed that residual values gave low value in almost all series, excepting in series 1 for compounds 3a and 4a, and in series 4 for compounds 1a, 2a and 3a, giving a low value for 4a. Consequently, equations seems to be specific according to the structure of the evaluated compound, that means, series 1 fits better for compound 1a, series 3 or 4 fits better for compounds 3a or 4a. Same behavior was observed in the butyrylcholinesterase (BChE). Therefore, obtained equations in this QSAR study could be employed to calculate the inhibition constant (Ki) value for compounds having a similar structure as N-aryl derivatives described here. The QSAR study showed that bond lengths, molecular electrostatic potential and frontier orbital energies are important in both ChE targets. Docking studies revealed that despite the multiple conformations obtained through MD simulations on both ChEs, the ligand recognition properties were conserved. In fact, the complex formed between ChEs and the best N-aryl compound reproduced the binding mode experimentally reported, where the ligand was coupled into the choline-binding site and stabilized through π-π interactions with Trp82 or Trp86 for BChE and AChE, respectively, suggesting that this compound could be an efficient inhibitor and supporting our model. Copyright © 2014. Published by Elsevier Ireland Ltd.

Classification and source determination of medium petroleum distillates by chemometric and artificial neural networks: a self organizing feature approach.

PubMed

Mat-Desa, Wan N S; Ismail, Dzulkiflee; NicDaeid, Niamh

2011-10-15

Three different medium petroleum distillate (MPD) products (white spirit, paint brush cleaner, and lamp oil) were purchased from commercial stores in Glasgow, Scotland. Samples of 10, 25, 50, 75, 90, and 95% evaporated product were prepared, resulting in 56 samples in total which were analyzed using gas chromatography-mass spectrometry. Data sets from the chromatographic patterns were examined and preprocessed for unsupervised multivariate analyses using principal component analysis (PCA), hierarchical cluster analysis (HCA), and a self organizing feature map (SOFM) artificial neural network. It was revealed that data sets comprised of higher boiling point hydrocarbon compounds provided a good means for the classification of the samples and successfully linked highly weathered samples back to their unevaporated counterpart in every case. The classification abilities of SOFM were further tested and validated for their predictive abilities where one set of weather data in each case was withdrawn from the sample set and used as a test set of the retrained network. This revealed SOFM to be an outstanding mechanism for sample discrimination and linkage over the more conventional PCA and HCA methods often suggested for such data analysis. SOFM also has the advantage of providing additional information through the evaluation of component planes facilitating the investigation of underlying variables that account for the classification. © 2011 American Chemical Society

[Method of traditional Chinese medicine formula design based on 3D-database pharmacophore search and patent retrieval].

PubMed

He, Yu-su; Sun, Zhi-yi; Zhang, Yan-ling

2014-11-01

By using the pharmacophore model of mineralocorticoid receptor antagonists as a starting point, the experiment stud- ies the method of traditional Chinese medicine formula design for anti-hypertensive. Pharmacophore models were generated by 3D-QSAR pharmacophore (Hypogen) program of the DS3.5, based on the training set composed of 33 mineralocorticoid receptor antagonists. The best pharmacophore model consisted of two Hydrogen-bond acceptors, three Hydrophobic and four excluded volumes. Its correlation coefficient of training set and test set, N, and CAI value were 0.9534, 0.6748, 2.878, and 1.119. According to the database screening, 1700 active compounds from 86 source plant were obtained. Because of lacking of available anti-hypertensive medi cation strategy in traditional theory, this article takes advantage of patent retrieval in world traditional medicine patent database, in order to design drug formula. Finally, two formulae was obtained for antihypertensive.

Inverse sequential procedures for the monitoring of time series

NASA Technical Reports Server (NTRS)

Radok, Uwe; Brown, Timothy

1993-01-01

Climate changes traditionally have been detected from long series of observations and long after they happened. The 'inverse sequential' monitoring procedure is designed to detect changes as soon as they occur. Frequency distribution parameters are estimated both from the most recent existing set of observations and from the same set augmented by 1,2,...j new observations. Individual-value probability products ('likelihoods') are then calculated which yield probabilities for erroneously accepting the existing parameter(s) as valid for the augmented data set and vice versa. A parameter change is signaled when these probabilities (or a more convenient and robust compound 'no change' probability) show a progressive decrease. New parameters are then estimated from the new observations alone to restart the procedure. The detailed algebra is developed and tested for Gaussian means and variances, Poisson and chi-square means, and linear or exponential trends; a comprehensive and interactive Fortran program is provided in the appendix.

Atom-based 3D-QSAR, induced fit docking, and molecular dynamics simulations study of thieno[2,3-b]pyridines negative allosteric modulators of mGluR5.

PubMed

Vijaya Prabhu, Sitrarasu; Singh, Sanjeev Kumar

2018-05-28

Atom-based three dimensional-quantitative structure-activity relationship (3D-QSAR) model was developed on the basis of 5-point pharmacophore hypothesis (AARRR) with two hydrogen bond acceptors (A) and three aromatic rings for the derivatives of thieno[2,3-b]pyridine, which modulates the activity to inhibit the mGluR5 receptor. Generation of a highly predictive 3D-QSAR model was performed using the alignment of predicted pharmacophore hypothesis for the training set (R 2  = 0.84, SD = 0.26, F = 45.8, N = 29) and test set (Q 2  = 0.74, RMSE = 0.235, Pearson-R = 0.94, N = 9). The best pharmacophore hypothesis AARRR was selected, and developed three dimensional-quantitative structure activity relationship (3D-QSAR) model also supported the outcome of this study by means of favorable and unfavorable electron withdrawing group and hydrophobic regions of most active compound 42d and least active compound 18b. Following, induced fit docking and binding free energy calculations reveals the reliable binding orientation of the compounds. Finally, molecular dynamics simulations for 100 ns were performed to depict the protein-ligand stability. We anticipate that the resulted outcome could be supportive to discover potent negative allosteric modulators for metabotropic glutamate receptor 5 (mGluR5).

Novel 1,4-naphthoquinone-based sulfonamides: Synthesis, QSAR, anticancer and antimalarial studies.

PubMed

Pingaew, Ratchanok; Prachayasittikul, Veda; Worachartcheewan, Apilak; Nantasenamat, Chanin; Prachayasittikul, Supaluk; Ruchirawat, Somsak; Prachayasittikul, Virapong

2015-10-20

A novel series of 1,4-naphthoquinones (33-44) tethered by open and closed chain sulfonamide moieties were designed, synthesized and evaluated for their cytotoxic and antimalarial activities. All quinone-sulfonamide derivatives displayed a broad spectrum of cytotoxic activities against all of the tested cancer cell lines including HuCCA-1, HepG2, A549 and MOLT-3. Most quinones (33-36 and 38-43) exerted higher anticancer activity against HepG2 cell than that of the etoposide. The open chain analogs 36 and 42 were shown to be the most potent compounds. Notably, the restricted sulfonamide analog 38 with 6,7-dimethoxy groups exhibited the most potent antimalarial activity (IC₅₀ = 2.8 μM). Quantitative structure-activity relationships (QSAR) study was performed to reveal important chemical features governing the biological activities. Five constructed QSAR models provided acceptable predictive performance (Rcv 0.5647-0.9317 and RMSEcv 0.1231-0.2825). Four additional sets of structurally modified compounds were generated in silico (34a-34d, 36a-36k, 40a-40d and 42a-42k) in which their activities were predicted using the constructed QSAR models. A comprehensive discussion of the structure-activity relationships was made and a set of promising compounds (i.e., 33, 36, 38, 42, 36d, 36f, 42e, 42g and 42f) was suggested for further development as anticancer and antimalarial agents. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Combined 3D-QSAR and molecular docking study on 7,8-dialkyl-1,3-diaminopyrrolo-[3,2-f] Quinazoline series compounds to understand the binding mechanism of DHFR inhibitors

NASA Astrophysics Data System (ADS)

Aouidate, Adnane; Ghaleb, Adib; Ghamali, Mounir; Chtita, Samir; Choukrad, M'barek; Sbai, Abdelouahid; Bouachrine, Mohammed; Lakhlifi, Tahar

2017-07-01

A series of nineteen DHFR inhibitors was studied based on the combination of two computational techniques namely, three-dimensional quantitative structure activity relationship (3D-QSAR) and molecular docking. The comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) were developed using 19 molecules having pIC50 ranging from 9.244 to 5.839. The best CoMFA and CoMSIA models show conventional determination coefficients R2 of 0.96 and 0.93 as well as the Leave One Out cross-validation determination coefficients Q2 of 0.64 and 0.72, respectively. The predictive ability of those models was evaluated by the external validation using a test set of five compounds with predicted determination coefficients R2test of 0.92 and 0.94, respectively. The binding mode between this kind of compounds and the DHFR enzyme in addition to the key amino acid residues were explored by molecular docking simulation. Contour maps and molecular docking identified that the R1 and R2 natures at the pyrazole moiety are the important features for the optimization of the binding affinity to the DHFR receptor. According to the good concordance between the CoMFA/CoMSIA contour maps and docking results, the obtained information was explored to design novel molecules.

Recognition of the Component Odors in Mixtures

PubMed Central

Fletcher, Dane B; Hettinger, Thomas P

2017-01-01

Abstract Natural olfactory stimuli are volatile-chemical mixtures in which relative perceptual saliencies determine which odor-components are identified. Odor identification also depends on rapid selective adaptation, as shown for 4 odor stimuli in an earlier experimental simulation of natural conditions. Adapt-test pairs of mixtures of water-soluble, distinct odor stimuli with chemical features in common were studied. Identification decreased for adapted components but increased for unadapted mixture-suppressed components, showing compound identities were retained, not degraded to individual molecular features. Four additional odor stimuli, 1 with 2 perceptible odor notes, and an added “water-adapted” control tested whether this finding would generalize to other 4-compound sets. Selective adaptation of mixtures of the compounds (odors): 3 mM benzaldehyde (cherry), 5 mM maltol (caramel), 1 mM guaiacol (smoke), and 4 mM methyl anthranilate (grape-smoke) again reciprocally unmasked odors of mixture-suppressed components in 2-, 3-, and 4-component mixtures with 2 exceptions. The cherry note of “benzaldehyde” (itself) and the shared note of “methyl anthranilate and guaiacol” (together) were more readily identified. The pervasive mixture-component dominance and dynamic perceptual salience may be mediated through peripheral adaptation and central mutual inhibition of neural responses. Originating in individual olfactory receptor variants, it limits odor identification and provides analytic properties for momentary recognition of a few remaining mixture-components. PMID:28641388

Probabilistic neural networks modeling of the 48-h LC50 acute toxicity endpoint to Daphnia magna.

PubMed

Niculescu, S P; Lewis, M A; Tigner, J

2008-01-01

Two modeling experiments based on the maximum likelihood estimation paradigm and targeting prediction of the Daphnia magna 48-h LC50 acute toxicity endpoint for both organic and inorganic compounds are reported. The resulting models computational algorithms are implemented as basic probabilistic neural networks with Gaussian kernel (statistical corrections included). The first experiment uses strictly D. magna information for 971 structures as training/learning data and the resulting model targets practical applications. The second experiment uses the same training/learning information plus additional data on another 29 compounds whose endpoint information is originating from D. pulex and Ceriodaphnia dubia. It only targets investigation of the effect of mixing strictly D. magna 48-h LC50 modeling information with small amounts of similar information estimated from related species, and this is done as part of the validation process. A complementary 81 compounds dataset (involving only strictly D. magna information) is used to perform external testing. On this external test set, the Gaussian character of the distribution of the residuals is confirmed for both models. This allows the use of traditional statistical methodology to implement computation of confidence intervals for the unknown measured values based on the models predictions. Examples are provided for the model targeting practical applications. For the same model, a comparison with other existing models targeting the same endpoint is performed.

Fish embryo toxicity test: identification of compounds with weak toxicity and analysis of behavioral effects to improve prediction of acute toxicity for neurotoxic compounds.

PubMed

Klüver, Nils; König, Maria; Ortmann, Julia; Massei, Riccardo; Paschke, Albrecht; Kühne, Ralph; Scholz, Stefan

2015-06-02

The fish embryo toxicity test has been proposed as an alternative for the acute fish toxicity test, but concerns have been raised for its predictivity given that a few compounds have been shown to exhibit a weak acute toxicity in the fish embryo. In order to better define the applicability domain and improve the predictive capacity of the fish embryo test, we performed a systematic analysis of existing fish embryo and acute fish toxicity data. A correlation analysis of a total of 153 compounds identified 28 compounds with a weaker or no toxicity in the fish embryo test. Eleven of these compounds exhibited a neurotoxic mode of action. We selected a subset of eight compounds with weaker or no embryo toxicity (cyanazine, picloram, aldicarb, azinphos-methyl, dieldrin, diquat dibromide, endosulfan, and esfenvalerate) to study toxicokinetics and a neurotoxic mode of action as potential reasons for the deviating fish embryo toxicity. Published fish embryo LC50 values were confirmed by experimental analysis of zebrafish embryo LC50 according to OECD guideline 236. Except for diquat dibromide, internal concentration analysis did not indicate a potential relation of the low sensitivity of fish embryos to a limited uptake of the compounds. Analysis of locomotor activity of diquat dibromide and the neurotoxic compounds in 98 hpf embryos (exposed for 96 h) indicated a specific effect on behavior (embryonic movement) for the neurotoxic compounds. The EC50s of behavior for neurotoxic compounds were close to the acute fish toxicity LC50. Our data provided the first evidence that the applicability domain of the fish embryo test (LC50s determination) may exclude neurotoxic compounds. However, neurotoxic compounds could be identified by changes in embryonic locomotion. Although a quantitative prediction of acute fish toxicity LC50 using behavioral assays in fish embryos may not yet be possible, the identification of neurotoxicity could trigger the conduction of a conventional fish acute toxicity test or application of assessment factors while considering the very good fish embryo-acute fish toxicity correlation for other compounds.

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.

Intermediate Volatility Organic Compound Emissions from On-Road Diesel Vehicles: Chemical Composition, Emission Factors, and Estimated Secondary Organic Aerosol Production.

PubMed

Zhao, Yunliang; Nguyen, Ngoc T; Presto, Albert A; Hennigan, Christopher J; May, Andrew A; Robinson, Allen L

2015-10-06

Emissions of intermediate-volatility organic compounds (IVOCs) from five on-road diesel vehicles and one off-road diesel engine were characterized during dynamometer testing. The testing evaluated the effects of driving cycles, fuel composition and exhaust aftertreatment devices. On average, more than 90% of the IVOC emissions were not identified on a molecular basis, instead appearing as an unresolved complex mixture (UCM) during gas-chromatography mass-spectrometry analysis. Fuel-based emissions factors (EFs) of total IVOCs (speciated + unspeciated) depend strongly on aftertreatment technology and driving cycle. Total-IVOC emissions from vehicles equipped with catalyzed diesel particulate filters (DPF) are substantially lower (factor of 7 to 28, depending on driving cycle) than from vehicles without any exhaust aftertreatment. Total-IVOC emissions from creep and idle operations are substantially higher than emissions from high-speed operations. Although the magnitude of the total-IVOC emissions can vary widely, there is little variation in the IVOC composition across the set of tests. The new emissions data are combined with published yield data to investigate secondary organic aerosol (SOA) formation. SOA production from unspeciated IVOCs is estimated using surrogate compounds, which are assigned based on gas-chromatograph retention time and mass spectral signature of the IVOC UCM. IVOCs contribute the vast majority of the SOA formed from exhaust from on-road diesel vehicles. The estimated SOA production is greater than predictions by previous studies and substantially higher than primary organic aerosol. Catalyzed DPFs substantially reduce SOA formation potential of diesel exhaust, except at low speed operations.

QSAR studies on triazole derivatives as sglt inhibitors via CoMFA and CoMSIA

NASA Astrophysics Data System (ADS)

Zhi, Hui; Zheng, Junxia; Chang, Yiqun; Li, Qingguo; Liao, Guochao; Wang, Qi; Sun, Pinghua

2015-10-01

Forty-six sodium-dependent glucose cotransporters-2 (SGLT-2) inhibitors with hypoglycemic activity were selected to develop three-dimensional quantitative structure-activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. A training set of 39 compounds were used to build up the models, which were then evaluated by a series of internal and external cross-validation techniques. A test set of 7 compounds was used for the external validation. The CoMFA model predicted a q2 value of 0.792 and an r2 value of 0.985. The best CoMSIA model predicted a q2 value of 0.633 and an r2 value of 0.895 based on a combination of steric, electrostatic, hydrophobic and hydrogen-bond acceptor effects. The predictive correlation coefficients (rpred2) of CoMFA and CoMSIA models were 0.872 and 0.839, respectively. The analysis of the contour maps from each model provided insight into the structural requirements for the development of more active sglt inhibitors, and on the basis of the models 8 new sglt inhibitors were designed and predicted.

A Chemoinformatics Approach to the Discovery of Lead-Like Molecules from Marine and Microbial Sources En Route to Antitumor and Antibiotic Drugs

PubMed Central

Pereira, Florbela; Latino, Diogo A. R. S.; Gaudêncio, Susana P.

2014-01-01

The comprehensive information of small molecules and their biological activities in the PubChem database allows chemoinformatic researchers to access and make use of large-scale biological activity data to improve the precision of drug profiling. A Quantitative Structure–Activity Relationship approach, for classification, was used for the prediction of active/inactive compounds relatively to overall biological activity, antitumor and antibiotic activities using a data set of 1804 compounds from PubChem. Using the best classification models for antibiotic and antitumor activities a data set of marine and microbial natural products from the AntiMarin database were screened—57 and 16 new lead compounds for antibiotic and antitumor drug design were proposed, respectively. All compounds proposed by our approach are classified as non-antibiotic and non-antitumor compounds in the AntiMarin database. Recently several of the lead-like compounds proposed by us were reported as being active in the literature. PMID:24473174

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

Screening of benzamidine-based thrombin inhibitors via a linear interaction energy in continuum electrostatics model

NASA Astrophysics Data System (ADS)

Nicolotti, Orazio; Giangreco, Ilenia; Miscioscia, Teresa Fabiola; Convertino, Marino; Leonetti, Francesco; Pisani, Leonardo; Carotti, Angelo

2010-02-01

A series of 27 benzamidine inhibitors covering a wide range of biological activity and chemical diversity was analysed to derive a Linear Interaction Energy in Continuum Electrostatics (LIECE) model for analysing the thrombin inhibitory activity. The main interactions occurring at the thrombin binding site and the preferred binding conformations of inhibitors were explicitly biased by including into the LIECE model 10 compounds extracted from X-ray solved thrombin-inhibitor complexes available from the Protein Data Bank (PDB). Supported by a robust statistics ( r 2 = 0.698; q 2 = 0.662), the LIECE model was successful in predicting the inhibitory activity for about 76% of compounds ( r ext 2 ≥ 0.600) from a larger external test set encompassing 88 known thrombin inhibitors and, more importantly, in retrieving, at high sensitivity and with better performance than docking and shape-based methods, active compounds from a thrombin combinatorial library of 10240 mimetic chemical products. The herein proposed LIECE model has the potential for successfully driving the design of novel thrombin inhibitors with benzamidine and/or benzamidine-like chemical structure.

Comparative study of photocatalytic oxidation on the degradation of formaldehyde and fuzzy mathematics evaluation of filters

NASA Astrophysics Data System (ADS)

Yu, Huili; Zhang, Jieting

2012-04-01

In this study, formaldehyde, one of the major volatile organic compounds, is chosen as the target pollutant. The polytetrafluoroethylene (PTFE) filter, a low cost and commonly used material in industry, is employed as the substrate for nano TiO2 photocatalyst coating at room temperature, which has been scarcely used compared to ceramics or glass beads. Furthermore, a specific experimental set-up that is similar to actual air purification system is developed for the testing. The degradation mechanisms of photolysis reaction, adsorption and photocatalytic oxidation reaction on volatile organic compounds are present respectively. The influences of three aspects mentioned above are compared by a serial of experimental data. The high efficiency of volatile organic compounds on the degradation of formaldehyde is assured. Furthermore, the purification characteristics of three kinds of activated carbon filters and PTFE filter with nano TiO2 are evaluated with the method of fuzzy mathematics. In the end, the result shows that the filter with nano TiO2 has the optimal comprehensive performances.

Comparative study of photocatalytic oxidation on the degradation of formaldehyde and fuzzy mathematics evaluation of filters

NASA Astrophysics Data System (ADS)

Yu, Huili; Zhang, Jieting

2011-11-01

In this study, formaldehyde, one of the major volatile organic compounds, is chosen as the target pollutant. The polytetrafluoroethylene (PTFE) filter, a low cost and commonly used material in industry, is employed as the substrate for nano TiO2 photocatalyst coating at room temperature, which has been scarcely used compared to ceramics or glass beads. Furthermore, a specific experimental set-up that is similar to actual air purification system is developed for the testing. The degradation mechanisms of photolysis reaction, adsorption and photocatalytic oxidation reaction on volatile organic compounds are present respectively. The influences of three aspects mentioned above are compared by a serial of experimental data. The high efficiency of volatile organic compounds on the degradation of formaldehyde is assured. Furthermore, the purification characteristics of three kinds of activated carbon filters and PTFE filter with nano TiO2 are evaluated with the method of fuzzy mathematics. In the end, the result shows that the filter with nano TiO2 has the optimal comprehensive performances.

The essential roles of chemistry in high-throughput screening triage

PubMed Central

Dahlin, Jayme L; Walters, Michael A

2015-01-01

It is increasingly clear that academic high-throughput screening (HTS) and virtual HTS triage suffers from a lack of scientists trained in the art and science of early drug discovery chemistry. Many recent publications report the discovery of compounds by screening that are most likely artifacts or promiscuous bioactive compounds, and these results are not placed into the context of previous studies. For HTS to be most successful, it is our contention that there must exist an early partnership between biologists and medicinal chemists. Their combined skill sets are necessary to design robust assays and efficient workflows that will weed out assay artifacts, false positives, promiscuous bioactive compounds and intractable screening hits, efforts that ultimately give projects a better chance at identifying truly useful chemical matter. Expertise in medicinal chemistry, cheminformatics and purification sciences (analytical chemistry) can enhance the post-HTS triage process by quickly removing these problematic chemotypes from consideration, while simultaneously prioritizing the more promising chemical matter for follow-up testing. It is only when biologists and chemists collaborate effectively that HTS can manifest its full promise. PMID:25163000

Occupational hazards of missile operations with special regard to the hydrazine propellants.

PubMed

Back, K C; Carter, V L; Thomas, A A

1978-04-01

The second generation of ballistic missiles and boosters, characterized by increased range and quick reaction capability, required the development of new high-energy storage propellants. This exploration led to the introduction of hydrazine (Hz), monomethylhydrazine (MMH), and 1,1-dimethylhydrazine (UDMH) into the USAF inventory. These compounds are all storable, noncryogenic, high-energy fuels which may be used alone or in combination as mixed amine fuels. Early toxicology experiments were to produce data on acute and subacute effects of the propellants in order to set standards for test and operational procedures to protect propellant handlers. The early work indicated that, despite similar chemical characteristics, there were marked differences between the compounds in terms of toxicological mechanisms. Since the propellant systems have been used for some 15 years, recent emphasis on toxicology has been centered on the more chronic effects and on an increasing body of evidence from animal experiments that the compounds may possess oncogenic potential as well as chronic systemic effects. This paper addresses itself to data leading up to current occupational standards.

Time dependent analysis of assay comparability: a novel approach to understand intra- and inter-site variability over time

NASA Astrophysics Data System (ADS)

Winiwarter, Susanne; Middleton, Brian; Jones, Barry; Courtney, Paul; Lindmark, Bo; Page, Ken M.; Clark, Alan; Landqvist, Claire

2015-09-01

We demonstrate here a novel use of statistical tools to study intra- and inter-site assay variability of five early drug metabolism and pharmacokinetics in vitro assays over time. Firstly, a tool for process control is presented. It shows the overall assay variability but allows also the following of changes due to assay adjustments and can additionally highlight other, potentially unexpected variations. Secondly, we define the minimum discriminatory difference/ratio to support projects to understand how experimental values measured at different sites at a given time can be compared. Such discriminatory values are calculated for 3 month periods and followed over time for each assay. Again assay modifications, especially assay harmonization efforts, can be noted. Both the process control tool and the variability estimates are based on the results of control compounds tested every time an assay is run. Variability estimates for a limited set of project compounds were computed as well and found to be comparable. This analysis reinforces the need to consider assay variability in decision making, compound ranking and in silico modeling.

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.

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.

Application of an Artificial Neural Network to the Prediction of OH Radical Reaction Rate Constants for Evaluating Global Warming Potential.

PubMed

Allison, Thomas C

2016-03-03

Rate constants for reactions of chemical compounds with hydroxyl radical are a key quantity used in evaluating the global warming potential of a substance. Experimental determination of these rate constants is essential, but it can also be difficult and time-consuming to produce. High-level quantum chemistry predictions of the rate constant can suffer from the same issues. Therefore, it is valuable to devise estimation schemes that can give reasonable results on a variety of chemical compounds. In this article, the construction and training of an artificial neural network (ANN) for the prediction of rate constants at 298 K for reactions of hydroxyl radical with a diverse set of molecules is described. Input to the ANN consists of counts of the chemical bonds and bends present in the target molecule. The ANN is trained using 792 (•)OH reaction rate constants taken from the NIST Chemical Kinetics Database. The mean unsigned percent error (MUPE) for the training set is 12%, and the MUPE of the testing set is 51%. It is shown that the present methodology yields rate constants of reasonable accuracy for a diverse set of inputs. The results are compared to high-quality literature values and to another estimation scheme. This ANN methodology is expected to be of use in a wide range of applications for which (•)OH reaction rate constants are required. The model uses only information that can be gathered from a 2D representation of the molecule, making the present approach particularly appealing, especially for screening applications.

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.

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.

Embryotoxic and pharmacologic potency ranking of six azoles in the rat whole embryo culture by morphological and transcriptomic analysis

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

Dimopoulou, Myrto, E-mail: myrto.dimopoulou@wur.nl

Differential gene expression analysis in the rat whole embryo culture (WEC) assay provides mechanistic insight into the embryotoxicity of test compounds. In our study, we hypothesized that comparative analysis of the transcriptomes of rat embryos exposed to six azoles (flusilazole, triadimefon, ketoconazole, miconazole, difenoconazole and prothioconazole) could lead to a better mechanism-based understanding of their embryotoxicity and pharmacological action. For evaluating embryotoxicity, we applied the total morphological scoring system (TMS) in embryos exposed for 48 h. The compounds tested showed embryotoxicity in a dose-response fashion. Functional analysis of differential gene expression after 4 h exposure at the ID{sub 10} (effectivemore » dose for 10% decreased TMS), revealed the sterol biosynthesis pathway and embryonic development genes, dominated by genes in the retinoic acid (RA) pathway, albeit in a differential way. Flusilazole, ketoconazole and triadimefon were the most potent compounds affecting the RA pathway, while in terms of regulation of sterol function, difenoconazole and ketoconazole showed the most pronounced effects. Dose-dependent analysis of the effects of flusilazole revealed that the RA pathway related genes were already differentially expressed at low dose levels while the sterol pathway showed strong regulation at higher embryotoxic doses, suggesting that this pathway is less predictive for the observed embryotoxicity. A similar analysis at the 24-hour time point indicated an additional time-dependent difference in the aforementioned pathways regulated by flusilazole. In summary, the rat WEC assay in combination with transcriptomics could add a mechanistic insight into the embryotoxic potency ranking and pharmacological mode of action of the tested compounds. - Highlights: • Embryonic exposure to azoles revealed concentration-dependent malformations. • Transcriptomics could enhance the mechanistic knowledge of embryotoxicants. • Retinoic acid gene set identifies early embryotoxic responses to azoles. • Toxic versus pharmacologic potency determines functional efficacy.« less

Use of the Monte Carlo Method for OECD Principles-Guided QSAR Modeling of SIRT1 Inhibitors.

PubMed

Kumar, Ashwani; Chauhan, Shilpi

2017-01-01

SIRT1 inhibitors offer therapeutic potential for the treatment of a number of diseases including cancer and human immunodeficiency virus infection. A diverse series of 45 compounds with reported SIRT1 inhibitory activity has been employed for the development of quantitative structure-activity relationship (QSAR) models using the Monte Carlo optimization method. This method makes use of simplified molecular input line entry system notation of the molecular structure. The QSAR models were built up according to OECD principles. Three subsets of three splits were examined and validated by respective external sets. All the three described models have good statistical quality. The best model has the following statistical characteristics: R 2  = 0.8350, Q 2 test  = 0.7491 for the test set and R 2  = 0.9655, Q 2 ext  = 0.9261 for the validation set. In the mechanistic interpretation, structural attributes responsible for the endpoint increase and decrease are defined. Further, the design of some prospective SIRT1 inhibitors is also presented on the basis of these structural attributes. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

Inferences of drug responses in cancer cells from cancer genomic features and compound chemical and therapeutic properties

PubMed Central

Wang, Yongcui; Fang, Jianwen; Chen, Shilong

2016-01-01

Accurately predicting the response of a cancer patient to a therapeutic agent is a core goal of precision medicine. Existing approaches were mainly relied primarily on genomic alterations in cancer cells that have been treated with different drugs. Here we focus on predicting drug response based on integration of the heterogeneously pharmacogenomics data from both cell and drug sides. Through a systematical approach, named as PDRCC (Predict Drug Response in Cancer Cells), the cancer genomic alterations and compound chemical and therapeutic properties were incorporated to determine the chemotherapeutic response in cancer patients. Using the Cancer Cell Line Encyclopedia (CCLE) study as the benchmark dataset, all pharmacogenomics data exhibited their roles in inferring the relationships between cancer cells and drugs. When integrating both genomic resources and compound information, the prediction coverage was significantly increased. The validity of PDRCC was also supported by its effective in uncovering the unknown cell-drug associations with database and literature evidences. It set the stage for clinical testing of novel therapeutic strategies, such as the sensitive association between cancer cell ‘A549_LUNG’ and compound ‘Topotecan’. In conclusion, PDRCC offers the possibility for faster, safer, and cheaper the development of novel anti-cancer therapeutics in the early-stage clinical trails. PMID:27645580

Inferences of drug responses in cancer cells from cancer genomic features and compound chemical and therapeutic properties

NASA Astrophysics Data System (ADS)

Wang, Yongcui; Fang, Jianwen; Chen, Shilong

2016-09-01

Accurately predicting the response of a cancer patient to a therapeutic agent is a core goal of precision medicine. Existing approaches were mainly relied primarily on genomic alterations in cancer cells that have been treated with different drugs. Here we focus on predicting drug response based on integration of the heterogeneously pharmacogenomics data from both cell and drug sides. Through a systematical approach, named as PDRCC (Predict Drug Response in Cancer Cells), the cancer genomic alterations and compound chemical and therapeutic properties were incorporated to determine the chemotherapeutic response in cancer patients. Using the Cancer Cell Line Encyclopedia (CCLE) study as the benchmark dataset, all pharmacogenomics data exhibited their roles in inferring the relationships between cancer cells and drugs. When integrating both genomic resources and compound information, the prediction coverage was significantly increased. The validity of PDRCC was also supported by its effective in uncovering the unknown cell-drug associations with database and literature evidences. It set the stage for clinical testing of novel therapeutic strategies, such as the sensitive association between cancer cell ‘A549_LUNG’ and compound ‘Topotecan’. In conclusion, PDRCC offers the possibility for faster, safer, and cheaper the development of novel anti-cancer therapeutics in the early-stage clinical trails.

Determination of the ultrasound power effects on flavonoid compounds from Psidium guajava L. using ANFIS

NASA Astrophysics Data System (ADS)

Ratu Ayu, Humairoh; Suryono, Suryono; Endro Suseno, Jatmiko; Kurniawati, Ratna

2018-05-01

The Adaptive Neural Fuzzy Inference System (ANFIS) model was used to predict and optimize the content of flavonoid compounds in guava leaves (Psidium Guajava L.). The extraction process was carried out by using ultrasound assisted extraction (UAE) with the variable parameters: temperature ranging from 25°C to 35°C, ultrasonic frequency (30 - 40 kHz) and extraction time (20 - 40 minutes). ANFIS learning procedure began by providing the input variable data set (temperature, frequency and time) and the output of the flavonoid compounds from the experiments that had been done. Subtractive clustering methods was used in the manufacture of FIS (fuzzy inference system) structures by varying the range of influence parameters to generate the ANFIS system. The ANFIS trainingsconducted wereaimed at minimum error value. The results showed that the best ANFIS models used a subtractive clustering method, in which the ranges of influence 0.1 were 0.70 x 10-4 for training RMSE, 8.11 for testing RMSE, 2.7 % MAPE, and 7.72 MAE. The optimum condition was obtained at a temperature of 35°C and frequency of 40 kHz, for 30 minutes. This result proves that the ANFIS model can be used to predict the content of flavonoid compounds in guava leaves.

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.

Kinetics of biological methane oxidation in the presence of non-methane organic compounds in landfill bio-covers

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

Albanna, Muna, E-mail: muna.albanna@gju.edu.j; Warith, Mostafa; Fernandes, Leta

2010-02-15

In this experimental program, the effects of non-methane organic compounds (NMOCs) on the biological methane (CH{sub 4}) oxidation process were examined. The investigation was performed on compost experiments incubated with CH{sub 4} and selected NMOCs under different environmental conditions. The selected NMOCs had different concentrations and their effects were tested as single compounds and mixtures of compounds. The results from all experimental sets showed a decrease in CH{sub 4} oxidation capacity of the landfill bio-cover with the increase in NMOCs concentrations. For example, in the experiment using compost with 100% moisture content at 35 deg. C without any NMOCs themore » V{sub max} value was 35.0 mug CH{sub 4}h{sup -1}g{sub wetwt}{sup -1}. This value was reduced to 19.1 mug CH{sub 4}h{sup -1}g{sub wetwt}{sup -1} when mixed NMOCs were present in the batch reactors under the same environmental conditions. The experimental oxidation rates of CH{sub 4} in the presence of single and mixed NMOCs were modeled using the uncompetitive inhibition model and kinetic parameters, including the dissociation constants, were obtained. Additionally, the degradation rates of the NMOCs and co-metabolic abilities of methanotrophic bacteria were estimated.« less

Inferences of drug responses in cancer cells from cancer genomic features and compound chemical and therapeutic properties.

PubMed

Wang, Yongcui; Fang, Jianwen; Chen, Shilong

2016-09-20

Accurately predicting the response of a cancer patient to a therapeutic agent is a core goal of precision medicine. Existing approaches were mainly relied primarily on genomic alterations in cancer cells that have been treated with different drugs. Here we focus on predicting drug response based on integration of the heterogeneously pharmacogenomics data from both cell and drug sides. Through a systematical approach, named as PDRCC (Predict Drug Response in Cancer Cells), the cancer genomic alterations and compound chemical and therapeutic properties were incorporated to determine the chemotherapeutic response in cancer patients. Using the Cancer Cell Line Encyclopedia (CCLE) study as the benchmark dataset, all pharmacogenomics data exhibited their roles in inferring the relationships between cancer cells and drugs. When integrating both genomic resources and compound information, the prediction coverage was significantly increased. The validity of PDRCC was also supported by its effective in uncovering the unknown cell-drug associations with database and literature evidences. It set the stage for clinical testing of novel therapeutic strategies, such as the sensitive association between cancer cell 'A549_LUNG' and compound 'Topotecan'. In conclusion, PDRCC offers the possibility for faster, safer, and cheaper the development of novel anti-cancer therapeutics in the early-stage clinical trails.

Bilayer Effects of Antimalarial Compounds

PubMed Central

Ramsey, Nicole B.; Andersen, Olaf S.

2015-01-01

Because of the perpetual development of resistance to current therapies for malaria, the Medicines for Malaria Venture developed the Malaria Box to facilitate the drug development process. We tested the 80 most potent compounds from the box for bilayer-mediated effects on membrane protein conformational changes (a measure of likely toxicity) in a gramicidin-based stopped flow fluorescence assay. Among the Malaria Box compounds tested, four compounds altered membrane properties (p< 0.05); MMV007384 stood out as a potent bilayer-perturbing compound that is toxic in many cell-based assays, suggesting that testing for membrane perturbation could help identify toxic compounds. In any case, MMV007384 should be approached with caution, if at all. PMID:26551613

Bilayer Effects of Antimalarial Compounds.

PubMed

Ramsey, Nicole B; Andersen, Olaf S

2015-01-01

Because of the perpetual development of resistance to current therapies for malaria, the Medicines for Malaria Venture developed the Malaria Box to facilitate the drug development process. We tested the 80 most potent compounds from the box for bilayer-mediated effects on membrane protein conformational changes (a measure of likely toxicity) in a gramicidin-based stopped flow fluorescence assay. Among the Malaria Box compounds tested, four compounds altered membrane properties (p< 0.05); MMV007384 stood out as a potent bilayer-perturbing compound that is toxic in many cell-based assays, suggesting that testing for membrane perturbation could help identify toxic compounds. In any case, MMV007384 should be approached with caution, if at all.

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.

Fungi from industrial tannins: potential application in biotransformation and bioremediation of tannery wastewaters.

PubMed

Prigione, Valeria; Trocini, Bruno; Spina, Federica; Poli, Anna; Romanisio, Davide; Giovando, Samuele; Varese, Giovanna Cristina

2018-05-01

Tannins are a complex family of polyphenolic compounds, widely distributed in the plant kingdom where they act as growth inhibitors towards many microorganisms including bacteria, yeasts, and fungi. Tannins are one of the major components of tannery wastewaters and may cause serious environmental pollution. In the present study, four different tannins (the hydrolysable chestnut ellagitannin and tara gallotannin and the condensed quebracho and wattle tannins) were characterized from a mycological point of view with the aim of selecting fungal strains capable of growing in the presence of high tannin concentration and thus potentially useful in industrial biotransformations of these compounds or in the bioremediation of tannery wastewaters. A total of 125 isolates of filamentous fungi belonging to 10 species and four genera (Aspergillus, Paecilomyces, Penicillium, and Talaromyces) were isolated from the tannin industrial preparations. Miniaturized biotransformation tests were set up with 10 fungal strains and the high-performance liquid chromatography (HPLC) analysis pointed out a strong activity of all the tested fungi on both chestnut and tara tannins. Two strains (Aspergillus tubingensis MUT 990 and Paecilomyces variotii MUT 1125), tested against a real tannery wastewater, were particularly efficient in chemical oxygen demand (COD) and tannin removal (> 60%), with a detoxification above 74%. These results indicate that these fungi are potentially exploitable in the treatment of tannery wastewaters.

Deciphering the crowd: modeling and identification of pedestrian group motion.

PubMed

Yücel, Zeynep; Zanlungo, Francesco; Ikeda, Tetsushi; Miyashita, Takahiro; Hagita, Norihiro

2013-01-14

Associating attributes to pedestrians in a crowd is relevant for various areas like surveillance, customer profiling and service providing. The attributes of interest greatly depend on the application domain and might involve such social relations as friends or family as well as the hierarchy of the group including the leader or subordinates. Nevertheless, the complex social setting inherently complicates this task. We attack this problem by exploiting the small group structures in the crowd. The relations among individuals and their peers within a social group are reliable indicators of social attributes. To that end, this paper identifies social groups based on explicit motion models integrated through a hypothesis testing scheme. We develop two models relating positional and directional relations. A pair of pedestrians is identified as belonging to the same group or not by utilizing the two models in parallel, which defines a compound hypothesis testing scheme. By testing the proposed approach on three datasets with different environmental properties and group characteristics, it is demonstrated that we achieve an identification accuracy of 87% to 99%. The contribution of this study lies in its definition of positional and directional relation models, its description of compound evaluations, and the resolution of ambiguities with our proposed uncertainty measure based on the local and global indicators of group relation.

Deciphering the Crowd: Modeling and Identification of Pedestrian Group Motion

PubMed Central

Yücel, Zeynep; Zanlungo, Francesco; Ikeda, Tetsushi; Miyashita, Takahiro; Hagita, Norihiro

2013-01-01

Associating attributes to pedestrians in a crowd is relevant for various areas like surveillance, customer profiling and service providing. The attributes of interest greatly depend on the application domain and might involve such social relations as friends or family as well as the hierarchy of the group including the leader or subordinates. Nevertheless, the complex social setting inherently complicates this task. We attack this problem by exploiting the small group structures in the crowd. The relations among individuals and their peers within a social group are reliable indicators of social attributes. To that end, this paper identifies social groups based on explicit motion models integrated through a hypothesis testing scheme. We develop two models relating positional and directional relations. A pair of pedestrians is identified as belonging to the same group or not by utilizing the two models in parallel, which defines a compound hypothesis testing scheme. By testing the proposed approach on three datasets with different environmental properties and group characteristics, it is demonstrated that we achieve an identification accuracy of 87% to 99%. The contribution of this study lies in its definition of positional and directional relation models, its description of compound evaluations, and the resolution of ambiguities with our proposed uncertainty measure based on the local and global indicators of group relation. PMID:23344382

Integrated in silico strategy for PBT assessment and prioritization under REACH.

PubMed

Pizzo, Fabiola; Lombardo, Anna; Manganaro, Alberto; Cappelli, Claudia I; Petoumenou, Maria I; Albanese, Federica; Roncaglioni, Alessandra; Brandt, Marc; Benfenati, Emilio

2016-11-01

Chemicals may persist in the environment, bioaccumulate and be toxic for humans and wildlife, posing great concern. These three properties, persistence (P), bioaccumulation (B), and toxicity (T) are the key targets of the PBT-hazard assessment. The European regulation for the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) requires assessment of PBT-properties for all chemicals that are produced or imported in Europe in amounts exceeding 10 tonnes per year, checking whether the criteria set out in REACH Annex XIII are met, so the substance should therefore be considered to have properties of very high concern. Considering how many substances can fall under the REACH regulation, there is a pressing need for new strategies to identify and screen large numbers fast and inexpensively. An efficient non-testing screening approach to identify PBT candidates is necessary, as a valuable alternative to money- and time-consuming laboratory tests and a good start for prioritization since few tools exist (e.g. the PBT profiler developed by US EPA). The aim of this work was to offer a conceptual scheme for identifying and prioritizing chemicals for further assessment and if appropriate further testing, based on their PBT-potential, using a non-testing screening approach. We integrated in silico models (using existing and developing new ones) in a final algorithm for screening and ranking PBT-potential, which uses experimental and predicted values as well as associated uncertainties. The Multi-Criteria Decision-Making (MCDM) theory was used to integrate the different values. Then we compiled a new set of data containing known PBT and non-PBT substances, in order to check how well our approach clearly differentiated compounds labeled as PBT from those labeled as non-PBT. This indicated that the integrated model distinguished between PBT from non-PBT compounds. Copyright © 2016 Elsevier Inc. All rights reserved.

Effect of lignocellulosic degradation compounds from steam explosion pretreatment on ethanol fermentation by thermotolerant yeast Kluyveromyces marxianus.

PubMed

Oliva, Jose Miguel; Sáez, Felicia; Ballesteros, Ignacio; González, Alberto; Negro, Maria José; Manzanares, Paloma; Ballesteros, Mercedes

2003-01-01

The filtrate from steam-pretreated poplar was analyzed to identify degradation compounds. The effect of selected compounds on growth and ethanolic fermentation of the thermotolerant yeast strain Kluyveromyces marxianus CECT 10875 was tested. Several fermentations on glucose medium, containing individual inhibitory compounds found in the hydrolysate, were carried out. The degree of inhibition on yeast strain growth and ethanolic fermentation was determined. At concentrations found in the prehy-drolysate, none of the individual compounds significantly affected the fermentation. For all tested compounds, growth was inhibited to a lesser extent than ethanol production. Lower concentrations of catechol (0.96 g/L) and 4-hydroxybenzaldehyde (1.02 g/L) were required to produce the 50% reduction in cell mass in comparison to other tested compounds.

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...

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

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.

A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy.

PubMed

Ji, Zhiwei; Wang, Bing; Yan, Ke; Dong, Ligang; Meng, Guanmin; Shi, Lei

2017-12-21

In recent years, the integration of 'omics' technologies, high performance computation, and mathematical modeling of biological processes marks that the systems biology has started to fundamentally impact the way of approaching drug discovery. The LINCS public data warehouse provides detailed information about cell responses with various genetic and environmental stressors. It can be greatly helpful in developing new drugs and therapeutics, as well as improving the situations of lacking effective drugs, drug resistance and relapse in cancer therapies, etc. In this study, we developed a Ternary status based Integer Linear Programming (TILP) method to infer cell-specific signaling pathway network and predict compounds' treatment efficacy. The novelty of our study is that phosphor-proteomic data and prior knowledge are combined for modeling and optimizing the signaling network. To test the power of our approach, a generic pathway network was constructed for a human breast cancer cell line MCF7; and the TILP model was used to infer MCF7-specific pathways with a set of phosphor-proteomic data collected from ten representative small molecule chemical compounds (most of them were studied in breast cancer treatment). Cross-validation indicated that the MCF7-specific pathway network inferred by TILP were reliable predicting a compound's efficacy. Finally, we applied TILP to re-optimize the inferred cell-specific pathways and predict the outcomes of five small compounds (carmustine, doxorubicin, GW-8510, daunorubicin, and verapamil), which were rarely used in clinic for breast cancer. In the simulation, the proposed approach facilitates us to identify a compound's treatment efficacy qualitatively and quantitatively, and the cross validation analysis indicated good accuracy in predicting effects of five compounds. In summary, the TILP model is useful for discovering new drugs for clinic use, and also elucidating the potential mechanisms of a compound to targets.

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

In-flight performance optimization for rotorcraft with redundant controls

NASA Astrophysics Data System (ADS)

Ozdemir, Gurbuz Taha

A conventional helicopter has limits on performance at high speeds because of the limitations of main rotor, such as compressibility issues on advancing side or stall issues on retreating side. Auxiliary lift and thrust components have been suggested to improve performance of the helicopter substantially by reducing the loading on the main rotor. Such a configuration is called the compound rotorcraft. Rotor speed can also be varied to improve helicopter performance. In addition to improved performance, compound rotorcraft and variable RPM can provide a much larger degree of control redundancy. This additional redundancy gives the opportunity to further enhance performance and handling qualities. A flight control system is designed to perform in-flight optimization of redundant control effectors on a compound rotorcraft in order to minimize power required and extend range. This "Fly to Optimal" (FTO) control law is tested in simulation using the GENHEL model. A model of the UH-60, a compound version of the UH-60A with lifting wing and vectored thrust ducted propeller (VTDP), and a generic compound version of the UH-60A with lifting wing and propeller were developed and tested in simulation. A model following dynamic inversion controller is implemented for inner loop control of roll, pitch, yaw, heave, and rotor RPM. An outer loop controller regulates airspeed and flight path during optimization. A Golden Section search method was used to find optimal rotor RPM on a conventional helicopter, where the single redundant control effector is rotor RPM. The FTO builds off of the Adaptive Performance Optimization (APO) method of Gilyard by performing low frequency sweeps on a redundant control for a fixed wing aircraft. A method based on the APO method was used to optimize trim on a compound rotorcraft with several redundant control effectors. The controller can be used to optimize rotor RPM and compound control effectors through flight test or simulations in order to establish a schedule. The method has been expanded to search a two-dimensional control space. Simulation results demonstrate the ability to maximize range by optimizing stabilator deflection and an airspeed set point. Another set of results minimize power required in high speed flight by optimizing collective pitch and stabilator deflection. Results show that the control laws effectively hold the flight condition while the FTO method is effective at improving performance. Optimizations show there can be issues when the control laws regulating altitude push the collective control towards it limits. So a modification was made to the control law to regulate airspeed and altitude using propeller pitch and angle of attack while the collective is held fixed or used as an optimization variable. A dynamic trim limit avoidance algorithm is applied to avoid control saturation in other axes during optimization maneuvers. Range and power optimization FTO simulations are compared with comprehensive sweeps of trim solutions and FTO optimization shown to be effective and reliable in reaching an optimal when optimizing up to two redundant controls. Use of redundant controls is shown to be beneficial for improving performance. The search method takes almost 25 minutes of simulated flight for optimization to be complete. The optimization maneuver itself can sometimes drive the power required to high values, so a power limit is imposed to restrict the search to avoid conditions where power is more than5% higher than that of the initial trim state. With this modification, the time the optimization maneuver takes to complete is reduced down to 21 minutes without any significant change in the optimal power value.

Pharmacological effects of primaquine ureas and semicarbazides on the central nervous system in mice and antimalarial activity in vitro.

PubMed

Kedzierska, Ewa; Orzelska, Jolanta; Perković, Ivana; Knežević, Danijel; Fidecka, Sylwia; Kaiser, Marcel; Zorc, Branka

2016-02-01

New primaquine (PQ) urea and semicarbazide derivatives 1-4 were screened for the first time for central nervous system (CNS) and antimalarial activity. Behavioural tests were performed on mice. In vitro cytotoxicity on L-6 cells and activity against erythrocytic stages of Plasmodium falciparum was determined. Compound 4 inhibited 'head-twitch' responses and decreased body temperature of mice, which suggests some involvement of the serotonergic system. Compound 4 protected mice against clonic seizures and was superior in the antimalarial test. A hybrid of two PQ urea 2 showed a strong antimalarial activity, confirming the previous findings of the high activity of bis(8-aminoquinolines) and other bisantimalarial drugs. All the compounds decreased the locomotor activity of mice, what suggests their weak depressive effects on the CNS, while PQ derivatives 1 and 2 increased amphetamine-induced hyperactivity. None of the compounds impaired coordination, what suggests a lack of their neurotoxicity. All the tested compounds presented an antinociceptive activity in the 'writhing' test. Compounds 3 and 4 were active in nociceptive tests, and those effects were reversed by naloxone. Compound 4 could be a useful lead compound in the development of CNS active agents and antimalarials, whereas compound 3 may be considered as the most promising lead for new antinociceptive agents. © 2015 Société Française de Pharmacologie et de Thérapeutique.

Practical Synthesis, Antidepressant, and Anticonvulsant Activity of 3-Phenyliminoindolin-2-one Derivatives.

PubMed

Ma, Jian-Yin; Quan, Ying-Chun; Jin, Hong-Guo; Zhen, Xing-Hua; Zhang, Xue-Wu; Guan, Li-Ping

2016-03-01

Herein, a series of 3-phenyliminoindolin-2-one derivatives were designed, synthesized, and screened for their antidepressant and anticonvulsant activities. The IR spectra of the compounds afforded NH stretching (3340-3346 cm(-1)) bands and C=O stretching (1731-1746 cm(-1)). In the (1)H-NMR spectra of the compounds, N-H protons of indoline ring were observed at 10.65-10.89 ppm generally as broad bands, and (13)C-NMR spectra of the compounds C=O were seen at 161.72-169.27 ppm. Interestingly, compounds 3o, 3p and 3r significantly shortened immobility time in the The forced swimming test (FST) and The tail suspension test (TST) at 50 mg/kg dose levels. In addition, compound 3r exhibited higher levels of efficacy than the reference standard fluoxetine but had no effect on locomotor activity in the open-field test. Compound 3r significantly increased serotonin and norepinephrine and the metabolite 5-hydroxyindoleacetic acid in mouse brain, suggesting that the effects of compound 3r may be mediated through these neurotransmitters. In the seizure screen, 15 compounds showed some degree against PTZ-induced seizure at a dose of 100 mg/kg, and the tested compounds did not show any neurotoxicity at a dose of 300 mg/kg in the rotarod test. © 2015 John Wiley & Sons A/S.

DFT and 3D-QSAR Studies of Anti-Cancer Agents m-(4-Morpholinoquinazolin-2-yl) Benzamide Derivatives for Novel Compounds Design

NASA Astrophysics Data System (ADS)

Zhao, Siqi; Zhang, Guanglong; Xia, Shuwei; Yu, Liangmin

2018-06-01

As a group of diversified frameworks, quinazolin derivatives displayed a broad field of biological functions, especially as anticancer. To investigate the quantitative structure-activity relationship, 3D-QSAR models were generated with 24 quinazolin scaffold molecules. The experimental and predicted pIC50 values for both training and test set compounds showed good correlation, which proved the robustness and reliability of the generated QSAR models. The most effective CoMFA and CoMSIA were obtained with correlation coefficient r 2 ncv of 1.00 (both) and leave-one-out coefficient q 2 of 0.61 and 0.59, respectively. The predictive abilities of CoMFA and CoMSIA were quite good with the predictive correlation coefficients ( r 2 pred ) of 0.97 and 0.91. In addition, the statistic results of CoMFA and CoMSIA were used to design new quinazolin molecules.

New sulfurated derivatives of cinnamic acids and rosmaricine as inhibitors of STAT3 and NF-κB transcription factors.

PubMed

Gabriele, Elena; Brambilla, Dario; Ricci, Chiara; Regazzoni, Luca; Taguchi, Kyoko; Ferri, Nicola; Asai, Akira; Sparatore, Anna

2017-12-01

A set of new sulfurated drug hybrids, mainly derived from caffeic and ferulic acids and rosmaricine, has been synthesized and their ability to inhibit both STAT3 and NF-κB transcription factors have been evaluated. Results showed that most of the new hybrid compounds were able to strongly and selectively bind to STAT3, whereas the parent drugs were devoid of this ability at the tested concentrations. Some of them were also able to inhibit the NF-κB transcriptional activity in HCT-116 cell line and inhibited HCT-116 cell proliferation in vitro with IC 50 in micromolar range, thus suggesting a potential anticancer activity. Taken together, our study described the identification of new derivatives with dual STAT3/NF-κB inhibitory activity, which may represent hit compounds for developing multi-target anticancer agents.

Determination of ruthenium in pharmaceutical compounds by graphite furnace atomic absorption spectroscopy.

PubMed

Jia, Xiujuan; Wang, Tiebang; Bu, Xiaodong; Tu, Qiang; Spencer, Sandra

2006-04-11

A graphite furnace atomic absorption (GFAA) spectrometric method for the determination of ruthenium (Rh) in solid and liquid pharmaceutical compounds has been developed. Samples are dissolved or diluted in dimethyl sulfoxide (DMSO) without any other treatment before they were analyzed by GFAA with a carefully designed heating program to avoid pre-atomization signal loss and to achieve suitable sensitivity. Various inorganic and organic solvents were tested and compared and DMSO was found to be the most suitable. In addition, ruthenium was found to be stable in DMSO for at least 5 days. Spike recoveries ranged from 81 to 100% and the limit of quantitation (LOQ) was determined to be 0.5 microg g(-1) for solid samples or 0.005 microg ml(-1) for liquid samples based a 100-fold dilution. The same set of samples was also analyzed by ICP-MS with a different sample preparation method, and excellent agreement was achieved.

Computational Prediction of Electron Ionization Mass Spectra to Assist in GC/MS Compound Identification.

PubMed

Allen, Felicity; Pon, Allison; Greiner, Russ; Wishart, David

2016-08-02

We describe a tool, competitive fragmentation modeling for electron ionization (CFM-EI) that, given a chemical structure (e.g., in SMILES or InChI format), computationally predicts an electron ionization mass spectrum (EI-MS) (i.e., the type of mass spectrum commonly generated by gas chromatography mass spectrometry). The predicted spectra produced by this tool can be used for putative compound identification, complementing measured spectra in reference databases by expanding the range of compounds able to be considered when availability of measured spectra is limited. The tool extends CFM-ESI, a recently developed method for computational prediction of electrospray tandem mass spectra (ESI-MS/MS), but unlike CFM-ESI, CFM-EI can handle odd-electron ions and isotopes and incorporates an artificial neural network. Tests on EI-MS data from the NIST database demonstrate that CFM-EI is able to model fragmentation likelihoods in low-resolution EI-MS data, producing predicted spectra whose dot product scores are significantly better than full enumeration "bar-code" spectra. CFM-EI also outperformed previously reported results for MetFrag, MOLGEN-MS, and Mass Frontier on one compound identification task. It also outperformed MetFrag in a range of other compound identification tasks involving a much larger data set, containing both derivatized and nonderivatized compounds. While replicate EI-MS measurements of chemical standards are still a more accurate point of comparison, CFM-EI's predictions provide a much-needed alternative when no reference standard is available for measurement. CFM-EI is available at https://sourceforge.net/projects/cfm-id/ for download and http://cfmid.wishartlab.com as a web service.

Simultaneous determination of five major compounds in the traditional medicine Pyeongwee-San by high performance liquid chromatography-diode array detection and liquid chromatography-mass spectrometry/mass spectrometry.

PubMed

Lee, Bohyoung; Weon, Jin Bae; Yun, Bo-Ra; Lee, Jiwoo; Eom, Min Rye; Ma, Choong Je

2014-01-01

Pyeongwee-San (PWS) has been widely used for treating acute gastritis, chronic, and gastritis. In this paper, simultaneous determination of five compounds (naringin, hesperidin, glycyrrhizin, atractylenolide III, and magnolol) from traditional medicine PWS using the high performance liquid chromatography (HPLC) was established for quality control. Optimum separations were obtained with a SHISEIDO C18 reverse-phase column by gradient elution with 0.1% Trifluoroacetic acid (TFA) water-acetonitrile as the mobile phase. The flow rate was 1 mL/min and detection wavelength was set at 205 nm and 250 nm. Validation of the analytical method was evaluated by linearity, precision, and accuracy test. The calibration curves were linear over the established range with R (2) > 0.9978. The limit of detection (LOD) and limit of quantification (LOQ) ranged from 0.09 to 0.43 and 0.27 to 1.29 μg/mL. The method exhibited intra-day and inter-day precision range between 0.01-1.86% and 0.04-0.35% respectively. The recoveries of five compounds in PWS were in the range between 93.18-106.40%, and 0.20-1.51%. The application of this method was identified through the successful analysis of five compounds in 12 batches of PWS. In addition, identification of five compounds was confirmed by a liquid chromatography method and mass spectrometry. The HPLC method was could be accomplished to the quality control and stable experiment for the preparations consisted of five major compounds.

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.

Synthesis and Antidepressant Activity Profile of Some Novel Benzothiazole Derivatives.

PubMed

Demir Özkay, Ümide; Kaya, Ceren; Acar Çevik, Ulviye; Can, Özgür Devrim

2017-09-07

Within the scope of our new antidepressant drug development efforts, in this study, we synthesized eight novel benzothiazole derivatives 3a - 3h . The chemical structures of the synthesized compounds were elucidated by spectroscopic methods. Test compounds were administered orally at a dose of 40 mg/kg to mice 24, 5 and 1 h before performing tail suspension, modified forced swimming, and activity cage tests. The obtained results showed that compounds 3c , 3d , 3f - 3h reduced the immobility time of mice as assessed in the tail suspension test. Moreover, in the modified forced swimming tests, the same compounds significantly decreased the immobility, but increased the swimming frequencies of mice, without any alteration in the climbing frequencies. These results, similar to the results induced by the reference drug fluoxetine (20 mg/kg, po), indicated the antidepressant-like activities of the compounds 3c , 3d , 3f - 3h . Owing to the fact that test compounds did not induce any significant alteration in the total number of spontaneous locomotor activities, the antidepressant-like effects of these derivatives seemed to be specific. In order to predict ADME parameters of the synthesized compounds 3a - 3h , some physicochemical parameters were calculated. The ADME prediction study revealed that all synthesized compounds may possess good pharmacokinetic profiles.

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).

Changes in Volatile Compounds of Chinese Luzhou-Flavor Liquor during the Fermentation and Distillation Process.

PubMed

Ding, Xiaofei; Wu, Chongde; Huang, Jun; Zhou, Rongqing

2015-11-01

The aim of this study was to investigate the dynamic of volatile compounds in the Zaopei during the fermentation and distillation process by headspace solid-phase microextraction-gas chromatography mass spectrometry (HS-SPME-GCMS). Physicochemical properties analysis of Zaopei (fermented grains [FG], fermented grains mixed with sorghum [FGS], streamed grains [SG], and streamed grains mixed with Daqu [SGD]) showed distinct changes. A total number of 66 volatile compounds in the Zaopei were identified, in which butanoic acid, hexanoic acid, ethyl hexanoate, ethyl lactate, ethyl octanoate, hexyl hexanoate, ethyl hydrocinnamate, ethyl oleate, ethyl hexadecanoate, and ethyl linoleate were considered to be the dominant compounds due to their high concentrations. FG had the highest volatile compounds (112.43 mg/kg), which significantly decreased by 17.05% in the FGS, 67.12% in the SG, and 73.75% in the SGD. Furthermore, about 61.49% of volatile compounds of FGS were evaporated into raw liquor, whereas head, heart, and tail liquor accounted for 29.84%, 39.49%, and 30.67%, respectively. Each volatile class generally presented a decreasing trend, except for furans. Especially, the percentage of esters was 55.51% to 67.41% in the Zaopei, and reached 92.60% to 97.67% in the raw liquor. Principal component analysis based ordination of volatile compounds data segregated FGS and SGD samples. In addition, radar diagrams of the odor activity values suggested that intense flavor of fruit was weakened most from FG to SGD. The dynamic of volatile compounds in the Zaopei during the fermentation and distillation process was tested by SPME-GCMS. The result of this study demonstrated that both volatile compounds of Zaopei and thermal reaction during distillation simply determined the unique feature of raw liquor. This study was conducted based on the real products from liquor manufactory, so it is practicable that the method can be used in an industry setting. © 2015 Institute of Food Technologists®

A combined cell based approach to identify P-glycoprotein substrates and inhibitors in a single assay.

PubMed

Balimane, Praveen V; Chong, Saeho

2005-09-14

The objective of this project was to develop a cell based in vitro experimental procedure that can differentiate P-glycoprotein (P-gp) substrates from inhibitors in a single assay. Caco-2 cells grown to confluency on 12-well Transwell were used for this study. The efflux permeability (B to A) of P-gp specific probe (viz., digoxin) in the presence of test compounds (e.g. substrates, inhibitors and non-substrates of P-gp) was monitored, and the influx permeability (A to B) of test compounds was evaluated after complete P-gp blockade. Radiolabelled digoxin was added on the basolateral side with buffer on the apical side. The digoxin concentration appearing on the apical side represents digoxin efflux permeability during the control phase (0-1 h period). After 1 h, a test compound (10 microM) was added on the apical side. The reduced efflux permeability of digoxin suggests that the added test compound is an inhibitor. The influx permeability of test compound is also determined during the 1-2 h study period by measuring the concentration of the test compound in the basolateral side. At the end of 2 h, a potent P-gp inhibitor (GF120918) was added. The increased influx permeability of test compound during the 2-3 h incubation period indicates that the added test compound is a substrate. Samples were taken from both sides at the end of 1-3 h and the concentrations of the test compounds and digoxin were quantitated. Digoxin efflux permeability remained unchanged when incubated with P-gp substrates (e.g., etoposide, rhodamine123, taxol). However, when a P-gp inhibitor was added to the apical side, the digoxin efflux (B to A permeability) was significantly reduced (ketoconazole=51% reduction) as expected. The influx permeability of substrates increased significantly (rhodamine123=70%, taxol=220%, digoxin=290%) after the P-gp inhibitor (GF120918) was introduced, whereas the influx permeability of P-gp inhibitor and non-substrates was not affected by GF120918. Thus, this combined assay provides an efficient cell based in vitro screening tool to simultaneously distinguish compounds that are P-gp substrates from P-gp inhibitors.

Evaluation of OASIS QSAR Models Using ToxCast™ in Vitro Estrogen and Androgen Receptor Binding Data and Application in an Integrated Endocrine Screening Approach

PubMed Central

Bhhatarai, Barun; Wilson, Daniel M.; Price, Paul S.; Marty, Sue; Parks, Amanda K.; Carney, Edward

2016-01-01

Background: Integrative testing strategies (ITSs) for potential endocrine activity can use tiered in silico and in vitro models. Each component of an ITS should be thoroughly assessed. Objectives: We used the data from three in vitro ToxCast™ binding assays to assess OASIS, a quantitative structure-activity relationship (QSAR) platform covering both estrogen receptor (ER) and androgen receptor (AR) binding. For stronger binders (described here as AC50 < 1 μM), we also examined the relationship of QSAR predictions of ER or AR binding to the results from 18 ER and 10 AR transactivation assays, 72 ER-binding reference compounds, and the in vivo uterotrophic assay. Methods: NovaScreen binding assay data for ER (human, bovine, and mouse) and AR (human, chimpanzee, and rat) were used to assess the sensitivity, specificity, concordance, and applicability domain of two OASIS QSAR models. The binding strength relative to the QSAR-predicted binding strength was examined for the ER data. The relationship of QSAR predictions of binding to transactivation- and pathway-based assays, as well as to in vivo uterotrophic responses, was examined. Results: The QSAR models had both high sensitivity (> 75%) and specificity (> 86%) for ER as well as both high sensitivity (92–100%) and specificity (70–81%) for AR. For compounds within the domains of the ER and AR QSAR models that bound with AC50 < 1 μM, the QSAR models accurately predicted the binding for the parent compounds. The parent compounds were active in all transactivation assays where metabolism was incorporated and, except for those compounds known to require metabolism to manifest activity, all assay platforms where metabolism was not incorporated. Compounds in-domain and predicted to bind by the ER QSAR model that were positive in ToxCast™ ER binding at AC50 < 1 μM were active in the uterotrophic assay. Conclusions: We used the extensive ToxCast™ HTS binding data set to show that OASIS ER and AR QSAR models had high sensitivity and specificity when compounds were in-domain of the models. Based on this research, we recommend a tiered screening approach wherein a) QSAR is used to identify compounds in-domain of the ER or AR binding models and predicted to bind; b) those compounds are screened in vitro to assess binding potency; and c) the stronger binders (AC50 < 1 μM) are screened in vivo. This scheme prioritizes compounds for integrative testing and risk assessment. Importantly, compounds that are not in-domain, that are predicted either not to bind or to bind weakly, that are not active in in vitro, that require metabolism to manifest activity, or for which in vivo AR testing is in order, need to be assessed differently. Citation: Bhhatarai B, Wilson DM, Price PS, Marty S, Parks AK, Carney E. 2016. Evaluation of OASIS QSAR models using ToxCast™ in vitro estrogen and androgen receptor binding data and application in an integrated endocrine screening approach. Environ Health Perspect 124:1453–1461; http://dx.doi.org/10.1289/EHP184 PMID:27152837

Field Evaluation of Anti-Biofouling Compounds on Optical Instrumentation

NASA Technical Reports Server (NTRS)

McLean, Scott; Schofield, Bryan; Zibordi, Giuseppe; Lewis, Marlon; Hooker, Stanford; Weidemann, Alan

1997-01-01

Biofouling has been a serious question in the stability of optical measurements in the ocean, particularly in moored and drifting buoy applications. Many investigators coat optical surfaces with various compounds to reduce the amount of fouling; to our knowledge, however, there are no objective, in-situ comparative testing of these compounds to evaluate their effectiveness with respect to optical stability relative to untreated controls. We have tested a wide range of compounds at in-situ locations in Halifax Harbour and in the Adriatic Sea on passive optical sensors. Compounds tested include a variety of TBT formulations, antifungal agents, and low-friction silicone-based compounds; time-scales of up to four months were evaluated. The results of these experiments are discussed.

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.

Biodegradation tests of mercaptocarboxylic acids, their esters, related divalent sulfur compounds and mercaptans.

PubMed

Rücker, Christoph; Mahmoud, Waleed M M; Schwartz, Dirk; Kümmerer, Klaus

2018-04-17

Mercaptocarboxylic acids and their esters, a class of difunctional compounds bearing both a mercapto and a carboxylic acid or ester functional group, are industrial chemicals of potential environmental concern. Biodegradation of such compounds was systematically investigated here, both by literature search and by experiments (Closed Bottle Test OECD 301D and Manometric Respirometry Test OECD 301F). These compounds were found either readily biodegradable or at least biodegradable to a significant extent. Some related compounds of divalent sulfur were tested for comparison (mercaptans, sulfides, disulfides). For the two relevant monofunctional compound classes, carboxylic acids/esters and mercaptans, literature data were compiled, and by comparison with structurally similar compounds without these functional groups, the influence of COOH/COOR' and SH groups on biodegradability was evaluated. Thereby, an existing rule of thumb for biodegradation of carboxylic acids/esters was supported by experimental data, and a rule of thumb could be formulated for mercaptans. Concurrent to biodegradation, abiotic processes were observed in the experiments, rapid oxidative formation of disulfides (dimerisation of monomercaptans and cyclisation of dimercaptans) and hydrolysis of esters. Some problems that compromise the reproducibility of biodegradation test results were discussed.

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.

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.

QSAR analyses of 3-(4-benzylpiperidin-1-yl)-N-phenylpropylamine derivatives as potent CCR5 antagonists.

PubMed

Roy, Kunal; Leonard, J Thomas

2005-01-01

CCR5 receptor binding affinity of a series of 3-(4-benzylpiperidin-1-yl)propylamine congeners was subjected to QSAR study using the linear free energy related (LFER) model of Hansch. Appropriate indicator variables encoding different group contributions and different physicochemical variables such as hydrophobicity (pi), electronic (Hammett sigma), and steric (molar refractivity, STERIMOL values) parameters of phenyl ring substituents of the compounds were used as predictor variables. The Hansch analysis explores the importance of the lipophilicity and electron-donating substituents for the binding affinity. However, this method could not give more insight into the structure-activity relationships because of the diverse molecular features in the data set. 3D-QSAR analyses of the same data set using Molecular Shape Analysis (MSA), Receptor Surface Analysis (RSA), and Molecular Field Analysis (MFA) techniques were also performed. The best model with acceptable statistical quality was derived from the MSA, which showed the importance of the relative negative charge (RNCG): substituents with a high RNCG value have more binding affinity than the unsubstituted piperidine and phenyl (R1 position) congeners. The relative negative charge surface area (RNCS) is detrimental (e.g. R2 = 3,4-Cl2) for the activity. An increase in the length of the molecule in the Z dimension (Lz) is conducive (e.g. R3 = sulfonylmorpholino), while an increase in the area of the molecular shadow in the XZ plane (Sxz) is detrimental (e.g. R1 = N-c-hexylmethyl-5-oxopyrrolidin-3-yl) for the binding affinity. The presence of a chiral center makes the molecule less active (e.g. R1 = N-methyl-5-oxopyrrolidin-3-yl). An increase in the van der Waals area, the molecular volume, and the difference between the volume of the individual molecule and the shape reference compound are conducive (e.g. R3 = (CH3)2NSO2-) for the binding affinity. Substituents with higher JursFPSA_2 values (fractional charged partial surface area) like the N-methylsulfonylpiperidin-4-yl (R1 position) group have better binding affinity than the substituents such as 4-chlorophenylamino (R1 position). Unsubstituted piperidines (R1 position) with less JursFNSA_1 values have lower binding affinity than the 4-chlorophenyl substituted compounds. The MFA derived equation shows interaction energies at different grid points, while the RSA model shows the importance of hydrophobicity and charge at different regions of the molecules. The models were validated through the leave-one-out, leave-15%-out, and leave-25%-out cross-validation techniques. The developed models were also subjected to a randomization test (99% confidence level). Although the MSA derived models had excellent statistical qualities both for the training as well as test sets, RSA and MFA results for the test sets are not comparable statistically with the MSA derived models.

Integrated testing strategies can be optimal for chemical risk classification.

PubMed

Raseta, Marko; Pitchford, Jon; Cussens, James; Doe, John

2017-08-01

There is an urgent need to refine strategies for testing the safety of chemical compounds. This need arises both from the financial and ethical costs of animal tests, but also from the opportunities presented by new in-vitro and in-silico alternatives. Here we explore the mathematical theory underpinning the formulation of optimal testing strategies in toxicology. We show how the costs and imprecisions of the various tests, and the variability in exposures and responses of individuals, can be assembled rationally to form a Markov Decision Problem. We compute the corresponding optimal policies using well developed theory based on Dynamic Programming, thereby identifying and overcoming some methodological and logical inconsistencies which may exist in the current toxicological testing. By illustrating our methods for two simple but readily generalisable examples we show how so-called integrated testing strategies, where information of different precisions from different sources is combined and where different initial test outcomes lead to different sets of future tests, can arise naturally as optimal policies. Copyright © 2017 Elsevier Inc. All rights reserved.

Trainable structure-activity relationship model for virtual screening of CYP3A4 inhibition.

PubMed

Didziapetris, Remigijus; Dapkunas, Justas; Sazonovas, Andrius; Japertas, Pranas

2010-11-01

A new structure-activity relationship model predicting the probability for a compound to inhibit human cytochrome P450 3A4 has been developed using data for >800 compounds from various literature sources and tested on PubChem screening data. Novel GALAS (Global, Adjusted Locally According to Similarity) modeling methodology has been used, which is a combination of baseline global QSAR model and local similarity based corrections. GALAS modeling method allows forecasting the reliability of prediction thus defining the model applicability domain. For compounds within this domain the statistical results of the final model approach the data consistency between experimental data from literature and PubChem datasets with the overall accuracy of 89%. However, the original model is applicable only for less than a half of PubChem database. Since the similarity correction procedure of GALAS modeling method allows straightforward model training, the possibility to expand the applicability domain has been investigated. Experimental data from PubChem dataset served as an example of in-house high-throughput screening data. The model successfully adapted itself to both data classified using the same and different IC₅₀ threshold compared with the training set. In addition, adjustment of the CYP3A4 inhibition model to compounds with a novel chemical scaffold has been demonstrated. The reported GALAS model is proposed as a useful tool for virtual screening of compounds for possible drug-drug interactions even prior to the actual synthesis.

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

NASA rotor system research aircraft flight-test data report: Helicopter and compound configuration

NASA Technical Reports Server (NTRS)

Erickson, R. E.; Kufeld, R. M.; Cross, J. L.; Hodge, R. W.; Ericson, W. F.; Carter, R. D. G.

1984-01-01

The flight test activities of the Rotor System Research Aircraft (RSRA), NASA 740, from June 30, 1981 to August 5, 1982 are reported. Tests were conducted in both the helicopter and compound configurations. Compound tests reconfirmed the Sikorsky flight envelope except that main rotor blade bending loads reached endurance at a speed about 10 knots lower than previously. Wing incidence changes were made from 0 to 10 deg.

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.

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.

How to Deal with Low-Resolution Target Structures: Using SAR, Ensemble Docking, Hydropathic Analysis, and 3D-QSAR to Definitively Map the αβ-Tubulin Colchicine Site

PubMed Central

Da, Chenxiao; Mooberry, Susan L.; Gupton, John T.; Kellogg, Glen E.

2013-01-01

αβ-tubulin colchicine site inhibitors (CSIs) from four scaffolds that we previously tested for antiproliferative activity were modeled to better understand their effect on microtubules. Docking models, constructed by exploiting the SAR of a pyrrole subset and HINT scoring, guided ensemble docking of all 59 compounds. This conformation set and two variants having progressively less structure knowledge were subjected to CoMFA, CoMFA+HINT, and CoMSIA 3D-QSAR analyses. The CoMFA+HINT model (docked alignment) showed the best statistics: leave-one-out q2 of 0.616, r2 of 0.949 and r2pred (internal test set) of 0.755. An external (tested in other laboratories) collection of 24 CSIs from eight scaffolds were evaluated with the 3D-QSAR models, which correctly ranked their activity trends in 7/8 scaffolds for CoMFA+HINT (8/8 for CoMFA). The combination of SAR, ensemble docking, hydropathic analysis and 3D-QSAR provides an atomic-scale colchicine site model more consistent with a target structure resolution much higher than the ~3.6 Å available for αβ-tubulin. PMID:23961916

Systematic study on the TD-DFT calculated electronic circular dichroism spectra of chiral aromatic nitro compounds: A comparison of B3LYP and CAM-B3LYP.

PubMed

Komjáti, Balázs; Urai, Ákos; Hosztafi, Sándor; Kökösi, József; Kováts, Benjámin; Nagy, József; Horváth, Péter

2016-02-15

B3LYP is one of the most widely used functional for the prediction of electronic circular dichroism spectra, however if the studied molecule contains aromatic nitro group computations may fail to produce reliable results. A test set of molecules of known stereochemistry were synthesized to study this phenomenon in detail. Spectra were computed by B3LYP and CAM-B3LYP functionals with 6-311++G(2d,2p) basis set. It was found that the range separated CAM-B3LYP gives better predictions than B3LYP for all test molecules. Fragment population analysis revealed that the nitro groups form highly localized molecule orbitals but the exact composition depends on the functional. CAM-B3LYP allows sufficient spatial overlap between the nitro group and distant parts of the molecule, which is necessary for the accurate description of excited states especially for charge transfer states. This phenomenon and the synthesized test molecules can be used to benchmark theoretical methods as well as to help the development of new functionals intended for spectroscopical studies. Copyright © 2015 Elsevier B.V. All rights reserved.

3D-QSAR CoMFA of a series of DABO derivatives as HIV-1 reverse transcriptase non-nucleoside inhibitors.

PubMed

de Brito, Monique Araújo; Rodrigues, Carlos Rangel; Cirino, José Jair Vianna; de Alencastro, Ricardo Bicca; Castro, Helena Carla; Albuquerque, Magaly Girão

2008-08-01

A series of 74 dihydroalkoxybenzyloxopyrimidines (DABOs), a class of highly potent non-nucleoside reverse transcriptase inhibitors (NNRTIs), was retrieved from the literature and studied by comparative molecular field analysis (CoMFA) in order to derive three-dimensional quantitative structure-activity relationship (3D-QSAR) models. The CoMFA study has been performed with a training set of 59 compounds, testing three alignments and four charge schemes (DFT, HF, AM1, and PM3) and using defaults probe atom (Csp (3), +1 charge), cutoffs (30 kcal.mol (-1) for both steric and electrostatic fields), and grid distance (2.0 A). The best model ( N = 59), derived from Alignment 1 and PM3 charges, shows q (2) = 0.691, SE cv = 0.475, optimum number of components = 6, r (2) = 0.930, SEE = 0.226, and F-value = 115.544. The steric and electrostatic contributions for the best model were 43.2% and 56.8%, respectively. The external predictive ability (r (2) pred = 0.918) of the resultant best model was evaluated using a test set of 15 compounds. In order to design more potent DABO analogues as anti-HIV/AIDS agents, attention should be taken in order to select a substituent for the 4-oxopyrimidine ring, since, as revealed by the best CoMFA model, there are a steric restriction at the C2-position, a electron-rich group restriction at the C6-position ( para-substituent of the 6-benzyl group), and a steric allowed region at the C5-position.

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.

Influence of substrate structure on turnover of the organic cation/H+ exchanger of the renal luminal membrane.

PubMed

Wright, S H; Wunz, T M

1998-08-01

We examined the influence of organic cation (OC) structure on the rate of turnover of the OC/H+ exchanger in rabbit renal brush-border membrane vesicles (BBMV). The rate of efflux of [14C]tetraethylammonium ([14C]TEA) from BBMV, measured in the presence of an inwardly directed chemical gradient for test agent, provided an indirect measure of activity of the OC/H+(OC) exchanger. The trans-stimulation of [14C]TEA efflux from BBMV was a saturable function of increasing extravesicular concentration of both unlabeled TEA and tetramethylammonium (TMA), with an apparent Michaelis constant (Kt) for the interaction of these compounds with the OC/H+(OC) exchanger of 25 microM and 1 mM, respectively. The effect on [14C]TEA efflux of saturating extravesicular concentrations of a series of n-tetraalkylammonium compounds was examined. Whereas the short-chain compounds TMA and TEA markedly stimulated [14C]TEA efflux (by 830% and 690%, respectively), the long-chain compounds tetrapropylammonium and tetrabutylammonium were less effective, increasing efflux by only 40% and 120%, respectively. When the exchanger was saturated with tetrapentylammonium, mediated efflux of [14C]TEA was reduced. Increasing alkyl chain length was also correlated with an increase in the inhibitory effect (as measured by the apparent inhibition constant, Ki, or the IC50 value) that these compounds had against transport of [14C]TEA by the OC/H+(OC) exchanger; i.e., there was a correlation between decreasing IC50 and decreasing turnover of the OC/H+(OC) exchanger. This same correlation was observed for a broader set of test agents of diverse molecular structure, including a series of n-tetraalkylammonium and -phosphonium compounds and the OCs, choline, N1-methyl nicotinamide, 1-methyl-4-phenylpyridinium, and amiloride. Because high affinity of substrates for the OC/H+(OC) exchanger is correlated with increasing substrate hydrophobicity, we conclude that the interaction of hydrophobic OCs with the renal OC/H+(OC) exchanger results in the formation of a substrate-exchanger complex that has a comparatively low rate of turnover.

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.

Evolved Gas Measurements Planned for the Lower Layers of the Gale Crater Mound with the Sample Analysis at Mars Instrument Suite

NASA Technical Reports Server (NTRS)

Mahaffy, Paul; Brunner, Anna; McAdam, Amy; Franz, Heather; Conrad, Pamela; Webster, Chris; Cabane, Michel

2009-01-01

The lower mound strata of Gale Crater provide a diverse set of chemical environments for exploration by the varied tools of the Curiosity Rover of the Mars Science Laboratory (MSL) Mission. Orbital imaging and spectroscopy clearly reveal distinct layers of hydrated minerals, sulfates, and clays with abundant evidence of a variety of fluvial processes. The three instruments of the MSL Sample Analysis at aMars (SAM) investigation, the Quadrupole Mass Spectrometer (QMS), the Tunable Laser Spectrometer (TLS), and the Gas Chromatograph (GC) are designed to analyze either atmospheric gases or volatiles thermally evolved or chemically extracted from powdered rock or soil. The presence or absence of organic compounds in these layers is of great interest since such an in situ search for this type of record has not been successfully implemented since the mid-60s Viking GCMS experiments. However, regardless of the outcome of the analysis for organics, the abundance and isotopic composition of thermally evolved inorganic compounds should also provide a rich data set to complement the mineralogical and elemental information provided by other MSL instruments. In addition, these evolved gas analysis (EGA) experiments will help test sedimentary models proposed by Malin and Edgett (2000) and then further developed by Milliken et al (2010) for Gale Crater. In the SAM EGA experiments the evolution temperatures of H2O, CO2, SO2, O2, or other simple compounds as the samples are heated in a helium stream to 1000 C provides information on mineral types and their associations. The isotopic composition of O, H, C, and S can be precisely determined in several evolved compounds and compared with the present day atmosphere. Such SAM results might be able to test mineralogical evidence of changing sedimentary and alteration processes over an extended period of time. For example, Bibring et al (2006) have suggested such a major shift from early nonacidic to later acidic alteration. We will illustrate through a variety of evolved gas experiments implemented under SAM-like gas flow and temperature ramp conditions on terrestrial analog minerals on high fidelity Sam breadboards the type of chemical information we expect SAM to provide.

Research in drug development against viral diseases of military importance (biological testing). Volume 1. Final report, 15 November 1985-31 January 1991

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

Shannon, W.M.; Arnett, G.; Brazier, A.D.

1991-03-01

The purpose of this program is to evaluate the efficacy of candidate antiviral compounds against a spectrum of viruses of military importance. This program involves (a) primary testing of chemical compounds and natural products for antiviral efficacy in vitro using standard CPE-inhibition assays, (b) primary testing of compounds for antiviral efficacy in vivo in animal model systems, and (c) secondary evaluation of the active candidate antiviral compounds. The target viruses for in vitro testing are Vaccinia Virus (VV), Adenovirus (AD2), Vesicular Stomatitis Virus (VSV), Punta Toro Virus (PT), Sandfly Fever Virus (SF), Yellow Fever Virus (YF), Venezuelan Equine Encephalomyelitis Virusmore » (VE), Japanese Encephalitis Virus and Vaccinia Virus infections of mice. Approximately 10,000 compounds have been received for in vitro evaluation and over 66,000 assays have been performed on this contract. Compounds have been identified in nearly all virus systems that have confirmed antiviral activity equal or exceeding that of the various positive control compounds (Ribavirin, Selenazofurin, Carbocyclic-3-deaza-adenosine, Adenosine dialdehyde, Ara-A, ddC and AZT). Many of these compounds represent potent and selective new antiviral agents.« less

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

Antiviral Screening of Multiple Compounds against Ebola Virus.

PubMed

Dowall, Stuart D; Bewley, Kevin; Watson, Robert J; Vasan, Seshadri S; Ghosh, Chandradhish; Konai, Mohini M; Gausdal, Gro; Lorens, James B; Long, Jason; Barclay, Wendy; Garcia-Dorival, Isabel; Hiscox, Julian; Bosworth, Andrew; Taylor, Irene; Easterbrook, Linda; Pitman, James; Summers, Sian; Chan-Pensley, Jenny; Funnell, Simon; Vipond, Julia; Charlton, Sue; Haldar, Jayanta; Hewson, Roger; Carroll, Miles W

2016-10-27

In light of the recent outbreak of Ebola virus (EBOV) disease in West Africa, there have been renewed efforts to search for effective antiviral countermeasures. A range of compounds currently available with broad antimicrobial activity have been tested for activity against EBOV. Using live EBOV, eighteen candidate compounds were screened for antiviral activity in vitro. The compounds were selected on a rational basis because their mechanisms of action suggested that they had the potential to disrupt EBOV entry, replication or exit from cells or because they had displayed some antiviral activity against EBOV in previous tests. Nine compounds caused no reduction in viral replication despite cells remaining healthy, so they were excluded from further analysis (zidovudine; didanosine; stavudine; abacavir sulphate; entecavir; JB1a; Aimspro; celgosivir; and castanospermine). A second screen of the remaining compounds and the feasibility of appropriateness for in vivo testing removed six further compounds (ouabain; omeprazole; esomeprazole; Gleevec; D-LANA-14; and Tasigna). The three most promising compounds (17-DMAG; BGB324; and NCK-8) were further screened for in vivo activity in the guinea pig model of EBOV disease. Two of the compounds, BGB324 and NCK-8, showed some effect against lethal infection in vivo at the concentrations tested, which warrants further investigation. Further, these data add to the body of knowledge on the antiviral activities of multiple compounds against EBOV and indicate that the scientific community should invest more effort into the development of novel and specific antiviral compounds to treat Ebola virus disease.

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

Material selection and evaluation of new encapsulation compounds for electric cables for launch support system

NASA Technical Reports Server (NTRS)

Ray, Asit K.

1992-01-01

Eight urethane compounds were evaluated as possible replacement for the existing encapsulating compoounds for electrical cables for the Launch Support System at Kennedy Space Center (KSC). The existing encapsulating compound, PR-1535, contains the curative MOCA 4-4'-Methylene-BIS (2-chloroaniline), which is a suspect carcinogen and hence may be the subject of further restrictions of its use by the Occupational Safety and Health Administration (OSHA). The samples made in the configuration of cable joints and in the form of disks were evaluated for flammability and hypergolic compatibility. These also underwent accelerated weatherability tests that measured the residual hardness of the exposed samples. Three candidates and the existing compound passed the hardness test. Of these, only one candidate and the existing compound passed the flammability test. The thermal and hydrolytic stability (weatherability) of these samples was studied using thermogravimetric analysis (DSC) techniques. The TMA and DSC data correlated with the residual hardness data; whereas, the TGA data showed no correlation. A hypergolic compatibility test will be conducted on the compound V-356-HE80, which passed both the flammability and accelerated weatherability tests.

QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1.

PubMed

Comelli, Nieves C; Duchowicz, Pablo R; Castro, Eduardo A

2014-10-01

The inhibitory activity of 103 thiophene and 33 imidazopyridine derivatives against Polo-Like Kinase 1 (PLK1) expressed as pIC50 (-logIC50) was predicted by QSAR modeling. Multivariate linear regression (MLR) was employed to model the relationship between 0D and 3D molecular descriptors and biological activities of molecules using the replacement method (MR) as variable selection tool. The 136 compounds were separated into several training and test sets. Two splitting approaches, distribution of biological data and structural diversity, and the statistical experimental design procedure D-optimal distance were applied to the dataset. The significance of the training set models was confirmed by statistically higher values of the internal leave one out cross-validated coefficient of determination (Q2) and external predictive coefficient of determination for the test set (Rtest2). The model developed from a training set, obtained with the D-optimal distance protocol and using 3D descriptor space along with activity values, separated chemical features that allowed to distinguish high and low pIC50 values reasonably well. Then, we verified that such model was sufficient to reliably and accurately predict the activity of external diverse structures. The model robustness was properly characterized by means of standard procedures and their applicability domain (AD) was analyzed by leverage method. Copyright © 2014 Elsevier B.V. All rights reserved.

Prediction of breast cancer risk with volatile biomarkers in breath.

PubMed

Phillips, Michael; Cataneo, Renee N; Cruz-Ramos, Jose Alfonso; Huston, Jan; Ornelas, Omar; Pappas, Nadine; Pathak, Sonali

2018-03-23

Human breath contains volatile organic compounds (VOCs) that are biomarkers of breast cancer. We investigated the positive and negative predictive values (PPV and NPV) of breath VOC biomarkers as indicators of breast cancer risk. We employed ultra-clean breath collection balloons to collect breath samples from 54 women with biopsy-proven breast cancer and 124 cancer-free controls. Breath VOCs were analyzed with gas chromatography (GC) combined with either mass spectrometry (GC MS) or surface acoustic wave detection (GC SAW). Chromatograms were randomly assigned to a training set or a validation set. Monte Carlo analysis identified significant breath VOC biomarkers of breast cancer in the training set, and these biomarkers were incorporated into a multivariate algorithm to predict disease in the validation set. In the unsplit dataset, the predictive algorithms generated discriminant function (DF) values that varied with sensitivity, specificity, PPV and NPV. Using GC MS, test accuracy = 90% (area under curve of receiver operating characteristic in unsplit dataset) and cross-validated accuracy = 77%. Using GC SAW, test accuracy = 86% and cross-validated accuracy = 74%. With both assays, a low DF value was associated with a low risk of breast cancer (NPV > 99.9%). A high DF value was associated with a high risk of breast cancer and PPV rising to 100%. Analysis of breath VOC samples collected with ultra-clean balloons detected biomarkers that accurately predicted risk of breast cancer.

Discovery and quantitative structure-activity relationship study of lepidopteran HMG-CoA reductase inhibitors as selective insecticides.

PubMed

Zang, Yang-Yang; Li, Yuan-Mei; Yin, Yue; Chen, Shan-Shan; Kai, Zhen-Peng

2017-09-01

In a previous study we have demonstrated that insect 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) can be a potential selective insecticide target. Three series of inhibitors were designed on the basis of the difference in HMGR structures from Homo sapiens and Manduca sexta, with the aim of discovering potent selective insecticide candidates. An in vitro bioassay showed that gem-difluoromethylenated statin analogues have potent effects on JH biosynthesis of M. sexta and high selectivity between H. sapiens and M. sexta. All series II compounds {1,3,5-trisubstituted [4-tert-butyl 2-(5,5-difluoro-2,2-dimethyl-6-vinyl-4-yl) acetate] pyrazoles} have some effect on JH biosynthesis, whereas most of them are inactive on human HMGR. In particular, the IC 50 value of compound II-12 (37.8 nm) is lower than that of lovastatin (99.5 nm) and similar to that of rosuvastatin (24.2 nm). An in vivo bioassay showed that I-1, I-2, I-3 and II-12 are potential selective insecticides, especially for lepidopteran pest control. A predictable and statistically meaningful CoMFA model of 23 inhibitors (20 as training sets and three as test sets) was obtained with a value of q 2 and r 2 of 0.66 and 0.996 respectively. The final model suggested that a potent insect HMGR inhibitor should contain suitable small and non-electronegative groups in the ring part, and electronegative groups in the side chain. Four analogues were discovered as potent selective lepidopteran HMGR inhibitors, which can specifically be used for lepidopteran pest control. The CoMFA model will be useful for the design of new selective insect HMGR inhibitors that are structurally related to the training set compounds. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Design and synthesis of some new 1-phenyl-3/4-[4-(aryl/heteroaryl/alkyl-piperazine1-yl)-phenyl-ureas as potent anticonvulsant and antidepressant agents.

PubMed

Mishra, Chandra Bhushan; Kumari, Shikha; Tiwari, Manisha

2016-05-01

A series of 1-phenyl-3/4-[4-(aryl/heteroaryl/alkyl-piperazine1-yl)-phenyl-urea derivatives (29-42) were designed, synthesized and evaluated for their anticonvulsant activity by using maximal electroshock (MES), subcutaneous pentylenetetrazole (scPTZ) seizure tests. The acute neurotoxicity was checked by rotarod assay. Most of the test compounds were found effective in both seizure tests. Compound 30 (1-{4-[4-(4-chloro-phenyl)-piperazin-1-yl]-phenyl}-3-phenyl-urea) exhibited marked anticonvulsant activity in MES as well as scPTZ tests. The phase II anticonvulsant quantification study of compound 30 indicates the ED50 value of 28.5 mg/kg against MES induced seizures. In addition, this compound also showed considerable protection against pilocarpine induced status epilepticus in rats. Seizures induced by 3-mercaptopropionic acid model and thiosemicarbazide were significantly attenuated by compound 30, which suggested its broad spectrum of anticonvulsant activity. Interestingly, compound 30 displayed better antidepressant activity than standard drug fluoxetine. Moreover, compound 30 appeared as a non-toxic chemical entity in sub-acute toxicity studies.

Development of a canine model to enable the preclinical assessment of pH-dependent absorption of test compounds.

PubMed

Fancher, R Marcus; Zhang, Hongjian; Sleczka, Bogdan; Derbin, George; Rockar, Richard; Marathe, Punit

2011-07-01

A preclinical canine model capable of predicting a compound's potential for pH-dependent absorption in humans was developed. This involved the surgical insertion of a gastrostomy feeding tube into the stomach of a beagle dog. The tube was sutured in position to allow frequent withdrawal of gastric fluid for pH measurement. Therefore, it was possible to measure pH in the stomach and assess the effect of gastric pH-modifying agents on the absorption of various test compounds. Fasted gastric pH in the dog showed considerable inter- and intra-animal variability. Pretreatment of pentagastrin (6 µg/kg intramuscularly) 20 min prior to test compound administration was determined to be adequate for simulating fasting stomach pH in humans. Pretreatment with famotidine [40 mg orally] 1 h prior to test compound administration was determined to be adequate for simulating human gastric pH when acid-reducing agents are coadministered. Pentagastrin and famotidine pretreatments were used to test two discovery compounds and distinct differences in their potential for pH-dependent absorption were observed. The model described herein can be used preclinically to screen out compounds, differentiate compounds, and support the assessment of various formulation- and prodrug-based strategies to mitigate the pH effect. Copyright © 2011 Wiley-Liss, Inc. and the American Pharmacists Association

Grid-based Molecular Footprint Comparison Method for Docking and De Novo Design: Application to HIVgp41

PubMed Central

Mukherjee, Sudipto; Rizzo, Robert C.

2014-01-01

Scoring functions are a critically important component of computer-aided screening methods for the identification of lead compounds during early stages of drug discovery. Here, we present a new multi-grid implementation of the footprint similarity (FPS) scoring function that was recently developed in our laboratory which has proven useful for identification of compounds which bind to a protein on a per-residue basis in a way that resembles a known reference. The grid-based FPS method is much faster than its Cartesian-space counterpart which makes it computationally tractable for on-the-fly docking, virtual screening, or de novo design. In this work, we establish that: (i) relatively few grids can be used to accurately approximate Cartesian space footprint similarity, (ii) the method yields improved success over the standard DOCK energy function for pose identification across a large test set of experimental co-crystal structures, for crossdocking, and for database enrichment, and (iii) grid-based FPS scoring can be used to tailor construction of new molecules to have specific properties, as demonstrated in a series of test cases targeting the viral protein HIVgp41. The method will be made available in the program DOCK6. PMID:23436713