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Sample records for activity relationships qsar

  1. A novel algorithm for QSAR (quantitative structure-activity relationships)

    SciTech Connect

    Carter, S. ); Nikolic, S.; Trinajstic, N. )

    1989-01-01

    A novel approach to quantitative structure-activity relationships (QSAR) is proposed. It is based on the molecular descriptor named the stereo-identification (SID) number. The applicability of this approach to QSAR studies is tested on aquatic toxicities of phenols against fathead minnows (Phimephales promelas). Our approach reproduced successfully the bioactivities of phenols and is superior to the Hall-Kier model based on Randic's connectivity index.

  2. PREDICTING TOXICOLOGICAL ENDPOINTS OF CHEMICALS USING QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS (QSARS)

    EPA Science Inventory

    Quantitative structure-activity relationships (QSARs) are being developed to predict the toxicological endpoints for untested chemicals similar in structure to chemicals that have known experimental toxicological data. Based on a very large number of predetermined descriptors, a...

  3. Quantitative structure-activity relationship (QSAR) for insecticides: development of predictive in vivo insecticide activity models.

    PubMed

    Naik, P K; Singh, T; Singh, H

    2009-07-01

    Quantitative structure-activity relationship (QSAR) analyses were performed independently on data sets belonging to two groups of insecticides, namely the organophosphates and carbamates. Several types of descriptors including topological, spatial, thermodynamic, information content, lead likeness and E-state indices were used to derive quantitative relationships between insecticide activities and structural properties of chemicals. A systematic search approach based on missing value, zero value, simple correlation and multi-collinearity tests as well as the use of a genetic algorithm allowed the optimal selection of the descriptors used to generate the models. The QSAR models developed for both organophosphate and carbamate groups revealed good predictability with r(2) values of 0.949 and 0.838 as well as [image omitted] values of 0.890 and 0.765, respectively. In addition, a linear correlation was observed between the predicted and experimental LD(50) values for the test set data with r(2) of 0.871 and 0.788 for both the organophosphate and carbamate groups, indicating that the prediction accuracy of the QSAR models was acceptable. The models were also tested successfully from external validation criteria. QSAR models developed in this study should help further design of novel potent insecticides.

  4. Developing sensor activity relationships for the JPL electronic nose sensors using molecular modeling and QSAR techniques

    NASA Technical Reports Server (NTRS)

    Shevade, A. V.; Ryan, M. A.; Homer, M. L.; Jewell, A. D.; Zhou, H.; Manatt, K.; Kisor, A. K.

    2005-01-01

    We report a Quantitative Structure-Activity Relationships (QSAR) study using Genetic Function Approximations (GFA) to describe the polymer-carbon composite sensor activities in the JPL Electronic Nose, when exposed to chemical vapors at parts-per-million concentration levels.

  5. Quantitative Structure-Activity Relationships (QSARs) - Applications and Methodology

    NASA Astrophysics Data System (ADS)

    Cronin, Mark T. D.

    The aim of this introduction is to describe briefly the applications and methodologies involved in (Q)SAR and relate these to the various chapters in this volume. This chapter gives the reader an overview of how, why and where in silico methods, including (Q)SAR, have been utilized to predict endpoints as diverse as those from pharmacology and toxicology. It provides an illustration of how all the various topics in this book interweave to form a single coherent area of science.

  6. Development of quantitative structure activity relationship (QSAR) model for disinfection byproduct (DBP) research: A review of methods and resources.

    PubMed

    Chen, Baiyang; Zhang, Tian; Bond, Tom; Gan, Yiqun

    2015-12-15

    Quantitative structure-activity relationship (QSAR) models are tools for linking chemical activities with molecular structures and compositions. Due to the concern about the proliferating number of disinfection byproducts (DBPs) in water and the associated financial and technical burden, researchers have recently begun to develop QSAR models to investigate the toxicity, formation, property, and removal of DBPs. However, there are no standard procedures or best practices regarding how to develop QSAR models, which potentially limit their wide acceptance. In order to facilitate more frequent use of QSAR models in future DBP research, this article reviews the processes required for QSAR model development, summarizes recent trends in QSAR-DBP studies, and shares some important resources for QSAR development (e.g., free databases and QSAR programs). The paper follows the four steps of QSAR model development, i.e., data collection, descriptor filtration, algorithm selection, and model validation; and finishes by highlighting several research needs. Because QSAR models may have an important role in progressing our understanding of DBP issues, it is hoped that this paper will encourage their future use for this application.

  7. The current status and future applicability of quantitative structure-activity relationships (QSARs) in predicting toxicity.

    PubMed

    Cronin, Mark T D

    2002-12-01

    The current status of quantitative structure-activity relationships (QSARs) in predicting toxicity is assessed. Widespread use of these methods to predict toxicity from chemical structure is possible, both by industry to develop new compounds, and also by regulatory agencies. The current use of QSARs is restricted by the lack of suitable toxicity data available for modelling, the suitability of simplistic modelling approaches for the prediction of certain endpoints, and the poor definition and utilisation of the applicability domain of models. Suggestions to resolve these issues are made.

  8. Simplifying complex QSAR's (quantitative structure-activity relationships) in toxicity studies with multivariate statistics

    SciTech Connect

    Niemi, G.J.; McKim, J.M.

    1988-07-01

    During the past several decades many quantitative structure-activity relationships (QSAR's) have been derived from relatively small data sets of chemicals in a homologous series and selected empirical observations. An alternative approach is to analyze large data sets consisting of heterogeneous groups of chemicals and to explore QSAR's among these chemicals for generalized patterns of chemical behavior. The use of exploratory multivariate statistical techniques for simplifying complex QSAR problems is demonstrated through the use of research data on biodegradation and mode of toxic action. In these examples, a large number of explanatory variables were examined to explore which variables might best explain whether a chemical biodegrades or whether a toxic response by an organism can be used to identify a mode of toxic action. In both cases, the procedures reduced the number of potential explanatory variables and generated hypotheses about biodegradation and mode of toxic action for future research without explicitly testing an existing hypothesis.

  9. Validation of Quantitative Structure-Activity Relationship (QSAR) Model for Photosensitizer Activity Prediction

    PubMed Central

    Frimayanti, Neni; Yam, Mun Li; Lee, Hong Boon; Othman, Rozana; Zain, Sharifuddin M.; Rahman, Noorsaadah Abd.

    2011-01-01

    Photodynamic therapy is a relatively new treatment method for cancer which utilizes a combination of oxygen, a photosensitizer and light to generate reactive singlet oxygen that eradicates tumors via direct cell-killing, vasculature damage and engagement of the immune system. Most of photosensitizers that are in clinical and pre-clinical assessments, or those that are already approved for clinical use, are mainly based on cyclic tetrapyrroles. In an attempt to discover new effective photosensitizers, we report the use of the quantitative structure-activity relationship (QSAR) method to develop a model that could correlate the structural features of cyclic tetrapyrrole-based compounds with their photodynamic therapy (PDT) activity. In this study, a set of 36 porphyrin derivatives was used in the model development where 24 of these compounds were in the training set and the remaining 12 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression analysis (MLRA) method. Based on the method, r2 value, r2 (CV) value and r2 prediction value of 0.87, 0.71 and 0.70 were obtained. The QSAR model was also employed to predict the experimental compounds in an external test set. This external test set comprises 20 porphyrin-based compounds with experimental IC50 values ranging from 0.39 μM to 7.04 μM. Thus the model showed good correlative and predictive ability, with a predictive correlation coefficient (r2 prediction for external test set) of 0.52. The developed QSAR model was used to discover some compounds as new lead photosensitizers from this external test set. PMID:22272096

  10. Quantitative structure-activity relationships (QSARs) for the transformation of organic micropollutants during oxidative water treatment.

    PubMed

    Lee, Yunho; von Gunten, Urs

    2012-12-01

    Various oxidants such as chlorine, chlorine dioxide, ferrate(VI), ozone, and hydroxyl radicals can be applied for eliminating organic micropollutant by oxidative transformation during water treatment in systems such as drinking water, wastewater, and water reuse. Over the last decades, many second-order rate constants (k) have been determined for the reaction of these oxidants with model compounds and micropollutants. Good correlations (quantitative structure-activity relationships or QSARs) are often found between the k-values for an oxidation reaction of closely related compounds (i.e. having a common organic functional group) and substituent descriptor variables such as Hammett or Taft sigma constants. In this study, we developed QSARs for the oxidation of organic and some inorganic compounds and organic micropollutants transformation during oxidative water treatment. A number of 18 QSARs were developed based on overall 412 k-values for the reaction of chlorine, chlorine dioxide, ferrate, and ozone with organic compounds containing electron-rich moieties such as phenols, anilines, olefins, and amines. On average, 303 out of 412 (74%) k-values were predicted by these QSARs within a factor of 1/3-3 compared to the measured values. For HO(·) reactions, some principles and estimation methods of k-values (e.g. the Group Contribution Method) are discussed. The developed QSARs and the Group Contribution Method could be used to predict the k-values for various emerging organic micropollutants. As a demonstration, 39 out of 45 (87%) predicted k-values were found within a factor 1/3-3 compared to the measured values for the selected emerging micropollutants. Finally, it is discussed how the uncertainty in the predicted k-values using the QSARs affects the accuracy of prediction for micropollutant elimination during oxidative water treatment. PMID:22939392

  11. Synthesis, biological activities, and quantitative structure-activity relationship (QSAR) study of novel camptothecin analogues.

    PubMed

    Wu, Dan; Zhang, Shao-Yong; Liu, Ying-Qian; Wu, Xiao-Bing; Zhu, Gao-Xiang; Zhang, Yan; Wei, Wei; Liu, Huan-Xiang; Chen, An-Liang

    2015-05-13

    In continuation of our program aimed at the development of natural product-based pesticidal agents, three series of novel camptothecin derivatives were designed, synthesized, and evaluated for their biological activities against T. Cinnabarinus, B. brassicae, and B. xylophilus. All of the derivatives showed good-to-excellent activity against three insect species tested, with LC50 values ranging from 0.00761 to 0.35496 mmol/L. Remarkably, all of the compounds were more potent than CPT against T. Cinnabarinus, and compounds 4d and 4c displayed superior activity (LC50 0.00761 mmol/L and 0.00942 mmol/L, respectively) compared with CPT (LC50 0.19719 mmol/L) against T. Cinnabarinus. Based on the observed bioactivities, preliminary structure-activity relationship (SAR) correlations were also discussed. Furthermore, a three-dimensional quantitative structure-activity relationship (3D-QSAR) model using comparative molecular field analysis (CoMFA) was built. The model gave statistically significant results with the cross-validated q2 values of 0.580 and correlation coefficient r2 of 0.991 and  of 0.993. The QSAR analysis indicated that the size of the substituents play an important in the activity of 7-modified camptothecin derivatives. These findings will pave the way for further design, structural optimization, and development of camptothecin-derived compounds as pesticidal agents.

  12. Toxicity challenges in environmental chemicals: Prediction of human plasma protein binding through quantitative structure-activity relationship (QSAR) models

    EPA Science Inventory

    The present study explores the merit of utilizing available pharmaceutical data to construct a quantitative structure-activity relationship (QSAR) for prediction of the fraction of a chemical unbound to plasma protein (Fub) in environmentally relevant compounds. Independent model...

  13. Quantitative structure-activity relationship (QSAR) study of a series of benzimidazole derivatives as inhibitors of Saccharomyces cerevisiae.

    PubMed

    Podunavac-Kuzmanović, Sonja O; Cvetković, Dragoljub D; Jevrić, Lidija R; Uzelac, Natasa J

    2013-01-01

    A quantitative structure activity relationship (QSAR) has been carried out on a series of benzimidazole derivatives to identify the structural requirements for their inhibitory activity against yeast Saccharomyces cerevisiae. A multiple linear regression (MLR) procedure was used to model the relationships between various physicochemical, steric, electronic, and structural molecular descriptors and antifungal activity of benzimidazole derivatives. The QSAR expressions were generated using a training set of 16 compounds and the predictive ability of the resulting models was evaluated against a test set of 8 compounds. The best QSAR models were further validated by leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. Therefore, satisfactory relationships between antifungal activity and molecular descriptors were found. QSAR analysis reveals that lipophilicity descriptor (logP), dipole moment (DM) and surface area grid (SAG) govern the inhibitory activity of compounds studied against Saccharomyces cerevisiae.

  14. Quantitative structure-activity/ecotoxicity relationships (QSAR/QEcoSAR) of a series of phosphonates.

    PubMed

    Petrescu, Alina-Maria; Putz, Mihai V; Ilia, Gheorghe

    2015-11-01

    In this paper the structure-toxicity relationship studies were performed for a series of 60 phosphonates. The toxicity of the compounds was determined by two ways: by quantifying the measured toxicity values, Mlog(1/MRIC50) collected by literature, for rodents species; second by using EcoSAR software version 1.11, for calculating the toxicity for fish species, considered as dependent variables and they were related to structural features obtained by molecular and quantum mechanics calculations. The QSAR/QEcoSAR was validated by multiple linear regression (MLR), although the purpose of this work was not to validate the model proposed, but rather to test the influence of structural parameters of the proposed model QSAR/QEcoSAR. The obtained models showed that the toxicity of phosphonates was influenced by steric and molecular geometry which cause inhibition of cholinesterase activity.

  15. Polychlorinated biphenyls: correlation between in vivo and in vitro quantitative structure-activity relationships (QSARs)

    SciTech Connect

    Leece, B.; Denomme, M.A.; Towner, R.; Li, S.M.A.; Safe, S.

    1985-01-01

    The in vivo quantitative structure-activity relationships (QSARs) for several polychlorinated biphenyls (PCBs) were determined in the immature male Wistar rat. The ED25 and ED50 values for hepatic microsomal aryl hydrocarbon hydroxylase (AHH) and ethoxyresorufin O-deethylase (EROD) induction as well as for body weight loss and for thymic atrophy were determined for nine PCB congeners and 4'-bromo-2,3,4,5-tetrachlorobiphenyl. The most active compounds were the coplanar PCB congeners, 3,3',4,4',5-penta- and 3,3',4,4',5,5'-hexachlorobiphenyl; for example, their ED50 values for body weight loss were 3.25 and 15.1 ..mu..mol/kg, respectively. The in vivo toxicity of the coplanar PCB, 3,3',4,4'-tetrachlorobiphenyl, was significantly lower (ED50 for body weight loss = 730 ..mu..mol/kg) than the values observed for the more highly chlorinated homologs, and this was consistent with the more rapid metabolism of the lower chlorinated congener. The dose-response biologic and toxic effects of several mono-ortho-chloro-substituted analogs of the coplanar PCBs, including 2,3,4,4',5-, 2,3,3',4,4'-, 2',3,4,4',5- and 2,3',4,4',5-penta-, 2,3,3',4,4',5- and 2,3,3',4,4',5-hexachlorobiphenyl were also determined, and members of this group of compounds were all less toxic than 3,3',4,4',5-penta and 3,3',4,4',5,5'-hexachlorobiphenyl. There was a good rank order correlation between the in vivo QSAR data and the in vitro QSAR data and the in vitro QSARs for PCBs that were developed from their relative receptor binding affinities and potencies as inducers of AHH and EROD in rat hepatoma H-4-II E cells in culture.

  16. Blood-brain barrier permeability mechanisms in view of quantitative structure-activity relationships (QSAR).

    PubMed

    Bujak, Renata; Struck-Lewicka, Wiktoria; Kaliszan, Michał; Kaliszan, Roman; Markuszewski, Michał J

    2015-04-10

    The goal of the present paper was to develop a quantitative structure-activity relationship (QSAR) method using a simple statistical approach, such as multiple linear regression (MLR) for predicting the blood-brain barrier (BBB) permeability of chemical compounds. The "best" MLR models, comprised logP and either molecular mass (M) or isolated atomic energy (E(isol)), tested on a structurally diverse set of 66 compounds, is characterized the by correlation coefficients (R) around 0.8. The obtained models were validated using leave-one-out (LOO) cross-validation technique and the correlation coefficient of leave-one-out- R(LOO)(2) (Q(2)) was at least 0.6. Analysis of a case from legal medicine demonstrated informative value of our QSAR model. To best authors' knowledge the present study is a first application of the developed QSAR models of BBB permeability to case from the legal medicine. Our data indicate that molecular energy-related descriptors, in combination with the well-known descriptors of lipophilicity may have a supportive value in predicting blood-brain distribution, which is of utmost importance in drug development and toxicological studies.

  17. Quantitative Structure--Activity Relationship (QSAR) for the Oxidation of Trace Organic Contaminants by Sulfate Radical.

    PubMed

    Xiao, Ruiyang; Ye, Tiantian; Wei, Zongsu; Luo, Shuang; Yang, Zhihui; Spinney, Richard

    2015-11-17

    The sulfate radical anion (SO4•–) based oxidation of trace organic contaminants (TrOCs) has recently received great attention due to its high reactivity and low selectivity. In this study, a meta-analysis was conducted to better understand the role of functional groups on the reactivity between SO4•– and TrOCs. The results indicate that compounds in which electron transfer and addition channels dominate tend to exhibit a faster second-order rate constants (kSO4•–) than that of H–atom abstraction, corroborating the SO4•– reactivity and mechanisms observed in the individual studies. Then, a quantitative structure activity relationship (QSAR) model was developed using a sequential approach with constitutional, geometrical, electrostatic, and quantum chemical descriptors. Two descriptors, ELUMO and EHOMO energy gap (ELUMO–EHOMO) and the ratio of oxygen atoms to carbon atoms (#O:C), were found to mechanistically and statistically affect kSO4•– to a great extent with the standardized QSAR model: ln kSO4•– = 26.8–3.97 × #O:C – 0.746 × (ELUMO–EHOMO). In addition, the correlation analysis indicates that there is no dominant reaction channel for SO4•– reactions with various structurally diverse compounds. Our QSAR model provides a robust predictive tool for estimating emerging micropollutants removal using SO4•– during wastewater treatment processes.

  18. Structure-hepatoprotective activity relationship study of sesquiterpene lactones: A QSAR analysis

    NASA Astrophysics Data System (ADS)

    Paukku, Yuliya; Rasulev, Bakhtiyor; Syrov, Vladimir; Khushbaktova, Zainab; Leszczynski, Jerzy

    This study has been carried out using quantitative structure-activity relationship analysis (QSAR) for 22 sesquiterpene lactones to correlate and predict their hepatoprotective activity. Sesquiterpenoids, the largest class of terpenoids, are a widespread group of substances occurring in various plant organisms. QSAR analysis was carried out using methods such as genetic algorithm for variables selection among generated and calculated descriptors and multiple linear regression analysis. Quantum-chemical calculations have been performed by density functional theory at B3LYP/6-311G(d, p) level for evaluation of electronic properties using reference geometries optimized by semi-empirical AM1 approach. Three models describing hepatoprotective activity values for series of sesquiterpene lactones are proposed. The obtained models are useful for description of sesquiterpene lactones hepatoprotective activity and can be used to estimate the hepatoprotective activity of new substituted sesquiterpene lactones. The models obtained in our study show not only statistical significance, but also good predictive ability. The estimated predictive ability (rtest2) of these models lies within 0.942-0.969.

  19. Quantitative Structure--Activity Relationship (QSAR) for the Oxidation of Trace Organic Contaminants by Sulfate Radical.

    PubMed

    Xiao, Ruiyang; Ye, Tiantian; Wei, Zongsu; Luo, Shuang; Yang, Zhihui; Spinney, Richard

    2015-11-17

    The sulfate radical anion (SO4•–) based oxidation of trace organic contaminants (TrOCs) has recently received great attention due to its high reactivity and low selectivity. In this study, a meta-analysis was conducted to better understand the role of functional groups on the reactivity between SO4•– and TrOCs. The results indicate that compounds in which electron transfer and addition channels dominate tend to exhibit a faster second-order rate constants (kSO4•–) than that of H–atom abstraction, corroborating the SO4•– reactivity and mechanisms observed in the individual studies. Then, a quantitative structure activity relationship (QSAR) model was developed using a sequential approach with constitutional, geometrical, electrostatic, and quantum chemical descriptors. Two descriptors, ELUMO and EHOMO energy gap (ELUMO–EHOMO) and the ratio of oxygen atoms to carbon atoms (#O:C), were found to mechanistically and statistically affect kSO4•– to a great extent with the standardized QSAR model: ln kSO4•– = 26.8–3.97 × #O:C – 0.746 × (ELUMO–EHOMO). In addition, the correlation analysis indicates that there is no dominant reaction channel for SO4•– reactions with various structurally diverse compounds. Our QSAR model provides a robust predictive tool for estimating emerging micropollutants removal using SO4•– during wastewater treatment processes. PMID:26451961

  20. Applying quantitative structure-activity relationship (QSAR) methodology for modeling postmortem redistribution of benzodiazepines and tricyclic antidepressants.

    PubMed

    Giaginis, Constantinos; Tsantili-Kakoulidou, Anna; Theocharis, Stamatios

    2014-06-01

    Postmortem redistribution (PMR) constitutes a multifaceted process, which complicates the interpretation of drug concentrations by forensic toxicologists. The present study aimed to apply quantitative structure-activity relationship (QSAR) analysis for modeling PMR data of structurally related drugs, 10 benzodiazepines and 10 tricyclic antidepressants. For benzodiazepines, an adequate QSAR model was obtained (R(2) = 0.98, Q(2) = 0.88, RMSEE = 0.12), in which energy, ionization and molecular size exerted significant impact. For tricyclic antidepressants, an adequate QSAR model with slightly inferior statistics (R(2) = 0.95, Q(2) = 0.87, RMSEE = 0.29) was established after exclusion of maprotiline, in which energy parameters, basicity character and lipophilicity exerted significant contribution. Thus, QSAR analysis could be used as a complementary tool to provide an informative illustration of the contributing molecular, physicochemical and structural properties in PMR process. However, the complexity, non-static and time-dependent nature of PMR endpoints raises serious concerns whether QSAR methodology could predict the degree of redistribution, highlighting the need for animal-derived PMR data.

  1. Development of acute toxicity quantitative structure activity relationships (QSAR) and their use in linear alkylbenzene sulfonate species sensitivity distributions.

    PubMed

    Belanger, Scott E; Brill, Jessica L; Rawlings, Jane M; Price, Brad B

    2016-07-01

    Linear Alkylbenzene Sulfonate (LAS) is high tonnage and widely dispersed anionic surfactant used by the consumer products sector. A range of homologous structures are used in laundry applications that differ primarily on the length of the hydrophobic alkyl chain. This research summarizes the development of a set of acute toxicity QSARs (Quantitative Structure Activity Relationships) for fathead minnows (Pimephales promelas) and daphnids (Daphnia magna, Ceriodaphnia dubia) using accepted test guideline approaches. A series of studies on pure chain length LAS from C10 to C14 were used to develop the QSARs and the robustness of the QSARs was tested by evaluation of two technical mixtures of differing compositions. All QSARs were high quality (R(2) were 0.965-0.997, p < 0.0001). Toxicity normalization employing QSARs is used to interpret a broader array of tests on LAS chain length materials to a diverse group of test organisms with the objective of developing Species Sensitivity Distributions (SSDs) for various chain lengths of interest. Mixtures include environmental distributions measured from exposure monitoring surveys of wastewater effluents, various commercial mixtures, or specific chain lengths. SSD 5th percentile hazardous concentrations (HC5s) ranged from 0.129 to 0.254 mg/L for wastewater effluents containing an average of 11.26-12 alkyl carbons. The SSDs are considered highly robust given the breadth of species (n = 19), use of most sensitive endpoints from true chronic studies and the quality of the underlying statistical properties of the SSD itself. The data continue to indicate a low hazard to the environment relative to expected environmental concentrations.

  2. Development of acute toxicity quantitative structure activity relationships (QSAR) and their use in linear alkylbenzene sulfonate species sensitivity distributions.

    PubMed

    Belanger, Scott E; Brill, Jessica L; Rawlings, Jane M; Price, Brad B

    2016-07-01

    Linear Alkylbenzene Sulfonate (LAS) is high tonnage and widely dispersed anionic surfactant used by the consumer products sector. A range of homologous structures are used in laundry applications that differ primarily on the length of the hydrophobic alkyl chain. This research summarizes the development of a set of acute toxicity QSARs (Quantitative Structure Activity Relationships) for fathead minnows (Pimephales promelas) and daphnids (Daphnia magna, Ceriodaphnia dubia) using accepted test guideline approaches. A series of studies on pure chain length LAS from C10 to C14 were used to develop the QSARs and the robustness of the QSARs was tested by evaluation of two technical mixtures of differing compositions. All QSARs were high quality (R(2) were 0.965-0.997, p < 0.0001). Toxicity normalization employing QSARs is used to interpret a broader array of tests on LAS chain length materials to a diverse group of test organisms with the objective of developing Species Sensitivity Distributions (SSDs) for various chain lengths of interest. Mixtures include environmental distributions measured from exposure monitoring surveys of wastewater effluents, various commercial mixtures, or specific chain lengths. SSD 5th percentile hazardous concentrations (HC5s) ranged from 0.129 to 0.254 mg/L for wastewater effluents containing an average of 11.26-12 alkyl carbons. The SSDs are considered highly robust given the breadth of species (n = 19), use of most sensitive endpoints from true chronic studies and the quality of the underlying statistical properties of the SSD itself. The data continue to indicate a low hazard to the environment relative to expected environmental concentrations. PMID:27105149

  3. Synthesis and quantitative structure activity relationship (QSAR) of arylidene (benzimidazol-1-yl)acetohydrazones as potential antibacterial agents.

    PubMed

    El-Kilany, Yeldez; Nahas, Nariman M; Al-Ghamdi, Mariam A; Badawy, Mohamed E I; El Ashry, El Sayed H

    2015-01-01

    Ethyl (benzimidazol-1-yl)acetate was subjected to hydrazinolysis with hydrazine hydrate to give (benzimidazol-1-yl)acetohydrazide. The latter was reacted with various aromatic aldehydes to give the respective arylidene (1H-benzimidazol-1-yl)acetohydrazones. Solutions of the prepared hydrazones were found to contain two geometric isomers. Similarly (2-methyl-benzimidazol-1-yl)acetohydrazide was reacted with various aldehydes to give the corresponding hydrazones. The antibacterial activity was evaluated in vitro by minimum inhibitory concentration (MIC) against Agrobacterium tumefaciens (A. tumefaciens), Erwinia carotovora (E. carotovora), Corynebacterium fascians (C. fascians) and Pseudomonas solanacearum (P. solanacearum). MIC result demonstrated that salicylaldehyde(1H-benzimidazol-1-yl)acetohydrazone (4) was the most active compound (MIC = 20, 35, 25 and 30 mg/L against A. tumefaciens, C. fascians, E. carotovora and P. solanacearum, respectively). Quantitative structure activity relationship (QSAR) investigation using Hansch analysis was applied to find out the correlation between antibacterial activity and physicochemical properties. Various physicochemical descriptors and experimentally determined MIC values for different microorganisms were used as independent and dependent variables, respectively. pMICs of the compounds exhibited good correlation (r = 0.983, 0.914, 0.960 and 0.958 for A. tumefaciens, C. fascians, E. carotovora and P. solanacearum, respectively) with the prediction made by the model. QSAR study revealed that the hydrophobic parameter (ClogP), the aqueous solubility (LogS), calculated molar refractivity, topological polar surface area and hydrogen bond acceptor were found to have overall significant correlation with antibacterial activity. The statistical results of training set, correlation coefficient (r and r (2)), the ratio between regression and residual variances (f, Fisher's statistic), the standard error of estimates and

  4. Structure-based and multiple potential three-dimensional quantitative structure-activity relationship (SB-MP-3D-QSAR) for inhibitor design.

    PubMed

    Du, Qi-Shi; Gao, Jing; Wei, Yu-Tuo; Du, Li-Qin; Wang, Shu-Qing; Huang, Ri-Bo

    2012-04-23

    The inhibitions of enzymes (proteins) are determined by the binding interactions between ligands and targeting proteins. However, traditional QSAR (quantitative structure-activity relationship) is a one-side technique, only considering the structures and physicochemical properties of inhibitors. In this study, the structure-based and multiple potential three-dimensional quantitative structure-activity relationship (SB-MP-3D-QSAR) is presented, in which the structural information of host protein is involved in the QSAR calculations. The SB-MP-3D-QSAR actually is a combinational method of docking approach and QSAR technique. Multiple docking calculations are performed first between the host protein and ligand molecules in a training set. In the targeting protein, the functional residues are selected, which make the major contribution to the binding free energy. The binding free energy between ligand and targeting protein is the summation of multiple potential energies, including van der Waals energy, electrostatic energy, hydrophobic energy, and hydrogen-bond energy, and may include nonthermodynamic factors. In the foundational QSAR equation, two sets of weighting coefficients {aj} and {bp} are assigned to the potential energy terms and to the functional residues, respectively. The two coefficient sets are solved by using iterative double least-squares (IDLS) technique in the training set. Then, the two sets of weighting coefficients are used to predict the bioactivities of inquired ligands. In an application example, the new developed method obtained much better results than that of docking calculations.

  5. A Structure-Activity Relationship Study of Imidazole-5-Carboxylic Acid Derivatives as Angiotensin II Receptor Antagonists Combining 2D and 3D QSAR Methods.

    PubMed

    Sharma, Mukesh C

    2016-03-01

    Two-dimensional (2D) and three-dimensional (3D) quantitative structure-activity relationship (QSAR) studies were performed for correlating the chemical composition of imidazole-5-carboxylic acid analogs and their angiotensin II [Formula: see text] receptor antagonist activity using partial least squares and k-nearest neighbor, respectively. For comparing the three different feature selection methods of 2D-QSAR, k-nearest neighbor models were used in conjunction with simulated annealing (SA), genetic algorithm and stepwise coupled with partial least square (PLS) showed variation in biological activity. The statistically significant best 2D-QSAR model having good predictive ability with statistical values of [Formula: see text] and [Formula: see text] was developed by SA-partial least square with the descriptors like [Formula: see text]count, 5Chain count, SdsCHE-index, and H-acceptor count, showing that increase in the values of these descriptors is beneficial to the activity. The 3D-QSAR studies were performed using the SA-PLS. A leave-one-out cross-validated correlation coefficient [Formula: see text] and predicate activity [Formula: see text] = 0.7226 were obtained. The information rendered by QSAR models may lead to a better understanding of structural requirements of substituted imidazole-5-carboxylic acid derivatives and also aid in designing novel potent antihypertensive molecules.

  6. Semisynthesis and quantitative structure-activity relationship (QSAR) study of some cholesterol-based hydrazone derivatives as insecticidal agents.

    PubMed

    Yang, Chun; Shao, Yonghua; Zhi, Xiaoyan; Huan, Qu; Yu, Xiang; Yao, Xiaojun; Xu, Hui

    2013-09-01

    In continuation of our program aimed at the discovery and development of natural-product-based insecticidal agents, four series of novel cholesterol-based hydrazone derivatives were synthesized, and their insecticidal activity was tested against the pre-third-instar larvae of oriental armyworm, Mythimna separata (Walker) in vivo at 1mg/mL. All the derivatives showed the better insecticidal activity than their precursor cholesterol. Quantitative structure-activity relationship (QSAR) model demonstrated that six descriptors such as RDF085v, Mor06u, Mor11u, Dv, HATS0v and H-046, are likely to influence the insecticidal activity of these compounds. Among them, two important ones are the Mor06u and RDF085v.

  7. Structural development of benzhydrol-type 1'-acetoxychavicol acetate (ACA) analogs as human leukemia cell-growth inhibitors based on quantitative structure-activity relationship (QSAR) analysis.

    PubMed

    Misawa, Takashi; Aoyama, Hiroshi; Furuyama, Taniyuki; Dodo, Kosuke; Sagawa, Morihiko; Miyachi, Hiroyuki; Kizaki, Masahiro; Hashimoto, Yuichi

    2008-10-01

    Benzhydrol-type analogs of 1'-acetoxychavicol acetate (ACA) were developed as inhibitors of human leukemia HL-60 cell growth based on quantitative structure-activity relationship (QSAR) analysis. An analog containing an anthracenyl moiety (8) was a potent inhibitor with the IC(50) value of 0.12 microM.

  8. Hepatoprotection of sesquiterpenoids: a quantitative structure-activity relationship (QSAR) approach.

    PubMed

    Vinholes, Juliana; Rudnitskaya, Alisa; Gonçalves, Pedro; Martel, Fátima; Coimbra, Manuel A; Rocha, Sílvia M

    2014-03-01

    The relative hepatoprotection effect of fifteen sesquiterpenoids, commonly found in plants and plant-derived foods and beverages was assessed. Endogenous lipid peroxidation (assay A) and induced lipid peroxidation (assay B) were evaluated in liver homogenates from Wistar rats by the thiobarbituric acid reactive species test. Sesquiterpenoids with different chemical structures were tested: trans,trans-farnesol, cis-nerolidol, (-)-α-bisabolol, trans-β-farnesene, germacrene D, α-humulene, β-caryophyllene, isocaryophyllene, (+)-valencene, guaiazulene, (-)-α-cedrene, (+)-aromadendrene, (-)-α-neoclovene, (-)-α-copaene, and (+)-cyclosativene. Ascorbic acid was used as a positive antioxidant control. With the exception of α-humulene, all the sesquiterpenoids under study (1mM) were effective in reducing the malonaldehyde levels in both endogenous and induced lipid peroxidation up to 35% and 70%, respectively. The 3D-QSAR models developed, relating the hepatoprotection activity with molecular properties, showed good fit (Radj(2) 0.819 and 0.972 for the assays A and B, respectively) with good prediction power (Q(2)>0.950 and SDEP<2%, for both models A and B). A network of effects associated with structural and chemical features of sesquiterpenoids such as shape, branching, symmetry, and presence of electronegative fragments, can modulate the hepatoprotective activity observed for these compounds.

  9. Quantitative structure-activity relationships (QSAR) for nitroaromatics to fathead minnow

    SciTech Connect

    Lang, P.Z.; Lu, G.H.; Ma, X.F.

    1994-12-31

    QSAR was studied with the reported semi-flow-through 96 h-LC{sub 50} values for fathead minnow using six physicochemical descriptors. The parameters used include {sup 1}X{sup v}, {sup 1}K{alpha}, {Sigma}{sigma}-, log P, I and E{sub LUMO}. The values of I were cited from literature (Hall et al., 1989). E{sub LUMO} was used instead of the reported E{sub 1/2}. E{sub LUMO} of 51 compounds was predicted by CNDO/2. By regression, a QSAR equation was obtained: {minus}log LC{sub 50} = 3.514 + 0.416 I + 0.344 {Sigma}{sigma}- (n = 35, r = 0.965, s = 0.194). The nitroaromatics include dinitrobenzenes, trinitrobenzenes, trinitrotoluenes and some substituted benzenes containing one or two -NO{sub 2} and one of -CH{sub 3}, -Cl, -F, -OH, -NH{sub 2} or -OCH{sub 3}. The QSAR equation was used to estimate LC{sub 50} for 16 nitroaromatics. Inferred from the obtained equation, of the compounds studied here, the dinitro compounds (their I and {Sigma}{sigma}- are all high) are reactive and the mononitro compounds are less reactive or non-reactive.

  10. A quantitative structure activity relationships (QSAR) analysis of triarylmethane dye tracers

    NASA Astrophysics Data System (ADS)

    Mon, Jarai; Flury, Markus; Harsh, James B.

    2006-01-01

    Dyes are important hydrological tracers. Many different dyes have been proposed as optimal tracers, but none of these dyes can be considered an ideal water tracer. Some dyes are toxic and most sorb to subsurface materials. The objective of this study was to find the molecular structure of an optimal water tracer. We used QSAR to screen a large number of hypothetical molecules, belonging to the class of triarylmethane dyes, in regard to their sorption characteristics to a sandy soil. The QSAR model was based on experimental sorption data obtained from four triarylmethane dyes: C.I. Food Blue 2 (C.I. 42090; Brilliant Blue FCF), C.I. Food Green 3 (C.I. 42053; FD&C Green No. 3), C.I. Acid Blue 7 (C.I. 42080; ORCOacid Blue A 150%), and C.I. Acid Green 9 (C.I. 42100; ORCOacid Fast Green B). Sorption characteristics of the dyes to the sandy soil were expressed with the Langmuir isotherm. Our premise was that dye sorption can be reduced by attachment of sulfonic acid (SO 3) groups to the triarylmethane template. About 70 hypothetical dyes were created and QSAR were used to estimate sorption characteristics. The results indicated that both the position and the number of SO 3 groups affected dye sorption. Sorption decreased with increasing number of SO 3 groups attached to the molecule. Increasing the number of sulfonic acid groups also decreases the toxicity of the compounds. An optimal triarylmethane water tracer contains 4 to 6 SO 3 groups.

  11. A review of quantitative structure activity relationships (QSARs) for assessing the ecotoxicity of phthalate esters

    SciTech Connect

    Parkerton, T.F.

    1995-12-31

    Dialkyl phthalate esters represent an important class of high production volume, industrial chemicals spanning a wide range of chemical properties. Over the last two decades, numerous studies have been conducted to characterize the ecotoxicity of phthalate esters. The purpose of this presentation is to provide a synthesis of the available ecotoxicity literature using a QSAR paradigm. Results from this analysis provide several important insights. First, a mechanistic explanation is provided to account for the general lack of ecotoxicity observed for higher molecular weight phthalates possessing alkyl chains of six or more carbons. Second, studies that appear as outliers are identified due to either experimental artifacts (e.g., physical effects on daphnids due to testing at concentrations exceeding water solubility) or questionable experimental methods (e.g., toxicity tests based on nominal concentrations). Lastly, differences in ecotoxicity between species appear to be due, in part, to differences in test organisms biotransformation capacities. The utility of adopting a QSAR-based approach for risk assessment will be discussed.

  12. Quantitative structure-activity relationships (QSAR) of some 2,2-diphenyl propionate (DPP) derivatives of muscarinic antagonists

    SciTech Connect

    Gordon, R.K.; Breuer, E.; Padilla, F.N.; Chiang, P.K.

    1987-05-01

    QSAR between biological activities and molecular-chemical properties were investigated to aid in designing more effective and potent antimuscarinic pharmacophores. A molecular modeling program was used to calculate geometrical and topological values of a series of DPP pharmacophores. The newly synthesized pharmacophores were tested for their antagonist activities by: (1) inhibition of (N-methyl-/sup 3/H)scopolamine binding assay to the muscarinic receptors of N4TG1 neuroblastoma cells; (2) blocking of acetylcholine-induced contraction of guinea pig ileum; and (3) inhibition of carbachol-induced ..cap alpha..-amylase release from rat pancreas. The differences in the log of these biological activities were directly and significantly related to the distances between the carbonyl oxygen of the DPP and the quaternary nitrogen of the modified pharmacophores. The biological activities, while depending on each particular assay, varied between three and four logs of activity. The charge remained the same in all the pharmacophores. There were no QSAR correlations between molecular volume, molecular connectivity, or principle moments and their antagonistic activities, although multivariate QSAR was not employed. Thus, based on distance geometry, potent muscarinic pharmacophores can be predicted.

  13. Docking-based three-dimensional quantitative structure-activity relationship (3D-QSAR) predicts binding affinities to aryl hydrocarbon receptor for polychlorinated dibenzodioxins, dibenzofurans, and biphenyls.

    PubMed

    Yuan, Jintao; Pu, Yuepu; Yin, Lihong

    2013-07-01

    Polychlorinated dibenzodioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), and polychlorinated biphenyls (PCBs) cause toxic effects after binding to an intracellular cytosolic receptor called the aryl hydrocarbon receptor (AhR). Thymic atrophy, weight loss, immunotoxicity, acute lethality, and induction of cytochrome P4501A1 have all been correlated with the binding affinity to AhR. To study the key molecular features for determining binding affinity to AhR, a homology model of AhR ligand-binding domains was developed, a molecular docking approach was employed to obtain docking-based conformations of all molecules in the whole set, and 3-dimensional quantitative structure-activity relationship (3D-QSAR) methodology, namely, comparative molecular field analysis (CoMFA), was applied. A partial least square analysis was performed, and QSAR models were generated for a training set of 59 compounds. The generated QSAR model showed good internal and external statistical reliability, and in a comparison with other reported CoMFA models using different alignment methods, the docking-based CoMFA model showed some advantages.

  14. Utilization of quantitative structure-activity relationships (QSARs) in risk assessment: Alkylphenols

    SciTech Connect

    Beck, B.D.; Toole, A.P.; Callahan, B.G.; Siddhanti, S.K. )

    1991-12-01

    Alkylphenols are a class of environmentally pervasive compounds, found both in natural (e.g., crude oils) and in anthropogenic (e.g., wood tar, coal gasification waste) materials. Despite the frequent environmental occurrence of these chemicals, there is a limited toxicity database on alkylphenols. The authors have therefore developed a 'toxicity equivalence approach' for alkylphenols which is based on their ability to inhibit, in a specific manner, the enzyme cyclooxygenase. Enzyme-inhibiting ability for individual alkylphenols can be estimated based on the quantitative structure-activity relationship developed by Dewhirst (1980) and is a function of the free hydroxyl group, electron-donating ring substituents, and hydrophobic aromatic ring substituents. The authors evaluated the toxicological significance of cyclooxygenase inhibition by comparison of the inhibitory capacity of alkylphenols with the inhibitory capacity of acetylsalicylic acid, or aspirin, a compound whose low-level effects are due to cyclooxygenase inhibition. Since nearly complete absorption for alkylphenols and aspirin is predicted, based on estimates of hydrophobicity and fraction of charged molecules at gastrointestinal pHs, risks from alkylphenols can be expressed directly in terms of 'milligram aspirin equivalence,' without correction for absorption differences. They recommend this method for assessing risks of mixtures of alkylphenols, especially for those compounds with no chronic toxicity data.38 references.

  15. QSAR models for anti-malarial activity of 4-aminoquinolines.

    PubMed

    Masand, Vijay H; Toropov, Andrey A; Toropova, Alla P; Mahajan, Devidas T

    2014-03-01

    In the present study, predictive quantitative structure - activity relationship (QSAR) models for anti-malarial activity of 4-aminoquinolines have been developed. CORAL, which is freely available on internet (http://www.insilico.eu/coral), has been used as a tool of QSAR analysis to establish statistically robust QSAR model of anti-malarial activity of 4-aminoquinolines. Six random splits into the visible sub-system of the training and invisible subsystem of validation were examined. Statistical qualities for these splits vary, but in all these cases, statistical quality of prediction for anti-malarial activity was quite good. The optimal SMILES-based descriptor was used to derive the single descriptor based QSAR model for a data set of 112 aminoquinolones. All the splits had r(2)> 0.85 and r(2)> 0.78 for subtraining and validation sets, respectively. The three parametric multilinear regression (MLR) QSAR model has Q(2) = 0.83, R(2) = 0.84 and F = 190.39. The anti-malarial activity has strong correlation with presence/absence of nitrogen and oxygen at a topological distance of six. PMID:24801104

  16. Synthesis and quantitative structure-activity relationship (QSAR) analysis of some novel oxadiazolo[3,4-d]pyrimidine nucleosides derivatives as antiviral agents.

    PubMed

    Xu, Xiaojuan; Wang, Jun; Yao, Qizheng

    2015-01-15

    We have synthesized a series of 4H,6H-[1,2,5]oxadiazolo[3,4-d]pyrimidine-5,7-dione 1-oxide nucleoside and their anti-vesicular stomatitis virus (VSV) activities in Wish cell were also investigated in vitro. It was found that most compounds showed obvious anti-VSV activities and compound 9 with ribofuranoside improved the anti-VSV activity by approximately 10 times and 18 times compared to didanosine (DDI) and acyclovir, respectively. A quantitative structure-activity relationship (QSAR) study of these compounds as well as previous reported oxadiazolo[3,4-d]pyrimidine nucleoside derivatives indicated that compounds with high activity should have small values of logP(o/w), vsurf_G and a large logS value. These findings and results provide a base for further investigations.

  17. Oxidation of substituted phenols in the environment: A QSAR analysis of rate constants for reaction with singlet oxygen. [Quantitative Structure-Activity Relationship

    SciTech Connect

    Tratnyek, P.G.; Holgne, J. , Duebendorf )

    1991-09-01

    Substituted phenols can be oxidized by singlet oxygen ({sup 1}O{sub 2}), which is formed in sunlit surface waters, and it has been suggested that this reaction may contribute to the environmental fate of phenolic substances. In aqueous solution, the observed rate of phenol disappearance is due to reaction of both the phenolate anion and the undissociated phenol. In order to quantify the effect of substituents on the rates of these reactions, second-order rate constants have been measured for both species for 22 substituted phenols by use of a model system containing the sensitizer rose bengal. Correlation analysis based on half-wave oxidation potentials, E{sub 1/2}, and on {sigma} constants reveals significant quantitative structure-activity relationships (QSARs) for both the undissociated phenols and the phenolate anions. Ortho- and multisubstituted phenols have been included in the correlations. These QSARs are consistent with the rate-limiting formation of a precursor complex with a small amount of charge-transfer character and can be used to predict additional rate constants for a wide range of environmentally significant substituted phenols.

  18. DEVELOPMENT OF QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS (QSARS) TO PREDICT TOXICITY FOR A VARIETY OF HUMAN AND ECOLOGICAL ENDPOINTS

    EPA Science Inventory

    In general, the accuracy of a predicted toxicity value increases with increase in similarity between the query chemical and the chemicals used to develop a QSAR model. A toxicity estimation methodology employing this finding has been developed. A hierarchical based clustering t...

  19. Synthesis and quantitative structure-activity relationship (QSAR) study of novel 4-acyloxypodophyllotoxin derivatives modified in the A and C rings as insecticidal agents.

    PubMed

    He, Shuzhen; Shao, Yonghua; Fan, Lingling; Che, Zhiping; Xu, Hui; Zhi, Xiaoyan; Wang, Juanjuan; Yao, Xiaojun; Qu, Huan

    2013-01-23

    In continuation of our program aimed at the discovery and development of natural-product-based insecticidal agents, we have synthesized three series of novel 4-acyloxy compounds derived from podophyllotoxin modified in the A and C rings, which is isolated as the main secondary metabolite from the roots and rhizomes of Podophyllum hexandrum . Their insecticidal activity was preliminarily evaluated against the pre-third-instar larvae of Mythimna separata in vivo. Compound 9g displayed the best promising insecticidal activity. It revealed that cleavage of the 6,7-methylenedioxy group of podophyllotoxin will lead to a less active compound and that the C-4 position of podophyllotoxin was the important modification location. A quantitative structure-activity relationship (QSAR) model was developed by genetic algorithm combined with multiple linear regression (GA-MLR). For this model, the squared correlation coefficient (R(2)) is 0.914, the leave-one-out cross-validation correlation coefficient (Q(2)(LOO)) is 0.881, and the root-mean-square error (RMSE) is 0.024. Five descriptors, BEHm2, Mor14v, Wap, G1v, and RDF020e, are likely to influence the biological activity of these compounds. Among them, two important ones are BEHm2 and Mor14v. This study will pave the way for further design, structural modification, and development of podophyllotoxin derivatives as insecticidal agents.

  20. Drug interaction study of natural steroids from herbs specifically toward human UDP-glucuronosyltransferase (UGT) 1A4 and their quantitative structure activity relationship (QSAR) analysis for prediction.

    PubMed

    Xu, Min; Dong, Peipei; Tian, Xiangge; Wang, Chao; Huo, Xiaokui; Zhang, Baojing; Wu, Lijun; Deng, Sa; Ma, Xiaochi

    2016-08-01

    The wide application of herbal medicines and foods containing steroids has resulted in the high risk of herb-drug interactions (HDIs). The present study aims to evaluate the inhibition potential of 43 natural steroids from herb medicines toward human UDP- glucuronosyltransferases (UGTs). A remarkable structure-dependent inhibition toward UGT1A4 was observed in vitro. Some natural steroids such as gitogenin, tigogenin, and solasodine were found to be the novel selective inhibitors of UGT1A4, and did not inhibit the activities of major human CYP isoforms. To clarify the possibility of the in vivo interaction of common steroids and clinical drugs, the kinetic inhibition type and related kinetic parameters (Ki) were measured. The target compounds 2-6 and 15, competitively inhibited the UGT1A4-catalyzed trifluoperazine glucuronidation reaction, with Ki values of 0.6, 0.18, 1.1, 0.7, 0.8, and 12.3μM, respectively. And this inhibition of steroids towards UGT1A4 was also verified in human primary hepatocytes. Furthermore, a quantitative structure-activity relationship (QSAR) of steroids with inhibitory effects toward human UGT1A4 isoform was established using the computational methods. Our findings elucidate the potential for in vivo HDI effects of steroids in herbal medicine and foods, with the clinical dr ugs eliminated by UGT1A4, and reveal the vital pharamcophoric requirement of natural steroids for UGT1A4 inhibition activity. PMID:27208893

  1. Drug interaction study of natural steroids from herbs specifically toward human UDP-glucuronosyltransferase (UGT) 1A4 and their quantitative structure activity relationship (QSAR) analysis for prediction.

    PubMed

    Xu, Min; Dong, Peipei; Tian, Xiangge; Wang, Chao; Huo, Xiaokui; Zhang, Baojing; Wu, Lijun; Deng, Sa; Ma, Xiaochi

    2016-08-01

    The wide application of herbal medicines and foods containing steroids has resulted in the high risk of herb-drug interactions (HDIs). The present study aims to evaluate the inhibition potential of 43 natural steroids from herb medicines toward human UDP- glucuronosyltransferases (UGTs). A remarkable structure-dependent inhibition toward UGT1A4 was observed in vitro. Some natural steroids such as gitogenin, tigogenin, and solasodine were found to be the novel selective inhibitors of UGT1A4, and did not inhibit the activities of major human CYP isoforms. To clarify the possibility of the in vivo interaction of common steroids and clinical drugs, the kinetic inhibition type and related kinetic parameters (Ki) were measured. The target compounds 2-6 and 15, competitively inhibited the UGT1A4-catalyzed trifluoperazine glucuronidation reaction, with Ki values of 0.6, 0.18, 1.1, 0.7, 0.8, and 12.3μM, respectively. And this inhibition of steroids towards UGT1A4 was also verified in human primary hepatocytes. Furthermore, a quantitative structure-activity relationship (QSAR) of steroids with inhibitory effects toward human UGT1A4 isoform was established using the computational methods. Our findings elucidate the potential for in vivo HDI effects of steroids in herbal medicine and foods, with the clinical dr ugs eliminated by UGT1A4, and reveal the vital pharamcophoric requirement of natural steroids for UGT1A4 inhibition activity.

  2. Quantitative structure-activity relationship (QSAR) prediction of (eco)toxicity of short aliphatic protic ionic liquids.

    PubMed

    Peric, Brezana; Sierra, Jordi; Martí, Esther; Cruañas, Robert; Garau, Maria Antonia

    2015-05-01

    Ionic liquids (ILs) are considered as a group of very promising compounds due to their excellent properties (practical non-volatility, high thermal stability and very good and diverse solving capacity). The ILs have a good prospect of replacing traditional organic solvents in vast variety of applications. However, the complete information on their environmental impact is still not available. There is also an enormous number of possible combinations of anions and cations which can form ILs, the fact that requires a method allowing the prediction of toxicity of existing and potential ILs. In this study, a group contribution QSAR model has been used in order to predict the (eco)toxicity of protic and aprotic ILs for five tests (Microtox®, Pseudokirchneriella subcapitata and Lemna minor growth inhibition test, and Acetylcholinestherase inhibition and Cell viability assay with IPC-81 cells). The predicted and experimental toxicity are well correlated. A prediction of EC50 for these (eco)toxicity tests has also been made for eight representatives of the new family of short aliphatic protic ILs, whose toxicity has not been determined experimentally to date. The QSAR model applied in this study can allow the selection of potentially less toxic ILs amongst the existing ones (e.g. in the case of aprotic ILs), but it can also be very helpful in directing the synthesis efforts toward developing new "greener" ILs respectful with the environment (e.g. short aliphatic protic ILs). PMID:25728357

  3. Quantitative structure-activity relationship (QSAR) prediction of (eco)toxicity of short aliphatic protic ionic liquids.

    PubMed

    Peric, Brezana; Sierra, Jordi; Martí, Esther; Cruañas, Robert; Garau, Maria Antonia

    2015-05-01

    Ionic liquids (ILs) are considered as a group of very promising compounds due to their excellent properties (practical non-volatility, high thermal stability and very good and diverse solving capacity). The ILs have a good prospect of replacing traditional organic solvents in vast variety of applications. However, the complete information on their environmental impact is still not available. There is also an enormous number of possible combinations of anions and cations which can form ILs, the fact that requires a method allowing the prediction of toxicity of existing and potential ILs. In this study, a group contribution QSAR model has been used in order to predict the (eco)toxicity of protic and aprotic ILs for five tests (Microtox®, Pseudokirchneriella subcapitata and Lemna minor growth inhibition test, and Acetylcholinestherase inhibition and Cell viability assay with IPC-81 cells). The predicted and experimental toxicity are well correlated. A prediction of EC50 for these (eco)toxicity tests has also been made for eight representatives of the new family of short aliphatic protic ILs, whose toxicity has not been determined experimentally to date. The QSAR model applied in this study can allow the selection of potentially less toxic ILs amongst the existing ones (e.g. in the case of aprotic ILs), but it can also be very helpful in directing the synthesis efforts toward developing new "greener" ILs respectful with the environment (e.g. short aliphatic protic ILs).

  4. MIA-QSAR: a simple 2D image-based approach for quantitative structure activity relationship analysis

    NASA Astrophysics Data System (ADS)

    Freitas, Matheus P.; Brown, Steven D.; Martins, José A.

    2005-03-01

    An accessible and quite simple QSAR method, based on 2D image analysis, is reported. A case study is carried out in order to compare this model with a previously reported sophisticated methodology. A well known set of ( S)- N-[(1-ethyl-2-pyrrolidinyl)methyl]-6-methoxybenzamides, compounds with affinity to the dopamine D 2 receptor subtype, was divided in 40 calibration compounds and 18 test compounds and the descriptors were generated from pixels of 2D structures of each compound, which can be drawn with aid of any appropriate program. Bilinear (conventional) PLS was utilized as the regression method and leave-one-out cross-validation was performed using the NIPALS algorithm. The good predicted Q2 value obtained for the series of test compounds (0.58), together with the similar prediction quality obtained to other data sets (nAChR ligands, HIV protease inhibitors, COX-2 inhibitors and anxiolytic agents), suggests that the model is robust and seems to be as applicable as more complex methods.

  5. Synthesis and quantitative structure-activity relationship (QSAR) study of novel N-arylsulfonyl-3-acylindole arylcarbonyl hydrazone derivatives as nematicidal agents.

    PubMed

    Che, Zhiping; Zhang, Shaoyong; Shao, Yonghua; Fan, Lingling; Xu, Hui; Yu, Xiang; Zhi, Xiaoyan; Yao, Xiaojun; Zhang, Rui

    2013-06-19

    In continuation of our program aimed at the discovery and development of natural-product-based pesticidal agents, 54 novel N-arylsulfonyl-3-acylindole arylcarbonyl hydrazone derivatives were prepared, and their structures were well characterized by ¹H NMR, ¹³C NMR, HRMS, ESI-MS, and mp. Their nematicidal activity was evaluated against that of the pine wood nematode, Bursaphelenchus xylophilus in vivo. Among all of the derivatives, especially V-12 and V-39 displayed the best promising nematicidal activity with LC₅₀ values of 1.0969 and 1.2632 mg/L, respectively. This suggested that introduction of R¹ and R² together as the electron-withdrawing substituents, R³ as the methyl group, and R⁴ as the phenyl with the electron-donating substituents could be taken into account for further preparation of these kinds of compounds as nematicidal agents. Six selected descriptors are a WHIM descriptor (E1m), two GETAWAY descriptors (R1m+ and R3m+), a Burden eigenvalues descriptor (BEHm8), and two edge-adjacency index descriptors (EEig05x and EEig13d). Quantitative structure-activity relationship (QSAR) studies demonstrated that the structural factors, such as molecular mass (a negative correlation with the bioactivity) and molecular polarity (a positive correlation with bioactivity), are likely to govern the nematicidal activities of these compounds. For this model, the correlation coefficient (R²(training set)), the leave-one-out cross-validation correlation coefficient (Q²(LOO)), and the 7-fold cross-validation correlation coefficient (Q²(7-fold)) were 0.791, 0.701, and 0.715, respectively. The external cross-validation correlation coefficient (Q²ext) and the root-mean-square error for the test set (RMSE(test set)) were 0.774 and 3.412, respectively. This study will pave the way for future design, structural modification, and development of indole derivatives as nematicidal agents.

  6. SAR and QSAR of the antioxidant activity of flavonoids.

    PubMed

    Amić, Dragan; Davidović-Amić, Dusanka; Beslo, Drago; Rastija, Vesna; Lucić, Bono; Trinajstić, Nenad

    2007-01-01

    Flavonoids are a group of naturally occurring phytochemicals abundantly present in fruits, vegetables, and beverages such as wine and tea. In the past two decades, flavonoids have gained enormous interest because of their beneficial health effects such as anti-inflammatory, cardio-protective and anticancer activities. These findings have contributed to the dramatic increase in the consumption and use of dietary supplements containing high concentrations of plant flavonoids. The pharmacological effect of flavonoids is mainly due to their antioxidant activity and their inhibition of certain enzymes. In spite of abundant data, structural requirements and mechanisms underlying these effects have not been fully understood. This review presents the current knowledge about structure-activity relationships (SARs) and quantitative structure-activity relationships (QSARs) of the antioxidant activity of flavonoids. SAR and QSAR can provide useful tools for revealing the nature of flavonoid antioxidant action. They may also help in the design of new and efficient flavonoids, which could be used as potential therapeutic agents.

  7. Hologram QSAR studies of antiprotozoal activities of sesquiterpene lactones.

    PubMed

    Trossini, Gustavo H G; Maltarollo, Vinícius G; Schmidt, Thomas J

    2014-01-01

    Infectious diseases such as trypanosomiasis and leishmaniasis are considered neglected tropical diseases due the lack for many years of research and development into new drug treatments besides the high incidence of mortality and the lack of current safe and effective drug therapies. Natural products such as sesquiterpene lactones have shown activity against T. brucei and L. donovani, the parasites responsible for these neglected diseases. To evaluate structure activity relationships, HQSAR models were constructed to relate a series of 40 sesquiterpene lactones (STLs) with activity against T. brucei, T. cruzi, L. donovani and P. falciparum and also with their cytotoxicity. All constructed models showed good internal (leave-one-out q2 values ranging from 0.637 to 0.775) and external validation coefficients (r2test values ranging from 0.653 to 0.944). From HQSAR contribution maps, several differences between the most and least potent compounds were found. The fragment contribution of PLS-generated models confirmed the results of previous QSAR studies that the presence of α,β-unsatured carbonyl groups is fundamental to biological activity. QSAR models for the activity of these compounds against T. cruzi, L. donovani and P. falciparum are reported here for the first time. The constructed HQSAR models are suitable to predict the activity of untested STLs.

  8. Synthesis, antifungal activity, and QSAR study of novel trichodermin derivatives.

    PubMed

    Cheng, Jing-Li; Zheng, Min; Yao, Ting-Ting; Li, Xiao-Liang; Zhao, Jin-Hao; Xia, Min; Zhu, Guo-Nian

    2015-01-01

    In an attempt to discover more potential antifungal agents, in this study, 21 novel trichodermin derivatives containing conjugated oxime ester (5a-5u) were designed and synthesized and were screened for in vitro antifungal activity. The bioassay tests showed that some of them exhibited good inhibitory activity against the tested pathogenic fungi. Compound 5a exhibited better activity against Pyricularia oryzae and Sclerotonia sclerotiorum than trichodermin, and compound 5j showed particular activity against P.oryzae and Botrytis cinerea. The quantitative structure-activity relationship (QSAR) indicated that log P and hardness were two critical parameters for the biological activities. The result suggested that these would be potential lead compounds for the development of fungicides with further structure modification. PMID:25290081

  9. A quantitative structure-activity relationship (QSAR) study on a few series of potent, highly selective inhibitors of nitric oxide synthase.

    PubMed

    Bharti, Vishwa Deepak; Gupta, Satya P; Kumar, Harish

    2014-02-01

    QSAR study was performed on a series of 1,2-dihydro-4-quinazolinamines, 4,5-dialkylsubstituted-2-imino-1,3-thiazolidine derivatives and 4,5-disubstituted-1,3-oxazolidin-2-imine derivatives studied by Tinker et al. [J Med Chem (2003), 46, 913-916], Ueda et al. [Bioorg Med Chem (2004) 12, 4101-4116] and Ueda et al. [Bioorg Med Chem Lett (2004) 14, 313-316], respectively, as potent, highly selective inhibitors of inducible nitric oxide synthase (iNOS). The iNOS inhibition activity of the whole series of compounds was analyzed in relation to the physicochemical and molecular properties of the compounds. The QSAR analysis revealed that the inhibition potency of the compounds was controlled by a topological parameter 1chi(v) (Kier's first order valence molecular connectivity index), density (D), surface tension (St) and length (steric parameter) of a substituent. This suggested that the drug-receptor interaction predominantly involved the dispersion interaction, but the bulky molecule would face steric problem because of which the molecule may not completely fit in active sites of the receptor and thus may not have the optimum interaction.

  10. Interspecies quantitative structure-activity relationships (QSARs) for eco-toxicity screening of chemicals: the role of physicochemical properties.

    PubMed

    Furuhama, A; Hasunuma, K; Aoki, Y

    2015-01-01

    In addition to molecular structure profiles, descriptors based on physicochemical properties are useful for explaining the eco-toxicities of chemicals. In a previous study we reported that a criterion based on the difference between the partition coefficient (log POW) and distribution coefficient (log D) values of chemicals enabled us to identify aromatic amines and phenols for which interspecies relationships with strong correlations could be developed for fish-daphnid and algal-daphnid toxicities. The chemicals that met the log D-based criterion were expected to have similar toxicity mechanisms (related to membrane penetration). Here, we investigated the applicability of log D-based criteria to the eco-toxicity of other kinds of chemicals, including aliphatic compounds. At pH 10, use of a log POW - log D > 0 criterion and omission of outliers resulted in the selection of more than 100 chemicals whose acute fish toxicities or algal growth inhibition toxicities were almost equal to their acute daphnid toxicities. The advantage of log D-based criteria is that they allow for simple, rapid screening and prioritizing of chemicals. However, inorganic molecules and chemicals containing certain structural elements cannot be evaluated, because calculated log D values are unavailable.

  11. Virulence Factor-activity Relationships: Workshop Summary

    EPA Science Inventory

    The concept or notion of virulence factor–activity relationships (VFAR) is an approach for identifying an analogous process to the use of qualitative structure–activity relationships (QSAR) for identifying new microbial contaminants. In QSAR, it is hypothesized that, for new chem...

  12. Adaptive Neuro-Fuzzy Inference System Applied QSAR with Quantum Chemical Descriptors for Predicting Radical Scavenging Activities of Carotenoids.

    PubMed

    Jhin, Changho; Hwang, Keum Taek

    2015-01-01

    One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models. PMID:26474167

  13. Adaptive Neuro-Fuzzy Inference System Applied QSAR with Quantum Chemical Descriptors for Predicting Radical Scavenging Activities of Carotenoids

    PubMed Central

    Jhin, Changho; Hwang, Keum Taek

    2015-01-01

    One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models. PMID:26474167

  14. Combinatorial QSAR Modeling of Rat Acute Toxicity by Oral Exposure

    EPA Science Inventory

    Quantitative Structure-Activity Relationship (QSAR) toxicity models have become popular tools for identifying potential toxic compounds and prioritizing candidates for animal toxicity tests. However, few QSAR studies have successfully modeled large, diverse mammalian toxicity end...

  15. A review of QSAR studies to discover new drug-like compounds actives against leishmaniasis and trypanosomiasis.

    PubMed

    Castillo-Garit, Juan Alberto; Abad, Concepción; Rodríguez-Borges, J Enrique; Marrero-Ponce, Yovani; Torrens, Francisco

    2012-01-01

    The neglected tropical diseases (NTDs) affect more than one billion people (one-sixth of the world's population) and occur primarily in undeveloped countries in sub-Saharan Africa, Asia, and Latin America. Available drugs for these diseases are decades old and present an important number of limitations, especially high toxicity and, more recently, the emergence of drug resistance. In the last decade several Quantitative Structure-Activity Relationship (QSAR) studies have been developed in order to identify new organic compounds with activity against the parasites responsible for these diseases, which are reviewed in this paper. The topics summarized in this work are: 1) QSAR studies to identify new organic compounds actives against Chaga's disease; 2) Development of QSAR studies to discover new antileishmanial drusg; 3) Computational studies to identify new drug-like compounds against human African trypanosomiasis. Each topic include the general characteristics, epidemiology and chemotherapy of the disease as well as the main QSAR approaches to discovery/identification of new actives compounds for the corresponding neglected disease. The last section is devoted to a new approach know as multi-target QSAR models developed for antiparasitic drugs specifically those actives against trypanosomatid parasites. At present, as a result of these QSAR studies several promising compounds, active against these parasites, are been indentify. However, more efforts will be required in the future to develop more selective (specific) useful drugs.

  16. Antibacterial Activity of Imidazolium-Based Ionic Liquids Investigated by QSAR Modeling and Experimental Studies.

    PubMed

    Hodyna, Diana; Kovalishyn, Vasyl; Rogalsky, Sergiy; Blagodatnyi, Volodymyr; Petko, Kirill; Metelytsia, Larisa

    2016-09-01

    Predictive QSAR models for the inhibitors of B. subtilis and Ps. aeruginosa among imidazolium-based ionic liquids were developed using literary data. The regression QSAR models were created through Artificial Neural Network and k-nearest neighbor procedures. The classification QSAR models were constructed using WEKA-RF (random forest) method. The predictive ability of the models was tested by fivefold cross-validation; giving q(2) = 0.77-0.92 for regression models and accuracy 83-88% for classification models. Twenty synthesized samples of 1,3-dialkylimidazolium ionic liquids with predictive value of activity level of antimicrobial potential were evaluated. For all asymmetric 1,3-dialkylimidazolium ionic liquids, only compounds containing at least one radical with alkyl chain length of 12 carbon atoms showed high antibacterial activity. However, the activity of symmetric 1,3-dialkylimidazolium salts was found to have opposite relationship with the length of aliphatic radical being maximum for compounds based on 1,3-dioctylimidazolium cation. The obtained experimental results suggested that the application of classification QSAR models is more accurate for the prediction of activity of new imidazolium-based ILs as potential antibacterials. PMID:27086199

  17. Quantitative Structure--Activity Relationship Modeling of Rat Acute Toxicity by Oral Exposure

    EPA Science Inventory

    Background: Few Quantitative Structure-Activity Relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity endpoints. Objective: In this study, a combinatorial QSAR approach has been employed for the creation of robust and predictive models of acute toxi...

  18. QSAR and docking studies on xanthone derivatives for anticancer activity targeting DNA topoisomerase IIα

    PubMed Central

    Alam, Sarfaraz; Khan, Feroz

    2014-01-01

    Due to the high mortality rate in India, the identification of novel molecules is important in the development of novel and potent anticancer drugs. Xanthones are natural constituents of plants in the families Bonnetiaceae and Clusiaceae, and comprise oxygenated heterocycles with a variety of biological activities along with an anticancer effect. To explore the anticancer compounds from xanthone derivatives, a quantitative structure activity relationship (QSAR) model was developed by the multiple linear regression method. The structure–activity relationship represented by the QSAR model yielded a high activity–descriptors relationship accuracy (84%) referred by regression coefficient (r2=0.84) and a high activity prediction accuracy (82%). Five molecular descriptors – dielectric energy, group count (hydroxyl), LogP (the logarithm of the partition coefficient between n-octanol and water), shape index basic (order 3), and the solvent-accessible surface area – were significantly correlated with anticancer activity. Using this QSAR model, a set of virtually designed xanthone derivatives was screened out. A molecular docking study was also carried out to predict the molecular interaction between proposed compounds and deoxyribonucleic acid (DNA) topoisomerase IIα. The pharmacokinetics parameters, such as absorption, distribution, metabolism, excretion, and toxicity, were also calculated, and later an appraisal of synthetic accessibility of organic compounds was carried out. The strategy used in this study may provide understanding in designing novel DNA topoisomerase IIα inhibitors, as well as for other cancer targets. PMID:24516330

  19. Uncertainty in QSAR predictions.

    PubMed

    Sahlin, Ullrika

    2013-03-01

    It is relevant to consider uncertainty in individual predictions when quantitative structure-activity (or property) relationships (QSARs) are used to support decisions of high societal concern. Successful communication of uncertainty in the integration of QSARs in chemical safety assessment under the EU Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) system can be facilitated by a common understanding of how to define, characterise, assess and evaluate uncertainty in QSAR predictions. A QSAR prediction is, compared to experimental estimates, subject to added uncertainty that comes from the use of a model instead of empirically-based estimates. A framework is provided to aid the distinction between different types of uncertainty in a QSAR prediction: quantitative, i.e. for regressions related to the error in a prediction and characterised by a predictive distribution; and qualitative, by expressing our confidence in the model for predicting a particular compound based on a quantitative measure of predictive reliability. It is possible to assess a quantitative (i.e. probabilistic) predictive distribution, given the supervised learning algorithm, the underlying QSAR data, a probability model for uncertainty and a statistical principle for inference. The integration of QSARs into risk assessment may be facilitated by the inclusion of the assessment of predictive error and predictive reliability into the "unambiguous algorithm", as outlined in the second OECD principle.

  20. Daphnia and fish toxicity of (benzo)triazoles: validated QSAR models, and interspecies quantitative activity-activity modelling.

    PubMed

    Cassani, Stefano; Kovarich, Simona; Papa, Ester; Roy, Partha Pratim; van der Wal, Leon; Gramatica, Paola

    2013-08-15

    Due to their chemical properties synthetic triazoles and benzo-triazoles ((B)TAZs) are mainly distributed to the water compartments in the environment, and because of their wide use the potential effects on aquatic organisms are cause of concern. Non testing approaches like those based on quantitative structure-activity relationships (QSARs) are valuable tools to maximize the information contained in existing experimental data and predict missing information while minimizing animal testing. In the present study, externally validated QSAR models for the prediction of acute (B)TAZs toxicity in Daphnia magna and Oncorhynchus mykiss have been developed according to the principles for the validation of QSARs and their acceptability for regulatory purposes, proposed by the Organization for Economic Co-operation and Development (OECD). These models are based on theoretical molecular descriptors, and are statistically robust, externally predictive and characterized by a verifiable structural applicability domain. They have been applied to predict acute toxicity for over 300 (B)TAZs without experimental data, many of which are in the pre-registration list of the REACH regulation. Additionally, a model based on quantitative activity-activity relationships (QAAR) has been developed, which allows for interspecies extrapolation from daphnids to fish. The importance of QSAR/QAAR, especially when dealing with specific chemical classes like (B)TAZs, for screening and prioritization of pollutants under REACH, has been highlighted.

  1. 3D QSAR investigations on locomotor activity of 5-cyano-N1,6-disubstituted 2-thiouracil derivatives.

    PubMed

    Kuchekar, B S; Pore, Y V

    2010-06-01

    Three dimensional quantitative structure activity relationship (3D QSAR) investigations were carried out on a series of 5-cyano-N1,6-disubstituted 2-thiouracil derivatives for their locomotor activity. The structures of all compounds were built on a workspace of VlifeMDS3.5 molecular modeling software and 3D QSAR models were generated by applying a partial least square (PLS) linear regression analysis coupled with a stepwise variable selection method. Both derived models were found to be statistically significant in terms of regression and internal and external predictive ability (r(2) = 0.9414 and 0.8511, q(2) = 0.8582 and 0.6222, pred_r(2) = 0.5142 and 0.7917). The QSAR models indicated that both electrostatic and steric interaction energies were contributing significantly to locomotor activity of thiouracil derivatives. PMID:22491179

  2. Study on the activity of non-purine xanthine oxidase inhibitor by 3D-QSAR modeling and molecular docking

    NASA Astrophysics Data System (ADS)

    Li, Peizhen; Tian, Yueli; Zhai, Honglin; Deng, Fangfang; Xie, Meihong; Zhang, Xiaoyun

    2013-11-01

    Non-purine derivatives have been shown to be promising novel drug candidates as xanthine oxidase inhibitors. Based on three-dimensional quantitative structure-activity relationship (3D-QSAR) methods including comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), two 3D-QSAR models for a series of non-purine xanthine oxidase (XO) inhibitors were established, and their reliability was supported by statistical parameters. Combined 3D-QSAR modeling and the results of molecular docking between non-purine xanthine oxidase inhibitors and XO, the main factors that influenced activity of inhibitors were investigated, and the obtained results could explain known experimental facts. Furthermore, several new potential inhibitors with higher activity predicted were designed, which based on our analyses, and were supported by the simulation of molecular docking. This study provided some useful information for the development of non-purine xanthine oxidase inhibitors with novel structures.

  3. Ligand Biological Activity Predictions Using Fingerprint-Based Artificial Neural Networks (FANN-QSAR)

    PubMed Central

    Myint, Kyaw Z.; Xie, Xiang-Qun

    2015-01-01

    This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In addition, the FANN-QSAR model was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds. We discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research. PMID:25502380

  4. QSAR classification models for the prediction of endocrine disrupting activity of brominated flame retardants.

    PubMed

    Kovarich, Simona; Papa, Ester; Gramatica, Paola

    2011-06-15

    The identification of potential endocrine disrupting (ED) chemicals is an important task for the scientific community due to their diffusion in the environment; the production and use of such compounds will be strictly regulated through the authorization process of the REACH regulation. To overcome the problem of insufficient experimental data, the quantitative structure-activity relationship (QSAR) approach is applied to predict the ED activity of new chemicals. In the present study QSAR classification models are developed, according to the OECD principles, to predict the ED potency for a class of emerging ubiquitary pollutants, viz. brominated flame retardants (BFRs). Different endpoints related to ED activity (i.e. aryl hydrocarbon receptor agonism and antagonism, estrogen receptor agonism and antagonism, androgen and progesterone receptor antagonism, T4-TTR competition, E2SULT inhibition) are modeled using the k-NN classification method. The best models are selected by maximizing the sensitivity and external predictive ability. We propose simple QSARs (based on few descriptors) characterized by internal stability, good predictive power and with a verified applicability domain. These models are simple tools that are applicable to screen BFRs in relation to their ED activity, and also to design safer alternatives, in agreement with the requirements of REACH regulation at the authorization step.

  5. Determinants of activity at human Toll-like receptors 7 and 8: quantitative structure-activity relationship (QSAR) of diverse heterocyclic scaffolds.

    PubMed

    Yoo, Euna; Salunke, Deepak B; Sil, Diptesh; Guo, Xiaoqiang; Salyer, Alex C D; Hermanson, Alec R; Kumar, Manoj; Malladi, Subbalakshmi S; Balakrishna, Rajalakshmi; Thompson, Ward H; Tanji, Hiromi; Ohto, Umeharu; Shimizu, Toshiyuki; David, Sunil A

    2014-10-01

    Toll-like receptor (TLR) 7 and 8 agonists are potential vaccine adjuvants, since they directly activate APCs and enhance Th1-driven immune responses. Previous SAR investigations in several scaffolds of small molecule TLR7/8 activators pointed to the strict dependence of the selectivity for TLR7 vis-à-vis TLR8 on the electronic configurations of the heterocyclic systems, which we sought to examine quantitatively with the goal of developing "heuristics" to define structural requisites governing activity at TLR7 and/or TLR8. We undertook a scaffold-hopping approach, entailing the syntheses and biological evaluations of 13 different chemotypes. Crystal structures of TLR8 in complex with the two most active compounds confirmed important binding interactions playing a key role in ligand occupancy and biological activity. Density functional theory based quantum chemical calculations on these compounds followed by linear discriminant analyses permitted the classification of inactive, TLR8-active, and TLR7/8 dual-active compounds, confirming the critical role of partial charges in determining biological activity.

  6. Determinants of Activity at Human Toll-like Receptors 7 and 8: Quantitative Structure–Activity Relationship (QSAR) of Diverse Heterocyclic Scaffolds

    PubMed Central

    2015-01-01

    Toll-like receptor (TLR) 7 and 8 agonists are potential vaccine adjuvants, since they directly activate APCs and enhance Th1-driven immune responses. Previous SAR investigations in several scaffolds of small molecule TLR7/8 activators pointed to the strict dependence of the selectivity for TLR7 vis-à-vis TLR8 on the electronic configurations of the heterocyclic systems, which we sought to examine quantitatively with the goal of developing “heuristics” to define structural requisites governing activity at TLR7 and/or TLR8. We undertook a scaffold-hopping approach, entailing the syntheses and biological evaluations of 13 different chemotypes. Crystal structures of TLR8 in complex with the two most active compounds confirmed important binding interactions playing a key role in ligand occupancy and biological activity. Density functional theory based quantum chemical calculations on these compounds followed by linear discriminant analyses permitted the classification of inactive, TLR8-active, and TLR7/8 dual-active compounds, confirming the critical role of partial charges in determining biological activity. PMID:25192394

  7. Molecular determinants of ligand binding modes in the histamine H(4) receptor: linking ligand-based three-dimensional quantitative structure-activity relationship (3D-QSAR) models to in silico guided receptor mutagenesis studies.

    PubMed

    Istyastono, Enade P; Nijmeijer, Saskia; Lim, Herman D; van de Stolpe, Andrea; Roumen, Luc; Kooistra, Albert J; Vischer, Henry F; de Esch, Iwan J P; Leurs, Rob; de Graaf, Chris

    2011-12-01

    The histamine H(4) receptor (H(4)R) is a G protein-coupled receptor (GPCR) that plays an important role in inflammation. Similar to the homologous histamine H(3) receptor (H(3)R), two acidic residues in the H(4)R binding pocket, D(3.32) and E(5.46), act as essential hydrogen bond acceptors of positively ionizable hydrogen bond donors in H(4)R ligands. Given the symmetric distribution of these complementary pharmacophore features in H(4)R and its ligands, different alternative ligand binding mode hypotheses have been proposed. The current study focuses on the elucidation of the molecular determinants of H(4)R-ligand binding modes by combining (3D) quantitative structure-activity relationship (QSAR), protein homology modeling, molecular dynamics simulations, and site-directed mutagenesis studies. We have designed and synthesized a series of clobenpropit (N-(4-chlorobenzyl)-S-[3-(4(5)-imidazolyl)propyl]isothiourea) derivatives to investigate H(4)R-ligand interactions and ligand binding orientations. Interestingly, our studies indicate that clobenpropit (2) itself can bind to H(4)R in two distinct binding modes, while the addition of a cyclohexyl group to the clobenpropit isothiourea moiety allows VUF5228 (5) to adopt only one specific binding mode in the H(4)R binding pocket. Our ligand-steered, experimentally supported protein modeling method gives new insights into ligand recognition by H(4)R and can be used as a general approach to elucidate the structure of protein-ligand complexes.

  8. QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni.

    PubMed

    Melo-Filho, Cleber C; Dantas, Rafael F; Braga, Rodolpho C; Neves, Bruno J; Senger, Mario R; Valente, Walter C G; Rezende-Neto, João M; Chaves, Willian T; Muratov, Eugene N; Paveley, Ross A; Furnham, Nicholas; Kamentsky, Lee; Carpenter, Anne E; Silva-Junior, Floriano P; Andrade, Carolina H

    2016-07-25

    Schistosomiasis is a neglected tropical disease that affects millions of people worldwide. Thioredoxin glutathione reductase of Schistosoma mansoni (SmTGR) is a validated drug target that plays a crucial role in the redox homeostasis of the parasite. We report the discovery of new chemical scaffolds against S. mansoni using a combi-QSAR approach followed by virtual screening of a commercial database and confirmation of top ranking compounds by in vitro experimental evaluation with automated imaging of schistosomula and adult worms. We constructed 2D and 3D quantitative structure-activity relationship (QSAR) models using a series of oxadiazoles-2-oxides reported in the literature as SmTGR inhibitors and combined the best models in a consensus QSAR model. This model was used for a virtual screening of Hit2Lead set of ChemBridge database and allowed the identification of ten new potential SmTGR inhibitors. Further experimental testing on both shistosomula and adult worms showed that 4-nitro-3,5-bis(1-nitro-1H-pyrazol-4-yl)-1H-pyrazole (LabMol-17) and 3-nitro-4-{[(4-nitro-1,2,5-oxadiazol-3-yl)oxy]methyl}-1,2,5-oxadiazole (LabMol-19), two compounds representing new chemical scaffolds, have high activity in both systems. These compounds will be the subjects for additional testing and, if necessary, modification to serve as new schistosomicidal agents. PMID:27253773

  9. 3D-QSAR Study of 7,8-Dialkyl-1,3-diaminopyrrolo-[3,2-f] Quinazolines with Anticancer Activity as DHFR Inhibitors

    NASA Astrophysics Data System (ADS)

    Chen, Jin-can; Chen, Lan-mei; Liao, Si-yan; Qian, Li; Zheng, Kang-cheng

    2009-06-01

    A three-dimensional quantitative structure-activity relationship (3D-QSAR) study of a series of 7,8-dialkyl-1,3-diaminopyrrolo-[3,2-f] quinazolines with anticancer activity as dihydrofolate reductase (DHFR) inhibitors was carried out by using the comparative molecular field analysis (CoMFA), on the basis of our reported 2D-QSAR of these compounds. The established 3D-QSAR model has good quality of statistics and good prediction ability; the non cross-validation correlation coefficient and the cross-validation value of this model are 0.993 and 0.619, respectively, the F value is 193.4, and the standard deviation SD is 0.208. This model indicates that the steric field factor plays a much more important role than the electrostatic one, in satisfying agreement with the published 2D-QSAR model. However, the 3D-QSAR model offers visual images of the steric field and the electrostatic field. The 3D-QSAR study further suggests the following: to improve the activity, the substituent R' should be selected to be a group with an adaptive bulk like Et or i-Pr, and the substituent R should be selected to be a larger alkyl. In particular, based on our present 3D-QSAR as well as the published 2D-QSAR, the experimentally-proposed hydrophobic binding mechanism on the receptor-binding site of the DHFR can be further explained in theory. Therefore, the QSAR studies help to further understand the “hydrophobic binding" action mechanism of this kind of compounds, and to direct the molecular design of new drugs with higher activity.

  10. A new computer program for QSAR-analysis: ARTE-QSAR.

    PubMed

    Van Damme, Sofie; Bultinck, Patrick

    2007-08-01

    A new computer program has been designed to build and analyze quantitative-structure activity relationship (QSAR) models through regression analysis. The user is provided with a range of regression and validation techniques. The emphasis of the program lies mainly in the validation of QSAR models in chemical applications. ARTE-QSAR produces an easy interpretable output from which the user can conclude if the obtained model is suitable for prediction and analysis. PMID:17394240

  11. A new computer program for QSAR-analysis: ARTE-QSAR.

    PubMed

    Van Damme, Sofie; Bultinck, Patrick

    2007-08-01

    A new computer program has been designed to build and analyze quantitative-structure activity relationship (QSAR) models through regression analysis. The user is provided with a range of regression and validation techniques. The emphasis of the program lies mainly in the validation of QSAR models in chemical applications. ARTE-QSAR produces an easy interpretable output from which the user can conclude if the obtained model is suitable for prediction and analysis.

  12. QSAR study of anti-prion activity of 2-aminothiazoles

    PubMed Central

    Mandi, Prasit; Nantasenamat, Chanin; Srungboonmee, Kakanand; Isarankura-Na-Ayudhya, Chartchalerm; Prachayasittikul, Virapong

    2012-01-01

    2-aminothiazoles is a class of compounds capable of treating life-threatening prion diseases. QSAR studies on a set of forty-seven 2-aminothiazole derivatives possessing anti-prion activity were performed using multivariate analysis, which comprised of multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM). The results indicated that MLR afforded reasonable performance with a correlation coefficient (r) and root mean squared error (RMSE) of 0.9073 and 0.2977, respectively, as obtained from leave-one-out cross-validation (LOO-CV). More sophisticated learning methods such as SVM provided models with the highest accuracy with r and RMSE of 0.9471 and 0.2264, respectively, while ANN gave reasonable performance with r and RMSE of 0.9023 and 0.3043, respectively, as obtained LOO-CV calculations. Descriptor analysis from the regression coefficients of the MLR model suggested that compounds should be asymmetrical molecule with low propensity to form hydrogen bonds and high frequency of N content at topological distance 02 in order to provide good activities. Insights from QSAR studies is anticipated to be useful in the design of novel derivatives based on the 2-aminothiazole scaffold as potent therapeutic agents against prion diseases. PMID:27418919

  13. QSAR and Docking Studies on Capsazepine Derivatives for Immunomodulatory and Anti-Inflammatory Activity

    PubMed Central

    Shukla, Aparna; Sharma, Pooja; Prakash, Om; Singh, Monika; Kalani, Komal; Khan, Feroz; Bawankule, Dnyaneshwar Umrao; Luqman, Suaib; Srivastava, Santosh Kumar

    2014-01-01

    Capsazepine, an antagonist of capsaicin, is discovered by the structure and activity relationship. In previous studies it has been found that capsazepine has potency for immunomodulation and anti-inflammatory activity and emerging as a favourable target in quest for efficacious and safe anti-inflammatory drug. Thus, a 2D quantitative structural activity relationship (QSAR) model against target tumor necrosis factor-α (TNF-α) was developed using multiple linear regression method (MLR) with good internal prediction (r2 = 0.8779) and external prediction (r2pred = 0.5865) using Discovery Studio v3.5 (Accelrys, USA). The predicted activity was further validated by in vitro experiment. Capsazepine was tested in lipopolysaccharide (LPS) induced inflammation in peritoneal mouse macrophages. Anti-inflammatory profile of capsazepine was assessed by its potency to inhibit the production of inflammatory mediator TNF-α. The in vitro experiment indicated that capsazepine is an efficient anti-inflammatory agent. Since, the developed QSAR model showed significant correlations between chemical structure and anti-inflammatory activity, it was successfully applied in the screening of forty-four virtual derivatives of capsazepine, which finally afforded six potent derivatives, CPZ-29, CPZ-30, CPZ-33, CPZ-34, CPZ-35 and CPZ-36. To gain more insights into the molecular mechanism of action of capsazepine and its derivatives, molecular docking and in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) studies were performed. The results of QSAR, molecular docking, in silico ADMET screening and in vitro experimental studies provide guideline and mechanistic scope for the identification of more potent anti-inflammatory & immunomodulatory drug. PMID:25003344

  14. Comparison of Different 2D and 3D-QSAR Methods on Activity Prediction of Histamine H3 Receptor Antagonists

    PubMed Central

    Dastmalchi, Siavoush; Hamzeh-Mivehroud, Maryam; Asadpour-Zeynali, Karim

    2012-01-01

    Histamine H3 receptor subtype has been the target of several recent drug development programs. Quantitative structure-activity relationship (QSAR) methods are used to predict the pharmaceutically relevant properties of drug candidates whenever it is applicable. The aim of this study was to compare the predictive powers of three different QSAR techniques, namely, multiple linear regression (MLR), artificial neural network (ANN), and HASL as a 3D QSAR method, in predicting the receptor binding affinities of arylbenzofuran histamine H3 receptor antagonists. Genetic algorithm coupled partial least square as well as stepwise multiple regression methods were used to select a number of calculated molecular descriptors to be used in MLR and ANN-based QSAR studies. Using the leave-group-out cross-validation technique, the performances of the MLR and ANN methods were evaluated. The calculated values for the mean absolute percentage error (MAPE), ranging from 2.9 to 3.6, and standard deviation of error of prediction (SDEP), ranging from 0.31 to 0.36, for both MLR and ANN methods were statistically comparable, indicating that both methods perform equally well in predicting the binding affinities of the studied compounds toward the H3 receptors. On the other hand, the results from 3D-QSAR studies using HASL method were not as good as those obtained by 2D methods. It can be concluded that simple traditional approaches such as MLR method can be as reliable as those of more advanced and sophisticated methods like ANN and 3D-QSAR analyses. PMID:25317190

  15. QSAR for cholinesterase inhibition by organophosphorus esters and CNDO/2 calculations for organophosphorus ester hydrolysis. [quantitative structure-activity relationship, complete neglect of differential overlap

    NASA Technical Reports Server (NTRS)

    Johnson, H.; Kenley, R. A.; Rynard, C.; Golub, M. A.

    1985-01-01

    Quantitative structure-activity relationships were derived for acetyl- and butyrylcholinesterase inhibition by various organophosphorus esters. Bimolecular inhibition rate constants correlate well with hydrophobic substituent constants, and with the presence or absence of cationic groups on the inhibitor, but not with steric substituent constants. CNDO/2 calculations were performed on a separate set of organophosphorus esters, RR-primeP(O)X, where R and R-prime are alkyl and/or alkoxy groups and X is fluorine, chlorine or a phenoxy group. For each subset with the same X, the CNDO-derived net atomic charge at the central phosphorus atom in the ester correlates well with the alkaline hydrolysis rate constant. For the whole set of esters with different X, two equations were derived that relate either charge and leaving group steric bulk, or orbital energy and bond order to the hydrolysis rate constant.

  16. QSAR and 3D QSAR of inhibitors of the epidermal growth factor receptor

    NASA Astrophysics Data System (ADS)

    Pinto-Bazurco, Mariano; Tsakovska, Ivanka; Pajeva, Ilza

    This article reports quantitative structure-activity relationships (QSAR) and 3D QSAR models of 134 structurally diverse inhibitors of the epidermal growth factor receptor (EGFR) tyrosine kinase. Free-Wilson analysis was used to derive the QSAR model. It identified the substituents in aniline, the polycyclic system, and the substituents at the 6- and 7-positions of the polycyclic system as the most important structural features. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used in the 3D QSAR modeling. The steric and electrostatic interactions proved the most important for the inhibitory effect. Both QSAR and 3D QSAR models led to consistent results. On the basis of the statistically significant models, new structures were proposed and their inhibitory activities were predicted.

  17. QSAR prediction of estrogen activity for a large set of diverse chemicals under the guidance of OECD principles.

    PubMed

    Liu, Huanxiang; Papa, Ester; Gramatica, Paola

    2006-11-01

    A large number of environmental chemicals, known as endocrine-disrupting chemicals, are suspected of disrupting endocrine functions by mimicking or antagonizing natural hormones, and such chemicals may pose a serious threat to the health of humans and wildlife. They are thought to act through a variety of mechanisms, mainly estrogen-receptor-mediated mechanisms of toxicity. However, it is practically impossible to perform thorough toxicological tests on all potential xenoestrogens, and thus, the quantitative structure--activity relationship (QSAR) provides a promising method for the estimation of a compound's estrogenic activity. Here, QSAR models of the estrogen receptor binding affinity of a large data set of heterogeneous chemicals have been built using theoretical molecular descriptors, giving full consideration to the new OECD principles in regulation for QSAR acceptability, during model construction and assessment. An unambiguous multiple linear regression (MLR) algorithm was used to build the models, and model predictive ability was validated by both internal and external validation. The applicability domain was checked by the leverage approach to verify prediction reliability. The results obtained using several validation paths indicate that the proposed QSAR model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogen activity of organic compounds.

  18. QSAR prediction of HIV-1 protease inhibitory activities using docking derived molecular descriptors.

    PubMed

    Fatemi, Mohammad H; Heidari, Afsane; Gharaghani, Sajjad

    2015-03-21

    In this study, application of a new hybrid docking-quantitative structure activity relationship (QSAR) methodology to model and predict the HIV-1 protease inhibitory activities of a series of newly synthesized chemicals is reported. This hybrid docking-QSAR approach can provide valuable information about the most important chemical and structural features of the ligands that affect their inhibitory activities. Docking studies were used to find the actual conformations of chemicals in active site of HIV-1 protease. Then the molecular descriptors were calculated from these conformations. Multiple linear regression (MLR) and least square support vector machine (LS-SVM) were used as QSAR models, respectively. The obtained results reveal that statistical parameters of the LS-SVM model are better than the MLR model, which indicate that there are some non-linear relations between selected molecular descriptors and anti-HIV activities of interested chemicals. The correlation coefficient (R), root mean square error (RMSE) and average absolute error (AAE) for LS-SVM are: R=0.988, RMSE=0.207 and AAE=0.145 for the training set, and R=0.965, RMSE=0.403 and AAE=0.338 for the test set. Leave one out cross validation test was used for assessment of the predictive power and validity of models which led to cross-validation correlation coefficient QUOTE of 0.864 and 0.850 and standardized predicted relative error sum of squares (SPRESS) of 0.553 and 0.581 for LS-SVM and MLR models, respectively.

  19. Use of QSARs in international decision-making frameworks to predict health effects of chemical substances.

    PubMed Central

    Cronin, Mark T D; Jaworska, Joanna S; Walker, John D; Comber, Michael H I; Watts, Christopher D; Worth, Andrew P

    2003-01-01

    This article is a review of the use of quantitative (and qualitative) structure-activity relationships (QSARs and SARs) by regulatory agencies and authorities to predict acute toxicity, mutagenicity, carcinogenicity, and other health effects. A number of SAR and QSAR applications, by regulatory agencies and authorities, are reviewed. These include the use of simple QSAR analyses, as well as the use of multivariate QSARs, and a number of different expert system approaches. PMID:12896862

  20. p38 Mitogen-activated protein kinase inhibitors: a review on pharmacophore mapping and QSAR studies.

    PubMed

    Gangwal, Rahul P; Bhadauriya, Anuseema; Damre, Mangesh V; Dhoke, Gaurao V; Sangamwar, Abhay T

    2013-01-01

    p38 mitogen-activated protein (MAP) kinases are the serine/threonine protein kinases, which play a vital role in cellular responses to external stress signals. p38 MAP kinase inhibitors have shown anti-inflammatory effects in the preclinical disease models, primarily through inhibition of the expression of inflammatory mediators. A number of structurally diverse p38 MAP kinase inhibitors have been developed as potential anti-inflammatory agents. Most of the inhibitors have failed in the clinical trials either due to poor pharmacokinetic profile or selectivity issue, which makes p38 MAP kinase a promising target for molecular modelling studies. Several quantitative structure activity relationships (QSAR) and pharmacophore models have been developed to identify the structural requirements essential for p38 MAP kinase inhibitory activity. In this review, we provide an overview of the presently known p38 MAP kinase inhibitors and how QSAR analyses among series of compounds have led to the development of molecular models and pharmacophores, allowing the design of novel inhibitors.

  1. A QSAR study of radical scavenging antioxidant activity of a series of flavonoids using DFT based quantum chemical descriptors--the importance of group frontier electron density.

    PubMed

    Sarkar, Ananda; Middya, Tapas Ranjan; Jana, Atish Dipnakar

    2012-06-01

    In a pursuit of electronic level understanding of the antioxidant activity of a series of flavonoids, quantitative structure-activity relationship (QSAR) studies have been carried out using density functional theory (DFT) based quantum chemical descriptors. The best QSAR model have been selected for which the computed square correlation coefficient r(2) = 0.937 and cross-validated squared correlation coefficient q(2) =0.916. The QSAR model indicates that hardness (η), group electrophilic frontier electron density (F(E)(A)) and group philicity (ω(B)(+)) of individual molecules are responsible for in vitro biological activity. To the best our knowledge, the group electrophilic frontier electron density (F(E)(A)) has been used for the first time to explain the radical scavenging activity (RSA) of flavonoids. The excellent correlation between the RSA and the above mentioned DFT based descriptors lead us to predict new antioxidants having very good antioxidant activity.

  2. Multi-Target QSAR Approaches for Modeling Protein Inhibitors. Simultaneous Prediction of Activities Against Biomacromolecules Present in Gram-Negative Bacteria.

    PubMed

    Speck-Planche, Alejandro; Cordeiro, M N D S

    2015-01-01

    Drug discovery is aimed at finding therapeutic agents for the treatment of many diverse diseases and infections. However, this is a very slow an expensive process, and for this reason, in silico approaches are needed to rationalize the search for new molecular entities with desired biological profiles. Models focused on quantitative structure-activity relationships (QSAR) have constituted useful complementary tools in medicinal chemistry, allowing the virtual predictions of dissimilar pharmacological activities of compounds. In the last 10 years, multi-target (mt) QSAR models have been reported, representing great advances with respect to those models generated from classical approaches. Thus, mt- QSAR models can simultaneously predict activities against different biological targets (proteins, microorganisms, cell lines, etc.) by using large and heterogeneous datasets of chemicals. The present review is devoted to discuss the most promising mt-QSAR models, particularly those developed for the prediction of protein inhibitors. We also report the first multi-tasking QSAR (mtk-QSAR) model for simultaneous prediction of inhibitors against biomacromolecules (specifically proteins) present in Gram-negative bacteria. This model allowed us to consider both different proteins and multiple experimental conditions under which the inhibitory activities of the chemicals were determined. The mtk-QSAR model exhibited accuracies higher than 98% in both training and prediction sets, also displaying a very good performance in the classification of active and inactive cases that depended on the specific elements of the experimental conditions. The physicochemical interpretations of the molecular descriptors were also analyzed, providing important insights regarding the molecular patterns associated with the appearance/enhancement of the inhibitory potency. PMID:25961517

  3. Profile-QSAR: a novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity.

    PubMed

    Martin, Eric; Mukherjee, Prasenjit; Sullivan, David; Jansen, Johanna

    2011-08-22

    Profile-QSAR is a novel 2D predictive model building method for kinases. This "meta-QSAR" method models the activity of each compound against a new kinase target as a linear combination of its predicted activities against a large panel of 92 previously studied kinases comprised from 115 assays. Profile-QSAR starts with a sparse incomplete kinase by compound (KxC) activity matrix, used to generate Bayesian QSAR models for the 92 "basis-set" kinases. These Bayesian QSARs generate a complete "synthetic" KxC activity matrix of predictions. These synthetic activities are used as "chemical descriptors" to train partial-least squares (PLS) models, from modest amounts of medium-throughput screening data, for predicting activity against new kinases. The Profile-QSAR predictions for the 92 kinases (115 assays) gave a median external R²(ext) = 0.59 on 25% held-out test sets. The method has proven accurate enough to predict pairwise kinase selectivities with a median correlation of R²(ext) = 0.61 for 958 kinase pairs with at least 600 common compounds. It has been further expanded by adding a "C(k)XC" cellular activity matrix to the KxC matrix to predict cellular activity for 42 kinase driven cellular assays with median R²(ext) = 0.58 for 24 target modulation assays and R²(ext) = 0.41 for 18 cell proliferation assays. The 2D Profile-QSAR, along with the 3D Surrogate AutoShim, are the foundations of an internally developed iterative medium-throughput screening (IMTS) methodology for virtual screening (VS) of compound archives as an alternative to experimental high-throughput screening (HTS). The method has been applied to 20 actual prospective kinase projects. Biological results have so far been obtained in eight of them. Q² values ranged from 0.3 to 0.7. Hit-rates at 10 uM for experimentally tested compounds varied from 25% to 80%, except in K5, which was a special case aimed specifically at finding "type II" binders, where none of the compounds were predicted to be

  4. Optimization of antiproliferative activity of substituted phenyl 4-(2-oxoimidazolidin-1-yl) benzenesulfonates: QSAR and CoMFA analyses.

    PubMed

    Masand, Vijay H; Mahajan, Devidas T; Alafeefy, Ahmed M; Bukhari, Syed Nasir Abbas; Elsayed, Nahed N

    2015-09-18

    Multiple separate quantitative structure-activity relationships (QSARs) models were built for the antiproliferative activity of substituted Phenyl 4-(2-Oxoimidazolidin-1-yl)-benzenesulfonates (PIB-SOs). A variety of descriptors were considered for PIB-SOs through QSAR model building. Genetic algorithm (GA), available in QSARINS, was employed to select optimum number and set of descriptors to build the multi-linear regression equations for a dataset of PIB-SOs. The best three parametric models were subjected to thorough internal and external validation along with Y-randomization using QSARINS, according to the OECD principles for QSAR model validation. The models were found to be statistically robust with high external predictivity. The best three parametric model, based on steric, 3D- and finger print descriptors, was found to have R(2)=0.91, R(2)ex=0.89, and CCCex=0.94. The CoMFA model, which is based on a combination of steric and electrostatic effects and graphically inferred using contour plots, gave F=229.34, R(2)CV=0.71 and R(2)=0.94. Steric repulsion, frequency of occurrence of carbon and nitrogen at topological distance of seven, and internal electronic environment of the molecule were found to have correlation with the anti-tumor activity of PIB-SOs. PMID:26066412

  5. QSAR Models for Regulatory Purposes: Experiences and Perspectives

    NASA Astrophysics Data System (ADS)

    Benfenati, Emilio

    Quantitative structure-activity relationships (QSARs) are more and more discussed and used in several situations. Their application to legislative purposes stimulated a large debate in Europe on the recent legislation on industrial chemicals. To correctly assess the suitability of QSAR, the discussion has to be done depending on the target. Different targets modify the model evaluation and use. The application of QSAR for legislative purposes requires keeping into account the use of the values obtained through the QSAR models. False negatives should be minimized. The model should be robust, verified, and validated. Reproducibility and transparency are other important characteristics.

  6. QSAR in Chromatography: Quantitative Structure-Retention Relationships (QSRRs)

    NASA Astrophysics Data System (ADS)

    Kaliszan, Roman; Bączek, Tomasz

    To predict a given physicochemical or biological property, the relationships can be identified between the chemical structure and the desired property. Ideally these relationships should be described in reliable quantitative terms. To obtain statistically significant relationships, one needs relatively large series of property parameters. Chromatography is a unique method which can provide a great amount of quantitatively precise, reproducible, and comparable retention data for large sets of structurally diversified compounds (analytes). On the other hand, chemometrics is recognized as a valuable tool for accomplishing a variety of tasks in a chromatography laboratory. Chemometrics facilitates the interpretation of large sets of complex chromatographic and structural data. Among various chemometric methods, multiple regression analysis is most often performed to process retention data and to extract chemical information on analytes. And the methodology of quantitative structure-(chromatographic) retention relationships (QSRRs) is mainly based on multiple regression analysis. QSRR can be a valuable source of knowledge on both the nature of analytes and of the macromolecules forming the stationary phases. Therefore, quantitative structure-retention relationships have been considered as a model approach to establish strategy and methods of property predictions.

  7. Probing the origins of aromatase inhibitory activity of disubstituted coumarins via QSAR and molecular docking

    PubMed Central

    Worachartcheewan, Apilak; Suvannang, Naravut; Prachayasittikul, Supaluk; Prachayasittikul, Virapong; Nantasenamat, Chanin

    2014-01-01

    This study investigated the quantitative structure-activity relationship (QSAR) of imidazole derivatives of 4,7-disubstituted coumarins as inhibitors of aromatase, a potential therapeutic protein target for the treatment of breast cancer. Herein, a series of 3,7- and 4,7-disubstituted coumarin derivatives (1-34) with R1 and R2 substituents bearing aromatase inhibitory activity were modeled as a function of molecular and quantum chemical descriptors derived from low-energy conformer geometrically optimized at B3LYP/6-31G(d) level of theory. Insights on origins of aromatase inhibitory activity was afforded by the computed set of 7 descriptors comprising of F10[N-O], Inflammat-50, Psychotic-80, H-047, BELe1, B10[C-O] and MAXDP. Such significant descriptors were used for QSAR model construction and results indicated that model 4 afforded the best statistical performance. Good predictive performance were achieved as verified from the internal (comprising the training and the leave-one-out cross-validation (LOO-CV) sets) and external sets affording the following statistical parameters: R2Tr = 0.9576 and RMSETr = 0.0958 for the training set; Q2CV = 0.9239 and RMSECV = 0.1304 for the LOO-CV set as well as Q2Ext = 0.7268 and RMSEExt = 0.2927 for the external set. Significant descriptors showed correlation with functional substituents, particularly, R1 in governing high potency as aromatase inhibitor. Molecular docking calculations suggest that key residues interacting with the coumarins were predominantly lipophilic or non-polar while a few were polar and positively-charged. Findings illuminated herein serve as the impetus that can be used to rationally guide the design of new aromatase inhibitors. PMID:26417339

  8. Quantitative structure-activity relationships for chemical toxicity to environmental bacteria

    SciTech Connect

    Blum, D.J.; Speece, R.E. )

    1991-10-01

    Quantitative structure-activity relationships (QSARs) were developed for nonreactive chemical toxicity to each of four groups of bacteria of importance in environmental engineering: aerobic heterotrophs, methanogens, Nitrosomonas, and Microtox. The QSARs were based on chemicals covering a range of structures and including important environmental pollutants (i.e., chlorinated and other substituted benzenes, phenols, and aliphatic hydrocarbons). QSARs were developed for each chemical class and for combinations of chemical classes. Three QSAR methods (groups of chemical describing parameters) were evaluated for their accuracy and ease of use: log P, linear solvation energy relationships (LSER), and molecular connectivity. Successful QSARs were found for each group of bacteria and by each method, with correlation coefficients (adjusted r2) between 0.79 and 0.95. LSER QSARs incorporated the widest range of chemicals with the greatest accuracy. Log P and molecular connectivity QSARs are easier to use because their parameters are readily available. Outliers from the QSARs likely due to reactive toxicity included acryls, low pKa compounds, and aldehydes. Nitro compounds and chlorinated aliphatic hydrocarbons and alcohols showed enhanced toxicity to the methanogens only. Chemicals with low IC50 concentrations (log IC50 mumol/liter less than 1.5) were often outliers for Nitrosomonas. QSARs were validated statistically and with literature data. A suggested method is provided for use of the QSARs.

  9. QSAR study on the antibacterial activity of some sulfa drugs: building blockers of Mannich bases.

    PubMed

    Mandloi, Dheeraj; Joshi, Sheela; Khadikar, Padmakar V; Khosla, Navita

    2005-01-17

    Sulfa drugs are building blockers of several types of Mannich bases. Consequently, the antibacterial activities of sulfa drugs are reported in this paper, which will help in explaining and understanding antibacterial activities of Mannich bases. Reported QSAR is carried out using distance-based topological indices and discussed critically on the basis of statistical parameters.

  10. QSAR analysis of antitumor activities of 3,4-ethylenedioxythiphene derivatives

    NASA Astrophysics Data System (ADS)

    Rastija, Vesna; Bajić, Miroslav; Stolić, Ivana; Krstulović, Luka; Jukić, Marijana; Glavaš-Obrovac, Ljubica

    2015-12-01

    QSAR analysis was performed for the antitumor activity of 27 derivatives of 3,4-ethylenedioxythiophene against six carcinoma cell lines. The best models were obtained with surface area (SAG) in combination with lipohilicity (log P) as descriptors. Results have shown that molecules with smaller solvent accessible surface area and higher lipophilicy should have higher biological activity against carcinoma cell.

  11. QSAR study and conformational analysis of 4-arylthiazolylhydrazones derived from 1-indanones with anti-Trypanosoma cruzi activity.

    PubMed

    Noguera, Guido J; Fabian, Lucas E; Lombardo, Elisa; Finkielsztein, Liliana

    2015-10-12

    A set of 4-arylthiazolylhydrazones derived from 1-indanones (TZHs) previously synthesized and assayed against Trypanosoma cruzi, the causative agent of Chagas disease, were explored in terms of conformational analysis. We found that TZHs can adopt four minimum energy conformations: cis (A, B and C) and trans. The possible bioactive conformation was selected by a 3D-QSAR model. Different molecular parameters were calculated to produce QSAR second-generation models. These QSAR results are discussed in conjunction with conformational analysis from molecular modeling studies. The main factor to determine the activity of the compounds was the partial charge at the N(3) atom (qN3). The predictive ability of the QSAR equations proposed was experimentally validated. The QSAR models developed in this study will be helpful to design novel potent TZHs.

  12. QSAR Modeling: Where have you been? Where are you going to?

    PubMed Central

    Cherkasov, Artem; Muratov, Eugene N.; Fourches, Denis; Varnek, Alexandre; Baskin, Igor I.; Cronin, Mark; Dearden, John; Gramatica, Paola; Martin, Yvonne C.; Todeschini, Roberto; Consonni, Viviana; Kuz'min, Victor E.; Cramer, Richard; Benigni, Romualdo; Yang, Chihae; Rathman, James; Terfloth, Lothar; Gasteiger, Johann; Richard, Ann; Tropsha, Alexander

    2014-01-01

    Quantitative Structure-Activity Relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss: (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists towards collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making. PMID:24351051

  13. QSAR modeling: where have you been? Where are you going to?

    PubMed

    Cherkasov, Artem; Muratov, Eugene N; Fourches, Denis; Varnek, Alexandre; Baskin, Igor I; Cronin, Mark; Dearden, John; Gramatica, Paola; Martin, Yvonne C; Todeschini, Roberto; Consonni, Viviana; Kuz'min, Victor E; Cramer, Richard; Benigni, Romualdo; Yang, Chihae; Rathman, James; Terfloth, Lothar; Gasteiger, Johann; Richard, Ann; Tropsha, Alexander

    2014-06-26

    Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

  14. QSAR modeling: where have you been? Where are you going to?

    PubMed

    Cherkasov, Artem; Muratov, Eugene N; Fourches, Denis; Varnek, Alexandre; Baskin, Igor I; Cronin, Mark; Dearden, John; Gramatica, Paola; Martin, Yvonne C; Todeschini, Roberto; Consonni, Viviana; Kuz'min, Victor E; Cramer, Richard; Benigni, Romualdo; Yang, Chihae; Rathman, James; Terfloth, Lothar; Gasteiger, Johann; Richard, Ann; Tropsha, Alexander

    2014-06-26

    Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making. PMID:24351051

  15. (Q)SAR: A Tool for the Toxicologist.

    PubMed

    Steinbach, Thomas; Gad-McDonald, Samantha; Kruhlak, Naomi; Powley, Mark; Greene, Nigel

    2015-01-01

    A continuing education (CE) course at the 2014 American College of Toxicology annual meeting covered the topic of (Quantitative) Structure-Activity Relationships [(Q)SAR]. The (Q)SAR methodologies use predictive computer modeling based on predefined rules to describe the relationship between chemical structure and a chemical's associated biological activity or statistical tools to find correlations between biologic activity and the molecular structure or properties of a compound. The (Q)SAR has applications in risk assessment, drug discovery, and regulatory decision making. Pressure within industry to reduce the cost of drug development and societal pressure for government regulatory agencies to produce more accurate and timely risk assessment of drugs and chemicals have necessitated the use of (Q)SAR. Producing a high-quality (Q)SAR model depends on many factors including the choice of statistical methods and descriptors, but first and foremost the quality of the data input into the model. Understanding how a (Q)SAR model is developed and applied is critical to the successful use of such a tool. The CE session covered the basic principles of (Q)SAR, practical applications of these computational models in toxicology, how regulatory agencies use and interpret (Q)SAR models, and potential pitfalls of using them.

  16. (Quantitative structure-activity relationships in environmental toxicology)

    SciTech Connect

    Turner, J.E.

    1990-10-04

    The traveler attended the Fourth International Workshop on QSAR (Quantitative Structure-Activity Relationships) in Environmental Toxicology. He was an author or co-author on one platform and two poster presentations. The subject of the workshop offers a framework for analyzing and predicting the fate of chemical pollutants in organisms and the environment. QSAR is highly relevant to the ORNL program on the physicochemical characterization of chemical pollutants for health protection.

  17. The effect of various atomic partial charge schemes to elucidate consensus activity-correlating molecular regions: a test case of diverse QSAR models.

    PubMed

    Kumar, Sivakumar Prasanth; Jha, Prakash C; Jasrai, Yogesh T; Pandya, Himanshu A

    2016-01-01

    The estimation of atomic partial charges of the small molecules to calculate molecular interaction fields (MIFs) is an important process in field-based quantitative structure-activity relationship (QSAR). Several studies showed the influence of partial charge schemes that drastically affects the prediction accuracy of the QSAR model and focused on the selection of appropriate charge models that provide highest cross-validated correlation coefficient ([Formula: see text] or q(2)) to explain the variation in chemical structures against biological endpoints. This study shift this focus in a direction to understand the molecular regions deemed to explain SAR in various charge models and recognize a consensus picture of activity-correlating molecular regions. We selected eleven diverse dataset and developed MIF-based QSAR models using various charge schemes including Gasteiger-Marsili, Del Re, Merck Molecular Force Field, Hückel, Gasteiger-Hückel, and Pullman. The generalized resultant QSAR models were then compared with Open3DQSAR model to interpret the MIF descriptors decisively. We suggest the regions of activity contribution or optimization can be effectively determined by studying various charge-based models to understand SAR precisely.

  18. QSAR classification models for the screening of the endocrine-disrupting activity of perfluorinated compounds.

    PubMed

    Kovarich, S; Papa, E; Li, J; Gramatica, P

    2012-01-01

    Perfluorinated compounds (PFCs) are a class of emerging pollutants still widely used in different materials as non-adhesives, waterproof fabrics, fire-fighting foams, etc. Their toxic effects include potential for endocrine-disrupting activity, but the amount of experimental data available for these pollutants is limited. The use of predictive strategies such as quantitative structure-activity relationships (QSARs) is recommended under the REACH regulation, to fill data gaps and to screen and prioritize chemicals for further experimentation, with a consequent reduction of costs and number of tested animals. In this study, local classification models for PFCs were developed to predict their T4-TTR (thyroxin-transthyretin) competing potency. The best models were selected by maximizing the sensitivity and external predictive ability. These models, characterized by robustness, good predictive power and a defined applicability domain, were applied to predict the activity of 33 other PFCs of environmental concern. Finally, classification models recently published by our research group for T4-TTR binding of brominated flame retardants and for estrogenic and anti-androgenic activity were applied to the studied perfluorinated chemicals to compare results and to further evaluate the potential for these PFCs to cause endocrine disruption.

  19. Synthesis, algal inhibition activities and QSAR studies of novel gramine compounds containing ester functional groups

    NASA Astrophysics Data System (ADS)

    Li, Xia; Yu, Liangmin; Jiang, Xiaohui; Xia, Shuwei; Zhao, Haizhou

    2009-05-01

    2,5,6-Tribromo-1-methylgramine (TBG), isolated from bryozoan Zoobotryon pellucidum was shown to be very efficient in preventing recruitment of larval settlement. In order to improve the compatibility of TBG and its analogues with other ingredients in antifouling paints, structural modification of TBG was focused mainly on halogen substitution and N-substitution. Two halogen-substitute gramines and their derivatives which contain ester functional groups at N-position of gramines were synthesized. Algal inhibition activities of the synthesized compounds against algae Nitzschia closterium were evaluated and the Median Effective Concentration (EC50) range was 1.06-6.74 μg ml-1. Compounds that had a long chain ester group exhibited extremely high antifouling activity. Quantitive Structure Activity Relationship (QSAR) studies with multiple linear regression analysis were applied to find correlation between different calculated molecular descriptors and biological activity of the synthesized compounds. The results show that the toxicity (log (1/EC50)) is correlated well with the partition coefficient log P. Thus, these products have potential function as antifouling agents.

  20. In Vitro Antioxidant Activity of Selected 4-Hydroxy-chromene-2-one Derivatives—SAR, QSAR and DFT Studies

    PubMed Central

    Mladenović, Milan; Mihailović, Mirjana; Bogojević, Desanka; Matić, Sanja; Nićiforović, Neda; Mihailović, Vladimir; Vuković, Nenad; Sukdolak, Slobodan; Solujić, Slavica

    2011-01-01

    The series of fifteen synthesized 4-hydroxycoumarin derivatives was subjected to antioxidant activity evaluation in vitro, through total antioxidant capacity, 1,1-diphenyl-2-picryl-hydrazyl (DPPH), hydroxyl radical, lipid peroxide scavenging and chelating activity. The highest activity was detected during the radicals scavenging, with 2b, 6b, 2c, and 4c noticed as the most active. The antioxidant activity was further quantified by the quantitative structure-activity relationships (QSAR) studies. For this purpose, the structures were optimized using Paramethric Method 6 (PM6) semi-empirical and Density Functional Theory (DFT) B3LYP methods. Bond dissociation enthalpies of coumarin 4-OH, Natural Bond Orbital (NBO) gained hybridization of the oxygen, acidity of the hydrogen atom and various molecular descriptors obtained, were correlated with biological activity, after which we designed 20 new antioxidant structures, using the most favorable structural motifs, with much improved predicted activity in vitro. PMID:21686153

  1. QSAR Methods.

    PubMed

    Gini, Giuseppina

    2016-01-01

    In this chapter, we introduce the basis of computational chemistry and discuss how computational methods have been extended to some biological properties and toxicology, in particular. Since about 20 years, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Then we see how animal experiments, aimed at providing a standardized result about a biological property, can be mimicked by new in silico methods. Our emphasis here is on toxicology and on predicting properties through chemical structures. Two main streams of such models are available: models that consider the whole molecular structure to predict a value, namely QSAR (Quantitative Structure Activity Relationships), and models that find relevant substructures to predict a class, namely SAR. The term in silico discovery is applied to chemical design, to computational toxicology, and to drug discovery. We discuss how the experimental practice in biological science is moving more and more toward modeling and simulation. Such virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals. PMID:27311459

  2. QSAR DataBank - an approach for the digital organization and archiving of QSAR model information

    PubMed Central

    2014-01-01

    Background Research efforts in the field of descriptive and predictive Quantitative Structure-Activity Relationships or Quantitative Structure–Property Relationships produce around one thousand scientific publications annually. All the materials and results are mainly communicated using printed media. The printed media in its present form have obvious limitations when they come to effectively representing mathematical models, including complex and non-linear, and large bodies of associated numerical chemical data. It is not supportive of secondary information extraction or reuse efforts while in silico studies poses additional requirements for accessibility, transparency and reproducibility of the research. This gap can and should be bridged by introducing domain-specific digital data exchange standards and tools. The current publication presents a formal specification of the quantitative structure-activity relationship data organization and archival format called the QSAR DataBank (QsarDB for shorter, or QDB for shortest). Results The article describes QsarDB data schema, which formalizes QSAR concepts (objects and relationships between them) and QsarDB data format, which formalizes their presentation for computer systems. The utility and benefits of QsarDB have been thoroughly tested by solving everyday QSAR and predictive modeling problems, with examples in the field of predictive toxicology, and can be applied for a wide variety of other endpoints. The work is accompanied with open source reference implementation and tools. Conclusions The proposed open data, open source, and open standards design is open to public and proprietary extensions on many levels. Selected use cases exemplify the benefits of the proposed QsarDB data format. General ideas for future development are discussed. PMID:24910716

  3. Developing a QSAR model for hepatotoxicity screening of the active compounds in traditional Chinese medicines.

    PubMed

    Huang, Shan-Han; Tung, Chun-Wei; Fülöp, Ferenc; Li, Jih-Heng

    2015-04-01

    The perception that natural substances are deemed safe has made traditional Chinese medicine (TCM) popular in the treatment and prevention of disease globally. However, such an assumption is often misleading owing to a lack of scientific validation. To assess the safety of TCM, in silico screening provides major advantages over the classical laboratory approaches in terms of resource- and time-saving and full reproducibility. To screen the hepatotoxicity of the active compounds of TCMs, a quantitative structure-activity relationship (QSAR) model was firstly established by utilizing drugs from the Liver Toxicity Knowledge Base. These drugs were annotated with drug-induced liver injury information obtained from clinical trials and post-marketing surveillance. The performance of the model after nested 10-fold cross-validation was 79.1%, 91.2%, 53.8% for accuracy, sensitivity, and specificity, respectively. The external validation of 91 well-known ingredients of common herbal medicines yielded a high accuracy (87%). After screening the TCM Database@Taiwan, the world's largest TCM database, a total of 6853 (74.8%) ingredients were predicted to have hepatotoxic potential. The one-hundred chemical ingredients predicted to have the highest hepatotoxic potential by our model were further verified by published literatures. Our study indicated that this model can serve as a complementary tool to evaluate the safety of TCM.

  4. Developing a QSAR model for hepatotoxicity screening of the active compounds in traditional Chinese medicines.

    PubMed

    Huang, Shan-Han; Tung, Chun-Wei; Fülöp, Ferenc; Li, Jih-Heng

    2015-04-01

    The perception that natural substances are deemed safe has made traditional Chinese medicine (TCM) popular in the treatment and prevention of disease globally. However, such an assumption is often misleading owing to a lack of scientific validation. To assess the safety of TCM, in silico screening provides major advantages over the classical laboratory approaches in terms of resource- and time-saving and full reproducibility. To screen the hepatotoxicity of the active compounds of TCMs, a quantitative structure-activity relationship (QSAR) model was firstly established by utilizing drugs from the Liver Toxicity Knowledge Base. These drugs were annotated with drug-induced liver injury information obtained from clinical trials and post-marketing surveillance. The performance of the model after nested 10-fold cross-validation was 79.1%, 91.2%, 53.8% for accuracy, sensitivity, and specificity, respectively. The external validation of 91 well-known ingredients of common herbal medicines yielded a high accuracy (87%). After screening the TCM Database@Taiwan, the world's largest TCM database, a total of 6853 (74.8%) ingredients were predicted to have hepatotoxic potential. The one-hundred chemical ingredients predicted to have the highest hepatotoxic potential by our model were further verified by published literatures. Our study indicated that this model can serve as a complementary tool to evaluate the safety of TCM. PMID:25660478

  5. The kinetics and QSAR of abiotic reduction of mononitro aromatic compounds catalyzed by activated carbon.

    PubMed

    Gong, Wenwen; Liu, Xinhui; Gao, Ding; Yu, Yanjun; Fu, Wenjun; Cheng, Dengmiao; Cui, Baoshan; Bai, Junhong

    2015-01-01

    The kinetics of abiotic reduction of mono-nitro aromatic compounds (mono-NACs) catalyzed by activated carbon (AC) in an anaerobic system were examined. There were 6 types of substituent groups on nitrobenzene, including methyl, chlorine, amino, carboxyl, hydroxyl and cyanogen groups, at the ortho, meta or para positions. Our results showed that reduction followed pseudo-first order reaction kinetics, and that the rate constant (logkSA) varied widely, ranging between -4.77 and -2.82, depending upon the type and position of the substituent. A quantitative structure-activity relationship (QSAR) model using 15 theoretical molecular descriptors and partial-least-squares (PLS) regression was developed for the reduction rates of mono-NACs catalyzed by AC. The cross-validated regression coefficient (Qcum(2), 0.861) and correlation coefficient (R(2), 0.898) indicated significantly high robustness of the model. The VIP (variable importance in the projection) values of energy of the lowest unoccupied molecular orbital (ELUMO) and the maximum net atomic charge on the aromatic carbon bound to the nitro group (QC(-)) were 1.15 and 1.01, respectively. These values indicated that the molecular orbital energies and the atomic net charges might play important roles in the reduction of mono-NACs catalyzed by AC in anaerobic systems.

  6. Compound profiling and 3D-QSAR studies of hydrazone derivatives with activity against intracellular Trypanosoma cruzi.

    PubMed

    Costa, Lívia Bandeira; Cardoso, Marcos Veríssimo de Oliveira; de Oliveira Filho, Gevanio Bezerra; de Moraes Gomes, Paulo André Teixeira; Espíndola, José Wanderlan Pontes; de Jesus Silva, Thays Gabrielle; Torres, Pedro Henrique Monteiro; Silva Junior, Floriano Paes; Martin, Julio; de Figueiredo, Regina Célia Bressan Queiroz; Leite, Ana Cristina Lima

    2016-04-15

    Chagas disease is a tropical disease caused by the parasite Trypanosoma cruzi, which is endemic in Central and South America. Few treatments are available with effectiveness limited to the early (acute) stage of disease, significant toxicity and widespread drug resistance. In this work we report the outcome of a HTS-ready assay chemical library screen to identify novel, nontoxic, small-molecule inhibitors of T. cruzi. We have selected 50 compounds that possess hydrazone as a common group. The compounds were screened using recombinant T. cruzi (Tulahuen strain) expressing beta-galactosidase. A 3D quantitative structure-activity relationship (QSAR) analysis was performed using descriptors calculated from comparative molecular field analysis (CoMFA). Our findings show that of the fifty selected hydrazones, compounds LpQM-19, 28 and 31 displayed the highest activity against T. cruzi, leading to a selectivity index (SI) of 20-fold. The 3D-QSAR analysis indicates that a particular electrostatic arrangement, where electron-deficient atoms are aligned along the molecule main axis positively correlates with compound biological activity. These results provide new candidate molecules for the development of treatments against Chagas disease. PMID:26964673

  7. QSAR Study on Thiazolidine-2,4-dione Derivatives for Antihyperglycemic Activity.

    PubMed

    Prashantha Kumar, B R; Nanjan, M J

    2008-09-01

    A set of seventy four molecules belonging to the class of thioglitazones were subjected to the QSAR analysis for their antihyperglycemic activity. All the molecules were subjected to energy minimization to get 3D structures, followed by conformational analysis to get the conformation of the molecule associated with the least energy and highest stability. Various physico-chemical parameters were then calculated using ALCHEMY 2000 software, namely, thermodynamic parameters, structure-dependant parameters, topological parameters and charge-dependant parameters. Multiple linear regression analysis was carried out on all the molecules. The final equation was developed by choosing optimal combination of descriptors after removing the outliers. Cross validation was performed by leave one out method to arrive at the final QSAR model for the chosen set of molecules to exhibit antihyperglycemic activity. PMID:21394250

  8. QSAR Study on Thiazolidine-2,4-dione Derivatives for Antihyperglycemic Activity

    PubMed Central

    Prashantha Kumar, B. R.; Nanjan, M. J.

    2008-01-01

    A set of seventy four molecules belonging to the class of thioglitazones were subjected to the QSAR analysis for their antihyperglycemic activity. All the molecules were subjected to energy minimization to get 3D structures, followed by conformational analysis to get the conformation of the molecule associated with the least energy and highest stability. Various physico-chemical parameters were then calculated using ALCHEMY 2000 software, namely, thermodynamic parameters, structure-dependant parameters, topological parameters and charge-dependant parameters. Multiple linear regression analysis was carried out on all the molecules. The final equation was developed by choosing optimal combination of descriptors after removing the outliers. Cross validation was performed by leave one out method to arrive at the final QSAR model for the chosen set of molecules to exhibit antihyperglycemic activity. PMID:21394250

  9. 3D-QSAR Studies on a Series of Dihydroorotate Dehydrogenase Inhibitors: Analogues of the Active Metabolite of Leflunomide

    PubMed Central

    Li, Shun-Lai; He, Mao-Yu; Du, Hong-Guang

    2011-01-01

    The active metabolite of the novel immunosuppressive agent leflunomide has been shown to inhibit the enzyme dihydroorotate dehydrogenase (DHODH). This enzyme catalyzes the fourth step in de novo pyrimidine biosynthesis. Self-organizing molecular field analysis (SOMFA), a simple three-dimensional quantitative structure-activity relationship (3D-QSAR) method is used to study the correlation between the molecular properties and the biological activities of a series of analogues of the active metabolite. The statistical results, cross-validated rCV2 (0.664) and non cross-validated r2 (0.687), show a good predictive ability. The final SOMFA model provides a better understanding of DHODH inhibitor-enzyme interactions, and may be useful for further modification and improvement of inhibitors of this important enzyme. PMID:21686163

  10. Synthesis and QSAR study of novel anti-inflammatory active mesalazine-metronidazole conjugates.

    PubMed

    Naumov, Roman N; Panda, Siva S; Girgis, Adel S; George, Riham F; Farhat, Michel; Katritzky, Alan R

    2015-06-01

    Novel, mesalazine, metronidazole conjugates 6a-e with amino acid linkers were synthesized utilizing benzotriazole chemistry. Biological data acquired for all the novel bis-conjugates showed (a) some bis-conjugates exhibit comparable anti-inflammatory activity with parent drugs and (b) the potent bis-conjugates show no visible stomach lesions. 3D-pharmacophore and 2D-QSAR modeling support the observed bio-properties. PMID:25937011

  11. Development and validation of a quantitative structure-activity relationship for chronic narcosis to fish.

    PubMed

    Claeys, Lieve; Iaccino, Federica; Janssen, Colin R; Van Sprang, Patrick; Verdonck, Frederik

    2013-10-01

    Vertebrate testing under the European Union's regulation on Registration, Evaluation, Authorisation and Restriction of Chemical substances (REACH) is discouraged, and the use of alternative nontesting approaches such as quantitative structure-activity relationships (QSARs) is encouraged. However, robust QSARs predicting chronic ecotoxicity of organic compounds to fish are not available. The Ecological Structure Activity Relationships (ECOSAR) Class Program is a computerized predictive system that estimates the acute and chronic toxicity of organic compounds for several chemical classes based on their log octanol-water partition coefficient (K(OW)). For those chemical classes for which chronic training data sets are lacking, acute to chronic ratios are used to predict chronic toxicity to aquatic organisms. Although ECOSAR reaches a high score against the Organisation for Economic Co-operation and Development (OECD) principles for QSAR validation, the chronic QSARs in ECOSAR are not fully compliant with OECD criteria in the framework of REACH or CLP (classification, labeling, and packaging) regulation. The objective of the present study was to develop a chronic ecotoxicity QSAR for fish for compounds acting via nonpolar and polar narcosis. These QSARs were built using a database of quality screened toxicity values, considering only chronic exposure durations and relevant end points. After statistical multivariate diagnostic analysis, literature-based, mechanistically relevant descriptors were selected to develop a multivariate regression model. Finally, these QSARs were tested for their acceptance for regulatory purposes and were found to be compliant with the OECD principles for the validation of a QSAR.

  12. Synthesis, antifungal activity, and QSAR studies of 1,6-dihydropyrimidine derivatives

    PubMed Central

    Rami, Chirag; Patel, Laxmanbhai; Patel, Chhaganbhai N.; Parmar, Jayshree P.

    2013-01-01

    Introduction: A practical synthesis of pyrimidinone would be very helpful for chemists because pyrimidinone is found in many bioactive natural products and exhibits a wide range of biological properties. The biological significance of pyrimidine derivatives has led us to the synthesis of substituted pyrimidine. Materials and Methods: With the aim of developing potential antimicrobials, new series of 5-cyano-6-oxo-1,6-dihydro-pyrimidine derivatives namely 2-(5-cyano-6-oxo-4-substituted (aryl)-1,6-dihydropyrimidin-2-ylthio)-N-substituted (phenyl) acetamide (C1-C41) were synthesized and characterized by Fourier transform infrared spectroscopy (FTIR), mass analysis, and proton nuclear magnetic resonance (1H NMR). All the compounds were screened for their antifungal activity against Candida albicans (MTCC, 227). Results and Discussion: Quantitative structure activity relationship (QSAR) studies of a series of 1,6-dihydro-pyrimidine were carried out to study various structural requirements for fungal inhibition. Various lipophilic, electronic, geometric, and spatial descriptors were correlated with antifungal activity using genetic function approximation. Developed models were found predictive as indicated by their square of predictive regression values (r2pred) and their internal and external cross-validation. Study reveals that CHI_3_C, Molecular_SurfaceArea, and Jurs_DPSA_1 contributed significantly to the activity along with some electronic, geometric, and quantum mechanical descriptors. Conclusion: A careful analysis of the antifungal activity data of synthesized compounds revealed that electron withdrawing substitution on N-phenyl acetamide ring of 1,6-dihydropyrimidine moiety possess good activity. PMID:24302836

  13. History and successes of QSAR in environmental applications

    SciTech Connect

    Veith, G.D.

    1994-12-31

    The history of the development of relationships between chemical structure and chemical behavior for assessing the safety of chemicals is marked by the struggle with timidity that so many areas of science face. Despite a continuous stream of successes for estimating properties and biological activity of chemicals from their structure, the field of QSAR has been met forcibly by the skeptics. In failing to articulate the potential savings in term of costs land test animals, QSAR researchers have enabled the skeptics to prevent a strategic QSAR program from being formed in either the private or the public sectors. QSAR must generate systematic, reference databases for the intrinsic properties of chemicals. Partitioning is such an intrinsic property. QSAR has succeeded not only in calculating hydrophobicity descriptors that control partitioning, but also in using these descriptors in counties relationships for specific endpoints. QSAR has also developed databases for potency. So many structure-toxicity relationships have been published that the potency of over 75 percent of all chemicals produced worldwide can be estimated without further animal testing. Biochemical persistence, as evidenced in biodegradability and/or tissue metabolism, lags behind due, in part, to a shortage of systematic databases. Several interesting approaches will be discussed. Finally, intrinsic reactivity and the selectivity of chemicals among competing interactions must be modeled. Since chemical reactivity holds the key to identifying genotoxic chemicals and other highly toxic chemicals, reactivity models are urgently needed. Recent QSAR advances for some forms of reactivity will be discussed.

  14. External validation of a QSAR for the acute toxicity of halogenated aliphatic hydrocarbons

    SciTech Connect

    Eriksson, L.; Jonsson, J. . Dept. of Organic Chemistry); Berglind, R. . NBC-Defense Research)

    1993-07-01

    The validation of the predictive capability of a quantitative structure-activity relationship (QSAR) is a significant step toward the construction of a reliable model. This point is discussed and illustrated with data for a class of halogenated aliphatic hydrocarbons. For this class of compounds, a QSAR concerning their acute toxicity toward rate was recently published. This QSAR is verified in this by selecting and testing an external validation set comprising six compounds. The QSAR is also used for predicting the acute toxicity of 28 nontested members of this class.

  15. Antioxidant activity of flavonoids: a QSAR modeling using Fukui indices descriptors.

    PubMed

    Djeradi, Houria; Rahmouni, Ali; Cheriti, Abdelkrim

    2014-10-01

    A QSAR model to predict the antioxidant activity of flavonoid compounds was developed. New electronic structure descriptors which are Fukui indices are correlated to the radical scavenging of flavonoids. These indices are obtained at DFT/B3LYP level of chemical quantum theory. The logIC50 experimental values of antioxidant activity are taken from the literature. The model is based on the multilinear regression method. Both experimental and calculated data of 36 flavonoids compounds were analyzed. A good correlation coefficient (R(2) = 0.8159) is obtained and the antioxidant activities of test compounds are well predicted. PMID:25311723

  16. 2 D - QSAR studies on CYP26A1 inhibitory activity of 1-[benzofuran-2-yl-(4-alkyl/aryl-phenyl)-methyl]- 1 H-triazoles.

    PubMed

    Yadav, Madhu

    2011-01-01

    The Quantitative Structure Activity Relationship (QSAR) study is performed over a set of 15, 4-alkyl/aryl-substituted 1- [benzofuran-2-yl-phenylmethyl]-1 H-triazoles derivatives. This study is based on the application of physicochemical parameters in QSAR. The parameters include (MR (molar refractivity), MW (molecular weight), Pc (parachor), St (surface tension), D (density), Ir (index of refraction) and log P (partition coefficient). The parameters describing physiochemical properties are used as independent variables and the biological activity (IC(50)) is considered as dependent variable in multiple regression analysis. Different models were generated with high co-efficient of determination (R(2)). The 2D-QSAR study identified compounds capable of inhibiting the metabolic breakdown of the retinoid (trans-retinoic acid (ATRA)) involved in the activation of specific nuclear Retinoic acid receptors (RARs). This study identifies R115866 as a potential inhibitor of the cytochrome P450 (CYP) mediated metabolism with increased RA levels for retinoid actions. PMID:22347780

  17. Prediction and evaluation of the lipase inhibitory activities of tea polyphenols with 3D-QSAR models

    PubMed Central

    Li, Yi-Fang; Chang, Yi-Qun; Deng, Jie; Li, Wei-Xi; Jian, Jie; Gao, Jia-Suo; Wan, Xin; Gao, Hao; Kurihara, Hiroshi; Sun, Ping-Hua; He, Rong-Rong

    2016-01-01

    The extraordinary hypolipidemic effects of polyphenolic compounds from tea have been confirmed in our previous study. To gain compounds with more potent activities, using the conformations of the most active compound revealed by molecular docking, a 3D-QSAR pancreatic lipase inhibitor model with good predictive ability was established and validated by CoMFA and CoMISA methods. With good statistical significance in CoMFA (r2cv = 0.622, r2 = 0.956, F = 261.463, SEE = 0.096) and CoMISA (r2cv = 0.631, r2 = 0.932, F = 75.408, SEE = 0.212) model, we summarized the structure-activity relationship between polyphenolic compounds and pancreatic lipase inhibitory activities and find the bulky substituents in R2, R4 and R5, hydrophilic substituents in R1 and electron withdrawing groups in R2 are the key factors to enhance the lipase inhibitory activities. Under the guidance of the 3D-QSAR results, (2R,3R,2′R,3′R)-desgalloyloolongtheanin-3,3′-O-digallate (DOTD), a potent lipase inhibitor with an IC50 of 0.08 μg/ml, was obtained from EGCG oxidative polymerization catalyzed by crude polyphenol oxidase. Furthermore, DOTD was found to inhibit lipid absorption in olive oil-loaded rats, which was related with inhibiting the activities of lipase in the intestinal mucosa and contents. PMID:27694956

  18. Combinatorial QSAR of ambergris fragrance compounds.

    PubMed

    Kovatcheva, Assia; Golbraikh, Alexander; Oloff, Scott; Xiao, Yun-De; Zheng, Weifan; Wolschann, Peter; Buchbauer, Gerhard; Tropsha, Alexander

    2004-01-01

    A combinatorial quantitative structure-activity relationships (Combi-QSAR) approach has been developed and applied to a data set of 98 ambergris fragrance compounds with complex stereochemistry. The Combi-QSAR approach explores all possible combinations of different independent descriptor collections and various individual correlation methods to obtain statistically significant models with high internal (for the training set) and external (for the test set) accuracy. Seven different descriptor collections were generated with commercially available MOE, CoMFA, CoMMA, Dragon, VolSurf, and MolconnZ programs; we also included chirality topological descriptors recently developed in our laboratory (Golbraikh, A.; Bonchev, D.; Tropsha, A. J. Chem. Inf. Comput. Sci. 2001, 41, 147-158). CoMMA descriptors were used in combination with MOE descriptors. MolconnZ descriptors were used in combination with chirality descriptors. Each descriptor collection was combined individually with four correlation methods, including k-nearest neighbors (kNN) classification, Support Vector Machines (SVM), decision trees, and binary QSAR, giving rise to 28 different types of QSAR models. Multiple diverse and representative training and test sets were generated by the divisions of the original data set in two. Each model with high values of leave-one-out cross-validated correct classification rate for the training set was subjected to extensive internal and external validation to avoid overfitting and achieve reliable predictive power. Two validation techniques were employed, i.e., the randomization of the target property (in this case, odor intensity) also known as the Y-randomization test and the assessment of external prediction accuracy using test sets. We demonstrate that not every combination of the data modeling technique and the descriptor collection yields a validated and predictive QSAR model. kNN classification in combination with CoMFA descriptors was found to be the best QSAR

  19. 3D-QSAR modelling dataset of bioflavonoids for predicting the potential modulatory effect on P-glycoprotein activity.

    PubMed

    Wongrattanakamon, Pathomwat; Lee, Vannajan Sanghiran; Nimmanpipug, Piyarat; Jiranusornkul, Supat

    2016-12-01

    The data is obtained from exploring the modulatory activities of bioflavonoids on P-glycoprotein function by ligand-based approaches. Multivariate Linear-QSAR models for predicting the induced/inhibitory activities of the flavonoids were created. Molecular descriptors were initially used as independent variables and a dependent variable was expressed as pFAR. The variables were then used in MLR analysis by stepwise regression calculation to build the linear QSAR data. The entire dataset consisted of 23 bioflavonoids was used as a training set. Regarding the obtained MLR QSAR model, R of 0.963, R (2)=0.927, [Formula: see text], SEE=0.197, F=33.849 and q (2)=0.927 were achieved. The true predictabilities of QSAR model were justified by evaluation with the external dataset (Table 4). The pFARs of representative flavonoids were predicted by MLR QSAR modelling. The data showed that internal and external validations may generate the same conclusion. PMID:27626051

  20. 3D-QSAR modelling dataset of bioflavonoids for predicting the potential modulatory effect on P-glycoprotein activity.

    PubMed

    Wongrattanakamon, Pathomwat; Lee, Vannajan Sanghiran; Nimmanpipug, Piyarat; Jiranusornkul, Supat

    2016-12-01

    The data is obtained from exploring the modulatory activities of bioflavonoids on P-glycoprotein function by ligand-based approaches. Multivariate Linear-QSAR models for predicting the induced/inhibitory activities of the flavonoids were created. Molecular descriptors were initially used as independent variables and a dependent variable was expressed as pFAR. The variables were then used in MLR analysis by stepwise regression calculation to build the linear QSAR data. The entire dataset consisted of 23 bioflavonoids was used as a training set. Regarding the obtained MLR QSAR model, R of 0.963, R (2)=0.927, [Formula: see text], SEE=0.197, F=33.849 and q (2)=0.927 were achieved. The true predictabilities of QSAR model were justified by evaluation with the external dataset (Table 4). The pFARs of representative flavonoids were predicted by MLR QSAR modelling. The data showed that internal and external validations may generate the same conclusion.

  1. Robust fuzzy mappings for QSAR studies.

    PubMed

    Kumar, Mohit; Thurow, Kerstin; Stoll, Norbert; Stoll, Regina

    2007-05-01

    This study presents a new robust method of developing quantitative structure-activity relationship (QSAR) models based on fuzzy mappings. An important issue in QSAR modelling is of robustness, i.e., model should not undergo overtraining and model performance should be least sensitive to the modelling errors associated with the chosen descriptors and structure of the model. We establish robust input-output mappings for QSAR studies based on fuzzy "if-then" rules. The identification of these mappings (i.e. the construction of fuzzy rules) is based on a robust criterion that the maximum possible value of energy-gain from modelling errors to the identification errors is minimum. The robustness of proposed approach has been illustrated with simulation studies and QSAR modelling examples. The method of robust fuzzy mappings has been compared with Bayesian regularized neural networks through the QSAR modelling examples of (1) carboquinones' data set, (2) benzodiazepine data set, and (3) predicting the rate constant for hydroxyl radical tropospheric degradation of 460 heterogeneous organic compounds.

  2. The Role of Qsar Methodology in the Regulatory Assessment of Chemicals

    NASA Astrophysics Data System (ADS)

    Worth, Andrew Paul

    The aim of this chapter is to outline the different ways in which quantitative structure-activity relationship (QSAR) methods can be used in the regulatory assessment of chemicals. The chapter draws on experience gained in the European Union in the assessment of industrial chemicals, as well as recently developed guidance for the use of QSARs within specific legislative frameworks such as REACH and the Water Framework Directive. This chapter reviews the concepts of QSAR validity, applicability, and acceptability and emphasises that the use of individual QSAR estimates is highly context-dependent, which has implications in terms of the confidence needed in the model validity. In addition to the potential use of QSAR models as stand-alone estimation methods, it is expected that QSARs will be used within the context of broader weight-of-evidence approaches, such as chemical categories and integrated testing strategies; therefore, the role of (Q)SARs within these approaches is explained. This chapter also refers to a range of freely available software tools being developed to facilitate the use of QSARs for regulatory purposes. Finally, some conclusions are drawn concerning current needs for the further development and uptake of QSARs

  3. Proceedings of the third international workshop on quantitative structure-activity relationships in environmental toxicology

    SciTech Connect

    Turner, J.E.; England, M.W.; Schultz, T.W.; Kwaak, N.J.

    1988-06-01

    The 3rd International Workshop on Quantitative Structure-Activity Relationships (QSAR) in Environmental Toxicology (QSAR-88) was organized to facilitate the exchange of ideas between experts in different areas working in QSAR. Invited participants were selected to provide a mixture of representatives from biology, chemistry, and statistics as well as industry, government, and academia. The theme for QSAR-88 was ''Interrelationships of QSAR and Mechanisms of Toxic Actions.'' The program was divided into four sessions of invited talks on statistics, molecular descriptors, fish QSARs, and non-fish QSARs and a poster session. These Proceedings contain the text of the 16 invited technical papers and descriptions of the 16 contributed poster presentations. In addition, we include a summary of the Workshop prepared by Dr. Kaiser. The use of structure-activity relationships to elucidate trends in toxicology has been documented for more than a century. However, it is only over the past fifteen years that the modern tools, initially developed for experimental drug design, have been brought to bear on the problem of environmental contamination. The very nature of the field has, from the start, required the collaboration of experts from several scientific disciplines.

  4. Structure-activity relationships: quantitative techniques for predicting the behavior of chemicals in the ecosystem

    SciTech Connect

    Nirmalakhandan, N.; Speece, R.E.

    1988-06-01

    Quantitative Structure-Activity Relationships (QSARs) are used increasingly to screen and predict the toxicity and the fate of chemicals released into the environment. The impetus to use QSAR methods in this area has been the large number of synthetic chemicals introduced into the ecosystem via intensive agriculture and industrialization. Because of the costly and time-consuming nature of environmental fate testing, QSARs have been effectively used to screen large classes of chemical compounds and flag those that appear to warrant more thorough testing.

  5. Modelling the effect of structural QSAR parameters on skin penetration using genetic programming

    NASA Astrophysics Data System (ADS)

    Chung, K. K.; Do, D. Q.

    2010-09-01

    In order to model relationships between chemical structures and biological effects in quantitative structure-activity relationship (QSAR) data, an alternative technique of artificial intelligence computing—genetic programming (GP)—was investigated and compared to the traditional method—statistical. GP, with the primary advantage of generating mathematical equations, was employed to model QSAR data and to define the most important molecular descriptions in QSAR data. The models predicted by GP agreed with the statistical results, and the most predictive models of GP were significantly improved when compared to the statistical models using ANOVA. Recently, artificial intelligence techniques have been applied widely to analyse QSAR data. With the capability of generating mathematical equations, GP can be considered as an effective and efficient method for modelling QSAR data.

  6. Quantitative structure-activity relationships for fluoroelastomer/chlorofluorocarbon systems

    SciTech Connect

    Paciorek, K.J.L.; Masuda, S.R.; Nakahara, J.H. ); Snyder, C.E. Jr.; Warner, W.M. )

    1991-12-01

    This paper reports on swell, tensile, and modulus data that were determined for a fluoroelastomer after exposure to a series of chlorofluorocarbon model fluids. Quantitative structure-activity relationships (QSAR) were developed for the swell as a function of the number of carbons and chlorines and for tensile strength as a function of carbon number and chlorine positions in the chlorofluorocarbons.

  7. ESTIMATION OF MICROBIAL REDUCTIVE TRANSFORMATION RATES FOR CHLORINATED BENZENES AND PHENOLS USING A QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP APPROACH

    EPA Science Inventory

    A set of literature data was used to derive several quantitative structure-activity relationships (QSARs) to predict the rate constants for the microbial reductive dehalogenation of chlorinated aromatics. Dechlorination rate constants for 25 chloroaromatics were corrected for th...

  8. The Use of Qsar and Computational Methods in Drug Design

    NASA Astrophysics Data System (ADS)

    Bajot, Fania

    The application of quantitative structure-activity relationships (QSARs) has significantly impacted the paradigm of drug discovery. Following the successful utilization of linear solvation free-energy relationships (LSERs), numerous 2D- and 3D-QSAR methods have been developed, most of them based on descriptors for hydrophobicity, polarizability, ionic interactions, and hydrogen bonding. QSAR models allow for the calculation of physicochemical properties (e.g., lipophilicity), the prediction of biological activity (or toxicity), as well as the evaluation of absorption, distribution, metabolism, and excretion (ADME). In pharmaceutical research, QSAR has a particular interest in the preclinical stages of drug discovery to replace tedious and costly experimentation, to filter large chemical databases, and to select drug candidates. However, to be part of drug discovery and development strategies, QSARs need to meet different criteria (e.g., sufficient predictivity). This chapter describes the foundation of modern QSAR in drug discovery and presents some current challenges and applications for the discovery and optimization of drug candidates

  9. Localized heuristic inverse quantitative structure activity relationship with bulk descriptors using numerical gradients.

    PubMed

    Stålring, Jonna; Almeida, Pedro R; Carlsson, Lars; Helgee Ahlberg, Ernst; Hasselgren, Catrin; Boyer, Scott

    2013-08-26

    State-of-the-art quantitative structure-activity relationship (QSAR) models are often based on nonlinear machine learning algorithms, which are difficult to interpret. From a pharmaceutical perspective, QSARs are used to enhance the chemical design process. Ultimately, they should not only provide a prediction but also contribute to a mechanistic understanding and guide modifications to the chemical structure, promoting compounds with desirable biological activity profiles. Global ranking of descriptor importance and inverse QSAR have been used for these purposes. This paper introduces localized heuristic inverse QSAR, which provides an assessment of the relative ability of the descriptors to influence the biological response in an area localized around the predicted compound. The method is based on numerical gradients with parameters optimized using data sets sampled from analytical functions. The heuristic character of the method reduces the computational requirements and makes it applicable not only to fragment based methods but also to QSARs based on bulk descriptors. The application of the method is illustrated on congeneric QSAR data sets, and it is shown that the predicted influential descriptors can be used to guide structural modifications that affect the biological response in the desired direction. The method is implemented into the AZOrange Open Source QSAR package. The current implementation of localized heuristic inverse QSAR is a step toward a generally applicable method for elucidating the structure activity relationship specifically for a congeneric region of chemical space when using QSARs based on bulk properties. Consequently, this method could contribute to accelerating the chemical design process in pharmaceutical projects, as well as provide information that could enhance the mechanistic understanding for individual scaffolds.

  10. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

    PubMed Central

    Eriksson, Lennart; Jaworska, Joanna; Worth, Andrew P; Cronin, Mark T D; McDowell, Robert M; Gramatica, Paola

    2003-01-01

    This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation. PMID:12896860

  11. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

    PubMed

    Eriksson, Lennart; Jaworska, Joanna; Worth, Andrew P; Cronin, Mark T D; McDowell, Robert M; Gramatica, Paola

    2003-08-01

    This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation.

  12. Pharmacophore modeling, molecular docking, QSAR, and in silico ADMET studies of gallic acid derivatives for immunomodulatory activity.

    PubMed

    Yadav, Dharmendra Kumar; Khan, Feroz; Negi, Arvind Singh

    2012-06-01

    Immunomodulation refers to an alteration in the immune response due to the intrusion of foreign molecules into the body. In the present communication, QSAR and docking studies of gallic acid derivatives were performed in relation to their immunomodulatory activities. Screening through the use of a QSAR model suggested that the compounds G-4, G-7, G-9, G-10, G-12, and G-13 possess immunomodulatory activity. Activity was predicted using a statistical model developed by the forward stepwise multiple linear regression method. The correlation coefficient (r(2)) and the prediction accuracy (rCV(2)) of the QSAR model were 0.99 and 0.96, respectively. The QSAR study indicated that chemical descriptors-dipole moment, steric energy, amide group count, λ(max) (UV-visible) and molar refractivity-are well correlated with activity, while decreases in the dipole moment, steric energy, and molar refractivity were negatively correlated. A molecular docking study showed that the compounds had high binding affinities for the INFα-2, IL-6, and IL-4 receptors. Binding site residues formed H-bonds with the designed gallic acid derivatives G-3, G-4, G-5, G-6, G-7, and G-10. Moreover, based on screening for oral bioavailability, in silico ADME, and toxicity risk assessment, we concluded that compound G-7 exhibits marked immunomodulatory activity, comparable to levamisole.

  13. Understanding the Structural Requirements of Hybrid (S)-6-((2-(4-Phenylpiperazin-1-yl)ethyl)(propyl)amino)-5,6,7,8-tetrahydronaphthalen-1-ol and its Analogs as D2/D3 Receptor Ligands: A Three-Dimensional Quantitative Structure-Activity Relationship (3D QSAR) Investigation

    PubMed Central

    Modi, Gyan; Sharma, Horrick; Kharkar, Prashant S.; Dutta, Aloke K.

    2014-01-01

    To gain insights into the structural requirements for dopamine D2 and D3 agonists in the treatment of Parkinson’s disease (PD) and to elucidate the basis of selectivity for D3 over D2 (D2/D3), 3D quantitative structure-activity relationship (3D QSAR) investigations using CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indices analysis) methods were performed on a series of 45 structurally related D2 and D3 dopaminergic ligands. Two alignment methods (atom-based and flexible) and two charge calculation methods (Gasteiger-Hückel and AM1) were used in the present study. Overall, D2 affinity and selectivity (D2/D3) models performed better with r2cv values of 0.71 and 0.63 for CoMFA and 0.71 and 0.79 for CoMSIA, respectively. The corresponding predictive r2 values for the CoMFA and CoMSIA models were 0.92 and 0.86 and 0.91 and 0.78, respectively. The CoMFA models generated using flexible alignment outperformed the models with the atom-based alignment in terms of relevant statistics and interpretability of the generated contour maps while CoMSIA models obtained using atom-based alignment showed superiority in terms of internal and external predictive abilities. The presence of carbonyl group (C=O) attached to the piperazine ring and the hydrophobic biphenyl ring were found to be the most important features responsible for the D3 selectivity over D2. This study can be further utilized to design and develop selective and potent dopamine agonists to treat PD. PMID:25221669

  14. Understanding the Structural Requirements of Hybrid (S)-6-((2-(4-Phenylpiperazin-1-yl)ethyl)(propyl)amino)-5,6,7,8-tetrahydronaphthalen-1-ol and its Analogs as D2/D3 Receptor Ligands: A Three-Dimensional Quantitative Structure-Activity Relationship (3D QSAR) Investigation.

    PubMed

    Modi, Gyan; Sharma, Horrick; Kharkar, Prashant S; Dutta, Aloke K

    2014-09-01

    To gain insights into the structural requirements for dopamine D2 and D3 agonists in the treatment of Parkinson's disease (PD) and to elucidate the basis of selectivity for D3 over D2 (D2/D3), 3D quantitative structure-activity relationship (3D QSAR) investigations using CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indices analysis) methods were performed on a series of 45 structurally related D2 and D3 dopaminergic ligands. Two alignment methods (atom-based and flexible) and two charge calculation methods (Gasteiger-Hückel and AM1) were used in the present study. Overall, D2 affinity and selectivity (D2/D3) models performed better with r(2)cv values of 0.71 and 0.63 for CoMFA and 0.71 and 0.79 for CoMSIA, respectively. The corresponding predictive r(2) values for the CoMFA and CoMSIA models were 0.92 and 0.86 and 0.91 and 0.78, respectively. The CoMFA models generated using flexible alignment outperformed the models with the atom-based alignment in terms of relevant statistics and interpretability of the generated contour maps while CoMSIA models obtained using atom-based alignment showed superiority in terms of internal and external predictive abilities. The presence of carbonyl group (C=O) attached to the piperazine ring and the hydrophobic biphenyl ring were found to be the most important features responsible for the D3 selectivity over D2. This study can be further utilized to design and develop selective and potent dopamine agonists to treat PD.

  15. Energy-Based Pharmacophore and Three-Dimensional Quantitative Structure--Activity Relationship (3D-QSAR) Modeling Combined with Virtual Screening To Identify Novel Small-Molecule Inhibitors of Silent Mating-Type Information Regulation 2 Homologue 1 (SIRT1).

    PubMed

    Pulla, Venkat Koushik; Sriram, Dinavahi Saketh; Viswanadha, Srikant; Sriram, Dharmarajan; Yogeeswari, Perumal

    2016-01-25

    Silent mating-type information regulation 2 homologue 1 (SIRT1), being the homologous enzyme of silent information regulator-2 gene in yeast, has multifaceted functions. It deacetylates a wide range of histone and nonhistone proteins; hence, it has good therapeutic importance. SIRT1 was believed to be overexpressed in many cancers (prostate, colon) and inflammatory disorders (rheumatoid arthritis). Hence, designing inhibitors against SIRT1 could be considered valuable. Both structure-based and ligand-based drug design strategies were employed to design novel inhibitors utilizing high-throughput virtual screening of chemical databases. An energy-based pharmacophore was generated using the crystal structure of SIRT1 bound with a small molecule inhibitor and compared with a ligand-based pharmacophore model that showed four similar features. A three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed and validated to be employed in the virtual screening protocol. Among the designed compounds, Lead 17 emerged as a promising SIRT1 inhibitor with IC50 of 4.34 μM and, at nanomolar concentration (360 nM), attenuated the proliferation of prostate cancer cells (LnCAP). In addition, Lead 17 significantly reduced production of reactive oxygen species, thereby reducing pro inflammatory cytokines such as IL6 and TNF-α. Furthermore, the anti-inflammatory potential of the compound was ascertained using an animal paw inflammation model induced by carrageenan. Thus, the identified SIRT1 inhibitors could be considered as potent leads to treat both cancer and inflammation.

  16. Energy-Based Pharmacophore and Three-Dimensional Quantitative Structure--Activity Relationship (3D-QSAR) Modeling Combined with Virtual Screening To Identify Novel Small-Molecule Inhibitors of Silent Mating-Type Information Regulation 2 Homologue 1 (SIRT1).

    PubMed

    Pulla, Venkat Koushik; Sriram, Dinavahi Saketh; Viswanadha, Srikant; Sriram, Dharmarajan; Yogeeswari, Perumal

    2016-01-25

    Silent mating-type information regulation 2 homologue 1 (SIRT1), being the homologous enzyme of silent information regulator-2 gene in yeast, has multifaceted functions. It deacetylates a wide range of histone and nonhistone proteins; hence, it has good therapeutic importance. SIRT1 was believed to be overexpressed in many cancers (prostate, colon) and inflammatory disorders (rheumatoid arthritis). Hence, designing inhibitors against SIRT1 could be considered valuable. Both structure-based and ligand-based drug design strategies were employed to design novel inhibitors utilizing high-throughput virtual screening of chemical databases. An energy-based pharmacophore was generated using the crystal structure of SIRT1 bound with a small molecule inhibitor and compared with a ligand-based pharmacophore model that showed four similar features. A three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed and validated to be employed in the virtual screening protocol. Among the designed compounds, Lead 17 emerged as a promising SIRT1 inhibitor with IC50 of 4.34 μM and, at nanomolar concentration (360 nM), attenuated the proliferation of prostate cancer cells (LnCAP). In addition, Lead 17 significantly reduced production of reactive oxygen species, thereby reducing pro inflammatory cytokines such as IL6 and TNF-α. Furthermore, the anti-inflammatory potential of the compound was ascertained using an animal paw inflammation model induced by carrageenan. Thus, the identified SIRT1 inhibitors could be considered as potent leads to treat both cancer and inflammation. PMID:26636371

  17. Prediction of activated carbon adsorption capacities for organic vapors using quantitative structure-activity relationship methods

    SciTech Connect

    Nirmalakhandan, N.N. ); Speece, R.E. )

    1993-08-01

    Quantitative structure-activity relationship (QSAR) methods were used to develop models to estimate and predict activated carbon adsorption capacities for organic vapors. Literature isothermal data from two sources for 22 organic contaminants on six different carbons were merged to form a training set of 75 data points. Two different QSAR approaches were evaluated: the molecular connectivity approach and the linear solvation energy relationship approach. The QSAR model developed in this study using the molecular connectivity approach was able to fit the experimental data with r = 0.96 and standard error of 0.09. The utility of the model was demonstrated by using predicted k values to calculate adsorption capacities of 12 chemicals on two different carbons and comparing them with experimentally determined values. 9 refs., 1 fig., 3 tabs.

  18. Synthesis, Evaluation of Anticancer Activity and QSAR Study of Heterocyclic Esters of Caffeic Acid

    PubMed Central

    Hajmohamad Ebrahim Ketabforoosh, Shima; Amini, Mohsen; Vosooghi, Mohsen; Shafiee, Abbas; Azizi, Ebrahim; Kobarfard, Farzad

    2013-01-01

    Caffeic acid phenethyl ester (CAPE) suppresses the growth of transformed cells such as human breast cancer cells, hepatocarcinoma , myeloid leukemia, colorectal cancer cells, fibrosarcoma, glioma and melanoma. A group of heterocyclic esters of caffeic acid was synthesized using Mitsunobu reaction and the esters were subjected to further structural modification by electrooxidation of the catechol ring of caffeic acid esters in the presence of sodium benzenesulfinate and sodium toluensulfinate as nucleophiles. Both heterocyclic esters of caffeic acid and their arylsulfonyl derivatives were evaluated for their cytotoxic activity against HeLa, SK-OV-3, and HT-29 cancer cell lines. HeLa cells showed the highest sensitivity to the compounds and heterocyclic esters with no substituent on catechol ring showed better activity compared to their substituted counterparts. QSAR studies reemphasized the importance of molecular shape of the compounds for their cytotoxic activity. PMID:24523750

  19. Molecular docking and 3D-QSAR studies on the glucocorticoid receptor antagonistic activity of hydroxylated polychlorinated biphenyls.

    PubMed

    Liu, S; Luo, Y; Fu, J; Zhou, J; Kyzas, G Z

    2016-01-01

    The glucocorticoid receptor (GR) antagonistic activities of hydroxylated polychlorinated biphenyls (HO-PCBs) were recently characterised. To further explore the interactions between HO-PCBs and the GR, and to elucidate structural characteristics that influence the GR antagonistic activity of HO-PCBs, molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed. Comparative molecular similarity indices analysis (CoMSIA) was performed using both ligand- and receptor-based alignment schemes. Results generated from the receptor-based model were found to be more satisfactory, with q(2) of 0.632 and r(2) of 0.931 compared with those from the ligand-based model. Some internal validation strategies (e.g. cross-validation analysis, bootstrapping analysis and Y-randomisation) and an external validation method were used respectively to further assess the stability and predictive ability of the derived model. Graphical interpretation of the model provided some insights into the structural features that affected the GR antagonistic activity of HO-PCBs. Molecular docking studies revealed that some key residues were critical for ligand-receptor interactions by forming hydrogen bonds (Glu540) and hydrophobic interactions with ligands (Ile539, Val543 and Trp577). Although CoMSIA sometimes depends on the alignment of the molecules, the information provided is beneficial for predicting the GR antagonistic activities of HO-PCB homologues and is helpful for understanding the binding mechanisms of HO-PCBs to GR. PMID:26848875

  20. Quantitative structure-activity relationships of selective antagonists of glucagon receptor using QuaSAR descriptors.

    PubMed

    Manoj Kumar, Palanivelu; Karthikeyan, Chandrabose; Hari Narayana Moorthy, Narayana Subbiah; Trivedi, Piyush

    2006-11-01

    In the present paper, quantitative structure activity relationship (QSAR) approach was applied to understand the affinity and selectivity of a novel series of triaryl imidazole derivatives towards glucagon receptor. Statistically significant and highly predictive QSARs were derived for glucagon receptor inhibition by triaryl imidazoles using QuaSAR descriptors of molecular operating environment (MOE) employing computer-assisted multiple regression procedure. The generated QSAR models revealed that factors related to hydrophobicity, molecular shape and geometry predominantly influences glucagon receptor binding affinity of the triaryl imidazoles indicating the relevance of shape specific steric interactions between the molecule and the receptor. Further, QSAR models formulated for selective inhibition of glucagon receptor over p38 mitogen activated protein (MAP) kinase of the compounds in the series highlights that the same structural features, which influence the glucagon receptor affinity, also contribute to their selective inhibition.

  1. Ecotoxicity quantitative structure-activity relationships for alcohol ethoxylate mixtures based on substance-specific toxicity predictions.

    PubMed

    Boeije, G M; Cano, M L; Marshall, S J; Belanger, S E; Van Compernolle, R; Dorn, P B; Gümbel, H; Toy, R; Wind, T

    2006-05-01

    Traditionally, ecotoxicity quantitative structure-activity relationships (QSARs) for alcohol ethoxylate (AE) surfactants have been developed by assigning the measured ecotoxicity for commercial products to the average structures (alkyl chain length and ethoxylate chain length) of these materials. Acute Daphnia magna toxicity tests for binary mixtures indicate that mixtures are more toxic than the individual AE substances corresponding with their average structures (due to the nonlinear relation of toxicity with structure). Consequently, the ecotoxicity value (expressed as effects concentration) attributed to the average structures that are used to develop the existing QSARs is expected to be too low. A new QSAR technique for complex substances, which interprets the mixture toxicity with regard to the "ethoxymers" distribution (i.e., the individual AE components) rather than the average structure, was developed. This new technique was then applied to develop new AE ecotoxicity QSARs for invertebrates, fish, and mesocosms. Despite the higher complexity, the fit and accuracy of the new QSARs are at least as good as those for the existing QSARs based on the same data set. As expected from typical ethoxymer distributions of commercial AEs, the new QSAR generally predicts less toxicity than the QSARs based on average structure. PMID:16256196

  2. Hyaluronidase Inhibitory Activity of Pentacylic Triterpenoids from Prismatomeris tetrandra (Roxb.) K. Schum: Isolation, Synthesis and QSAR Study

    PubMed Central

    Abdullah, Nor Hayati; Thomas, Noel Francis; Sivasothy, Yasodha; Lee, Vannajan Sanghiran; Liew, Sook Yee; Noorbatcha, Ibrahim Ali; Awang, Khalijah

    2016-01-01

    The mammalian hyaluronidase degrades hyaluronic acid by the cleavage of the β-1,4-glycosidic bond furnishing a tetrasaccharide molecule as the main product which is a highly angiogenic and potent inducer of inflammatory cytokines. Ursolic acid 1, isolated from Prismatomeris tetrandra, was identified as having the potential to develop inhibitors of hyaluronidase. A series of ursolic acid analogues were either synthesized via structure modification of ursolic acid 1 or commercially obtained. The evaluation of the inhibitory activity of these compounds on the hyaluronidase enzyme was conducted. Several structural, topological and quantum chemical descriptors for these compounds were calculated using semi empirical quantum chemical methods. A quantitative structure activity relationship study (QSAR) was performed to correlate these descriptors with the hyaluronidase inhibitory activity. The statistical characteristics provided by the best multi linear model (BML) (R2 = 0.9717, R2cv = 0.9506) indicated satisfactory stability and predictive ability of the developed model. The in silico molecular docking study which was used to determine the binding interactions revealed that the ursolic acid analog 22 had a strong affinity towards human hyaluronidase. PMID:26907251

  3. Quantitative structure-activity relationship of antifungal activity of rosin derivatives.

    PubMed

    Wang, Hui; Nguyen, Thi Thanh Hien; Li, Shujun; Liang, Tao; Zhang, Yuanyuan; Li, Jian

    2015-01-15

    To develop new rosin-based wood preservatives with good antifungal activity, 24 rosin derivatives were synthesized, bioassay tested with Trametes versicolor and Gloeophyllum trabeum, and subjected to analysis of their quantitative structure-activity relationships (QSAR). A QSAR analysis using Ampac 9.2.1 and Codessa 2.7.16 software built two QSAR models of antifungal ratio for T. versicolor and G. trabeum with values of R(2)=0.9740 and 0.9692, respectively. Based on the models, tri-N-(3-hydroabietoxy-2-hydroxy) propyl-triethyl ammonium chloride was designed and the bioassay test result proved its better inhibitory effect against the two selected fungi as expected.

  4. Quantitative structure-activity relationship of antifungal activity of rosin derivatives.

    PubMed

    Wang, Hui; Nguyen, Thi Thanh Hien; Li, Shujun; Liang, Tao; Zhang, Yuanyuan; Li, Jian

    2015-01-15

    To develop new rosin-based wood preservatives with good antifungal activity, 24 rosin derivatives were synthesized, bioassay tested with Trametes versicolor and Gloeophyllum trabeum, and subjected to analysis of their quantitative structure-activity relationships (QSAR). A QSAR analysis using Ampac 9.2.1 and Codessa 2.7.16 software built two QSAR models of antifungal ratio for T. versicolor and G. trabeum with values of R(2)=0.9740 and 0.9692, respectively. Based on the models, tri-N-(3-hydroabietoxy-2-hydroxy) propyl-triethyl ammonium chloride was designed and the bioassay test result proved its better inhibitory effect against the two selected fungi as expected. PMID:25466709

  5. Obscure phenomena in statistical analysis of quantitative structure-activity relationships. Part 1: Multicollinearity of physicochemical descriptors.

    PubMed

    Mager, P P; Rothe, H

    1990-10-01

    Multicollinearity of physicochemical descriptors leads to serious consequences in quantitative structure-activity relationship (QSAR) analysis, such as incorrect estimators and test statistics of regression coefficients of the ordinary least-squares (OLS) model applied usually to QSARs. Beside the diagnosis of the known simple collinearity, principal component regression analysis (PCRA) also allows the diagnosis of various types of multicollinearity. Only if the absolute values of PCRA estimators are order statistics that decrease monotonically, the effects of multicollinearity can be circumvented. Otherwise, obscure phenomena may be observed, such as good data recognition but low predictive model power of a QSAR model.

  6. Evaluation and QSAR modeling on multiple endpoints of estrogen activity based on different bioassays.

    PubMed

    Liu, Huanxiang; Papa, Ester; Gramatica, Paola

    2008-02-01

    There is a great need for an effective means of rapidly assessing endocrine-disrupting activity, especially estrogen-simulating activity, due to the large number of chemicals that have serious adverse effects on the environment. Many approaches using a variety of biological screening assays are used to identify endocrine disrupting chemicals. The present investigation analyzes the consistency and peculiarity of information from different experimental assays collected from a literature survey, by studying the correlation of the different endpoints. In addition, the activity values of more widely used selected bioassays have been combined by principle components analysis (PCA) to build one cumulative endpoint, the estrogen activity index (EAI), for priority setting to identify chemicals most likely possessing estrogen activity for early entry into screening. This index was then modeled using only a few theoretical molecular descriptors. The constructed MLR-QSAR model has been statistically validated for its predictive power, and can be proposed as a preliminary evaluative method to screen/prioritize estrogens according to their integrated estrogen activity, just starting from molecular structure.

  7. Molecular docking, molecular dynamics simulation, and structure-based 3D-QSAR studies on the aryl hydrocarbon receptor agonistic activity of hydroxylated polychlorinated biphenyls.

    PubMed

    Cao, Fu; Li, Xiaolin; Ye, Li; Xie, Yuwei; Wang, Xiaoxiang; Shi, Wei; Qian, Xiangping; Zhu, Yongliang; Yu, Hongxia

    2013-09-01

    The binding interactions between hydroxylated polychlorinated biphenyls (HO-PCBs) and the aryl hydrocarbon receptor (AhR) are suspected of causing toxic effects. To understand the binding mode between HO-PCBs and AhR, and to explore the structural characteristics that influence the AhR agonistic activities of HO-PCBs, the combination of molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR), and molecular dynamics (MD) simulations was performed. Using molecular docking, the HO-PCBs were docked into the binding pocket of AhR, which was generated by homology modeling. Comparative molecular similarity index analysis (CoMSIA) models were subsequently developed from three different alignment rules. The optimum 3D-QSAR model showed good predictive ability (q(2)=0.583, R(2)=0.913) and good mechanism interpretability. The statistical reliability of the CoMSIA model was also validated. In addition, molecular docking and MD simulations were applied to explore the binding modes between the ligands and AhR. The results obtained from this study may lead to a better understanding of the interaction mechanism between HO-PCBs and AhR.

  8. Monte Carlo QSAR models for predicting organophosphate inhibition of acetycholinesterase.

    PubMed

    Veselinović, J B; Nikolić, G M; Trutić, N V; Živković, J V; Veselinović, A M

    2015-06-01

    A series of 278 organophosphate compounds acting as acetylcholinesterase inhibitors has been studied. The Monte Carlo method was used as a tool for building up one-variable quantitative structure-activity relationship (QSAR) models for acetylcholinesterase inhibition activity based on the principle that the target endpoint is treated as a random event. As an activity, bimolecular rate constants were used. The QSAR models were based on optimal descriptors obtained from Simplified Molecular Input-Line Entry System (SMILES) used for the representation of molecular structure. Two modelling approaches were examined: (1) 'classic' training-test system where the QSAR model was built with one random split into a training, test and validation set; and (2) the correlation balance based QSAR models were built with two random splits into a sub-training, calibration, test and validation set. The DModX method was used for defining the applicability domain. The obtained results suggest that studied activity can be determined with the application of QSAR models calculated with the Monte Carlo method since the statistical quality of all build models was very good. Finally, structural indicators for the increase and the decrease of the bimolecular rate constant are defined. The possibility of using these results for the computer-aided design of new organophosphate compounds is presented.

  9. Predictive QSAR modeling workflow, model applicability domains, and virtual screening.

    PubMed

    Tropsha, Alexander; Golbraikh, Alexander

    2007-01-01

    Quantitative Structure Activity Relationship (QSAR) modeling has been traditionally applied as an evaluative approach, i.e., with the focus on developing retrospective and explanatory models of existing data. Model extrapolation was considered if only in hypothetical sense in terms of potential modifications of known biologically active chemicals that could improve compounds' activity. This critical review re-examines the strategy and the output of the modern QSAR modeling approaches. We provide examples and arguments suggesting that current methodologies may afford robust and validated models capable of accurate prediction of compound properties for molecules not included in the training sets. We discuss a data-analytical modeling workflow developed in our laboratory that incorporates modules for combinatorial QSAR model development (i.e., using all possible binary combinations of available descriptor sets and statistical data modeling techniques), rigorous model validation, and virtual screening of available chemical databases to identify novel biologically active compounds. Our approach places particular emphasis on model validation as well as the need to define model applicability domains in the chemistry space. We present examples of studies where the application of rigorously validated QSAR models to virtual screening identified computational hits that were confirmed by subsequent experimental investigations. The emerging focus of QSAR modeling on target property forecasting brings it forward as predictive, as opposed to evaluative, modeling approach.

  10. Variable selection for QSAR by artificial ant colony systems.

    PubMed

    Izrailev, S; Agrafiotis, D K

    2002-01-01

    Derivation of quantitative structure-activity relationships (QSAR) usually involves computational models that relate a set of input variables describing the structural properties of the molecules for which the activity has been measured to the output variable representing activity. Many of the input variables may be correlated, and it is therefore often desirable to select an optimal subset of the input variables that results in the most predictive model. In this paper we describe an optimization technique for variable selection based on artificial ant colony systems. The algorithm is inspired by the behavior of real ants, which are able to find the shortest path between a food source and their nest using deposits of pheromone as a communication agent. The underlying basic self-organizing principle is exploited for the construction of parsimonious QSAR models based on neural networks for several classical QSAR data sets. PMID:12184383

  11. Constrained NBMPR Analogue Synthesis, Pharmacophore Mapping and 3D-QSAR Modeling of Equilibrative nucleoside Transporter 1 (ENT1) Inhibitory Activity

    PubMed Central

    Zhu, Zhengxiang; Buolamwini, John K.

    2009-01-01

    Conformationally constrained analogue synthesis was undertaken to aid in pharmacophore mapping and 3D QSAR analysis of nitrobenzylmercaptopurine riboside (NBMPR) congeners as equilibriative nucleoside transporter 1 (ENT1) inhibitors. In our previous study (Zhu et al., J. Med. Chem. 46, 831–837, 2003), novel regioisomeric nitro-1, 2, 3, 4-tetrahydroisoquinoline conformationally constrained analogues of NBMPR were synthesized and evaluated as ENT1 ligands. 7-NO2-1, 2, 3, 4-tetrahydroisoquino-2-yl purine riboside was identified as the analogue with the nitro group in the best orientation at the NBMPR binding site of ENT1. In the present study, further conformational constraining was introduced by synthesizing 5′-O, 8-cyclo derivatives. The flow cytometrically determined binding affinities indicated that the additional 5′-O, 8-cyclo constraining was unfavorable for binding to the ENT1 transporter. The structure-activity relationship (SAR) acquired was applied to pharmacophore mapping using the PHASE program. The best pharmacophore hypothesis obtained embodied an anti-conformation with three H-bond acceptors, one hydrophobic center, and two aromatic rings involving the 3′-OH, 4′-oxygen, the NO2 group, the benzyl phenyl and the imidazole and pyrimidine portions of the purine ring, respectively. A PHASE 3D-QSAR model derived with this pharmacophore yielded an r2 of 0.916 for four (4) PLS components, and an excellent external test set predictive r2 of 0.78 for 39 compounds. This pharmacophore was used for molecular alignment in a comparative molecular field analysis (CoMFA) 3D-QSAR study that also afforded a predictive model with external test set validation predictive r2 of 0.73. Thus, although limited, this study suggests that the bioactive conformation for NBMPR at the ENT1 transporter could be anti. The study has also suggested an ENT1 inhibitory pharmacophore, and established a predictive CoMFA 3D-QSAR model that might be useful for novel ENT1 inhibitor

  12. Are the Chemical Structures in your QSAR Correct?

    EPA Science Inventory

    Quantitative structure-activity relationships (QSARs) are used to predict many different endpoints, utilize hundreds and even thousands of different parameters (or descriptors), and are created using a variety of approaches. The one thing they all have in common is the assumptio...

  13. QSAR models for estimating properties of persistent organic pollutants required in evaluation of their environmental fate and risk.

    PubMed

    Sabljic, A

    2001-04-01

    The molecular connectivity indices (MCIs) have been successfully used for over 20 years in quantitative structure activity relationships (QSAR) modelling in various areas of physics, chemistry, biology, drug design, and environmental sciences. With this review, we hope to assist present and future QSAR practitioners to apply MCIs more wisely and more critically. First, we have described the methods of calculation and systematics of MCIs. This section should be helpful in rational selection of MCIs for QSAR modelling. Then we have presented our long-term experience in the application of MCIs through several characteristic and successful QSAR models for estimating partitioning and chromatographic properties of persistent organic pollutants (POPs). We have also analysed the trends in calculated MCIs and discussed their physical interpretation. In conclusion, several practical recommendations and warnings, based on our research experience, have been given for the application of MCIs in the QSAR modelling. PMID:11302582

  14. 3D-QSAR - Applications, Recent Advances, and Limitations

    NASA Astrophysics Data System (ADS)

    Sippl, Wolfgang

    Three-dimensional quantitative structure-activity relationship (3D-QSAR) techniques are the most prominent computational means to support chemistry within drug design projects where no three-dimensional structure of the macromolecular target is available. The primary aim of these techniques is to establish a correlation of biological activities of a series of structurally and biologically characterized compounds with the spatial fingerprints of numerous field properties of each molecule, such as steric demand, lipophilicity, and electrostatic interactions. The number of 3D-QSAR studies has exponentially increased over the last decade, since a variety of methods are commercially available in user-friendly, graphically guided software. In this chapter, we will review recent advances, known limitations, and the application of receptor-based 3D-QSAR

  15. Quantitative Structure-Antifungal Activity Relationships for cinnamate derivatives.

    PubMed

    Saavedra, Laura M; Ruiz, Diego; Romanelli, Gustavo P; Duchowicz, Pablo R

    2015-12-01

    Quantitative Structure-Activity Relationships (QSAR) are established with the aim of analyzing the fungicidal activities of a set of 27 active cinnamate derivatives. The exploration of more than a thousand of constitutional, topological, geometrical and electronic molecular descriptors, which are calculated with Dragon software, leads to predictions of the growth inhibition on Pythium sp and Corticium rolfsii fungi species, in close agreement to the experimental values extracted from the literature. A set containing 21 new structurally related cinnamate compounds is prepared. The developed QSAR models are applied to predict the unknown fungicidal activity of this set, showing that cinnamates like 38, 28 and 42 are expected to be highly active for Pythium sp, while this is also predicted for 28 and 34 in C. rolfsii. PMID:26410195

  16. QSAR Models at the US FDA/NCTR.

    PubMed

    Hong, Huixiao; Chen, Minjun; Ng, Hui Wen; Tong, Weida

    2016-01-01

    Quantitative structure-activity relationship (QSAR) has been used in the scientific research community for many decades and applied to drug discovery and development in the industry. QSAR technologies are advancing fast and attracting possible applications in regulatory science. To facilitate the development of reliable QSAR models, the FDA had invested a lot of efforts in constructing chemical databases with a variety of efficacy and safety endpoint data, as well as in the development of computational algorithms. In this chapter, we briefly describe some of the often used databases developed at the FDA such as EDKB (Endocrine Disruptor Knowledge Base), EADB (Estrogenic Activity Database), LTKB (Liver Toxicity Knowledge Base), and CERES (Chemical Evaluation and Risk Estimation System) and the technologies adopted by the agency such as Mold(2) program for calculation of a large and diverse set of molecular descriptors and decision forest algorithm for QSAR model development. We also summarize some QSAR models that have been developed for safety evaluation of the FDA-regulated products. PMID:27311476

  17. Receptor-based 3D-QSAR in Drug Design: Methods and Applications in Kinase Studies.

    PubMed

    Fang, Cheng; Xiao, Zhiyan

    2016-01-01

    Receptor-based 3D-QSAR strategy represents a superior integration of structure-based drug design (SBDD) and three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis. It combines the accurate prediction of ligand poses by the SBDD approach with the good predictability and interpretability of statistical models derived from the 3D-QSAR approach. Extensive efforts have been devoted to the development of receptor-based 3D-QSAR methods and two alternative approaches have been exploited. One associates with computing the binding interactions between a receptor and a ligand to generate structure-based descriptors for QSAR analyses. The other concerns the application of various docking protocols to generate optimal ligand poses so as to provide reliable molecular alignments for the conventional 3D-QSAR operations. This review highlights new concepts and methodologies recently developed in the field of receptorbased 3D-QSAR, and in particular, covers its application in kinase studies.

  18. (Q)SAR assessments of potentially mutagenic impurities: a regulatory perspective on the utility of expert knowledge and data submission.

    PubMed

    Powley, Mark W

    2015-03-01

    (Quantitative) structure activity relationship [(Q)SAR] modeling is the primary tool used to evaluate the mutagenic potential associated with drug impurities. General recommendations regarding the use of (Q)SAR in regulatory decision making have recently been provided in the ICH M7 guideline. Although (Q)SAR alone is capable of achieving reasonable sensitivity and specificity, reliance on a simple positive or negative prediction can be problematic. The key to improving (Q)SAR performance is to integrate supporting information, also referred to as expert knowledge, into the final conclusion. In the regulatory context, expert knowledge is intended to (1) maximize confidence in a (Q)SAR prediction, (2) provide rationale to supersede a positive or negative (Q)SAR prediction, or (3) provide a basis for assessing mutagenicity in absence of a (Q)SAR prediction. Expert knowledge is subjective and is associated with great variability in regards to content and quality. However, it is still a critical component of impurity evaluations and its utility is acknowledged in the ICH M7 guideline. The current paper discusses the use of expert knowledge to support regulatory decision making, describes case studies, and provides recommendations for reporting data from (Q)SAR evaluations.

  19. Quantitative structure-activity relationship study on BTK inhibitors by modified multivariate adaptive regression spline and CoMSIA methods.

    PubMed

    Xu, A; Zhang, Y; Ran, T; Liu, H; Lu, S; Xu, J; Xiong, X; Jiang, Y; Lu, T; Chen, Y

    2015-01-01

    Bruton's tyrosine kinase (BTK) plays a crucial role in B-cell activation and development, and has emerged as a new molecular target for the treatment of autoimmune diseases and B-cell malignancies. In this study, two- and three-dimensional quantitative structure-activity relationship (2D and 3D-QSAR) analyses were performed on a series of pyridine and pyrimidine-based BTK inhibitors by means of genetic algorithm optimized multivariate adaptive regression spline (GA-MARS) and comparative molecular similarity index analysis (CoMSIA) methods. Here, we propose a modified MARS algorithm to develop 2D-QSAR models. The top ranked models showed satisfactory statistical results (2D-QSAR: Q(2) = 0.884, r(2) = 0.929, r(2)pred = 0.878; 3D-QSAR: q(2) = 0.616, r(2) = 0.987, r(2)pred = 0.905). Key descriptors selected by 2D-QSAR were in good agreement with the conclusions of 3D-QSAR, and the 3D-CoMSIA contour maps facilitated interpretation of the structure-activity relationship. A new molecular database was generated by molecular fragment replacement (MFR) and further evaluated with GA-MARS and CoMSIA prediction. Twenty-five pyridine and pyrimidine derivatives as novel potential BTK inhibitors were finally selected for further study. These results also demonstrated that our method can be a very efficient tool for the discovery of novel potent BTK inhibitors.

  20. Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization

    SciTech Connect

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

    2015-04-15

    Skin permeability is widely considered to be mechanistically implicated in chemically-induced skin sensitization. Although many chemicals have been identified as skin sensitizers, there have been very few reports analyzing the relationships between molecular structure and skin permeability of sensitizers and non-sensitizers. The goals of this study were to: (i) compile, curate, and integrate the largest publicly available dataset of chemicals studied for their skin permeability; (ii) develop and rigorously validate QSAR models to predict skin permeability; and (iii) explore the complex relationships between skin sensitization and skin permeability. Based on the largest publicly available dataset compiled in this study, we found no overall correlation between skin permeability and skin sensitization. In addition, cross-species correlation coefficient between human and rodent permeability data was found to be as low as R{sup 2} = 0.44. Human skin permeability models based on the random forest method have been developed and validated using OECD-compliant QSAR modeling workflow. Their external accuracy was high (Q{sup 2}{sub ext} = 0.73 for 63% of external compounds inside the applicability domain). The extended analysis using both experimentally-measured and QSAR-imputed data still confirmed the absence of any overall concordance between skin permeability and skin sensitization. This observation suggests that chemical modifications that affect skin permeability should not be presumed a priori to modulate the sensitization potential of chemicals. The models reported herein as well as those developed in the companion paper on skin sensitization suggest that it may be possible to rationally design compounds with the desired high skin permeability but low sensitization potential. - Highlights: • It was compiled the largest publicly-available skin permeability dataset. • Predictive QSAR models were developed for skin permeability. • No concordance between skin

  1. Comparative QSAR studies on peptide deformylase inhibitors.

    PubMed

    Lee, Ji Young; Doddareddy, Munikumar Reddy; Cho, Yong Seo; Choo, Hyunah; Koh, Hun Yeong; Kang, Jae-Hoon; No, Kyoung Tai; Pae, Ae Nim

    2007-05-01

    Comparative quantitative structure-activity relationship (QSAR) analyses of peptide deformylase (PDF) inhibitors were performed with a series of previously published (British Biotech Pharmaceuticals, Oxford, UK) reverse hydroxamate derivatives having antibacterial activity against Escherichia coli PDF, using 2D and 3D QSAR methods, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR). Statistically reliable models with good predictive power were generated from all three methods (CoMFA r (2) = 0.957, q (2) = 0.569; CoMSIA r (2) = 0.924, q (2) = 0.520; HQSAR r (2) = 0.860, q (2) = 0.578). The predictive capability of these models was validated by a set of compounds that were not included in the training set. The models based on CoMFA and CoMSIA gave satisfactory predictive r (2) values of 0.687 and 0.505, respectively. The model derived from the HQSAR method showed a low predictability of 0.178 for the test set. In this study, 3D prediction models showed better predictive power than 2D models for the test set. This might be because 3D information is more important in the case of datasets containing compounds with similar skeletons. Superimposition of CoMFA contour maps in the active site of the PDF crystal structure showed a meaningful correlation between receptor-ligand binding and biological activity. The final QSAR models, along with information gathered from 3D contour and 2D contribution maps, could be useful for the design of novel active inhibitors of PDF. PMID:17333308

  2. Quantitative structure-activity relationship models of chemical transformations from matched pairs analyses.

    PubMed

    Beck, Jeremy M; Springer, Clayton

    2014-04-28

    The concepts of activity cliffs and matched molecular pairs (MMP) are recent paradigms for analysis of data sets to identify structural changes that may be used to modify the potency of lead molecules in drug discovery projects. Analysis of MMPs was recently demonstrated as a feasible technique for quantitative structure-activity relationship (QSAR) modeling of prospective compounds. Although within a small data set, the lack of matched pairs, and the lack of knowledge about specific chemical transformations limit prospective applications. Here we present an alternative technique that determines pairwise descriptors for each matched pair and then uses a QSAR model to estimate the activity change associated with a chemical transformation. The descriptors effectively group similar transformations and incorporate information about the transformation and its local environment. Use of a transformation QSAR model allows one to estimate the activity change for novel transformations and therefore returns predictions for a larger fraction of test set compounds. Application of the proposed methodology to four public data sets results in increased model performance over a benchmark random forest and direct application of chemical transformations using QSAR-by-matched molecular pairs analysis (QSAR-by-MMPA).

  3. Use of QSARs in international decision-making frameworks to predict ecologic effects and environmental fate of chemical substances.

    PubMed Central

    Cronin, Mark T D; Walker, John D; Jaworska, Joanna S; Comber, Michael H I; Watts, Christopher D; Worth, Andrew P

    2003-01-01

    This article is a review of the use, by regulatory agencies and authorities, of quantitative structure-activity relationships (QSARs) to predict ecologic effects and environmental fate of chemicals. For many years, the U.S. Environmental Protection Agency has been the most prominent regulatory agency using QSARs to predict the ecologic effects and environmental fate of chemicals. However, as increasing numbers of standard QSAR methods are developed and validated to predict ecologic effects and environmental fate of chemicals, it is anticipated that more regulatory agencies and authorities will find them to be acceptable alternatives to chemical testing. PMID:12896861

  4. QSAR modeling of the inhibition of glycogen synthase kinase-3.

    PubMed

    Katritzky, Alan R; Pacureanu, Liliana M; Dobchev, Dimitar A; Fara, Dan C; Duchowicz, Pablo R; Karelson, Mati

    2006-07-15

    Quantitative structure-activity relationship (QSAR) models of the biological activity (pIC50) of 277 inhibitors of Glycogen Synthase Kinase-3 (GSK-3) are developed using geometrical, topological, quantum mechanical, and electronic descriptors calculated by CODESSA PRO. The linear (multilinear regression) and nonlinear (artificial neural network) models obtained link the structures to their reported activity pIC50. The results are discussed in the light of the main factors that influence the inhibitory activity of the GSK-3 enzyme.

  5. 5-N-Substituted-2-(substituted benzenesulphonyl) glutamines as antitumor agents. Part II: synthesis, biological activity and QSAR study.

    PubMed

    Samanta, Soma; Srikanth, K; Banerjee, Suchandra; Debnath, Bikash; Gayen, Shovanlal; Jha, Tarun

    2004-03-15

    Cancer is a major killer disease throughout human history. Thus, cancer becomes a major point of interest in life science. It was proved that cancer is a nitrogen trap and tumor cells are avid glutamine consumers. The non-essential amino acid glutamine, which is a glutamic acid derivative, supplies its amide nitrogen to tumor cells in the biosynthesis of purine and pyrimidine bases of nucleic acids as well as takes part in protein synthesis. Based on these and in continuation of our composite programme of development of new potential anticancer agents through rational drug design, 17 new 5-N-Substituted-2-(substituted benzenesulphonyl) glutamines were selected for synthesis. These compounds as well as 36 earlier synthesized glutamine analogues were screened for antitumor activity using percentage inhibition of tumor cell count as the activity parameter. QSAR study was performed with 53 compounds in order to design leads with increased effectiveness for antitumor activity using both physicochemical and topological parameters. QSAR study showed that steric effect on the aromatic ring is conducive to the activity. n-butyl substitution on aliphatic side chain and atom no 12 is important for antitumor activity of glutamine analogues.

  6. Comparative Analysis of QSAR-based vs. Chemical Similarity Based Predictors of GPCRs Binding Affinity.

    PubMed

    Luo, Man; Wang, Xiang S; Tropsha, Alexander

    2016-01-01

    Ligand based virtual screening (LBVS) approaches could be broadly divided into those relying on chemical similarity searches and those employing Quantitative Structure-Activity Relationship (QSAR) models. We have compared the predictive power of these approaches using some datasets of compounds tested against several G-Protein Coupled Receptors (GPCRs). The k-Nearest Neighbors (kNN) QSAR models were built for known ligands of each GPCR target independently, with a fraction of tested ligands for each target set aside as a validation set. The prediction accuracies of QSAR models for making active/inactive calls for compounds in both training and validation sets were compared to those achieved by the Prediction of Activity Spectra for Substances' (PASS) and the Similarity Ensemble Approach (SEA) tools both available online. Models developed with the kNN QSAR method showed the highest predictive power for almost all tested GPCR datasets. The PASS software, which incorporates multiple end-point specific QSAR models demonstrated a moderate predictive power, while SEA, a chemical similarity based approach, had the lowest prediction power. Our studies suggest that when sufficient amount of data is available to develop and rigorously validate QSAR models such models should be chosen as the preferred virtual screening tool in ligand-based computational drug discovery as compared to chemical similarity based approaches. PMID:27491652

  7. Residual-QSAR. Implications for genotoxic carcinogenesis

    PubMed Central

    2011-01-01

    Introduction Both main types of carcinogenesis, genotoxic and epigenetic, were examined in the context of non-congenericity and similarity, respectively, for the structure of ligand molecules, emphasizing the role of quantitative structure-activity relationship ((Q)SAR) studies in accordance with OECD (Organization for Economic and Cooperation Development) regulations. The main purpose of this report involves electrophilic theory and the need for meaningful physicochemical parameters to describe genotoxicity by a general mechanism. Residual-QSAR Method The double or looping multiple linear correlation was examined by comparing the direct and residual structural information against the observed activity. A self-consistent equation of observed-computed activity was assumed to give maximum correlation efficiency for those situations in which the direct correlations gave non-significant statistical information. Alternatively, it was also suited to describe slow and apparently non-noticeable cancer phenomenology, with special application to non-congeneric molecules involved in genotoxic carcinogenesis. Application and Discussions The QSAR principles were systematically applied to a given pool of molecules with genotoxic activity in rats to elucidate their carcinogenic mechanisms. Once defined, the endpoint associated with ligand-DNA interaction was used to select variables that retained the main Hansch physicochemical parameters of hydrophobicity, polarizability and stericity, computed by the custom PM3 semiempirical quantum method. The trial and test sets of working molecules were established by implementing the normal Gaussian principle of activities that applies when the applicability domain is not restrained to the congeneric compounds, as in the present study. The application of the residual, self-consistent QSAR method and the factor (or average) method yielded results characterized by extremely high and low correlations, respectively, with the latter resembling

  8. Antiproliferative Pt(IV) complexes: synthesis, biological activity, and quantitative structure-activity relationship modeling.

    PubMed

    Gramatica, Paola; Papa, Ester; Luini, Mara; Monti, Elena; Gariboldi, Marzia B; Ravera, Mauro; Gabano, Elisabetta; Gaviglio, Luca; Osella, Domenico

    2010-09-01

    Several Pt(IV) complexes of the general formula [Pt(L)2(L')2(L'')2] [axial ligands L are Cl-, RCOO-, or OH-; equatorial ligands L' are two am(m)ine or one diamine; and equatorial ligands L'' are Cl- or glycolato] were rationally designed and synthesized in the attempt to develop a predictive quantitative structure-activity relationship (QSAR) model. Numerous theoretical molecular descriptors were used alongside physicochemical data (i.e., reduction peak potential, Ep, and partition coefficient, log Po/w) to obtain a validated QSAR between in vitro cytotoxicity (half maximal inhibitory concentrations, IC50, on A2780 ovarian and HCT116 colon carcinoma cell lines) and some features of Pt(IV) complexes. In the resulting best models, a lipophilic descriptor (log Po/w or the number of secondary sp3 carbon atoms) plus an electronic descriptor (Ep, the number of oxygen atoms, or the topological polar surface area expressed as the N,O polar contribution) is necessary for modeling, supporting the general finding that the biological behavior of Pt(IV) complexes can be rationalized on the basis of their cellular uptake, the Pt(IV)-->Pt(II) reduction, and the structure of the corresponding Pt(II) metabolites. Novel compounds were synthesized on the basis of their predicted cytotoxicity in the preliminary QSAR model, and were experimentally tested. A final QSAR model, based solely on theoretical molecular descriptors to ensure its general applicability, is proposed.

  9. Quantitative structure-activity relationships and the prediction of MHC supermotifs.

    PubMed

    Doytchinova, Irini A; Guan, Pingping; Flower, Darren R

    2004-12-01

    The underlying assumption in quantitative structure-activity relationship (QSAR) methodology is that related chemical structures exhibit related biological activities. We review here two QSAR methods in terms of their applicability for human MHC supermotif definition. Supermotifs are motifs that characterise binding to more than one allele. Supermotif definition is the initial in silico step of epitope-based vaccine design. The first QSAR method we review here--the additive method--is based on the assumption that the binding affinity of a peptide depends on contributions from both amino acids and the interactions between them. The second method is a 3D-QSAR method: comparative molecular similarity indices analysis (CoMSIA). Both methods were applied to 771 peptides binding to 9 HLA alleles. Five of the alleles (A*0201, A*0202, A*0203, A*0206 and A*6802) belong to the HLA-A2 superfamily and the other four (A*0301, A*1101, A*3101 and A*6801) to the HLA-A3 superfamily. For each superfamily, supermotifs defined by the two QSAR methods agree closely and are supported by many experimental data. PMID:15542370

  10. Curating and Preparing High-Throughput Screening Data for Quantitative Structure-Activity Relationship Modeling.

    PubMed

    Kim, Marlene T; Wang, Wenyi; Sedykh, Alexander; Zhu, Hao

    2016-01-01

    Publicly available bioassay data often contains errors. Curating massive bioassay data, especially high-throughput screening (HTS) data, for Quantitative Structure-Activity Relationship (QSAR) modeling requires the assistance of automated data curation tools. Using automated data curation tools are beneficial to users, especially ones without prior computer skills, because many platforms have been developed and optimized based on standardized requirements. As a result, the users do not need to extensively configure the curation tool prior to the application procedure. In this chapter, a freely available automatic tool to curate and prepare HTS data for QSAR modeling purposes will be described.

  11. Curating and Preparing High-Throughput Screening Data for Quantitative Structure-Activity Relationship Modeling.

    PubMed

    Kim, Marlene T; Wang, Wenyi; Sedykh, Alexander; Zhu, Hao

    2016-01-01

    Publicly available bioassay data often contains errors. Curating massive bioassay data, especially high-throughput screening (HTS) data, for Quantitative Structure-Activity Relationship (QSAR) modeling requires the assistance of automated data curation tools. Using automated data curation tools are beneficial to users, especially ones without prior computer skills, because many platforms have been developed and optimized based on standardized requirements. As a result, the users do not need to extensively configure the curation tool prior to the application procedure. In this chapter, a freely available automatic tool to curate and prepare HTS data for QSAR modeling purposes will be described. PMID:27518634

  12. QSAR Study on Anti-HIV-1 Activity of 4-Oxo-1,4-dihydroquinoline and 4-Oxo-4H-pyrido[1,2-a]pyrimidine Derivatives Using SW-MLR, Artificial Neural Network and Filtering Methods

    PubMed Central

    Hajimahdi, Zahra; Ranjbar, Amin; Abolfazl Suratgar, Amir; Zarghi, Afshin

    2015-01-01

    Predictive quantitative structure–activity relationship was performed on the novel4-oxo-1,4-dihydroquinoline and 4-oxo-4H-pyrido[1,2-a]pyrimidine derivatives to explore relationship between the structure of synthesized compounds and their anti-HIV-1 activities. In this way, the suitable set of the molecular descriptors was calculated and the important descriptors using the variable selections of the stepwise technique were selected. Multiple linear regression (MLR) and artificial neural network (ANN) as nonlinear system were used for constructing QSAR models. The predictive quality of the quantitative structure–activity relationship models was tested for an external set of five compounds, randomly chosen out of 25 compounds. The findings exhibited that stepwise-ANN model was more efficient at prediction activity of both training and test sets with high statistical qualities. Based on QSAR models results, electronegativity, the atomic masses, the atomic van der Waals volumes, the molecular symmetry and polarizability were found to be important factors controlling the anti-HIV-1 activity. PMID:26185507

  13. Molecular docking and QSAR analyses for understanding the antimalarial activity of some 7-substituted-4-aminoquinoline derivatives.

    PubMed

    Shibi, I G; Aswathy, L; Jisha, R S; Masand, V H; Divyachandran, A; Gajbhiye, J M

    2015-09-18

    The quinoline moiety is one of the widely studied scaffolds for generating derivatives with various pharmacophoric groups due to its potential antimalarial activities. In the present study, a series of 7-substituted-4-aminoquinoline derivatives were selected to understand their antimalarial properties computationally by molecular modeling techniques including 2D QSAR, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and molecular docking. The 2D-QSAR model built with four descriptors selected by genetic algorithm technique and CoMFA model showed satisfactory statistical results (Q(2)=0.540, R(2)ncv=0.881, F value=157.09). A reliable CoMSIA model out of the fourteen different combinations has a Q(2) value of 0.638. The molecular docking studies of the compounds for 1CET as the protein target revealed that ten compounds showed maximum interactions with the binding site of the protein. The present study highlights the unique binding signatures of the ligands within the active site groove of the target and it explains the subtle differences in their EC50 values and their mechanism of inhibition.

  14. QSAR Studies and Design of Some Tetracyclic 1,4-Benzothiazines as Antimicrobial Agents.

    PubMed

    Mor, S; Nagoria, S; Kumar, A; Kumar, A; Kaushik, C P

    2016-08-01

    A quantitative structure-activity relationship (QSAR) analysis has been performed on a series of 20 tetracyclic 1,4-benzothiazines (1a-1t) with antimicrobial activity to explain the observed biological activity trend on structural basis. Multiple linear regression (MLR) method was employed to establish statistically significant QSAR models. The developed models are robust, predictive and free from chance correlation with good fitting ability and sufficient generalizability. These studies revealed the dominance of WHIM parameters in describing antimicrobial activity of the title compounds. Further, design of some more active compounds is presented. PMID:27389854

  15. Quantitative structure-antifungal activity relationships of some benzohydrazides against Botrytis cinerea.

    PubMed

    Reino, José L; Saiz-Urra, Liane; Hernandez-Galan, Rosario; Aran, Vicente J; Hitchcock, Peter B; Hanson, James R; Gonzalez, Maykel Perez; Collado, Isidro G

    2007-06-27

    Fourteen benzohydrazides have been synthesized and evaluated for their in vitro antifungal activity against the phytopathogenic fungus Botrytis cinerea. The best antifungal activity was observed for the N',N'-dibenzylbenzohydrazides 3b-d and for the N-aminoisoindoline-derived benzohydrazide 5. A quantitative structure-activity relationship (QSAR) study has been developed using a topological substructural molecular design (TOPS-MODE) approach to interpret the antifungal activity of these synthetic compounds. The model described 98.3% of the experimental variance, with a standard deviation of 4.02. The influence of an ortho substituent on the conformation of the benzohydrazides was investigated by X-ray crystallography and supported by QSAR study. Several aspects of the structure-activity relationships are discussed in terms of the contribution of different bonds to the antifungal activity, thereby making the relationships between structure and biological activity more transparent.

  16. The 18th European symposium on quantitative structure-activity relationships.

    PubMed

    Tsantili-Kakoulidou, Anna; Agrafiotis, Dimitris K

    2011-04-01

    The 18th European Symposium on Quantitative Structure-Activity Relationships (QSAR) took place in Rhodes, Greece, on 19 - 24 September 2010. It was organized by the Hellenic Society of Medicinal Chemistry and the Cheminformatics and QSAR Society, and co-sponsored by the European Federation of Medicinal Chemistry. The conference was thematically dedicated to discovery informatics and drug design and addressed the impact of informatics in all its variants (chemoinformatics, bioinformatics, pharmacoinformatics) on drug discovery in the broader context of biological complexity. The latest scientific and technological advances in QSAR as tools for the discovery of new, safer and more efficacious drugs were discussed during the meeting. This paper highlights the most important outcomes of the symposium, commenting briefly on some of the key presentations.

  17. A general method for exploiting QSAR models in lead optimization.

    PubMed

    Lewis, Richard A

    2005-03-10

    Computer-aided drug design tools can generate many useful and powerful models that explain structure-activity relationship (SAR) observations in a quantitative manner. These models can use many different descriptors, functional forms, and methods from simple linear equations through to multilayer neural nets. Using a model, a medicinal chemist can compute an activity, given a structure, but it is much harder to work out what changes are needed to make a structure more active. The impact of a model on the design process would be greatly enhanced if the model were more interpretable to the bench chemist. This paper describes a new protocol for performing automated iterative quantitative structure-activity relationship (QSAR) studies and presents the results of experiments on two QSAR sets from the literature. The fundamental goal of this work is to try to assist the chemist in his search for what to make next.

  18. Overview of data and conceptual approaches for derivation of quantitative structure-activity relationships for ecotoxicological effects of organic chemicals.

    PubMed

    Bradbury, Steven P; Russom, Christine L; Ankley, Gerald T; Schultz, T Wayne; Walker, John D

    2003-08-01

    The use of quantitative structure-activity relationships (QSARs) in assessing potential toxic effects of organic chemicals on aquatic organisms continues to evolve as computational efficiency and toxicological understanding advance. With the ever-increasing production of new chemicals, and the need to optimize resources to assess thousands of existing chemicals in commerce, regulatory agencies have turned to QSARs as essential tools to help prioritize tiered risk assessments when empirical data are not available to evaluate toxicological effects. Progress in designing scientifically credible QSARs is intimately associated with the development of empirically derived databases of well-defined and quantified toxicity endpoints, which are based on a strategic evaluation of diverse sets of chemical structures, modes of toxic action, and species. This review provides a brief overview of four databases created for the purpose of developing QSARs for estimating toxicity of chemicals to aquatic organisms. The evolution of QSARs based initially on general chemical classification schemes, to models founded on modes of toxic action that range from nonspecific partitioning into hydrophobic cellular membranes to receptor-mediated mechanisms is summarized. Finally, an overview of expert systems that integrate chemical-specific mode of action classification and associated QSAR selection for estimating potential toxicological effects of organic chemicals is presented. PMID:12924578

  19. Nonlinear QSAR modeling for predicting cytotoxicity of ionic liquids in leukemia rat cell line: an aid to green chemicals designing.

    PubMed

    Gupta, Shikha; Basant, Nikita; Singh, Kunwar P

    2015-08-01

    Safety assessment and designing of safer ionic liquids (ILs) are among the priorities of the chemists and toxicologists today. Computational approaches have been considered as appropriate methods for prior safety assessment of chemicals and tools to aid in structural designing. The present study is an attempt to investigate the chemical attributes of a wide variety of ILs towards their cytotoxicity in leukemia rat cell line IPC-81 through the development of nonlinear quantitative structure-activity relationship (QSAR) models in the light of the OECD principles for QSAR development. Here, the cascade correlation network (CCN), probabilistic neural network (PNN), and generalized regression neural networks (GRNN) QSAR models were established for the discrimination of ILs in four categories of cytotoxicity and their end-point prediction using few simple descriptors. The diversity and nonlinearity of the considered dataset were evaluated through computing the Euclidean distance and Brock-Dechert-Scheinkman statistics. The constructed QSAR models were validated with external test data. The predictive power of these models was established through a variety of stringent parameters recommended in QSAR literature. The classification QSARs rendered the accuracy of >86%, and the regression models yielded correlation (R(2)) of >0.90 in test data. The developed QSAR models exhibited high statistical confidence and identified the structural elements of the ILs responsible for their cytotoxicity and, hence, could be useful tools in structural designing of safer and green ILs.

  20. 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

  1. Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization

    PubMed Central

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

    2015-01-01

    Skin permeability is widely considered to be mechanistically implicated in chemically-induced skin sensitization. Although many chemicals have been identified as skin sensitizers, there have been very few reports analyzing the relationships between molecular structure and skin permeability of sensitizers and non-sensitizers. The goals of this study were to: (i) compile, curate, and integrate the largest publicly available dataset of chemicals studied for their skin permeability; (ii) develop and rigorously validate QSAR models to predict skin permeability; and (iii) explore the complex relationships between skin sensitization and skin permeability. Based on the largest publicly available dataset compiled in this study, we found no overall correlation between skin permeability and skin sensitization. In addition, cross-species correlation coefficient between human and rodent permeability data was found to be as low as R2=0.44. Human skin permeability models based on the random forest method have been developed and validated using OECD-compliant QSAR modeling workflow. Their external accuracy was high (Q2ext = 0.73 for 63% of external compounds inside the applicability domain). The extended analysis using both experimentally-measured and QSAR-imputed data still confirmed the absence of any overall concordance between skin permeability and skin sensitization. This observation suggests that chemical modifications that affect skin permeability should not be presumed a priori to modulate the sensitization potential of chemicals. The models reported herein as well as those developed in the companion paper on skin sensitization suggest that it may be possible to rationally design compounds with the desired high skin permeability but low sensitization potential. PMID:25560673

  2. Using theoretical descriptors in structure activity relationships: Validating toxicity predictions

    SciTech Connect

    Famini, G.R.; Wilson, L.Y.; Chester, N.A.; Sterling, P.A.

    1995-12-01

    Quantitative Structure Activity Relationships (QSAR) and Linear Free Energy Relationships (LFER) are very useful for correlating toxicological data, and in characterizating trends in terms of structural and electronic effects. Several years ago, we developed a series of equations correlating a number of toxicity tests with theoretically determined descriptors. One of these tests was the Microtox test, using the degradation in light from Photobacteriurn phosphoreum. Recently, several new compounds have been tested in our laboratory using the Microtox test, and compared against the predicted values. The agreement between experimental and theoretical results will be discussed, as will reasons for {open_quotes}good{close_quotes} or {open_quotes}poor{close_quotes} predictions.

  3. SAR/QSAR methods in public health practice

    SciTech Connect

    Demchuk, Eugene Ruiz, Patricia; Chou, Selene; Fowler, Bruce A.

    2011-07-15

    Methods of (Quantitative) Structure-Activity Relationship ((Q)SAR) modeling play an important and active role in ATSDR programs in support of the Agency mission to protect human populations from exposure to environmental contaminants. They are used for cross-chemical extrapolation to complement the traditional toxicological approach when chemical-specific information is unavailable. SAR and QSAR methods are used to investigate adverse health effects and exposure levels, bioavailability, and pharmacokinetic properties of hazardous chemical compounds. They are applied as a part of an integrated systematic approach in the development of Health Guidance Values (HGVs), such as ATSDR Minimal Risk Levels, which are used to protect populations exposed to toxic chemicals at hazardous waste sites. (Q)SAR analyses are incorporated into ATSDR documents (such as the toxicological profiles and chemical-specific health consultations) to support environmental health assessments, prioritization of environmental chemical hazards, and to improve study design, when filling the priority data needs (PDNs) as mandated by Congress, in instances when experimental information is insufficient. These cases are illustrated by several examples, which explain how ATSDR applies (Q)SAR methods in public health practice.

  4. Novel phenolic inhibitors of the sarco/endoplasmic reticulum calcium ATPase: identification and characterization by quantitative structure-activity relationship modeling and virtual screening.

    PubMed

    Paula, Stefan; Hofmann, Emily; Burden, John; Stanton, David T

    2015-02-01

    Inhibitors of the sarco/endoplasmic reticulum calcium ATPase (SERCA) are valuable research tools and hold promise as a new generation of anti-prostate cancer agents. Based on previously determined potencies of phenolic SERCA inhibitors, we created quantitative structure-activity relationship (QSAR) models using three independent development strategies. The obtained QSAR models facilitated virtual screens of several commercial compound collections for novel inhibitors. Sixteen compounds were subsequently evaluated in SERCA activity inhibition assays and 11 showed detectable potencies in the micro- to millimolar range. The experimental results were then incorporated into a comprehensive master QSAR model, whose physical interpretation by partial least squares analysis revealed that properly positioned substituents at the central phenyl ring capable of forming hydrogen bonds and of undergoing hydrophobic interactions were prerequisites for effective SERCA inhibition. The established SAR was in good agreement with findings from previous structural studies, even though it was obtained independently using standard QSAR methodologies.

  5. Predicting stability constants of various chelating agents using QSAR technology

    SciTech Connect

    Okey, R.W.; Lin, S.W.; Hong, P.K.A.

    1995-12-31

    The practice of capturing metals from contaminated soil slurry often involves the use of organics as chelators. This work was undertaken to develop information on the molecular characteristics which optimize the removal or the complexation of cadmium, copper, lead and zinc. Quantitative structure-activity relationship (QSAR) technology was employed using special techniques developed for the determination of the correct set of variables. The linear free energy relationship was applied using a 183 case data set to obtain regression coefficients. Equations obtained are provided. The differences in the coefficients and variables may be used as a guide in selecting the optimum chelator for a specific metal. The use of QSAR technology appears effective in furthering the understanding of metal-chelator relationships. A variable set combining molecular connectivity indices and fragments or groups can be used to minimize the size of the data set required for a valid regression and for the avoidance of collinearity problems.

  6. Synthesis, bioassay, and QSAR study of bronchodilatory active 4H-pyrano[3,2-c]pyridine-3-carbonitriles.

    PubMed

    Girgis, Adel S; Saleh, Dalia O; George, Riham F; Srour, Aladdin M; Pillai, Girinath G; Panda, Chandramukhi S; Katritzky, Alan R

    2015-01-01

    A statistically significant QSAR model describing the bioactivity of bronchodilatory active 4H-pyrano[3,2-c]pyridine-3-carbonitriles (N = 41, n = 8, R(2) = 0.824, R(2)cv = 0.724, F = 18.749, s(2) = 0.0018) was obtained employing CODESSA-Pro software. The bronchodilatory active 4H-pyrano[3,2-c]pyridine-3-carbonitriles 17-57 were synthesized through a facile approach via reaction of 1-alkyl-4-piperidones 1-4 with ylidenemalononitriles 5-16 in methanol in the presence of sodium. The bronchodilation properties of 17-57 were investigated in vitro using isolated guinea pig tracheal rings pre-contracted with histamine (standard method) and compared with theophylline (standard reference). Most of the compounds synthesized exhibit promising bronchodilation properties especially, compounds 25 and 28. PMID:25462283

  7. Summary of a workshop on regulatory acceptance of (Q)SARs for human health and environmental endpoints.

    PubMed Central

    Jaworska, Joanna S; Comber, M; Auer, C; Van Leeuwen, C J

    2003-01-01

    The "Workshop on Regulatory Use of (Q)SARs for Human Health and Environmental Endpoints," organized by the European Chemical Industry Council and the International Council of Chemical Associations, gathered more than 60 human health and environmental experts from industry, academia, and regulatory agencies from around the world. They agreed, especially industry and regulatory authorities, that the workshop initiated great potential for the further development and use of predictive models, that is, quantitative structure-activity relationships [(Q)SARs], for chemicals management in a much broader scope than is currently the case. To increase confidence in (Q)SAR predictions and minimization of their misuse, the workshop aimed to develop proposals for guidance and acceptability criteria. The workshop also described the broad outline of a system that would apply that guidance and acceptability criteria to a (Q)SAR when used for chemical management purposes, including priority setting, risk assessment, and classification and labeling. PMID:12896859

  8. Fragment-based QSAR: perspectives in drug design.

    PubMed

    Salum, Lívia B; Andricopulo, Adriano D

    2009-08-01

    Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. Quantitative structure-activity relationship (QSAR) methods are among the most important strategies that can be applied for the successful design of small molecule modulators having clinical utility. Hologram QSAR (HQSAR) is a modern 2D fragment-based QSAR method that employs specialized molecular fingerprints. HQSAR can be applied to large data sets of compounds, as well as traditional-size sets, being a versatile tool in drug design. The HQSAR approach has evolved from a classical use in the generation of standard QSAR models for data correlation and prediction into advanced drug design tools for virtual screening and pharmacokinetic property prediction. This paper provides a brief perspective on the evolution and current status of HQSAR, highlighting present challenges and new opportunities in drug design.

  9. Dataset Modelability by QSAR

    PubMed Central

    Golbraikh, Alexander; Muratov, Eugene; Fourches, Denis; Tropsha, Alexander

    2014-01-01

    We introduce a simple MODelability Index (MODI) that estimates the feasibility of obtaining predictive QSAR models (Correct Classification Rate above 0.7) for a binary dataset of bioactive compounds. MODI is defined as an activity class-weighted ratio of the number of the nearest neighbor pairs of compounds with the same activity class versus the total number of pairs. The MODI values were calculated for more than 100 datasets and the threshold of 0.65 was found to separate non-modelable from the modelable datasets. PMID:24251851

  10. Chemical domain of QSAR models from atom-centered fragments.

    PubMed

    Kühne, Ralph; Ebert, Ralf-Uwe; Schüürmann, Gerrit

    2009-12-01

    A methodology to characterize the chemical domain of qualitative and quantitative structure-activity relationship (QSAR) models based on the atom-centered fragment (ACF) approach is introduced. ACFs decompose the molecule into structural pieces, with each non-hydrogen atom of the molecule acting as an ACF center. ACFs vary with respect to their size in terms of the path length covered in each bonding direction starting from a given central atom and how comprehensively the neighbor atoms (including hydrogen) are described in terms of element type and bonding environment. In addition to these different levels of ACF definitions, the ACF match mode as degree of strictness of the ACF comparison between a test compound and a given ACF pool (such as from a training set) has to be specified. Analyses of the prediction statistics of three QSAR models with their training sets as well as with external test sets and associated subsets demonstrate a clear relationship between the prediction performance and the levels of ACF definition and match mode. The findings suggest that second-order ACFs combined with a borderline match mode may serve as a generic and at the same time a mechanistically sound tool to define and evaluate the chemical domain of QSAR models. Moreover, four standard categories of the ACF-based membership to a given chemical domain (outside, borderline outside, borderline inside, inside) are introduced that provide more specific information about the expected QSAR prediction performance. As such, the ACF-based characterization of the chemical domain appears to be particularly useful for QSAR applications in the context of REACH and other regulatory schemes addressing the safety evaluation of chemical compounds.

  11. 2D-QSAR study of some 2,5-diaminobenzophenone farnesyltransferase inhibitors by different chemometric methods

    PubMed Central

    Ghanbarzadeh, Saeed; Ghasemi, Saeed; Shayanfar, Ali; Ebrahimi-Najafabadi, Heshmatollah

    2015-01-01

    Quantitative structure activity relationship (QSAR) models can be used to predict the activity of new drug candidates in early stages of drug discovery. In the present study, the information of the ninety two 2,5-diaminobenzophenone-containing farnesyltranaferase inhibitors (FTIs) were taken from the literature. Subsequently, the structures of the molecules were optimized using Hyperchem software and molecular descriptors were obtained using Dragon software. The most suitable descriptors were selected using genetic algorithms-partial least squares and stepwise regression, where exhibited that the volume, shape and polarity of the FTIs are important for their activities. The two-dimensional QSAR models (2D-QSAR) were obtained using both linear methods (multiple linear regression) and non-linear methods (artificial neural networks and support vector machines). The proposed QSAR models were validated using internal validation method. The results showed that the proposed 2D-QSAR models were valid and they can be used for prediction of the activities of the 2,5-diaminobenzophenone-containing FTIs. In conclusion, the 2D-QSAR models (both linear and non-linear) showed good prediction capability and the non-linear models were exhibited more accuracy than the linear models. PMID:26600747

  12. Acute toxicity and QSAR of chlorophenols on Daphnia magna

    SciTech Connect

    Devillers, J.; Chambon, P.

    1986-10-01

    Chlorophenols which are released into natural waters from various industrial processes and from agricultural uses have been recognized as a group of chemical substances potentially hazardous to the aquatic environment. Therefore it is important to estimate their toxic impact on biota. Thus, the scope of this research was to obtain acute toxicity data for seventeen chlorophenols towards Daphnia magna and to explore the possibilities of deriving QSAR's (quantitative structure-activity relationship) from the above values.

  13. (Q)SAR modeling and safety assessment in regulatory review.

    PubMed

    Kruhlak, N L; Benz, R D; Zhou, H; Colatsky, T J

    2012-03-01

    The ability to predict clinical safety based on chemical structures is becoming an increasingly important part of regulatory decision making. (Quantitative) structure-activity relationship ((Q)SAR) models are currently used to evaluate late-arising safety concerns and possible nonclinical effects of a drug and its related compounds when adequate safety data are absent or equivocal. Regulatory use will likely increase with the standardization of analytical approaches, more complete and reliable data collection methods, and a better understanding of toxicity mechanisms.

  14. Quantum chemical parameters in QSAR: what do I use when?

    USGS Publications Warehouse

    Hickey, James P.; Ostrander, Gary K.

    1996-01-01

    This chapter provides a brief overview of the numerous quantum chemical parameters that have been/are currently being used in quantitative structure activity relationships (QSAR), along with a representative bibliography. The parameters will be grouped according to their mechanistic interpretations, and representative biological and physical chemical applications will be mentioned. Parmater computation methods and the appropriate software are highlighted, as are sources for software.

  15. Antiprotozoal Nitazoxanide Derivatives: Synthesis, Bioassays and QSAR Study Combined with Docking for Mechanistic Insight.

    PubMed

    Scior, Thomas; Lozano-Aponte, Jorge; Ajmani, Subhash; Hernández-Montero, Eduardo; Chávez-Silva, Fabiola; Hernández-Núñez, Emanuel; Moo-Puc, Rosa; Fraguela-Collar, Andres; Navarrete-Vázquez, Gabriel

    2015-01-01

    In view of the serious health problems concerning infectious diseases in heavily populated areas, we followed the strategy of lead compound diversification to evaluate the near-by chemical space for new organic compounds. To this end, twenty derivatives of nitazoxanide (NTZ) were synthesized and tested for activity against Entamoeba histolytica parasites. To ensure drug-likeliness and activity relatedness of the new compounds, the synthetic work was assisted by a quantitative structure-activity relationships study (QSAR). Many of the inherent downsides - well-known to QSAR practitioners - we circumvented thanks to workarounds which we proposed in prior QSAR publication. To gain further mechanistic insight on a molecular level, ligand-enzyme docking simulations were carried out since NTZ is known to inhibit the protozoal pyruvate ferredoxin oxidoreductase (PFOR) enzyme as its biomolecular target. PMID:25872791

  16. Causation or only correlation? Application of causal inference graphs for evaluating causality in nano-QSAR models

    NASA Astrophysics Data System (ADS)

    Sizochenko, Natalia; Gajewicz, Agnieszka; Leszczynski, Jerzy; Puzyn, Tomasz

    2016-03-01

    In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure-Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model. Verification of the relationships between descriptors and toxicity or other activity in the QSAR model has a vital role in understanding the mechanisms of action. The well-known phrase ``correlation does not imply causation'' reflects insight statistically correlated with the endpoint descriptor may not cause the emergence of this endpoint. Hence, paradigmatic shifts must be undertaken when moving from traditional statistical correlation analysis to causal analysis of multivariate data. Methods of causal discovery have been applied for broader physical insight into mechanisms of action and interpretation of the developed nano-QSAR models. Previously developed nano-QSAR models for toxicity of 17 nano-sized metal oxides towards E. coli bacteria have been validated by means of the causality criteria. Using the descriptors confirmed by the causal technique, we have developed new models consistent with the straightforward causal-reasoning account. It was proven that causal inference methods are able to provide a more robust mechanistic interpretation of the developed nano-QSAR models.In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure-Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model. Verification of the relationships between descriptors and toxicity or other activity in the QSAR model has a vital role in understanding the mechanisms of action. The well-known phrase ``correlation does not imply causation'' reflects insight statistically correlated with the endpoint descriptor may not cause the emergence of this endpoint. Hence, paradigmatic shifts must be undertaken when moving from traditional statistical correlation analysis to causal

  17. Three dimensional quantitative structure-activity relationships of sulfonamides binding monoclonal antibody by comparative molecular field analysis

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The three-dimensional quantitative structure-activity relationship (3D-QSAR) model of sulfonamide analogs, binding a monoclonal antibody (MabSMR) produced against sulfamerazine was carried out by comparative molecular field analysis (CoMFA). The affinities of MabSMR, expressed as Log10IC50, for 17 ...

  18. Quantitative structure-activity relationship investigation of the role of hydrophobicity in regulating mutagenicity in the Ames test: 2. Mutagenicity of aromatic and heteroaromatic nitro compounds in Salmonella typhimurium TA100

    SciTech Connect

    Debnath, A.K.; Hansch, C. ); Shusterman, A.J. ); Lopez de Compadre, R.L. )

    1992-01-01

    A quantitative structure-activity relationship (QSAR) has been derived for the mutagenic activity of 117 aromatic and heteroaromatic nitro compounds acting on Salmonella typhimurium TA100. Relative mutagenic activity is bilinearly dependent on hydrophobicity, with an optimal log P of 5.44, and is linearly dependent on the energy of the lowest unoccupied molecular orbital of the nitro compound. The dependence of mutagenic activity on hydrophobicity and electronic effects is very similar for TA98 and TA100. Mutagenic activity in TA100 does not depend on the size of the aromatic ring system, as it does in TA98. The effect of the choice of assay organism, TA98 versus TA100, on nitroarene QSAR is seen to be similar to the effect previously found for aminoarenes. Lateral verification of QSARs is presented as a tool for establishing the significance of a new QSAR.

  19. Quantitative structure-activity relationships of antimicrobial fatty acids and derivatives against Staphylococcus aureus *

    PubMed Central

    Zhang, Hui; Zhang, Lu; Peng, Li-juan; Dong, Xiao-wu; Wu, Di; Wu, Vivian Chi-Hua; Feng, Feng-qin

    2012-01-01

    Fatty acids and derivatives (FADs) are resources for natural antimicrobials. In order to screen for additional potent antimicrobial agents, the antimicrobial activities of FADs against Staphylococcus aureus were examined using a microplate assay. Monoglycerides of fatty acids were the most potent class of fatty acids, among which monotridecanoin possessed the most potent antimicrobial activity. The conventional quantitative structure-activity relationship (QSAR) and comparative molecular field analysis (CoMFA) were performed to establish two statistically reliable models (conventional QSAR: R 2=0.942, Q 2 LOO=0.910; CoMFA: R 2=0.979, Q 2=0.588, respectively). Improved forecasting can be achieved by the combination of these two models that provide a good insight into the structure-activity relationships of the FADs and that may be useful to design new FADs as antimicrobial agents. PMID:22302421

  20. Quantitative structure-activity relationship studies of a series of sulfa drugs as inhibitors of Pneumocystis carinii dihydropteroate synthetase.

    PubMed

    Johnson, T; Khan, I A; Avery, M A; Grant, J; Meshnick, S R

    1998-06-01

    Sulfone and sulfanilamide sulfa drugs have been shown to inhibit dihydropteroate synthetase (DHPS) isolated from Pneumocystis carinii. In order to develop a pharmacophoric model for this inhibition, quantitative structure-activity relationships (QSAR) for sulfa drugs active against DHPS have been studied. Accurate 50% inhibitory concentrations were collected for 44 analogs, and other parameters, such as partition coefficients and molar refractivity, were calculated. Conventional multiple regression analysis of these data did not provide acceptable QSAR. However, three-dimensional QSAR provided by comparative molecular field analysis did give excellent results. Upon removal of poorly correlated analogs, a data set of 36 analogs, all having a common NHSO2 group, provided a cross-validated r2 value of 0.699 and conventional r2 value of 0.964. The resulting pharmacophore model should be useful for understanding and predicting the binding of DHPS by new sulfa drugs.

  1. Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?

    EPA Science Inventory

    Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external dataset, the best way to validate the predictive ability of a model is to perform its s...

  2. The antibacterial activity of some sulfonamides and sulfonyl hydrazones, and 2D-QSAR study of a series of sulfonyl hydrazones

    NASA Astrophysics Data System (ADS)

    Aslan, H. Güzin; Özcan, Servet; Karacan, Nurcan

    2012-12-01

    Benzenesulfonicacid-1-methylhydrazide (1) and its four aromatic sulfonyl hydrazone derivatives (1a-1d), N-(3-amino-2-hydroxypropyl)benzene sulfonamide (2) and N-(2-hydroxyethyl)benzenesulfonamide (3) were synthesized and their structures were determined by IR, 1H NMR, 13C NMR, and LCMS techniques. Antibacterial activities of new synthesized compounds were evaluated against various bacteria strains by microdilution and disk diffusion methods. The experimental results show that presence of OH group on sulfonamides reduces the antimicrobial activity, and antimicrobial activities of the sulfonyl hydrazones (1a-1d) are smaller than that of the parent sulfonamide (1), except Candida albicans. In addition, 2D-QSAR analysis was performed on 28 aromatic sulfonyl hydrazones as antimicrobial agents against Escherichia coli and Staphylococcus aureus. In the QSAR models, the most important descriptor is total point-charge component of the molecular dipole for E. coli, and partial negative surface area (PNSA-1) for S. aureus.

  3. Imidazolium Ionic Liquids as Potential Anti-Candida Inhibitors: QSAR Modeling and Experimental Studies.

    PubMed

    Hodyna, Diana; Kovalishyn, Vasyl; Rogalsky, Sergiy; Blagodatnyi, Volodymyr; Metelytsia, Larisa

    2016-01-01

    Quantitative structure-activity relationships (QSAR) of imidazolium ionic liquids (ILs) as inhibitors of C. albicans collection strains (IOA-109, KCTC 1940, ATCC 10231) have been studied. Predictive QSAR models were built using different descriptor sets for a set of 88 ionic liquids with known minimum inhibitory concentrations (MIC) against C. albicans. We applied the state-of-the-art QSAR methodologies such as WEKA Random Forest (RF) as a binary classifier, Associative Neural Networks (ASNN) and k-Nearest Neighbors (k-NN) to build continuum non-linear regression models. The obtained models were validated using a 5-fold cross-validation approach and resulted in the prediction accuracies of 80% ± 5.0 for the classification models and q2 = 0.73-0.87 for the non-linear regression models. Biological testing of newly synthesized 1,3-dialkylimidazolium ionic liquids with predicted activity was performed by disco-diffusion method against C. albicans ATCC 10231 M885 strain and clinical isolates C. albicans, C. krusei and C. glabrata strains. The high percentage of coincidence between the QSAR predictions and the experimental results confirmed the high predictive power of the developed QSAR models within the applicability domain of new imidazolium ionic liquids. PMID:27160290

  4. Imidazolium Ionic Liquids as Potential Anti-Candida Inhibitors: QSAR Modeling and Experimental Studies.

    PubMed

    Hodyna, Diana; Kovalishyn, Vasyl; Rogalsky, Sergiy; Blagodatnyi, Volodymyr; Metelytsia, Larisa

    2016-01-01

    Quantitative structure-activity relationships (QSAR) of imidazolium ionic liquids (ILs) as inhibitors of C. albicans collection strains (IOA-109, KCTC 1940, ATCC 10231) have been studied. Predictive QSAR models were built using different descriptor sets for a set of 88 ionic liquids with known minimum inhibitory concentrations (MIC) against C. albicans. We applied the state-of-the-art QSAR methodologies such as WEKA Random Forest (RF) as a binary classifier, Associative Neural Networks (ASNN) and k-Nearest Neighbors (k-NN) to build continuum non-linear regression models. The obtained models were validated using a 5-fold cross-validation approach and resulted in the prediction accuracies of 80% ± 5.0 for the classification models and q2 = 0.73-0.87 for the non-linear regression models. Biological testing of newly synthesized 1,3-dialkylimidazolium ionic liquids with predicted activity was performed by disco-diffusion method against C. albicans ATCC 10231 M885 strain and clinical isolates C. albicans, C. krusei and C. glabrata strains. The high percentage of coincidence between the QSAR predictions and the experimental results confirmed the high predictive power of the developed QSAR models within the applicability domain of new imidazolium ionic liquids.

  5. Multispecies QSAR modeling for predicting the aquatic toxicity of diverse organic chemicals for regulatory toxicology.

    PubMed

    Singh, Kunwar P; Gupta, Shikha; Kumar, Anuj; Mohan, Dinesh

    2014-05-19

    The research aims to develop multispecies quantitative structure-activity relationships (QSARs) modeling tools capable of predicting the acute toxicity of diverse chemicals in various Organization for Economic Co-operation and Development (OECD) recommended test species of different trophic levels for regulatory toxicology. Accordingly, the ensemble learning (EL) approach based classification and regression QSAR models, such as decision treeboost (DTB) and decision tree forest (DTF) implementing stochastic gradient boosting and bagging algorithms were developed using the algae (P. subcapitata) experimental toxicity data for chemicals. The EL-QSAR models were successfully applied to predict toxicities of wide groups of chemicals in other test species including algae (S. obliguue), daphnia, fish, and bacteria. Structural diversity of the selected chemicals and those of the end-point toxicity data of five different test species were tested using the Tanimoto similarity index and Kruskal-Wallis (K-W) statistics. Predictive and generalization abilities of the constructed QSAR models were compared using statistical parameters. The developed QSAR models (DTB and DTF) yielded a considerably high classification accuracy in complete data of model building (algae) species (97.82%, 99.01%) and ranged between 92.50%-94.26% and 92.14%-94.12% in four test species, respectively, whereas regression QSAR models (DTB and DTF) rendered high correlation (R(2)) between the measured and model predicted toxicity end-point values and low mean-squared error in model building (algae) species (0.918, 0.15; 0.905, 0.21) and ranged between 0.575 and 0.672, 0.18-0.51 and 0.605-0.689 and 0.20-0.45 in four different test species. The developed QSAR models exhibited good predictive and generalization abilities in different test species of varied trophic levels and can be used for predicting the toxicities of new chemicals for screening and prioritization of chemicals for regulation.

  6. Multispecies QSAR modeling for predicting the aquatic toxicity of diverse organic chemicals for regulatory toxicology.

    PubMed

    Singh, Kunwar P; Gupta, Shikha; Kumar, Anuj; Mohan, Dinesh

    2014-05-19

    The research aims to develop multispecies quantitative structure-activity relationships (QSARs) modeling tools capable of predicting the acute toxicity of diverse chemicals in various Organization for Economic Co-operation and Development (OECD) recommended test species of different trophic levels for regulatory toxicology. Accordingly, the ensemble learning (EL) approach based classification and regression QSAR models, such as decision treeboost (DTB) and decision tree forest (DTF) implementing stochastic gradient boosting and bagging algorithms were developed using the algae (P. subcapitata) experimental toxicity data for chemicals. The EL-QSAR models were successfully applied to predict toxicities of wide groups of chemicals in other test species including algae (S. obliguue), daphnia, fish, and bacteria. Structural diversity of the selected chemicals and those of the end-point toxicity data of five different test species were tested using the Tanimoto similarity index and Kruskal-Wallis (K-W) statistics. Predictive and generalization abilities of the constructed QSAR models were compared using statistical parameters. The developed QSAR models (DTB and DTF) yielded a considerably high classification accuracy in complete data of model building (algae) species (97.82%, 99.01%) and ranged between 92.50%-94.26% and 92.14%-94.12% in four test species, respectively, whereas regression QSAR models (DTB and DTF) rendered high correlation (R(2)) between the measured and model predicted toxicity end-point values and low mean-squared error in model building (algae) species (0.918, 0.15; 0.905, 0.21) and ranged between 0.575 and 0.672, 0.18-0.51 and 0.605-0.689 and 0.20-0.45 in four different test species. The developed QSAR models exhibited good predictive and generalization abilities in different test species of varied trophic levels and can be used for predicting the toxicities of new chemicals for screening and prioritization of chemicals for regulation. PMID:24738471

  7. Design and prediction of new acetylcholinesterase inhibitor via quantitative structure activity relationship of huprines derivatives.

    PubMed

    Zhang, Shuqun; Hou, Bo; Yang, Huaiyu; Zuo, Zhili

    2016-05-01

    Acetylcholinesterase (AChE) is an important enzyme in the pathogenesis of Alzheimer's disease (AD). Comparative quantitative structure-activity relationship (QSAR) analyses on some huprines inhibitors against AChE were carried out using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR) methods. Three highly predictive QSAR models were constructed successfully based on the training set. The CoMFA, CoMSIA, and HQSAR models have values of r (2) = 0.988, q (2) = 0.757, ONC = 6; r (2) = 0.966, q (2) = 0.645, ONC = 5; and r (2) = 0.957, q (2) = 0.736, ONC = 6. The predictabilities were validated using an external test sets, and the predictive r (2) values obtained by the three models were 0.984, 0.973, and 0.783, respectively. The analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the AChE to further understand the vital interactions between huprines and the protease. On the basis of the QSAR study, 14 new potent molecules have been designed and six of them are predicted to be more active than the best active compound 24 described in the literature. The final QSAR models could be helpful in design and development of novel active AChE inhibitors.

  8. Combined 3D-QSAR, molecular docking and molecular dynamics study on thyroid hormone activity of hydroxylated polybrominated diphenyl ethers to thyroid receptors β

    SciTech Connect

    Li, Xiaolin; Ye, Li; Wang, Xiaoxiang; Wang, Xinzhou; Liu, Hongling; Zhu, Yongliang; Yu, Hongxia

    2012-12-15

    Several recent reports suggested that hydroxylated polybrominated diphenyl ethers (HO-PBDEs) may disturb thyroid hormone homeostasis. To illuminate the structural features for thyroid hormone activity of HO-PBDEs and the binding mode between HO-PBDEs and thyroid hormone receptor (TR), the hormone activity of a series of HO-PBDEs to thyroid receptors β was studied based on the combination of 3D-QSAR, molecular docking, and molecular dynamics (MD) methods. The ligand- and receptor-based 3D-QSAR models were obtained using Comparative Molecular Similarity Index Analysis (CoMSIA) method. The optimum CoMSIA model with region focusing yielded satisfactory statistical results: leave-one-out cross-validation correlation coefficient (q{sup 2}) was 0.571 and non-cross-validation correlation coefficient (r{sup 2}) was 0.951. Furthermore, the results of internal validation such as bootstrapping, leave-many-out cross-validation, and progressive scrambling as well as external validation indicated the rationality and good predictive ability of the best model. In addition, molecular docking elucidated the conformations of compounds and key amino acid residues at the docking pocket, MD simulation further determined the binding process and validated the rationality of docking results. -- Highlights: ► The thyroid hormone activities of HO-PBDEs were studied by 3D-QSAR. ► The binding modes between HO-PBDEs and TRβ were explored. ► 3D-QSAR, molecular docking, and molecular dynamics (MD) methods were performed.

  9. Design, synthesis, α-glucosidase inhibitory activity, molecular docking and QSAR studies of benzimidazole derivatives

    NASA Astrophysics Data System (ADS)

    Dinparast, Leila; Valizadeh, Hassan; Bahadori, Mir Babak; Soltani, Somaieh; Asghari, Behvar; Rashidi, Mohammad-Reza

    2016-06-01

    In this study the green, one-pot, solvent-free and selective synthesis of benzimidazole derivatives is reported. The reactions were catalyzed by ZnO/MgO containing ZnO nanoparticles as a highly effective, non-toxic and environmentally friendly catalyst. The structure of synthesized benzimidazoles was characterized using spectroscopic technics (FT-IR, 1HNMR, 13CNMR). Synthesized compounds were evaluated for their α-glucosidase inhibitory potential. Compounds 3c, 3e, 3l and 4n were potent inhibitors with IC50 values ranging from 60.7 to 168.4 μM. In silico studies were performed to explore the binding modes and interactions between enzyme and synthesized benzimidazoles. Developed linear QSAR model based on density and molecular weight could predict bioactivity of newly synthesized compounds well. Molecular docking studies revealed the availability of some hydrophobic interactions. In addition, the bioactivity of most potent compounds had good correlation with estimated free energy of binding (ΔGbinding) which was calculated according to docked best conformations.

  10. Design, synthesis and exploring the quantitative structure-activity relationship of some antioxidant flavonoid analogues.

    PubMed

    Das, Sreeparna; Mitra, Indrani; Batuta, Shaikh; Niharul Alam, Md; Roy, Kunal; Begum, Naznin Ara

    2014-11-01

    A series of flavonoid analogues were synthesized and screened for the in vitro antioxidant activity through their ability to quench 1,1-diphenyl-2-picryl hydrazyl (DPPH) radical. The activity of these compounds, measured in comparison to the well-known standard antioxidants (29-32), their precursors (38-42) and other bioactive moieties (38-42) resembling partially the flavone skeleton was analyzed further to develop Quantitative Structure-Activity Relationship (QSAR) models using the Genetic Function Approximation (GFA) technique. Based on the essential structural requirements predicted by the QSAR models, some analogues were designed, synthesized and tested for activity. The predicted and experimental activities of these compounds were well correlated. Flavone analogue 20 was found to be the most potent antioxidant.

  11. Quantitative structure-activity relationships and ecological risk assessment: an overview of predictive aquatic toxicology research.

    PubMed

    Bradbury, S P

    1995-09-01

    In the field of aquatic toxicology, quantitative structure-activity relationships (QSARs) have developed as scientifically credible tools for predicting the toxicity of chemicals when little or no empirical data are available. A fundamental understanding of toxicological principles has been considered an important component to the acceptance and application of QSAR approaches as biologically relevant in ecological risk assessments. As a consequence, there has been an evolution of QSAR development and application from that of a chemical-class perspective to one that is more consistent with assumptions regarding modes of toxic action. In this review, techniques to assess modes of toxic action from chemical structure are discussed, with consideration that toxicodynamic knowledge bases must be clearly defined with regard to exposure regimes, biological models/endpoints and compounds that adequately span the diversity of chemicals anticipated for future applications. With such knowledge bases, classification systems, including rule-based expert systems, have been established for use in predictive aquatic toxicology applications. The establishment of QSAR techniques that are based on an understanding of toxic mechanisms is needed to provide a link to physiologically based toxicokinetic and toxicodynamic models, which can provide the means to extrapolate adverse effects across species and exposure regimes. PMID:7570660

  12. QSAR study and the hydrolysis activity prediction of three alkaline lipases from different lipase-producing microorganisms.

    PubMed

    Wang, Haikuan; Wang, Xiaojie; Li, Xiaolu; Zhang, Yehong; Dai, Yujie; Guo, Changlu; Zheng, Heng

    2012-09-28

    The hydrolysis activities of three alkaline lipases, L-A1, L-A2 and L-A3 secreted by different lipase-producing microorganisms isolated from the Bay of Bohai, P. R. China were characterized with 16 kinds of esters. It was found that all the lipases have the ability to catalyze the hydrolysis of the glycerides, methyl esters, ethyl esters, especially for triglycerides, which shows that they have broad substrate spectra, and this property is very important for them to be used in detergent industry. Three QSAR models were built for L-A1, L-A2 and L-A3 respectively with GFA using Discovery studio 2.1. The models equations 1, 2 and 3 can explain 95.80%, 97.45% and 97.09% of the variances (R(2)(adj)) respectively while they could predict 95.44%, 89.61% and 93.41% of the variances (R(2)(cv)) respectively. With these models the hydrolysis activities of these lipases to mixed esters were predicted and the result showed that the predicted values are in good agreement with the measured values, which indicates that this method can be used as a simple tool to predict the lipase activities for single or mixed esters.

  13. QSAR study and the hydrolysis activity prediction of three alkaline lipases from different lipase-producing microorganisms.

    PubMed

    Wang, Haikuan; Wang, Xiaojie; Li, Xiaolu; Zhang, Yehong; Dai, Yujie; Guo, Changlu; Zheng, Heng

    2012-01-01

    The hydrolysis activities of three alkaline lipases, L-A1, L-A2 and L-A3 secreted by different lipase-producing microorganisms isolated from the Bay of Bohai, P. R. China were characterized with 16 kinds of esters. It was found that all the lipases have the ability to catalyze the hydrolysis of the glycerides, methyl esters, ethyl esters, especially for triglycerides, which shows that they have broad substrate spectra, and this property is very important for them to be used in detergent industry. Three QSAR models were built for L-A1, L-A2 and L-A3 respectively with GFA using Discovery studio 2.1. The models equations 1, 2 and 3 can explain 95.80%, 97.45% and 97.09% of the variances (R(2)(adj)) respectively while they could predict 95.44%, 89.61% and 93.41% of the variances (R(2)(cv)) respectively. With these models the hydrolysis activities of these lipases to mixed esters were predicted and the result showed that the predicted values are in good agreement with the measured values, which indicates that this method can be used as a simple tool to predict the lipase activities for single or mixed esters. PMID:23016923

  14. Molecular docking, molecular dynamics simulation, and structure-based 3D-QSAR studies on estrogenic activity of hydroxylated polychlorinated biphenyls.

    PubMed

    Li, Xiaolin; Ye, Li; Wang, Xiaoxiang; Wang, Xinzhou; Liu, Hongling; Qian, Xiangping; Zhu, Yongliang; Yu, Hongxia

    2012-12-15

    Hydroxylated polychlorinated biphenyls (HO-PCBs), major metabolites of PCBs, have been reported to present agonist or antagonist interactions with estrogen receptor α (ERα) and induce ER-mediated responses. In this work, a multistep framework combining molecular docking, molecular dynamics (MD) simulations, and structure-based three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed to explore the influence of structural features on the estrogenic activities of HO-PCBs, and to investigate the molecular mechanism of ERα-ligand interactions. The CoMSIA (comparative molecular similarity indices analysis) model was developed from the conformations obtained from molecular docking. The model exhibited statistically significant results as the cross-validated correlation coefficient q² was 0.648, the non-cross-validated correlation coefficient r² was 0.968, and the external predictive correlation coefficient r(pred)² was 0.625. The key amino acid residues were identified by molecular docking, and the detailed binding modes of the compounds with different activities were determined by MD simulations. The binding free energies correlated well with the experimental activity. An energetic analysis, MM-GBSA energy decomposition, revealed that the van der Waals interaction was the major driving force for the binding of compounds to ERα. The hydrogen bond interactions between the ligands and residue His524 help to stabilize the conformation of ligands at the binding pocket. These results are expected to be beneficial to predict estrogenic activities of other HO-PCB congeners and helpful for understanding the binding mechanism of HO-PCBs and ERα. PMID:23137989

  15. 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).

  16. Alert-QSAR. Implications for Electrophilic Theory of Chemical Carcinogenesis

    PubMed Central

    Putz, Mihai V.; Ionaşcu, Cosmin; Putz, Ana-Maria; Ostafe, Vasile

    2011-01-01

    Given the modeling and predictive abilities of quantitative structure activity relationships (QSARs) for genotoxic carcinogens or mutagens that directly affect DNA, the present research investigates structural alert (SA) intermediate-predicted correlations ASA of electrophilic molecular structures with observed carcinogenic potencies in rats (observed activity, A = Log[1/TD50], i.e., ASA=f(X1SA,X2SA,…)). The present method includes calculation of the recently developed residual correlation of the structural alert models, i.e., ARASA=f(A−ASA,X1SA,X2SA,…). We propose a specific electrophilic ligand-receptor mechanism that combines electronegativity with chemical hardness-associated frontier principles, equality of ligand-reagent electronegativities and ligand maximum chemical hardness for highly diverse toxic molecules against specific receptors in rats. The observed carcinogenic activity is influenced by the induced SA-mutagenic intermediate effect, alongside Hansch indices such as hydrophobicity (LogP), polarizability (POL) and total energy (Etot), which account for molecular membrane diffusion, ionic deformation, and stericity, respectively. A possible QSAR mechanistic interpretation of mutagenicity as the first step in genotoxic carcinogenesis development is discussed using the structural alert chemoinformation and in full accordance with the Organization for Economic Co-operation and Development QSAR guidance principles. PMID:21954348

  17. Quantitative Structure Activity Relationship Models for the Antioxidant Activity of Polysaccharides

    PubMed Central

    Nie, Kaiying; Wang, Zhaojing

    2016-01-01

    In this study, quantitative structure activity relationship (QSAR) models for the antioxidant activity of polysaccharides were developed with 50% effective concentration (EC50) as the dependent variable. To establish optimum QSAR models, multiple linear regressions (MLR), support vector machines (SVM) and artificial neural networks (ANN) were used, and 11 molecular descriptors were selected. The optimum QSAR model for predicting EC50 of DPPH-scavenging activity consisted of four major descriptors. MLR model gave EC50 = 0.033Ara-0.041GalA-0.03GlcA-0.025PC+0.484, and MLR fitted the training set with R = 0.807. ANN model gave the improvement of training set (R = 0.96, RMSE = 0.018) and test set (R = 0.933, RMSE = 0.055) which indicated that it was more accurately than SVM and MLR models for predicting the DPPH-scavenging activity of polysaccharides. 67 compounds were used for predicting EC50 of the hydroxyl radicals scavenging activity of polysaccharides. MLR model gave EC50 = 0.12PC+0.083Fuc+0.013Rha-0.02UA+0.372. A comparison of results from models indicated that ANN model (R = 0.944, RMSE = 0.119) was also the best one for predicting the hydroxyl radicals scavenging activity of polysaccharides. MLR and ANN models showed that Ara and GalA appeared critical in determining EC50 of DPPH-scavenging activity, and Fuc, Rha, uronic acid and protein content had a great effect on the hydroxyl radicals scavenging activity of polysaccharides. The antioxidant activity of polysaccharide usually was high in MW range of 4000–100000, and the antioxidant activity could be affected simultaneously by other polysaccharide properties, such as uronic acid and Ara. PMID:27685320

  18. Rational design, synthesis and 2D-QSAR study of novel vasorelaxant active benzofuran-pyridine hybrids.

    PubMed

    Srour, Aladdin M; Abd El-Karim, Somaia S; Saleh, Dalia O; El-Eraky, Wafaa I; Nofal, Zeinab M

    2016-05-15

    Reaction of 3-aryl-1-(benzofuran-2-yl)-2-propen-1-ones 3a-c with malononitrile in the presence of sufficient amount of sodium alkoxide in the corresponding alcohol proceeds in a regioselective manner to afford 2-alkoxy-4-aryl-6-(benzofuran-2-yl)-3-pyridinecarbonitriles 4-37, which also obtained by treating ylidenemalononitriles 6a-q with 2-acetylbenzofuran 1 in the presence of sufficient amount of sodium alkoxide in the corresponding alcohol. The new chemical entities showed significant vasodilation properties using isolated thoracic aortic rings of rats pre-contracted with norepinephrine hydrochloride standard technique. Compounds 11, 16, 21, 24 and 30 exhibited remarkable activity compared with amiodarone hydrochloride the reference standard used in the present study. CODESSA-Pro software was employing to obtain a statistically significant QSAR model describing the bioactivity of the newly synthesized analogs (N=31, n=5, R(2)=0.846, R(2)cvOO=0.765, R(2)cvMO=0.778, F=27.540. s(2)=0.002). PMID:27048942

  19. Rational design, synthesis and 2D-QSAR study of novel vasorelaxant active benzofuran-pyridine hybrids.

    PubMed

    Srour, Aladdin M; Abd El-Karim, Somaia S; Saleh, Dalia O; El-Eraky, Wafaa I; Nofal, Zeinab M

    2016-05-15

    Reaction of 3-aryl-1-(benzofuran-2-yl)-2-propen-1-ones 3a-c with malononitrile in the presence of sufficient amount of sodium alkoxide in the corresponding alcohol proceeds in a regioselective manner to afford 2-alkoxy-4-aryl-6-(benzofuran-2-yl)-3-pyridinecarbonitriles 4-37, which also obtained by treating ylidenemalononitriles 6a-q with 2-acetylbenzofuran 1 in the presence of sufficient amount of sodium alkoxide in the corresponding alcohol. The new chemical entities showed significant vasodilation properties using isolated thoracic aortic rings of rats pre-contracted with norepinephrine hydrochloride standard technique. Compounds 11, 16, 21, 24 and 30 exhibited remarkable activity compared with amiodarone hydrochloride the reference standard used in the present study. CODESSA-Pro software was employing to obtain a statistically significant QSAR model describing the bioactivity of the newly synthesized analogs (N=31, n=5, R(2)=0.846, R(2)cvOO=0.765, R(2)cvMO=0.778, F=27.540. s(2)=0.002).

  20. Robust Methods in Qsar

    NASA Astrophysics Data System (ADS)

    Walczak, Beata; Daszykowski, Michał; Stanimirova, Ivana

    A large progress in the development of robust methods as an efficient tool for processing of data contaminated with outlying objects has been made over the last years. Outliers in the QSAR studies are usually the result of an improper calculation of some molecular descriptors and/or experimental error in determining the property to be modelled. They influence greatly any least square model, and therefore the conclusions about the biological activity of a potential component based on such a model are misleading. With the use of robust approaches, one can solve this problem building a robust model describing the data majority well. On the other hand, the proper identification of outliers may pinpoint a new direction of a drug development. The outliers' assessment can exclusively be done with robust methods and these methods are to be described in this chapter

  1. Quantitative structure-activity relationship correlation between molecular structure and the Rayleigh enantiomeric enrichment factor.

    PubMed

    Jammer, S; Rizkov, D; Gelman, F; Lev, O

    2015-08-01

    It was recently demonstrated that under environmentally relevant conditions the Rayleigh equation is valid to describe the enantiomeric enrichment - conversion relationship, yielding a proportional constant called the enantiomeric enrichment factor, εER. In the present study we demonstrate a quantitative structure-activity relationship model (QSAR) that describes well the dependence of εER on molecular structure. The enantiomeric enrichment factor can be predicted by the linear Hansch model, which correlates biological activity with physicochemical properties. Enantioselective hydrolysis of sixteen derivatives of 2-(phenoxy)propionate (PPMs) have been analyzed during enzymatic degradation by lipases from Pseudomonas fluorescens (PFL), Pseudomonas cepacia (PCL), and Candida rugosa (CRL). In all cases the QSAR relationships were significant with R(2) values of 0.90-0.93, and showed high predictive abilities with internal and external validations providing QLOO(2) values of 0.85-0.87 and QExt(2) values of 0.8-0.91. Moreover, it is demonstrated that this model enables differentiation between enzymes with different binding site shapes. The enantioselectivity of PFL and PCL was dictated by electronic properties, whereas the enantioselectivity of CRL was determined by lipophilicity and steric factors. The predictive ability of the QSAR model demonstrated in the present study may serve as a helpful tool in environmental studies, assisting in source tracking of unstudied chiral compounds belonging to a well-studied homologous series.

  2. Antioxidant QSAR modeling as exemplified on polyphenols.

    PubMed

    Lucić, Bono; Amić, Dragan; Trinajstić, Nenad

    2008-01-01

    Methodology for deriving quantitative structure-activity relationship (QSAR) models based on computed molecular descriptors, representing numerically structural features of polyphenols, and applicable to the antioxidant activity of polyphenols is delineated. The application of this methodology is illustrated on a data set of 100 polyphenols. Prior to the computation of molecular descriptors, molecular structures are coded in the SMILES form, a computer-acceptable version of structure, and then converted to the 3D form by the CORINA program. Using 3D structures, molecular descriptors can be calculated by one of several programs developed (we used the DRAGON program in this study). Finally, using computer program for selection of most important descriptors in the model, a two-descriptor model is selected and its use is illustrated.

  3. Comparison between 5,10,15,20-tetraaryl- and 5,15-diarylporphyrins as photosensitizers: synthesis, photodynamic activity, and quantitative structure-activity relationship modeling.

    PubMed

    Banfi, Stefano; Caruso, Enrico; Buccafurni, Loredana; Murano, Roberto; Monti, Elena; Gariboldi, Marzia; Papa, Ester; Gramatica, Paola

    2006-06-01

    The synthesis of a panel of seven nonsymmetric 5,10,15,20-tetraarylporphyrins, 13 symmetric and nonsymmetric 5,15-diarylporphyrins, and one 5,15-diarylchlorin is described. In vitro photodynamic activities on HCT116 human colon adenocarcinoma cells were evaluated by standard cytotoxicity assays. A predictive quantitative structure-activity relationship (QSAR) regression model, based on theoretical holistic molecular descriptors, of a series of 34 tetrapyrrolic photosensitizers (PSs), including the 24 compounds synthesized in this work, was developed to describe the relationship between structural features and photodynamic activity. The present study demonstrates that structural features significantly influence the photodynamic activity of tetrapyrrolic derivatives: diaryl compounds were more active with respect to the tetraarylporphyrins, and among the diaryl derivatives, hydroxy-substituted compounds were more effective than the corresponding methoxy-substituted ones. Furthermore, three monoarylporphyrins, isolated as byproducts during diarylporphyrin synthesis, were considered for both photodynamic and QSAR studies; surprisingly they were found to be particularly active photosensitizers.

  4. Ranking of aquatic toxicity of esters modelled by QSAR.

    PubMed

    Papa, Ester; Battaini, Francesca; Gramatica, Paola

    2005-02-01

    Alternative methods like predictions based on Quantitative Structure-Activity Relationships (QSARs) are now accepted to fill data gaps and define priority lists for more expensive and time consuming assessments. A heterogeneous data set of 74 esters was studied for their aquatic toxicity, and available experimental toxicity data on algae, Daphnia and fish were used to develop statistically validated QSAR models, obtained using multiple linear regression (MLR) by the OLS (Ordinary Least Squares) method and GA-VSS (Variable Subset Selection by Genetic Algorithms) to predict missing values. An ESter Aquatic Toxicity INdex (ESATIN) was then obtained by combining, by PCA, experimental and predicted toxicity data, from which model outliers and esters highly influential due to their structure had been eliminated. Finally this integrated aquatic toxicity index, defined by the PC1 score, was modelled using only a few theoretical molecular descriptors. This last QSAR model, statistically validated for its predictive power, could be proposed as a preliminary evaluative method for screening/prioritising esters according to their integrated aquatic toxicity, just starting from their molecular structure.

  5. Design, synthesis, evaluation and QSAR analysis of N(1)-substituted norcymserine derivatives as selective butyrylcholinesterase inhibitors.

    PubMed

    Takahashi, Jun; Hijikuro, Ichiro; Kihara, Takeshi; Murugesh, Modachur G; Fuse, Shinichiro; Kunimoto, Ryo; Tsumura, Yoshinori; Akaike, Akinori; Niidome, Tetsuhiro; Okuno, Yasushi; Takahashi, Takashi; Sugimoto, Hachiro

    2010-03-01

    We synthesized a series of N(1)-substituted norcymserine derivatives 7a-p and evaluated their anti-cholinesterase activities. In vitro evaluation showed that the pyridinylethyl derivatives 7m-o and the piperidinylethyl derivative 7p improved the anti-butyrylcholinesterase activity by approximately threefold compared to N(1)-phenethylnorcymserine (PEC, 2). A quantitative structure-activity relationship (QSAR) study indicated that logS might be a key feature of the improved compounds.

  6. INFLUENCE OF MATRIX FORMULATION ON DERMAL PERCUTANEOUS ABSORPTION OF TRIAZOLE FUNGICIDES USING QSAR AND PBPK / PD MODELS

    EPA Science Inventory

    The objective of this work is to use the Exposure Related Dose Estimating Model (ERDEM) and quantitative structure-activity relationship (QSAR) models to develop an assessment tool for human exposure assessment to triazole fungicides. A dermal exposure route is used for the physi...

  7. Active site characterization and structure based 3D-QSAR studies on non-redox type 5-lipoxygenase inhibitors.

    PubMed

    Ul-Haq, Zaheer; Khan, Naveed; Zafar, Syed Kashif; Moin, Syed Tarique

    2016-06-10

    Structure-based 3D-QSAR study was performed on a class of 5-benzylidene-2-phenylthiazolinones non-redox type 5-LOX inhibitors. In this study, binding pocket of 5-Lipoxygenase (pdb id 3o8y) was identified by manual docking using 15-LOX (pdb id 2p0m) as a reference structure. Additionally, most of the binding site residues were found conserved in both structures. These non-redox inhibitors were then docked into the binding site of 5-LOX. To generate reliable CoMFA and CoMSIA models, atom fit data base alignment method using docked conformation of the most active compound was employed. The q(2)cv and r(2)ncv values for CoMFA model were found to be 0.549 and 0.702, respectively. The q(2)cv and r(2)ncv values for the selected CoMSIA model comprised four descriptors steric, electrostatic, hydrophobic and hydrogen bond donor fields were found to be 0.535 and 0.951, respectively. Obtained results showed that our generated model was statistically reliable. Furthermore, an external test set validates the reliability of the predicted model by calculating r(2)pred i.e.0.787 and 0.571 for CoMFA and CoMSIA model, respectively. 3D contour maps generated from CoMFA and CoMSIA models were utilized to determine the key structural features of ligands responsible for biological activities. The applied protocol will be helpful to design more potent and selective inhibitors of 5-LOX. PMID:27044904

  8. QSAR and Molecular Docking Studies of Oxadiazole-Ligated Pyrrole Derivatives as Enoyl-ACP (CoA) Reductase Inhibitors

    PubMed Central

    Asgaonkar, Kalyani D.; Mote, Ganesh D.; Chitre, Trupti S.

    2014-01-01

    A quantitative structure-activity relationship model was developed on a series of compounds containing oxadiazole-ligated pyrrole pharmacophore to identify key structural fragments required for anti-tubercular activity. Two-dimensional (2D) and three-dimensional (3D) QSAR studies were performed using multiple linear regression (MLR) analysis and k-nearest neighbour molecular field analysis (kNN-MFA), respectively. The developed QSAR models were found to be statistically significant with respect to training, cross-validation, and external validation. New chemical entities (NCEs) were designed based on the results of the 2D- and 3D-QSAR. NCEs were subjected to Lipinski’s screen to ensure the drug-like pharmacokinetic profile of the designed compounds in order to improve their bioavailability. Also, the binding ability of the NCEs with enoyl-ACP (CoA) reductase was assessed by docking. PMID:24634843

  9. 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

  10. A quantitative structure-activity relationship model for radical scavenging activity of flavonoids.

    PubMed

    Om, A; Kim, J H

    2008-03-01

    A quantitative structure-activity relationship (QSAR) study has been carried out for a training set of 29 flavonoids to correlate and predict the 1,1-diphenyl-2-picrylhydrazyl radical scavenging activity (RSA) values obtained from published data. Genetic algorithm and multiple linear regression were employed to select the descriptors and to generate the best prediction model that relates the structural features to the RSA activities using (1) three-dimensional (3D) Dragon (TALETE srl, Milan, Italy) descriptors and (2) semi-empirical descriptor calculations. The predictivity of the models was estimated by cross-validation with the leave-one-out method. The result showed that a significant improvement of the statistical indices was obtained by deleting outliers. Based on the data for the compounds used in this study, our results suggest a QSAR model of RSA that is based on the following descriptors: 3D-Morse, WHIM, and GETAWAY. Therefore, satisfactory relationships between RSA and the semi-empirical descriptors were found, demonstrating that the energy of the highest occupied molecular orbital, total energy, and energy of heat of formation contributed more significantly than all other descriptors.

  11. Predicting anti-androgenic activity of bisphenols using molecular docking and quantitative structure-activity relationships.

    PubMed

    Yang, Xianhai; Liu, Huihui; Yang, Qian; Liu, Jining; Chen, Jingwen; Shi, Lili

    2016-11-01

    Both in vivo and in vitro assay indicated that bisphenols can inhibit the androgen receptor. However, the underlying antagonistic mechanism is unclear. In this study, molecular docking was employed to probe the interaction mechanism between bisphenols and human androgen receptor (hAR). The binding pattern of ligands in hAR crystal structures was also analyzed. Results show that hydrogen bonding and hydrophobic interactions are the dominant interactions between the ligands and hAR. The critical amino acid residues involved in forming hydrogen bonding between bisphenols and hAR is Asn 705 and Gln 711. Furthermore, appropriate molecular structural descriptors were selected to characterize the non-bonded interactions. Stepwise multiple linear regressions (MLR) analysis was employed to develop quantitative structure-activity relationship (QSAR) models for predicting the anti-androgenic activity of bisphenols. Based on the QSAR development and validation guideline issued by OECD, the goodness-of-fit, robustness and predictive ability of constructed QSAR model were assessed. The model application domain was characterized by the Euclidean distance and Williams plot. The mechanisms of the constructed model were also interpreted based on the selected molecular descriptors i.e. the number of hydroxyl groups (nROH), the most positive values of the molecular surface potential (Vs,max) and the lowest unoccupied molecular orbital energy (ELUMO). Finally, based on the model developed, the data gap for other twenty-six bisphenols on their anti-androgenic activity was filled. The predicted results indicated that the anti-androgenic activity of seven bisphenols was higher than that of bisphenol A. PMID:27561732

  12. Flow network QSAR for the prediction of physicochemical properties by mapping an electrical resistance network onto a chemical reaction poset.

    PubMed

    Ivanciuc, Ovidiu; Ivanciuc, Teodora; Klein, Douglas J

    2013-06-01

    Usual quantitative structure-activity relationship (QSAR) models are computed from unstructured input data, by using a vector of molecular descriptors for each chemical in the dataset. Another alternative is to consider the structural relationships between the chemical structures, such as molecular similarity, presence of certain substructures, or chemical transformations between compounds. We defined a class of network-QSAR models based on molecular networks induced by a sequence of substitution reactions on a chemical structure that generates a partially ordered set (or poset) oriented graph that may be used to predict various molecular properties with quantitative superstructure-activity relationships (QSSAR). The network-QSAR interpolation models defined on poset graphs, namely average poset, cluster expansion, and spline poset, were tested with success for the prediction of several physicochemical properties for diverse chemicals. We introduce the flow network QSAR, a new poset regression model in which the dataset of chemicals, represented as a reaction poset, is transformed into an oriented network of electrical resistances in which the current flow results in a potential at each node. The molecular property considered in the QSSAR model is represented as the electrical potential, and the value of this potential at a particular node is determined by the electrical resistances assigned to each edge and by a system of batteries. Each node with a known value for the molecular property is attached to a battery that sets the potential on that node to the value of the respective molecular property, and no external battery is attached to nodes from the prediction set, representing chemicals for which the values of the molecular property are not known or are intended to be predicted. The flow network QSAR algorithm determines the values of the molecular property for the prediction set of molecules by applying Ohm's law and Kirchhoff's current law to the poset

  13. New Quantitative Structure-Activity Relationship Models Improve Predictability of Ames Mutagenicity for Aromatic Azo Compounds.

    PubMed

    Manganelli, Serena; Benfenati, Emilio; Manganaro, Alberto; Kulkarni, Sunil; Barton-Maclaren, Tara S; Honma, Masamitsu

    2016-10-01

    Existing Quantitative Structure-Activity Relationship (QSAR) models have limited predictive capabilities for aromatic azo compounds. In this study, 2 new models were built to predict Ames mutagenicity of this class of compounds. The first one made use of descriptors based on simplified molecular input-line entry system (SMILES), calculated with the CORAL software. The second model was based on the k-nearest neighbors algorithm. The statistical quality of the predictions from single models was satisfactory. The performance further improved when the predictions from these models were combined. The prediction results from other QSAR models for mutagenicity were also evaluated. Most of the existing models were found to be good at finding toxic compounds but resulted in many false positive predictions. The 2 new models specific for this class of compounds avoid this problem thanks to a larger set of related compounds as training set and improved algorithms.

  14. New Quantitative Structure-Activity Relationship Models Improve Predictability of Ames Mutagenicity for Aromatic Azo Compounds.

    PubMed

    Manganelli, Serena; Benfenati, Emilio; Manganaro, Alberto; Kulkarni, Sunil; Barton-Maclaren, Tara S; Honma, Masamitsu

    2016-10-01

    Existing Quantitative Structure-Activity Relationship (QSAR) models have limited predictive capabilities for aromatic azo compounds. In this study, 2 new models were built to predict Ames mutagenicity of this class of compounds. The first one made use of descriptors based on simplified molecular input-line entry system (SMILES), calculated with the CORAL software. The second model was based on the k-nearest neighbors algorithm. The statistical quality of the predictions from single models was satisfactory. The performance further improved when the predictions from these models were combined. The prediction results from other QSAR models for mutagenicity were also evaluated. Most of the existing models were found to be good at finding toxic compounds but resulted in many false positive predictions. The 2 new models specific for this class of compounds avoid this problem thanks to a larger set of related compounds as training set and improved algorithms. PMID:27413112

  15. QSAR and molecular docking studies on oxindole derivatives as VEGFR-2 tyrosine kinase inhibitors.

    PubMed

    Kang, Cong-Min; Liu, Dong-Qing; Zhao, Xu-Hao; Dai, Ying-Jie; Cheng, Jia-Gao; Lv, Ying-Tao

    2016-01-01

    The three-dimensional quantitative structure-activity relationships (3D-QSAR) were established for 30 oxindole derivatives as vascular endothelial growth factor receptor-2 (VEGFR-2) tyrosine kinase inhibitors by using comparative molecular field analysis (CoMFA) and comparative similarity indices analysis comparative molecular similarity indices analysis (CoMSIA) techniques. With the CoMFA model, the cross-validated value (q(2)) was 0.777, the non-cross-validated value (R(2)) was 0.987, and the external cross-validated value ([Formula: see text]) was 0.72. And with the CoMSIA model, the corresponding q(2), R(2) and [Formula: see text] values were 0.710, 0.988 and 0.78, respectively. Docking studies were employed to bind the inhibitors into the active site to determine the probable binding conformation. The binding mode obtained by molecular docking was in good agreement with the 3D-QSAR results. Based on the QSAR models and the docking binding mode, a set of new VEGFR-2 tyrosine kinase inhibitors were designed, which showed excellent predicting inhibiting potencies. The result revealed that both QSAR models have good predictive capability to guide the design and structural modification of homologic compounds. It is also helpful for further research and development of new VEGFR-2 tyrosine kinase inhibitors.

  16. Applying quantitative structure-activity relationship approaches to nanotoxicology: current status and future potential.

    PubMed

    Winkler, David A; Mombelli, Enrico; Pietroiusti, Antonio; Tran, Lang; Worth, Andrew; Fadeel, Bengt; McCall, Maxine J

    2013-11-01

    The potential (eco)toxicological hazard posed by engineered nanoparticles is a major scientific and societal concern since several industrial sectors (e.g. electronics, biomedicine, and cosmetics) are exploiting the innovative properties of nanostructures resulting in their large-scale production. Many consumer products contain nanomaterials and, given their complex life-cycle, it is essential to anticipate their (eco)toxicological properties in a fast and inexpensive way in order to mitigate adverse effects on human health and the environment. In this context, the application of the structure-toxicity paradigm to nanomaterials represents a promising approach. Indeed, according to this paradigm, it is possible to predict toxicological effects induced by chemicals on the basis of their structural similarity with chemicals for which toxicological endpoints have been previously measured. These structure-toxicity relationships can be quantitative or qualitative in nature and they can predict toxicological effects directly from the physicochemical properties of the entities (e.g. nanoparticles) of interest. Therefore, this approach can aid in prioritizing resources in toxicological investigations while reducing the ethical and monetary costs that are related to animal testing. The purpose of this review is to provide a summary of recent key advances in the field of QSAR modelling of nanomaterial toxicity, to identify the major gaps in research required to accelerate the use of quantitative structure-activity relationship (QSAR) methods, and to provide a roadmap for future research needed to achieve QSAR models useful for regulatory purposes. PMID:23165187

  17. Toxicity of ionic liquids: database and prediction via quantitative structure-activity relationship method.

    PubMed

    Zhao, Yongsheng; Zhao, Jihong; Huang, Ying; Zhou, Qing; Zhang, Xiangping; Zhang, Suojiang

    2014-08-15

    A comprehensive database on toxicity of ionic liquids (ILs) is established. The database includes over 4000 pieces of data. Based on the database, the relationship between IL's structure and its toxicity has been analyzed qualitatively. Furthermore, Quantitative Structure-Activity relationships (QSAR) model is conducted to predict the toxicities (EC50 values) of various ILs toward the Leukemia rat cell line IPC-81. Four parameters selected by the heuristic method (HM) are used to perform the studies of multiple linear regression (MLR) and support vector machine (SVM). The squared correlation coefficient (R(2)) and the root mean square error (RMSE) of training sets by two QSAR models are 0.918 and 0.959, 0.258 and 0.179, respectively. The prediction R(2) and RMSE of QSAR test sets by MLR model are 0.892 and 0.329, by SVM model are 0.958 and 0.234, respectively. The nonlinear model developed by SVM algorithm is much outperformed MLR, which indicates that SVM model is more reliable in the prediction of toxicity of ILs. This study shows that increasing the relative number of O atoms of molecules leads to decrease in the toxicity of ILs.

  18. Applying quantitative structure-activity relationship approaches to nanotoxicology: current status and future potential.

    PubMed

    Winkler, David A; Mombelli, Enrico; Pietroiusti, Antonio; Tran, Lang; Worth, Andrew; Fadeel, Bengt; McCall, Maxine J

    2013-11-01

    The potential (eco)toxicological hazard posed by engineered nanoparticles is a major scientific and societal concern since several industrial sectors (e.g. electronics, biomedicine, and cosmetics) are exploiting the innovative properties of nanostructures resulting in their large-scale production. Many consumer products contain nanomaterials and, given their complex life-cycle, it is essential to anticipate their (eco)toxicological properties in a fast and inexpensive way in order to mitigate adverse effects on human health and the environment. In this context, the application of the structure-toxicity paradigm to nanomaterials represents a promising approach. Indeed, according to this paradigm, it is possible to predict toxicological effects induced by chemicals on the basis of their structural similarity with chemicals for which toxicological endpoints have been previously measured. These structure-toxicity relationships can be quantitative or qualitative in nature and they can predict toxicological effects directly from the physicochemical properties of the entities (e.g. nanoparticles) of interest. Therefore, this approach can aid in prioritizing resources in toxicological investigations while reducing the ethical and monetary costs that are related to animal testing. The purpose of this review is to provide a summary of recent key advances in the field of QSAR modelling of nanomaterial toxicity, to identify the major gaps in research required to accelerate the use of quantitative structure-activity relationship (QSAR) methods, and to provide a roadmap for future research needed to achieve QSAR models useful for regulatory purposes.

  19. In Silico Study of In Vitro GPCR Assays by QSAR Modeling.

    PubMed

    Mansouri, Kamel; Judson, Richard S

    2016-01-01

    The US EPA's ToxCast program is screening thousands of chemicals of environmental interest in hundreds of in vitro high-throughput screening (HTS) assays. One goal is to prioritize chemicals for more detailed analyses based on activity in assays that target molecular initiating events (MIEs) of adverse outcome pathways (AOPs). However, the chemical space of interest for environmental exposure is much wider than ToxCast's chemical library. In silico methods such as quantitative structure-activity relationships (QSARs) are proven and cost-effective approaches to predict biological activity for untested chemicals. However, empirical data is needed to build and validate QSARs. ToxCast has developed datasets for about 2000 chemicals ideal for training and testing QSAR models. The overall goal of the present work was to develop QSAR models to fill the data gaps in larger environmental chemical lists. The specific aim of the current work was to build QSAR models for 18 G-protein-coupled receptor (GPCR) assays, part of the aminergic family. Two QSAR modeling strategies were adopted: classification models were developed to separate chemicals into active/non-active classes, and then regression models were built to predict the potency values of the bioassays for the active chemicals. Multiple software programs were used to calculate constitutional, topological, and substructural molecular descriptors from two-dimensional (2D) chemical structures. Model-fitting methods included PLSDA (partial least square discriminant analysis), SVMs (support vector machines), kNNs (k-nearest neighbors), and PLSs (partial least squares). Genetic algorithms (GAs) were applied as a variable selection technique to select the most predictive molecular descriptors for each assay. N-fold cross-validation (CV) coupled with multi-criteria decision-making fitting criteria was used to evaluate the models. Finally, the models were applied to make predictions within the established chemical space limits

  20. Improving quantitative structure-activity relationship models using Artificial Neural Networks trained with dropout.

    PubMed

    Mendenhall, Jeffrey; Meiler, Jens

    2016-02-01

    Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets. Use of dropout improved both enrichment false positive rate and log-scaled area under the receiver-operating characteristic curve (logAUC) by 22-46 % over conventional ANN implementations. Optimal dropout rates are found to be a function of the signal-to-noise ratio of the descriptor set, and relatively independent of the dataset. Dropout ANNs with 2D and 3D autocorrelation descriptors outperform conventional ANNs as well as optimized fingerprint similarity search methods.

  1. Structure-activity relationship of genotoxic polycyclic aromatic nitro compounds: Further evidence for the importance of hydrophobicity and molecular orbital energies in genetic toxicity

    SciTech Connect

    Debnath, A.K.; Hansch, C. )

    1992-01-01

    A quantitative structure-activity relationship (QSAR) has been formulated for 15 polycyclic aromatic nitro compounds acting on E. coli PQ37. Upon damage of DNA by these substances [beta]-galactosidase is induced and can be easily assayed colorimetrically, hence, this is a short-term test for mutagenicity. The QSAR (log SOSIP = 1.07 log P - 1.57 e[sub LUMO] - 6.41) is strikingly similar to that found earlier with nitroaromatics acting in the Ames test (TA100) and differs significantly for that found using TA98 organisms. The QSAR brings out in a unique manner the underlying similarity in the two test systems. 24 refs., 2 tabs.

  2. Structure-based approach to pharmacophore identification, in silico screening, and three-dimensional quantitative structure-activity relationship studies for inhibitors of Trypanosoma cruzi dihydrofolate reductase function

    SciTech Connect

    Schormann, N.; Senkovich, O.; Walker, K.; Wright, D.L.; Anderson, A.C.; Rosowsky, A.; Ananthan, S.; Shinkre, B.; Velu, S.; Chattopadhyay, D.

    2009-07-10

    We have employed a structure-based three-dimensional quantitative structure-activity relationship (3D-QSAR) approach to predict the biochemical activity for inhibitors of T. cruzi dihydrofolate reductase-thymidylate synthase (DHFR-TS). Crystal structures of complexes of the enzyme with eight different inhibitors of the DHFR activity together with the structure in the substrate-free state (DHFR domain) were used to validate and refine docking poses of ligands that constitute likely active conformations. Structural information from these complexes formed the basis for the structure-based alignment used as input for the QSAR study. Contrary to indirect ligand-based approaches the strategy described here employs a direct receptor-based approach. The goal is to generate a library of selective lead inhibitors for further development as antiparasitic agents. 3D-QSAR models were obtained for T. cruzi DHFR-TS (30 inhibitors in learning set) and human DHFR (36 inhibitors in learning set) that show a very good agreement between experimental and predicted enzyme inhibition data. For crossvalidation of the QSAR model(s), we have used the 10% leave-one-out method. The derived 3D-QSAR models were tested against a few selected compounds (a small test set of six inhibitors for each enzyme) with known activity, which were not part of the learning set, and the quality of prediction of the initial 3D-QSAR models demonstrated that such studies are feasible. Further refinement of the models through integration of additional activity data and optimization of reliable docking poses is expected to lead to an improved predictive ability.

  3. Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method.

    PubMed

    Rodgers, Amie D; Zhu, Hao; Fourches, Denis; Rusyn, Ivan; Tropsha, Alexander

    2010-04-19

    Adverse effects of drugs (AEDs) continue to be a major cause of drug withdrawals in both development and postmarketing. While liver-related AEDs are a major concern for drug safety, there are few in silico models for predicting human liver toxicity for drug candidates. We have applied the quantitative structure-activity relationship (QSAR) approach to model liver AEDs. In this study, we aimed to construct a QSAR model capable of binary classification (active vs inactive) of drugs for liver AEDs based on chemical structure. To build QSAR models, we have employed an FDA spontaneous reporting database of human liver AEDs (elevations in activity of serum liver enzymes), which contains data on approximately 500 approved drugs. Approximately 200 compounds with wide clinical data coverage, structural similarity, and balanced (40/60) active/inactive ratios were selected for modeling and divided into multiple training/test and external validation sets. QSAR models were developed using the k nearest neighbor method and validated using external data sets. Models with high sensitivity (>73%) and specificity (>94%) for the prediction of liver AEDs in external validation sets were developed. To test applicability of the models, three chemical databases (World Drug Index, Prestwick Chemical Library, and Biowisdom Liver Intelligence Module) were screened in silico, and the validity of predictions was determined, where possible, by comparing model-based classification with assertions in publicly available literature. Validated QSAR models of liver AEDs based on the data from the FDA spontaneous reporting system can be employed as sensitive and specific predictors of AEDs in preclinical screening of drug candidates for potential hepatotoxicity in humans. PMID:20192250

  4. Integrated QSAR study for inhibitors of hedgehog signal pathway against multiple cell lines:a collaborative filtering method

    PubMed Central

    2012-01-01

    Background The Hedgehog Signaling Pathway is one of signaling pathways that are very important to embryonic development. The participation of inhibitors in the Hedgehog Signal Pathway can control cell growth and death, and searching novel inhibitors to the functioning of the pathway are in a great demand. As the matter of fact, effective inhibitors could provide efficient therapies for a wide range of malignancies, and targeting such pathway in cells represents a promising new paradigm for cell growth and death control. Current research mainly focuses on the syntheses of the inhibitors of cyclopamine derivatives, which bind specifically to the Smo protein, and can be used for cancer therapy. While quantitatively structure-activity relationship (QSAR) studies have been performed for these compounds among different cell lines, none of them have achieved acceptable results in the prediction of activity values of new compounds. In this study, we proposed a novel collaborative QSAR model for inhibitors of the Hedgehog Signaling Pathway by integration the information from multiple cell lines. Such a model is expected to substantially improve the QSAR ability from single cell lines, and provide useful clues in developing clinically effective inhibitors and modifications of parent lead compounds for target on the Hedgehog Signaling Pathway. Results In this study, we have presented: (1) a collaborative QSAR model, which is used to integrate information among multiple cell lines to boost the QSAR results, rather than only a single cell line QSAR modeling. Our experiments have shown that the performance of our model is significantly better than single cell line QSAR methods; and (2) an efficient feature selection strategy under such collaborative environment, which can derive the commonly important features related to the entire given cell lines, while simultaneously showing their specific contributions to a specific cell-line. Based on feature selection results, we have

  5. A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, domain of application and prediction.

    PubMed

    Hamadache, Mabrouk; Benkortbi, Othmane; Hanini, Salah; Amrane, Abdeltif; Khaouane, Latifa; Si Moussa, Cherif

    2016-02-13

    Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q(2)ext and the root mean square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides.

  6. Structure-activity relationships for a class of selective inhibitors of the major cysteine protease from Trypanosoma cruzi.

    PubMed

    Guido, Rafael V C; Trossini, Gustavo H G; Castilho, Marcelo S; Oliva, Glaucius; Ferreira, Elizabeth I; Andricopulo, Adriano D

    2008-12-01

    Chagas' disease is a parasitic infection widely distributed throughout Latin America, with devastating consequences in terms of human morbidity and mortality. Cruzain, the major cysteine protease from Trypanosoma cruzi, is an attractive target for antitrypanosomal chemotherapy. In the present work, classical two-dimensional quantitative structure-activity relationships (2D QSAR) and hologram QSAR (HQSAR) studies were performed on a training set of 45 thiosemicarbazone and semicarbazone derivatives as inhibitors of T. cruzi cruzain. Significant statistical models (HQSAR, q(2) = 0.75 and r(2) = 0.96; classical QSAR, q(2) = 0.72 and r(2) = 0.83) were obtained, indicating their consistency for untested compounds. The models were then used to evaluate an external test set containing 10 compounds which were not included in the training set, and the predicted values were in good agreement with the experimental results (HQSAR, r(2)(pred) = 0.95; classical QSAR, r(2)(pred) = 0.91), indicating the existence of complementary between the two ligand-based drug design techniques.

  7. Prediction of anticancer property of bowsellic acid derivatives by quantitative structure activity relationship analysis and molecular docking study

    PubMed Central

    Satpathy, Raghunath; Guru, R. K.; Behera, R.; Nayak, B.

    2015-01-01

    Context: Boswellic acid consists of a series of pentacyclic triterpene molecules that are produced by the plant Boswellia serrata. The potential applications of Bowsellic acid for treatment of cancer have been focused here. Aims: To predict the property of the bowsellic acid derivatives as anticancer compounds by various computational approaches. Materials and Methods: In this work, all total 65 derivatives of bowsellic acids from the PubChem database were considered for the study. After energy minimization of the ligands various types of molecular descriptors were computed and corresponding two-dimensional quantitative structure activity relationship (QSAR) models were obtained by taking Andrews coefficient as the dependent variable. Statistical Analysis Used: Different types of comparative analysis were used for QSAR study are multiple linear regression, partial least squares, support vector machines and artificial neural network. Results: From the study geometrical descriptors shows the highest correlation coefficient, which indicates the binding factor of the compound. To evaluate the anticancer property molecular docking study of six selected ligands based on Andrews affinity were performed with nuclear factor-kappa protein kinase (Protein Data Bank ID 4G3D), which is an established therapeutic target for cancers. Along with QSAR study and docking result, it was predicted that bowsellic acid can also be treated as a potential anticancer compound. Conclusions: Along with QSAR study and docking result, it was predicted that bowsellic acid can also be treated as a potential anticancer compound. PMID:25709332

  8. 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. PMID:26397393

  9. QSAR development and bioavailability determination: the toxicity of chloroanilines to the soil dwelling springtail Folsomia candida.

    PubMed

    Giesen, Daniel; van Gestel, Cornelis A M

    2013-03-01

    Quantitative structure-activity relationships (QSARs) are an established tool in environmental risk assessment and a valuable alternative to the exhaustive use of test animals under REACH. In this study a QSAR was developed for the toxicity of a series of six chloroanilines to the soil-dwelling collembolan Folsomia candida in standardized natural LUFA2.2 soil. Toxicity endpoints incorporated in the QSAR were the concentrations causing 10% (EC10) and 50% (EC50) reduction in reproduction of F. candida. Toxicity was based on concentrations in interstitial water estimated from nominal concentrations in the soil and published soil-water partition coefficients. Estimated effect concentrations were negatively correlated with the lipophilicity of the compounds. Interstitial water concentrations for both the EC10 and EC50 for four compounds were determined by using solid-phase microextraction (SPME). Measured and estimated concentrations were comparable only for tetra- and pentachloroaniline. With decreasing chlorination the disparity between modelled and actual concentrations increased. Optimisation of the QSAR therefore could not be accomplished, showing the necessity to move from total soil to (bio)available concentration measurements. PMID:23276458

  10. A computational quantitative structure-activity relationship study of carbamate anticonvulsants using quantum pharmacological methods.

    PubMed

    Knight, J L; Weaver, D F

    1998-10-01

    A pattern recognition quantitative structure-activity relationship (QSAR) study has been performed to determine the molecular features of carbamate anticonvulsants which influence biological activity. Although carbamates, such as felbamate, have been used to treat epilepsy, their mechanisms of efficacy and toxicity are not completely understood. Quantum and classical mechanics calculations have been exploited to describe 46 carbamate drugs. Employing a principal component analysis and multiple linear regression calculations, five crucial structural descriptors were identified which directly relate to the bioactivity of the carbamate family. With the resulting mathematical model, the biological activity of carbamate analogues can be predicted with 85-90% accuracy.

  11. CURRENT PRACTICES IN QSAR DEVELOPMENT AND APPLICATIONS

    EPA Science Inventory

    Current Practices in QSAR Development and Applications

    Although it is commonly assumed that the structure and properties of a single chemical determines its activity in a particular biological system, it is only through study of how biological activity varies with changes...

  12. Quantitative structure-activity relationship modelling of oral acute toxicity and cytotoxic activity of fragrance materials in rodents.

    PubMed

    Papa, E; Luini, M; Gramatica, P

    2009-10-01

    Fragrance materials are used as ingredients in many consumer and personal care products. The wide and daily use of these substances, as well as their mainly uncontrolled discharge through domestic sewage, make fragrance materials both potential indoor and outdoor air pollutants which are also connected to possible toxic effects on humans (asthma, allergies, headaches). Unfortunately, little is known about the environmental fate and toxicity of these substances. However, the use of alternative, predictive approaches, such as quantitative structure-activity relationships (QSARs), can help in filling the data gap and in the characterization of the environmental and toxicological profile of these substances. In the proposed study, ordinary least squares regression-based QSAR models were developed for three toxicological endpoints: mouse oral LD(50), inhibition of NADH-oxidase (EC(50) NADH-Ox) and the effect on mitochondrial membrane potential (EC(50) DeltaPsim). Theoretical molecular descriptors were calculated by using DRAGON software, and the best QSAR models were developed according to the principles defined by the Organization for Economic Co-operation and Development.

  13. Assessment of the Potential Biological Activity of Low Molecular Weight Metabolites of Freshwater Macrophytes with QSAR

    PubMed Central

    Fedorova, Elena V.; Krylova, Julia V.

    2016-01-01

    The paper focuses on the assessment of the spectrum of biological activities (antineoplastic, anti-inflammatory, antifungal, and antibacterial) with PASS (Prediction of Activity Spectra for Substances) for the major components of three macrophytes widespread in the Holarctic species of freshwater, emergent macrophyte with floating leaves, Nuphar lutea (L.) Sm., and two species of submergent macrophyte groups, Ceratophyllum demersum L. and Potamogeton obtusifolius (Mert. et Koch), for the discovery of their ecological and pharmacological potential. The predicted probability of anti-inflammatory or antineoplastic activities above 0.8 was observed for twenty compounds. The same compounds were also characterized by high probability of antifungal and antibacterial activity. Six metabolites, namely, hexanal, pentadecanal, tetradecanoic acid, dibutyl phthalate, hexadecanoic acid, and manool, were a part of the major components of all three studied plants, indicating their high ecological significance and a certain universalism in their use by various species of water plants for the implementation of ecological and biochemical functions. This report underlines the role of identified compounds not only as important components in regulation of biochemical and metabolic pathways and processes in aquatic ecological systems, but also as potential pharmacological agents in the fight against different diseases. PMID:27200207

  14. Assessment of the Potential Biological Activity of Low Molecular Weight Metabolites of Freshwater Macrophytes with QSAR.

    PubMed

    Kurashov, Evgeny A; Fedorova, Elena V; Krylova, Julia V; Mitrukova, Galina G

    2016-01-01

    The paper focuses on the assessment of the spectrum of biological activities (antineoplastic, anti-inflammatory, antifungal, and antibacterial) with PASS (Prediction of Activity Spectra for Substances) for the major components of three macrophytes widespread in the Holarctic species of freshwater, emergent macrophyte with floating leaves, Nuphar lutea (L.) Sm., and two species of submergent macrophyte groups, Ceratophyllum demersum L. and Potamogeton obtusifolius (Mert. et Koch), for the discovery of their ecological and pharmacological potential. The predicted probability of anti-inflammatory or antineoplastic activities above 0.8 was observed for twenty compounds. The same compounds were also characterized by high probability of antifungal and antibacterial activity. Six metabolites, namely, hexanal, pentadecanal, tetradecanoic acid, dibutyl phthalate, hexadecanoic acid, and manool, were a part of the major components of all three studied plants, indicating their high ecological significance and a certain universalism in their use by various species of water plants for the implementation of ecological and biochemical functions. This report underlines the role of identified compounds not only as important components in regulation of biochemical and metabolic pathways and processes in aquatic ecological systems, but also as potential pharmacological agents in the fight against different diseases. PMID:27200207

  15. Quantitative structure-activity relationships for weak acid respiratory uncouplers to Vibrio fisheri

    SciTech Connect

    Schultz, T.W.; Cronin, M.T.D.

    1997-02-01

    Acute toxicity values of 16 organic compounds thought to elicit their response via the weak acid respiratory uncoupling mechanism of toxic action were secured from the literature. Regression analysis of toxicities revealed that a measured 5-min V. fisheri potency value can be used as a surrogate for the 30-min value. Regression analysis of toxicity versus hydrophobicity, measured as the 1-octanol/water partition coefficient (log K{sub ow}), was used to formulate a quantitative structure-activity relationship (QSAR). The equation log pT{sub 30}{sup {minus}1} = 0.489(log K{sub ow}) + 0.126 was found to be a highly predictive model. This V. fisheri QSAR is statistically similar to QSARs generated from weak acid uncoupler potency data for Pimephales promelas survivability and Tetrahymena pyriformis population growth impairment. This work, therefore, suggests that the weak acid respiratory uncoupling mechanism of toxic action is present in V. fisheri, and as such is not restricted to mitochondria-containing organisms.

  16. Synthesis, Biological Evaluation and QSAR Studies of Newer Isoxazole Derivatives.

    PubMed

    Asirvatham, Sahaya; Mahajan, Supriya

    2015-01-01

    A series of newer 3-(4'-methoxyphenyl)-5-substituted phenylisoxazoles derivatives have been synthesized by reacting hydroxylamine hydrochloride with chalcones. The chalcones were formed by reacting different aromatic aldehydes with 4-methoxyacetophenone in presence of aqueos potassium hydroxide (KOH). The purity of all the synthesized compounds was checked by recording their melting points and the retention Factors (Rf) values from thin layer chromatography. The structures of the compounds were characterized by recording their infrared (IR) spectra and confirmed by recording their nuclear magnetic resonance ((1)H NMR) spectra. The acute toxicity study was carried out on all the synthesized compounds and they were screened for their antiinflammatory activity by carrageenan induced rat paw edema method. Anti-inflammatory studies showed statistically significant activity when compared to the control, indomethacin. The two most potent compounds giving good anti-inflammatory activity were further evaluated for their antiulcer activity. The compounds were subjected to quantitative structure activity relationships (QSAR) studies. A close correlation between the observed and the predicted anti-inflammatory activity (Log % inhibition) for the compounds indicated the development of the best QSAR model. The synthesized compounds were found to be non-ulcerogenic as compared to the standard, aspirin.

  17. Parabolic quantitative structure-activity relationships and photodynamic therapy: application of a three-compartment model with clearance to the in vivo quantitative structure-activity relationships of a congeneric series of pyropheophorbide derivatives used as photosensitizers for photodynamic therapy.

    PubMed

    Potter, W R; Henderson, B W; Bellnier, D A; Pandey, R K; Vaughan, L A; Weishaupt, K R; Dougherty, T J

    1999-11-01

    An open three-compartment pharmacokinetic model was applied to the in vivo quantitative structure-activity relationship (QSAR) data of a homologous series of pyropheophorbide photosensitizers for photodynamic therapy (PDT). The physical model was a lipid compartment sandwiched between two identical aqueous compartments. The first compartment was assumed to clear irreversibly at a rate K0. The measured octanol-water partition coefficients, P(i) (where i is the number of carbons in the alkyl chain) and the clearance rate K0 determined the clearance kinetics of the drugs. Solving the coupled differential equations of the three-compartment model produced clearance kinetics for each of the sensitizers in each of the compartments. The third compartment was found to contain the target of PDT. This series of compounds is quite lipophilic. Therefore these drugs are found mainly in the second compartment. The drug level in the third compartment represents a small fraction of the tissue level and is thus not accessible to direct measurement by extraction. The second compartment of the model accurately predicted the clearance from the serum of mice of the hexyl ether of pyropheophorbide a, one member of this series of compounds. The diffusion and clearance rate constants were those found by fitting the pharmacokinetics of the third compartment to the QSAR data. This result validated the magnitude and mechanistic significance of the rate constants used to model the QSAR data. The PDT response to dose theory was applied to the kinetic behavior of the target compartment drug concentration. This produced a pharmacokinetic-based function connecting PDT response to dose as a function of time postinjection. This mechanistic dose-response function was fitted to published, single time point QSAR data for the pheophorbides. As a result, the PDT target threshold dose together with the predicted QSAR as a function of time postinjection was found.

  18. The prospects for using (Q)SARs in a changing political environment--high expectations and a key role for the European Commission's joint research centre.

    PubMed

    Worth, A P; Van Leeuwen, C J; Hartung, T

    2004-01-01

    Recent policy developments in the European union (EU) and within the Organisation for Economic Cooperation and Development (OECD) have placed increased emphasis on the use of structure-activity relationships (SARs) and quantitative structure-activity relationships (QSARs), collectively referred to as (Q)SARs, within various regulatory programmes for the assessment of chemicals and products. The most significant example within the EU is the European commission's proposal (of 29 October 2003) to introduce a new system for managing chemicals (called REACH), which calls for an increased use of (Q)SARs and other non-animal methods, especially for the assessment of low production volume chemicals. Another development within the EU is the Seventh Amendment to the Cosmetics Directive, which foresees the phasing out of animal testing on cosmetics, combined with the imposition of marketing bans on cosmetics that have been tested on animals after certain deadlines. At the same time, the Existing Chemicals programme within the OECD is investigating ways of increasing the use of chemical category approaches, which depend heavily on the use of (Q)SARs, activity-activity relationships and read-across. Such developments are placing an enormous challenge on industry, regulatory bodies, and on the European commission's Joint Research Centre (JRC), which is responsible for providing independent scientific advice to policy makers in the European Commission and the Member States. This paper reviews the different scientific and regulatory purposes for which reliable (Q)SARs could be used, and describes the current work of the JRC in providing scientific support for the development, validation and implementation of (Q)SARs. PMID:15669693

  19. Preselection of A- and B- modified d-homo lactone and d-seco androstane derivatives as potent compounds with antiproliferative activity against breast and prostate cancer cells - QSAR approach and molecular docking analysis.

    PubMed

    Kovačević, Strahinja Z; Podunavac-Kuzmanović, Sanja O; Jevrić, Lidija R; Vukić, Vladimir R; Savić, Marina P; Djurendić, Evgenija A

    2016-10-10

    The problem with trial-and-error approach in organic synthesis of targeted anticancer compounds can be successfully avoided by computational modeling of molecules, docking studies and chemometric tools. It has been proven that A- and B- modified d-homo lactone and d-seco androstane derivatives are compounds with significant antiproliferative activity against estrogen-independent breast adenocarcinoma (ER-, MDA-MB-231) and androgen-independent prostate cancer cells (AR-, PC-3). This paper presents the quantitative structure-activity relationship (QSAR) models based on artificial neural networks (ANNs) which are able to predict whether d-homo lactone and/or d-seco androstane-based compounds will express antiproliferative activity against breast cancer cells (MDA-MB-231) or not. Also, the present paper describes the molecular docking study of 3β-acetoxy-5α,6α-epoxy- (3) and 6α,7α-epoxy-1,4-dien-3-one (24) d-homo lactone androstane derivatives, as well as 4-en-3-one (15) d-seco androstane derivative, which are compounds with strong or moderate antiproliferative activity against prostate cancer cells (PC-3), and compares them with commercially available medicament for prostate cancer - abiraterone. The obtained promising results can be used as guidelines in further syntheses of novel d-homo lactone and d-seco androstane derivatives with antiproliferative activity against breast and prostate cancer cells.

  20. Structural and Physico-Chemical Interpretation (SPCI) of QSAR Models and Its Comparison with Matched Molecular Pair Analysis.

    PubMed

    Polishchuk, Pavel; Tinkov, Oleg; Khristova, Tatiana; Ognichenko, Ludmila; Kosinskaya, Anna; Varnek, Alexandre; Kuz'min, Victor

    2016-08-22

    This paper describes the Structural and Physico-Chemical Interpretation (SPCI) approach, which is an extension of a recently reported method for interpretation of quantitative structure-activity relationship (QSAR) models. This approach can efficiently be used to reveal structural motifs and the major physicochemical factors affecting the investigated properties. Its efficacy was demonstrated both on the classical Free-Wilson data set and on several data sets with different end points (permeability of the blood-brain barrier, fibrinogen receptor antagonists, acute oral toxicity). Structure-activity patterns extracted from QSAR models with SPCI were in good correspondence with experimentally observed relationships and molecular docking, regardless of the machine learning method used. Comparison of SPCI with the matched molecular pair (MMP) method clearly shows an advantage of our approach over MMP, especially for small or structurally diverse data sets. The developed approach has been implemented in the SPCI software tool with a graphical user interface, which is publicly available at http://qsar4u.com/pages/sirms_qsar.php . PMID:27419846

  1. Multiple receptor conformation docking, dock pose clustering and 3D QSAR studies on human poly(ADP-ribose) polymerase-1 (PARP-1) inhibitors.

    PubMed

    Fatima, Sabiha; Jatavath, Mohan Babu; Bathini, Raju; Sivan, Sree Kanth; Manga, Vijjulatha

    2014-10-01

    Poly(ADP-ribose) polymerase-1 (PARP-1) functions as a DNA damage sensor and signaling molecule. It plays a vital role in the repair of DNA strand breaks induced by radiation and chemotherapeutic drugs; inhibitors of this enzyme have the potential to improve cancer chemotherapy or radiotherapy. Three-dimensional quantitative structure activity relationship (3D QSAR) models were developed using comparative molecular field analysis, comparative molecular similarity indices analysis and docking studies. A set of 88 molecules were docked into the active site of six X-ray crystal structures of poly(ADP-ribose)polymerase-1 (PARP-1), by a procedure called multiple receptor conformation docking (MRCD), in order to improve the 3D QSAR models through the analysis of binding conformations. The docked poses were clustered to obtain the best receptor binding conformation. These dock poses from clustering were used for 3D QSAR analysis. Based on MRCD and QSAR information, some key features have been identified that explain the observed variance in the activity. Two receptor-based QSAR models were generated; these models showed good internal and external statistical reliability that is evident from the [Formula: see text], [Formula: see text] and [Formula: see text]. The identified key features enabled us to design new PARP-1 inhibitors. PMID:25046176

  2. Multiple receptor conformation docking, dock pose clustering and 3D QSAR studies on human poly(ADP-ribose) polymerase-1 (PARP-1) inhibitors.

    PubMed

    Fatima, Sabiha; Jatavath, Mohan Babu; Bathini, Raju; Sivan, Sree Kanth; Manga, Vijjulatha

    2014-10-01

    Poly(ADP-ribose) polymerase-1 (PARP-1) functions as a DNA damage sensor and signaling molecule. It plays a vital role in the repair of DNA strand breaks induced by radiation and chemotherapeutic drugs; inhibitors of this enzyme have the potential to improve cancer chemotherapy or radiotherapy. Three-dimensional quantitative structure activity relationship (3D QSAR) models were developed using comparative molecular field analysis, comparative molecular similarity indices analysis and docking studies. A set of 88 molecules were docked into the active site of six X-ray crystal structures of poly(ADP-ribose)polymerase-1 (PARP-1), by a procedure called multiple receptor conformation docking (MRCD), in order to improve the 3D QSAR models through the analysis of binding conformations. The docked poses were clustered to obtain the best receptor binding conformation. These dock poses from clustering were used for 3D QSAR analysis. Based on MRCD and QSAR information, some key features have been identified that explain the observed variance in the activity. Two receptor-based QSAR models were generated; these models showed good internal and external statistical reliability that is evident from the [Formula: see text], [Formula: see text] and [Formula: see text]. The identified key features enabled us to design new PARP-1 inhibitors.

  3. Investigation of antigen-antibody interactions of sulfonamides with a monoclonal antibody in a fluorescence polarization immunoassay using 3D-QSAR models

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A three-dimensional quantitative structure-activity relationship (3D-QSAR) model of sulfonamide analogs binding a monoclonal antibody (MAbSMR) produced against sulfamerazine was carried out by Distance Comparison (DISCOtech), comparative molecular field analysis (CoMFA), and comparative molecular si...

  4. Metabolic biotransformation half-lives in fish: QSAR modeling and consensus analysis.

    PubMed

    Papa, Ester; van der Wal, Leon; Arnot, Jon A; Gramatica, Paola

    2014-02-01

    Bioaccumulation in fish is a function of competing rates of chemical uptake and elimination. For hydrophobic organic chemicals bioconcentration, bioaccumulation and biomagnification potential are high and the biotransformation rate constant is a key parameter. Few measured biotransformation rate constant data are available compared to the number of chemicals that are being evaluated for bioaccumulation hazard and for exposure and risk assessment. Three new Quantitative Structure-Activity Relationships (QSARs) for predicting whole body biotransformation half-lives (HLN) in fish were developed and validated using theoretical molecular descriptors that seek to capture structural characteristics of the whole molecule and three data set splitting schemes. The new QSARs were developed using a minimal number of theoretical descriptors (n=9) and compared to existing QSARs developed using fragment contribution methods that include up to 59 descriptors. The predictive statistics of the models are similar thus further corroborating the predictive performance of the different QSARs; Q(2)ext ranges from 0.75 to 0.77, CCCext ranges from 0.86 to 0.87, RMSE in prediction ranges from 0.56 to 0.58. The new QSARs provide additional mechanistic insights into the biotransformation capacity of organic chemicals in fish by including whole molecule descriptors and they also include information on the domain of applicability for the chemical of interest. Advantages of consensus modeling for improving overall prediction and minimizing false negative errors in chemical screening assessments, for identifying potential sources of residual error in the empirical HLN database, and for identifying structural features that are not well represented in the HLN dataset to prioritize future testing needs are illustrated.

  5. QSAR models for predicting in vivo aquatic toxicity of chlorinated alkanes to fish.

    PubMed

    Zvinavashe, Elton; van den Berg, Hans; Soffers, Ans E M F; Vervoort, Jacques; Freidig, Andreas; Murk, Albertinka J; Rietjens, Ivonne M C M

    2008-03-01

    Quantitative structure-activity relationship (QSAR) models are expected to play a crucial role in reducing the number of animals to be used for toxicity testing resulting from the adoption of the new European Union chemical control system called Registration, Evaluation, and Authorization of Chemicals (REACH). The objective of the present study was to generate in vitro acute toxicity data that could be used to develop a QSAR model to describe acute in vivo toxicity of chlorinated alkanes. Cytotoxicity of a series of chlorinated alkanes to Chinese hamster ovary (CHO) cells was observed at concentrations similar to those that have been shown previously to be toxic to fish. Strong correlations exist between the acute in vitro toxicity of the chlorinated alkanes and (i) hydrophobicity [modeled by the calculated log K ow (octanol-water partition coefficient); r (2) = 0.883 and r int (2) = 0.854] and (ii) in vivo acute toxicity to fish ( r (2) = 0.758). A QSAR model has been developed to predict in vivo acute toxicity to fish, based on the in vitro data and even on in silico log K ow data only. The developed QSAR model is applicable to chlorinated alkanes with up to 10 carbon atoms, up to eight chlorine atoms, and log K ow values lying within the range from 1.71 to 5.70. Out of the 100204 compounds on the European Inventory of Existing Chemicals (EINECS), our QSAR model covers 77 (0.1%) of them. Our findings demonstrate that in vitro experiments and even in silico calculations can replace animal experiments in the prediction of the acute toxicity of chlorinated alkanes.

  6. Nanomaterials - the Next Great Challenge for Qsar Modelers

    NASA Astrophysics Data System (ADS)

    Puzyn, Tomasz; Gajewicz, Agnieszka; Leszczynska, Danuta; Leszczynski, Jerzy

    In this final chapter a new perspective for the application of QSAR in the nanosciences is discussed. The role of nanomaterials is rapidly increasing in many aspects of everyday life. This is promoting a wide range of research needs related to both the design of new materials with required properties and performing a comprehensive risk assessment of the manufactured nanoparticles. The development of nanoscience also opens new areas for QSAR modelers. We have begun this contribution with a detailed discussion on the remarkable physical-chemical properties of nanomaterials and their specific toxicities. Both these factors should be considered as potential endpoints for further nano-QSAR studies. Then, we have highlighted the status and research needs in the area of molecular descriptors applicable to nanomaterials. Finally, we have put together currently available nano-QSAR models related to the physico-chemical endpoints of nanoparticles and their activity. Although we have observed many problems (i.e., a lack of experimental data, insufficient and inadequate descriptors), we do believe that application of QSAR methodology will significantly support nanoscience in the near future. Development of reliable nano-QSARs can be considered as the next challenging task for the QSAR community.

  7. Approaches for externally validated QSAR modelling of Nitrated Polycyclic Aromatic Hydrocarbon mutagenicity.

    PubMed

    Gramatica, P; Pilutti, P; Papa, E

    2007-01-01

    Nitrated Polycyclic Aromatic Hydrocarbons (nitro-PAHs), ubiquitous environmental pollutants, are recognized mutagens and carcinogens. A set of mutagenicity data (TA100) for 48 nitro-PAHs was modeled by the Quantitative Structure-Activity Relationships (QSAR) regression method, and OECD principles for QSAR model validation were applied. The proposed Multiple Linear Regression (MLR) models are based on two topological molecular descriptors. The models were validated for predictivity by both internal and external validation. For the external validation, three different splitting approaches, D-optimal Experimental Design, Self Organizing Maps (SOM) and Random Selection by activity sampling, were applied to the original data set in order to compare these methodologies and to select the best descriptors able to model each prediction set chemicals independently of the splitting method applied. The applicability domain was verified by the leverage approach.

  8. Clobenpropit analogs as dual activity ligands for the histamine H3 and H4 receptors: synthesis, pharmacological evaluation, and cross-target QSAR studies.

    PubMed

    Lim, Herman D; Istyastono, Enade P; van de Stolpe, Andrea; Romeo, Giuseppe; Gobbi, Silvia; Schepers, Marjo; Lahaye, Roger; Menge, Wiro M B P; Zuiderveld, Obbe P; Jongejan, Aldo; Smits, Rogier A; Bakker, Remko A; Haaksma, Eric E J; Leurs, Rob; de Esch, Iwan J P

    2009-06-01

    Previous studies have demonstrated that clobenpropit (N-(4-chlorobenzyl)-S-[3-(4(5)-imidazolyl)propyl]isothiourea) binds to both the human histamine H(3) receptor (H(3)R) and H(4) receptor (H(4)R). In this paper, we describe the synthesis and pharmacological characterization of a series of clobenpropit analogs, which vary in the functional group adjacent to the isothiourea moiety in order to study structural requirements for H(3)R and H(4)R ligands. The compounds show moderate to high affinity for both the human H(3)R and H(4)R. Furthermore, the changes in the functional group attached to the isothiourea moiety modulate the intrinsic activity of the ligands at the H(4)R, ranging from neutral antagonism to full agonism. QSAR models have been generated in order to explain the H(3)R and H(4)R affinities.

  9. Oxidative toxicity of perfluorinated chemicals in green mussel and bioaccumulation factor dependent quantitative structure-activity relationship.

    PubMed

    Liu, Changhui; Chang, Victor W C; Gin, Karina Y H

    2014-10-01

    Concerns regarding perfluorinated chemicals (PFCs) have risen in recent years because of their ubiquitous presence and high persistency. However, data on the environmental impacts of PFCs on marine organisms are very limited. Oxidative toxicity has been suggested to be one of the major toxic pathways for PFCs to induce adverse effects on organisms. To investigate PFC-induced oxidative stress and oxidative toxicity, a series of antioxidant enzyme activities and oxidative damage biomarkers were examined to assess the adverse effects of the following 4 commonly detected compounds: perfluoro-octanesulfonate, perfluoro-ocanoic acid, perfluorononanoic acid, and perfluorodecanoic acid, on green mussel (Perna viridis). Quantitative structure-activity relationship (QSAR) models were also established. The results showed that all the tested PFCs are able to induce antioxidant response and oxidative damage on green mussels in a dose-dependent manner. At low exposure levels (0 µg/L-100 µg/L), activation of antioxidant enzymes (catalase [CAT] and superoxide dismutase [SOD]) was observed, which is an adaptive response to the excessive reactive oxygen species induced by PFCs, while at high exposure levels (100 µg/L-10 000 µg/L), PFCs were found to inhibit some enzyme activity (glutathione S-transferase and SOD) where the organism's ability to respond in an adaptive manner was compromised. The oxidative stress under high PFC exposure concentration also led to lipid and DNA damage. PFC-induced oxidative toxicity was found to be correlated with the bioaccumulation potential of PFCs. Based on this relationship, QSAR models were established using the bioaccumulation factor (BAF) as the molecular descriptor for the first time. Compared with previous octanol-water partition coefficient-dependent QSAR models, the BAF-dependent QSAR model is more suitable for the impact assessment of PFCs and thus provides a more accurate description of the toxic behavior of these compounds.

  10. Development of QSARs for predicting the joint effects between cyanogenic toxicants and aldehydes.

    PubMed

    Lin, Zhifen; Yin, Kedong; Shi, Ping; Wang, Liansheng; Yu, Hongxia

    2003-10-01

    Quantitative structure-activity relationship (QSAR) approaches are proposed in this study to predict the joint effects of mixture toxicity. The initial investigation studies the joint effects between cyanogenic toxicants and aldehydes to Photobacterium phosphoreum. Joint effects are found to result from the formation of a carbanion intermediate produced through the chemical interactions between cyanogenic toxicants and aldehydes. Further research indicates that the formation of carbanion intermediate is highly correlated with not only the charge of the carbon atom in the -CHO of aldehydes but also the charge of the carbon atom (C) in the carbochain of cyanogenic toxicants. The charge of the carbon atom in the -CHO of aldehydes is quantified by using the Hammett constant (sigma(p)), and then, sigma(p)-based QSAR models are proposed to describe the relationships between the joint effects and the chemical structures of the aldehydes. By using the charge of carbon atom (C) in the carbochain of cyanogenic toxicants, another QSAR model is proposed to describe the relationship between the joint effects and the chemical structures of cyanogenic toxicants.

  11. Toxicity in relation to mode of action for the nematode Caenorhabditis elegans: Acute-to-chronic ratios and quantitative structure-activity relationships.

    PubMed

    Ristau, Kai; Akgül, Yeliz; Bartel, Anna Sophie; Fremming, Jana; Müller, Marie-Theres; Reiher, Luise; Stapela, Frederike; Splett, Jan-Paul; Spann, Nicole

    2015-10-01

    Acute-to-chronic ratios (ACRs) and quantitative structure-activity relationships (QSARs) are of particular interest in chemical risk assessment. Previous studies focusing on the relationship between the size or variation of ACRs to substance classes and QSAR models were often based on data for standard test organisms, such as daphnids and fish. In the present study, acute and chronic toxicity tests were performed with the nematode Caenorhabditis elegans for a total of 11 chemicals covering 3 substance classes (nonpolar narcotics: 1-propanol, ethanol, methanol, 2-butoxyethanol; metals: copper, cadmium, zinc; and carbamates: methomyl, oxamyl, aldicarb, dioxacarb). The ACRs were variable, especially for the carbamates and metals, although there was a trend toward small and less variable ACRs for nonpolar narcotic substances. The octanol-water partition coefficient was a good predictor for explaining acute and chronic toxicity of nonpolar narcotic substances to C. elegans, but not for carbamates. Metal toxicity could be related to the covalent index χm2r. Overall, the results support earlier results from ACR and QSAR studies with standard freshwater test animals. As such C. elegans as a representative of small soil/sediment invertebrates would probably be protected by risk assessment strategies already in use. To increase the predictive power of ACRs and QSARs, further research should be expanded to other species and compounds and should also consider the target sites and toxicokinetics of chemicals.

  12. 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

  13. Novel Uses of In Vitro Data to Develop Quantitative Biological Activity Relationship Models for in Vivo Carcinogenicity Prediction.

    PubMed

    Pradeep, Prachi; Povinelli, Richard J; Merrill, Stephen J; Bozdag, Serdar; Sem, Daniel S

    2015-04-01

    The availability of large in vitro datasets enables better insight into the mode of action of chemicals and better identification of potential mechanism(s) of toxicity. Several studies have shown that not all in vitro assays can contribute as equal predictors of in vivo carcinogenicity for development of hybrid Quantitative Structure Activity Relationship (QSAR) models. We propose two novel approaches for the use of mechanistically relevant in vitro assay data in the identification of relevant biological descriptors and development of Quantitative Biological Activity Relationship (QBAR) models for carcinogenicity prediction. We demonstrate that in vitro assay data can be used to develop QBAR models for in vivo carcinogenicity prediction via two case studies corroborated with firm scientific rationale. The case studies demonstrate the similarities between QBAR and QSAR modeling in: (i) the selection of relevant descriptors to be used in the machine learning algorithm, and (ii) the development of a computational model that maps chemical or biological descriptors to a toxic endpoint. The results of both the case studies show: (i) improved accuracy and sensitivity which is especially desirable under regulatory requirements, and (ii) overall adherence with the OECD/REACH guidelines. Such mechanism based models can be used along with QSAR models for prediction of mechanistically complex toxic endpoints.

  14. Discovery of potent adenosine A2a antagonists as potential anti-Parkinson disease agents. Non-linear QSAR analyses integrated with pharmacophore modeling.

    PubMed

    Khanfar, Mohammad A; Al-Qtaishat, Saja; Habash, Maha; Taha, Mutasem O

    2016-07-25

    Adenosine A2A receptor antagonists are of great interest in the treatment for Parkinson's disease. In this study, we combined extensive pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis to explore the structural requirements for potent Adenosine A2A antagonists. Genetic function algorithm (GFA) joined with k nearest neighbor (kNN) analyses were applied to build predictive QSAR models. Successful pharmacophores were complemented with exclusion spheres to improve their receiver operating characteristic curve (ROC) profiles. Best QSAR models and their associated pharmacophore hypotheses were validated by identification of several novel Adenosine A2A antagonist leads retrieved from the National Cancer Institute (NCI) structural database. The most potent hit illustrated IC50 value of 545.7 nM. PMID:27216633

  15. Neuronal nicotinic acetylcholine receptor agonists: pharmacophores, evolutionary QSAR and 3D-QSAR models.

    PubMed

    Nicolotti, Orazio; Altomare, Cosimo; Pellegrini-Calace, Marialuisa; Carotti, Angelo

    2004-01-01

    Neuronal nicotinic acetylcholine ion channel receptors (nAChRs) exist as several subtypes and are involved in a variety of functions and disorders of the central nervous system (CNS), such as Alzheimer's and Parkinson's diseases. The lack of reliable information on the 3D structure of nAChRs prompted us to focus efforts on pharmacophore and structure-affinity relationships (SAFIRs). The use of DISCO (DIStance COmparison) and Catalyst/HipHop led to the formulation of a pharmacophore that is made of three geometrically unrelated features: (i) an ammonium head involved in coulombic and/or H-bond interactions, (ii) a lone pair of a pyridine nitrogen or a carbonyl oxygen, as H-bond acceptor site, and (iii) a hydrophobic molecular region generally constituted by aliphatic cycles. The quantitative SAFIR (QSAFIR) study was carried out on about three hundred nicotinoid agonists, and coherent results were obtained from classical Hansch-type approach, 3D QSAFIRs, based on Comparative Molecular Field Analysis (CoMFA), and trade-off models generated by Multi-objective Genetic QSAR (MoQSAR), a novel evolutionary software that makes use of Genetic Programming (GP) and multi-objective optimization (MO). Within each congeneric series, Hansch-type equations revealed detrimental steric effects as the major factors modulating the receptor affinity, whereas CoMFA allowed us to merge progressively single-class models in a more global one, whose robustness was supported by crossvalidation, high prediction statistics and satisfactory predictions of the affinity data of a true external ligand set (r(2)(pred) = 0.796). Next, MoQSAR was used to analyze a data set of 58 highly active nicotinoids characterized by 56 descriptors, that are log P, MR and 54 low inter-correlated WHIM (Weighted Holistic Invariant Molecular) indices. Equivalent QSAFIR models, that represent different compromises between structural model complexity, fitting and internal model complexity, were found. Our attention was

  16. Neuronal nicotinic acetylcholine receptor agonists: pharmacophores, evolutionary QSAR and 3D-QSAR models.

    PubMed

    Nicolotti, Orazio; Altomare, Cosimo; Pellegrini-Calace, Marialuisa; Carotti, Angelo

    2004-01-01

    Neuronal nicotinic acetylcholine ion channel receptors (nAChRs) exist as several subtypes and are involved in a variety of functions and disorders of the central nervous system (CNS), such as Alzheimer's and Parkinson's diseases. The lack of reliable information on the 3D structure of nAChRs prompted us to focus efforts on pharmacophore and structure-affinity relationships (SAFIRs). The use of DISCO (DIStance COmparison) and Catalyst/HipHop led to the formulation of a pharmacophore that is made of three geometrically unrelated features: (i) an ammonium head involved in coulombic and/or H-bond interactions, (ii) a lone pair of a pyridine nitrogen or a carbonyl oxygen, as H-bond acceptor site, and (iii) a hydrophobic molecular region generally constituted by aliphatic cycles. The quantitative SAFIR (QSAFIR) study was carried out on about three hundred nicotinoid agonists, and coherent results were obtained from classical Hansch-type approach, 3D QSAFIRs, based on Comparative Molecular Field Analysis (CoMFA), and trade-off models generated by Multi-objective Genetic QSAR (MoQSAR), a novel evolutionary software that makes use of Genetic Programming (GP) and multi-objective optimization (MO). Within each congeneric series, Hansch-type equations revealed detrimental steric effects as the major factors modulating the receptor affinity, whereas CoMFA allowed us to merge progressively single-class models in a more global one, whose robustness was supported by crossvalidation, high prediction statistics and satisfactory predictions of the affinity data of a true external ligand set (r(2)(pred) = 0.796). Next, MoQSAR was used to analyze a data set of 58 highly active nicotinoids characterized by 56 descriptors, that are log P, MR and 54 low inter-correlated WHIM (Weighted Holistic Invariant Molecular) indices. Equivalent QSAFIR models, that represent different compromises between structural model complexity, fitting and internal model complexity, were found. Our attention was

  17. More effective antimicrobial mastoparan derivatives, generated by 3D-QSAR-Almond and computational mutagenesis.

    PubMed

    Avram, Speranta; Buiu, Catalin; Borcan, Florin; Milac, Adina-Luminita

    2012-02-01

    Antimicrobial peptides are drugs used against a wide range of pathogens which present a great advantage: in contrast with antibiotics they do not develop resistance. The wide spectrum of antimicrobial peptides advertises them in the research and pharmaceutical industry as attractive starting points for obtaining new, more effective analogs. Here we predict the antimicrobial activity against Bacillus subtilis (expressed as minimal inhibitory concentration values) for 33 mastoparan analogs and their new derivatives by a non-aligned 3D-QSAR (quantitative structure-activity relationship) method. We establish the contribution to antimicrobial activity of molecular descriptors (hydrophobicity, hydrogen bond donor and steric), correlated with contributions from the membrane environment (sodium, potassium, chloride). Our best QSAR models show significant cross-validated correlation q(2) (0.55-0.75), fitted correlation r(2) (greater than 0.90) coefficients and standard error of prediction SDEP (less than 0.250). Moreover, based on our most accurate 3D-QSAR models, we propose nine new mastoparan analogs, obtained by computational mutagenesis, some of them predicted to have significantly improved antimicrobial activity compared to the parent compound.

  18. The Structure-Activity Relationship of the Antioxidant Peptides from Natural Proteins.

    PubMed

    Zou, Tang-Bin; He, Tai-Ping; Li, Hua-Bin; Tang, Huan-Wen; Xia, En-Qin

    2016-01-12

    Peptides derived from dietary proteins, have been reported to display significant antioxidant activity, which may exert notably beneficial effects in promoting human health and in food processing. Recently, much research has focused on the generation, separation, purification and identification of novel peptides from various protein sources. Some researchers have tried to discover the structural characteristics of antioxidant peptides in order to lessen or avoid the tedious and aimless work involving the ongoing generated peptide preparation schemes. This review aims to summarize the current knowledge on the relationship between the structural features of peptides and their antioxidant activities. The relationship between the structure of the precursor proteins and their abilities to release antioxidant fragments will also be summarized and inferred. The preparation methods and antioxidant capacity evaluation assays of peptides and a prediction scheme of quantitative structure-activity relationship (QSAR) will also be pointed out and discussed.

  19. Theoretical studies on QSAR and mechanism of 2-indolinone derivatives as tubulin inhibitors

    NASA Astrophysics Data System (ADS)

    Liao, Si Yan; Qian, Li; Miao, Ti Fang; Lu, Hai Liang; Zheng, Kang Cheng

    The theoretical studies on three-dimensional quantitative structure activity relationship (3D-QSAR) and action mechanism of a series of 2-indolinone derivatives as tubulin inhibitors against human breast cancer cell line MDA-MB-231 have been carried out. The established 3D-QSAR model from the comparative molecular field analysis (CoMFA) shows not only significant statistical quality but also predictive ability, with high correlation coefficient (R2 = 0.986) and cross-validation coefficient (q2 = 0.683). In particular, the appropriate binding orientations and conformations of these 2-indolinone derivatives interacting with tubulin are located by docking study, and it is very interesting to find that the plot of the energy scores of these compounds in DOCK versus the corresponding experimental pIC50 values exhibits a considerable linear correlation. Therefore, the inhibition mechanism that 2-indolinone derivatives were regarded as tubulin inhibitors can be theoretically confirmed. Based on such an inhibition mechanism along with 3D-QSAR results, some important factors improving the activities of these compounds were discussed in detail. These factors can be summarized as follows: the H atom adopted as substituent R1, the substituent R2 with higher electropositivity and smaller bulk, the substituents R4-R6 (on the phenyl ring) with higher electropositivity and larger bulk, and so on. These results can offer useful theoretical references for understanding the action mechanism, designing more potent inhibitors, and predicting their activities prior to synthesis.

  20. Molecular docking and 3D-QSAR studies on inhibitors of DNA damage signaling enzyme human PARP-1.

    PubMed

    Fatima, Sabiha; Bathini, Raju; Sivan, Sree Kanth; Manga, Vijjulatha

    2012-08-01

    Poly (ADP-ribose) polymerase-1 (PARP-1) operates in a DNA damage signaling network. Molecular docking and three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on human PARP-1 inhibitors. Docked conformation obtained for each molecule was used as such for 3D-QSAR analysis. Molecules were divided into a training set and a test set randomly in four different ways, partial least square analysis was performed to obtain QSAR models using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Derived models showed good statistical reliability that is evident from their r², q²(loo) and r²(pred) values. To obtain a consensus for predictive ability from all the models, average regression coefficient r²(avg) was calculated. CoMFA and CoMSIA models showed a value of 0.930 and 0.936, respectively. Information obtained from the best 3D-QSAR model was applied for optimization of lead molecule and design of novel potential inhibitors.

  1. Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis.

    PubMed

    Zhu, Hao; Tropsha, Alexander; Fourches, Denis; Varnek, Alexandre; Papa, Ester; Gramatica, Paola; Oberg, Tomas; Dao, Phuong; Cherkasov, Artem; Tetko, Igor V

    2008-04-01

    Selecting most rigorous quantitative structure-activity relationship (QSAR) approaches is of great importance in the development of robust and predictive models of chemical toxicity. To address this issue in a systematic way, we have formed an international virtual collaboratory consisting of six independent groups with shared interests in computational chemical toxicology. We have compiled an aqueous toxicity data set containing 983 unique compounds tested in the same laboratory over a decade against Tetrahymena pyriformis. A modeling set including 644 compounds was selected randomly from the original set and distributed to all groups that used their own QSAR tools for model development. The remaining 339 compounds in the original set (external set I) as well as 110 additional compounds (external set II) published recently by the same laboratory (after this computational study was already in progress) were used as two independent validation sets to assess the external predictive power of individual models. In total, our virtual collaboratory has developed 15 different types of QSAR models of aquatic toxicity for the training set. The internal prediction accuracy for the modeling set ranged from 0.76 to 0.93 as measured by the leave-one-out cross-validation correlation coefficient ( Q abs2). The prediction accuracy for the external validation sets I and II ranged from 0.71 to 0.85 (linear regression coefficient R absI2) and from 0.38 to 0.83 (linear regression coefficient R absII2), respectively. The use of an applicability domain threshold implemented in most models generally improved the external prediction accuracy but at the same time led to a decrease in chemical space coverage. Finally, several consensus models were developed by averaging the predicted aquatic toxicity for every compound using all 15 models, with or without taking into account their respective applicability domains. We find that consensus models afford higher prediction accuracy for the

  2. QSAR Analysis of Some Antagonists for p38 map kinase Using Combination of Principal Component Analysis and Artificial Intelligence.

    PubMed

    Doosti, Elham; Shahlaei, Mohsen

    2015-01-01

    Quantitative relationships between structures of a set of p38 map kinase inhibitors and their activities were investigated by principal component regression (PCR) and principal componentartificial neural network (PC-ANN). Latent variables (called components) generated by principal component analysis procedure were applied as the input of developed Quantitative structure- activity relationships (QSAR) models. An exact study of predictability of PCR and PC-ANN showed that the later model has much higher ability to calculate the biological activity of the investigated molecules. Also, experimental and estimated biological activities of compounds used in model development step have indicated a good correlation. Obtained results show that a non-linear model explaining the relationship between the pIC50s and the calculated principal components (that extract from structural descriptors of the studied molecules) is superior than linear model. Some typical figures of merit for QSAR studies explaining the accuracy and predictability of the suggested models were calculated. Therefore, to design novel inhibitors of p38 map kinase with high potency and low undesired effects the developed QSAR models were used to estimate biological pIC50 of the studied compounds.

  3. Augmented multivariate image analysis applied to quantitative structure-activity relationship modeling of the phytotoxicities of benzoxazinone herbicides and related compounds on problematic weeds.

    PubMed

    Freitas, Mirlaine R; Matias, Stella V B G; Macedo, Renato L G; Freitas, Matheus P; Venturin, Nelson

    2013-09-11

    Two of major weeds affecting cereal crops worldwide are Avena fatua L. (wild oat) and Lolium rigidum Gaud. (rigid ryegrass). Thus, development of new herbicides against these weeds is required; in line with this, benzoxazinones, their degradation products, and analogues have been shown to be important allelochemicals and natural herbicides. Despite earlier structure-activity studies demonstrating that hydrophobicity (log P) of aminophenoxazines correlates to phytotoxicity, our findings for a series of benzoxazinone derivatives do not show any relationship between phytotoxicity and log P nor with other two usual molecular descriptors. On the other hand, a quantitative structure-activity relationship (QSAR) analysis based on molecular graphs representing structural shape, atomic sizes, and colors to encode other atomic properties performed very accurately for the prediction of phytotoxicities of these compounds against wild oat and rigid ryegrass. Therefore, these QSAR models can be used to estimate the phytotoxicity of new congeners of benzoxazinone herbicides toward A. fatua L. and L. rigidum Gaud.

  4. 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.

  5. Identification of potential influenza virus endonuclease inhibitors through virtual screening based on the 3D-QSAR model.

    PubMed

    Kim, J; Lee, C; Chong, Y

    2009-01-01

    Influenza endonucleases have appeared as an attractive target of antiviral therapy for influenza infection. With the purpose of designing a novel antiviral agent with enhanced biological activities against influenza endonuclease, a three-dimensional quantitative structure-activity relationships (3D-QSAR) model was generated based on 34 influenza endonuclease inhibitors. The comparative molecular similarity index analysis (CoMSIA) with a steric, electrostatic and hydrophobic (SEH) model showed the best correlative and predictive capability (q(2) = 0.763, r(2) = 0.969 and F = 174.785), which provided a pharmacophore composed of the electronegative moiety as well as the bulky hydrophobic group. The CoMSIA model was used as a pharmacophore query in the UNITY search of the ChemDiv compound library to give virtual active compounds. The 3D-QSAR model was then used to predict the activity of the selected compounds, which identified three compounds as the most likely inhibitor candidates.

  6. Development of quantitative structure-activity relationship (QSAR) models to predict the carcinogenic potency of chemicals

    SciTech Connect

    Venkatapathy, Raghuraman Wang Chingyi; Bruce, Robert Mark; Moudgal, Chandrika

    2009-01-15

    Determining the carcinogenicity and carcinogenic potency of new chemicals is both a labor-intensive and time-consuming process. In order to expedite the screening process, there is a need to identify alternative toxicity measures that may be used as surrogates for carcinogenic potency. Alternative toxicity measures for carcinogenic potency currently being used in the literature include lethal dose (dose that kills 50% of a study population [LD{sub 50}]), lowest-observed-adverse-effect-level (LOAEL) and maximum tolerated dose (MTD). The purpose of this study was to investigate the correlation between tumor dose (TD{sub 50}) and three alternative toxicity measures as an estimator of carcinogenic potency. A second aim of this study was to develop a Classification and Regression Tree (CART) between TD{sub 50} and estimated/experimental predictor variables to predict the carcinogenic potency of new chemicals. Rat TD{sub 50}s of 590 structurally diverse chemicals were obtained from the Cancer Potency Database, and the three alternative toxicity measures considered in this study were estimated using TOPKAT, a toxicity estimation software. Though poor correlations were obtained between carcinogenic potency and the three alternative toxicity (both experimental and TOPKAT) measures for the CPDB chemicals, a CART developed using experimental data with no missing values as predictor variables provided reasonable estimates of TD{sub 50} for nine chemicals that were part of an external validation set. However, if experimental values for the three alternative measures, mutagenicity and logP are not available in the literature, then either the CART developed using missing experimental values or estimated values may be used for making a prediction.

  7. Development of Quantitative Structure-Activity Relationship (QSAR) Models to Predict the Carcinogenic Potency of Chemicals

    EPA Science Inventory

    Determining the carcinogenicity and carcinogenic potency of new chemicals is both a labor-intensive and time-consuming process. In order to expedite the screening process, there is a need to either: (1) identify alternative toxicity measures (shorter duration) that may be used as...

  8. QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP (QSAR) MODELS TO PREDICT CHEMICAL TOXICITY FOR VARIOUS HEALTH ENDPOINTS

    EPA Science Inventory

    Although ranking schemes based on exposure and toxicity have been developed to aid in the prioritization of research funds for identifying chemicals of regulatory concern, there are significant gaps in the availability of experimental toxicity data for most health endpoints. Pred...

  9. In-silico structure activity relationship study of toxicity endpoints by QSAR modeling (SOT)

    EPA Science Inventory

    Several thousand chemicals were tested in 700 toxicity-related in-vitro HTS bioassays through the EPA’s ToxCast and Tox21 projects. This chemical set only covers a portion of the chemical space of interest for environmental exposure, leading to a need to fill data gaps with alter...

  10. Towards understanding the mechanism of action of antibacterial N-alkyl-3-hydroxypyridinium salts: Biological activities, molecular modeling and QSAR studies.

    PubMed

    Dolezal, Rafael; Soukup, Ondrej; Malinak, David; Savedra, Ranylson M L; Marek, Jan; Dolezalova, Marie; Pasdiorova, Marketa; Salajkova, Sarka; Korabecny, Jan; Honegr, Jan; Ramalho, Teodorico C; Kuca, Kamil

    2016-10-01

    In this study, we have carried out a combined experimental and computational investigation to elucidate several bred-in-the-bone ideas standing out in rational design of novel cationic surfactants as antibacterial agents. Five 3-hydroxypyridinium salts differing in the length of N-alkyl side chain have been synthesized, analyzed by high performance liquid chromatography, tested for in vitro activity against a panel of pathogenic bacterial and fungal strains, computationally modeled in water by a SCRF B3LYP/6-311++G(d,p) method, and evaluated by a systematic QSAR analysis. Given the results of this work, the hypothesis suggesting that higher positive charge of the quaternary nitrogen should increase antimicrobial efficacy can be rejected since 3-hydroxyl group does increase the positive charge on the nitrogen but, simultaneously, it significantly derogates the antimicrobial activity by lowering the lipophilicity and by escalating the desolvation energy of the compounds in comparison with non-hydroxylated analogues. Herein, the majority of the prepared 3-hydroxylated substances showed notably lower potency than the parent pyridinium structures, although compound 8 with C12 alkyl chain proved a distinctly better antimicrobial activity in submicromolar range. Focusing on this anomaly, we have made an effort to reveal the reason of the observed activity through a molecular dynamics simulation of the interaction between the bacterial membrane and compound 8 in GROMACS software.

  11. Towards understanding the mechanism of action of antibacterial N-alkyl-3-hydroxypyridinium salts: Biological activities, molecular modeling and QSAR studies.

    PubMed

    Dolezal, Rafael; Soukup, Ondrej; Malinak, David; Savedra, Ranylson M L; Marek, Jan; Dolezalova, Marie; Pasdiorova, Marketa; Salajkova, Sarka; Korabecny, Jan; Honegr, Jan; Ramalho, Teodorico C; Kuca, Kamil

    2016-10-01

    In this study, we have carried out a combined experimental and computational investigation to elucidate several bred-in-the-bone ideas standing out in rational design of novel cationic surfactants as antibacterial agents. Five 3-hydroxypyridinium salts differing in the length of N-alkyl side chain have been synthesized, analyzed by high performance liquid chromatography, tested for in vitro activity against a panel of pathogenic bacterial and fungal strains, computationally modeled in water by a SCRF B3LYP/6-311++G(d,p) method, and evaluated by a systematic QSAR analysis. Given the results of this work, the hypothesis suggesting that higher positive charge of the quaternary nitrogen should increase antimicrobial efficacy can be rejected since 3-hydroxyl group does increase the positive charge on the nitrogen but, simultaneously, it significantly derogates the antimicrobial activity by lowering the lipophilicity and by escalating the desolvation energy of the compounds in comparison with non-hydroxylated analogues. Herein, the majority of the prepared 3-hydroxylated substances showed notably lower potency than the parent pyridinium structures, although compound 8 with C12 alkyl chain proved a distinctly better antimicrobial activity in submicromolar range. Focusing on this anomaly, we have made an effort to reveal the reason of the observed activity through a molecular dynamics simulation of the interaction between the bacterial membrane and compound 8 in GROMACS software. PMID:27341309

  12. Chemical graphs, molecular matrices and topological indices in chemoinformatics and quantitative structure-activity relationships.

    PubMed

    Ivanciuc, Ovidiu

    2013-06-01

    Chemical and molecular graphs have fundamental applications in chemoinformatics, quantitative structureproperty relationships (QSPR), quantitative structure-activity relationships (QSAR), virtual screening of chemical libraries, and computational drug design. Chemoinformatics applications of graphs include chemical structure representation and coding, database search and retrieval, and physicochemical property prediction. QSPR, QSAR and virtual screening are based on the structure-property principle, which states that the physicochemical and biological properties of chemical compounds can be predicted from their chemical structure. Such structure-property correlations are usually developed from topological indices and fingerprints computed from the molecular graph and from molecular descriptors computed from the three-dimensional chemical structure. We present here a selection of the most important graph descriptors and topological indices, including molecular matrices, graph spectra, spectral moments, graph polynomials, and vertex topological indices. These graph descriptors are used to define several topological indices based on molecular connectivity, graph distance, reciprocal distance, distance-degree, distance-valency, spectra, polynomials, and information theory concepts. The molecular descriptors and topological indices can be developed with a more general approach, based on molecular graph operators, which define a family of graph indices related by a common formula. Graph descriptors and topological indices for molecules containing heteroatoms and multiple bonds are computed with weighting schemes based on atomic properties, such as the atomic number, covalent radius, or electronegativity. The correlation in QSPR and QSAR models can be improved by optimizing some parameters in the formula of topological indices, as demonstrated for structural descriptors based on atomic connectivity and graph distance. PMID:23701000

  13. QSAR prediction of the competitive interaction of emerging halogenated pollutants with human transthyretin.

    PubMed

    Papa, E; Kovarich, S; Gramatica, P

    2013-01-01

    The determination of the potential endocrine disruption (ED) activity of chemicals such as poly/perfluorinated compounds (PFCs) and brominated flame retardants (BFRs) is still hindered by a limited availability of experimental data. Quantitative structure-activity relationship (QSAR) strategies can be applied to fill this data gap, help in the characterization of the ED potential, and screen PFCs and BFRs with a hazardous toxicological profile. This paper proposes the modelling of T4-TTR (thyroxin-transthyretin) competing potency and relative binding potency toward T4 (logT4-REP) of PFCs and BFRs by regression and classification QSAR models. This study is a follow up of a former work, which analysed separately the interaction of BFRs and PFCs with the carrier TTR. The new results demonstrate the possibility of developing robust and predictive QSARs, which include both BFRs and PFCs in the training set, obtaining larger applicability domains than the existing models developed separately for BFRs and PFCs. The selection of modelling molecular descriptors confirms the importance of structural features, such as the aromatic OH or the molecular length, to increase the binding of the studied chemicals to TTR. Additionally, the need of experimental tests for some chemicals, and in particular for some of the BFRs, is highlighted.

  14. Human intestinal transporter database: QSAR modeling and virtual profiling of drug uptake, efflux and interactions

    PubMed Central

    Sedykh, Alexander; Fourches, Denis; Duan, Jianmin; Hucke, Oliver; Garneau, Michel; Zhu, Hao; Bonneau, Pierre; Tropsha, Alexander

    2013-01-01

    Purpose Membrane transporters mediate many biological effects of chemicals and play a major role in pharmacokinetics and drug resistance. The selection of viable drug candidates among biologically active compounds requires the assessment of their transporter interaction profiles. Methods Using public sources, we have assembled and curated the largest, to our knowledge, human intestinal transporter database (>5,000 interaction entries for >3,700 molecules). This data was used to develop thoroughly validated classification Quantitative Structure-Activity Relationship (QSAR) models of transport and/or inhibition of several major transporters including MDR1, BCRP, MRP1-4, PEPT1, ASBT, OATP2B1, OCT1, and MCT1. Results & Conclusions QSAR models have been developed with advanced machine learning techniques such as Support Vector Machines, Random Forest, and k Nearest Neighbors using Dragon and MOE chemical descriptors. These models afforded high external prediction accuracies of 71–100% estimated by 5-fold external validation, and showed hit retrieval rates with up to 20-fold enrichment in the virtual screening of DrugBank compounds. The compendium of predictive QSAR models developed in this study can be used for virtual profiling of drug candidates and/or environmental agents with the optimal transporter profiles. PMID:23269503

  15. Flavonoids as CDK1 Inhibitors: Insights in Their Binding Orientations and Structure-Activity Relationship.

    PubMed

    Navarro-Retamal, Carlos; Caballero, Julio

    2016-01-01

    In the last years, the interactions of flavonoids with protein kinases (PKs) have been described by using crystallographic experiments. Interestingly, different orientations have been found for one flavonoid inside different PKs and different chemical substitutions lead to different orientations of the flavonoid scaffold inside one PK. Accordingly, orientation predictions of novel analogues could help to the design of flavonoids with high PK inhibitory activities. With this in mind, we studied the binding modes of 37 flavonoids (flavones and chalcones) inside the cyclin-dependent PK CDK1 using docking experiments. We found that the compounds under study adopted two different orientations into the active site of CDK1 (orientations I and II in the manuscript). In addition, quantitative structure-activity relationship (QSAR) models using CoMFA and CoMSIA methodologies were constructed to explain the trend of the CDK1 inhibitory activities for the studied flavonoids. Template-based and docking-based alignments were used. Models developed starting from docking-based alignment were applied for describing the whole dataset and compounds with orientation I. Adequate R2 and Q2 values were obtained by each method; interestingly, only hydrophobic and hydrogen bond donor fields describe the differential potency of the flavonoids as CDK1 inhibitors for both defined alignments and subsets. Our current application of docking and QSAR together reveals important elements to be drawn for the design of novel flavonoids with increased PK inhibitory activities. PMID:27517610

  16. Docking and quantitative structure-activity relationship of oxadiazole derivates as inhibitors of GSK3β.

    PubMed

    Quesada-Romero, Luisa; Caballero, Julio

    2014-02-01

    The binding modes of 42 oxadiazole derivates inside glycogen synthase kinase 3 beta (GSK3β were determined using docking experiments; thus, the preferred active conformations of these inhibitors are proposed. We found that these compounds adopt a scorpion-shaped conformation and they accept a hydrogen bond (HB) from the residue Val135 of the GSK3β ATP-binding site hinge region. In addition, quantitative structure-activity relationship (QSAR) models were constructed to explain the trend of the GSK3β inhibitory activities for the studied compounds. In a first approach, three-dimensional (3D) vectors were calculated using docking conformations and, by using multiple-linear regression, we assessed that GETAWAY vectors were able to describe the reported biological activities. In other QSAR approach, SMILES-based optimal descriptors were calculated. The best model included three-SMILES elements SSSβ leading to the identification of key molecular features that contribute to a high GSK3β inhibitory activity.

  17. Docking and quantitative structure-activity relationship of oxadiazole derivates as inhibitors of GSK3β.

    PubMed

    Quesada-Romero, Luisa; Caballero, Julio

    2014-02-01

    The binding modes of 42 oxadiazole derivates inside glycogen synthase kinase 3 beta (GSK3β were determined using docking experiments; thus, the preferred active conformations of these inhibitors are proposed. We found that these compounds adopt a scorpion-shaped conformation and they accept a hydrogen bond (HB) from the residue Val135 of the GSK3β ATP-binding site hinge region. In addition, quantitative structure-activity relationship (QSAR) models were constructed to explain the trend of the GSK3β inhibitory activities for the studied compounds. In a first approach, three-dimensional (3D) vectors were calculated using docking conformations and, by using multiple-linear regression, we assessed that GETAWAY vectors were able to describe the reported biological activities. In other QSAR approach, SMILES-based optimal descriptors were calculated. The best model included three-SMILES elements SSSβ leading to the identification of key molecular features that contribute to a high GSK3β inhibitory activity. PMID:24081608

  18. Flavonoids as CDK1 Inhibitors: Insights in Their Binding Orientations and Structure-Activity Relationship

    PubMed Central

    Navarro-Retamal, Carlos

    2016-01-01

    In the last years, the interactions of flavonoids with protein kinases (PKs) have been described by using crystallographic experiments. Interestingly, different orientations have been found for one flavonoid inside different PKs and different chemical substitutions lead to different orientations of the flavonoid scaffold inside one PK. Accordingly, orientation predictions of novel analogues could help to the design of flavonoids with high PK inhibitory activities. With this in mind, we studied the binding modes of 37 flavonoids (flavones and chalcones) inside the cyclin-dependent PK CDK1 using docking experiments. We found that the compounds under study adopted two different orientations into the active site of CDK1 (orientations I and II in the manuscript). In addition, quantitative structure–activity relationship (QSAR) models using CoMFA and CoMSIA methodologies were constructed to explain the trend of the CDK1 inhibitory activities for the studied flavonoids. Template-based and docking-based alignments were used. Models developed starting from docking-based alignment were applied for describing the whole dataset and compounds with orientation I. Adequate R2 and Q2 values were obtained by each method; interestingly, only hydrophobic and hydrogen bond donor fields describe the differential potency of the flavonoids as CDK1 inhibitors for both defined alignments and subsets. Our current application of docking and QSAR together reveals important elements to be drawn for the design of novel flavonoids with increased PK inhibitory activities. PMID:27517610

  19. Structure-thermodynamics-antioxidant activity relationships of selected natural phenolic acids and derivatives: an experimental and theoretical evaluation.

    PubMed

    Chen, Yuzhen; Xiao, Huizhi; Zheng, Jie; Liang, Guizhao

    2015-01-01

    Phenolic acids and derivatives have potential biological functions, however, little is known about the structure-activity relationships and the underlying action mechanisms of these phenolic acids to date. Herein we investigate the structure-thermodynamics-antioxidant relationships of 20 natural phenolic acids and derivatives using DPPH• scavenging assay, density functional theory calculations at the B3LYP/6-311++G(d,p) levels of theory, and quantitative structure-activity relationship (QSAR) modeling. Three main working mechanisms (HAT, SETPT and SPLET) are explored in four micro-environments (gas-phase, benzene, water and ethanol). Computed thermodynamics parameters (BDE, IP, PDE, PA and ETE) are compared with the experimental radical scavenging activities against DPPH•. Available theoretical and experimental investigations have demonstrated that the extended delocalization and intra-molecular hydrogen bonds are the two main contributions to the stability of the radicals. The C = O or C = C in COOH, COOR, C = CCOOH and C = CCOOR groups, and orthodiphenolic functionalities are shown to favorably stabilize the specific radical species to enhance the radical scavenging activities, while the presence of the single OH in the ortho position of the COOH group disfavors the activities. HAT is the thermodynamically preferred mechanism in the gas phase and benzene, whereas SPLET in water and ethanol. Furthermore, our QSAR models robustly represent the structure-activity relationships of these explored compounds in polar media. PMID:25803685

  20. Structure-thermodynamics-antioxidant activity relationships of selected natural phenolic acids and derivatives: an experimental and theoretical evaluation.

    PubMed

    Chen, Yuzhen; Xiao, Huizhi; Zheng, Jie; Liang, Guizhao

    2015-01-01

    Phenolic acids and derivatives have potential biological functions, however, little is known about the structure-activity relationships and the underlying action mechanisms of these phenolic acids to date. Herein we investigate the structure-thermodynamics-antioxidant relationships of 20 natural phenolic acids and derivatives using DPPH• scavenging assay, density functional theory calculations at the B3LYP/6-311++G(d,p) levels of theory, and quantitative structure-activity relationship (QSAR) modeling. Three main working mechanisms (HAT, SETPT and SPLET) are explored in four micro-environments (gas-phase, benzene, water and ethanol). Computed thermodynamics parameters (BDE, IP, PDE, PA and ETE) are compared with the experimental radical scavenging activities against DPPH•. Available theoretical and experimental investigations have demonstrated that the extended delocalization and intra-molecular hydrogen bonds are the two main contributions to the stability of the radicals. The C = O or C = C in COOH, COOR, C = CCOOH and C = CCOOR groups, and orthodiphenolic functionalities are shown to favorably stabilize the specific radical species to enhance the radical scavenging activities, while the presence of the single OH in the ortho position of the COOH group disfavors the activities. HAT is the thermodynamically preferred mechanism in the gas phase and benzene, whereas SPLET in water and ethanol. Furthermore, our QSAR models robustly represent the structure-activity relationships of these explored compounds in polar media.

  1. New QSAR models for polyhalogenated aromatics

    SciTech Connect

    Nevalainen, T.; Kolehmainen, E. . Dept. of Chemistry)

    1994-10-01

    Electronic properties of polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), polychlorinated biphenyls (PCBs), and polychlorinated diphenyl ethers (PCDEs) were calculated using the semi-empirical AM 1 method. The calculated electronic descriptions--the energy of the lowest unoccupied molecular orbital (E[sub LUMO]), the energy of the highest occupied molecular orbital (E[sub HOMO]), the E[sub LUMO]-E[sub HOMO] gap (dE), and molecular polarizability--are related to the Ah receptor binding affinity values of PCDDs, PCDFs, and PCBs and immunotoxicity values for PCDEs. The quantitative structure-activity relationships (QSARs) based on chlorine substitution patterns were also constructed, and they proved to be very predictive for Ah receptor binding. Significant correlations of the electronic factors were found between the dE and Ah receptor binding affinities for PCDDs, PCDFs, and PCBs and for immunotoxicity of PCDEs. A combination of descriptors E[sub LUMO] and the total number of chlorine atoms attached to a molecule (n[sub Cl]) gives significant correlation for the Ah receptor binding of PCDFs and for immunotoxicity of PCDEs. Hydrophobicity values taken from the literature were shown to be non-significant for toxicity prediction of these polychlorinated compounds.

  2. Prediction of Halocarbon Toxicity from Structure: A Hierarchical QSAR Approach

    SciTech Connect

    Gute, B D; Balasubramanian, K; Geiss, K; Basak, S C

    2003-04-11

    Mathematical structural invariants and quantum theoretical descriptors have been used extensively in quantitative structure-activity relationships (QSARs) for the estimation of pharmaceutical activities, biological properties, physicochemical properties, and the toxicities of chemicals. Recently our research team has explored the relative importance of various levels of chemodescriptors, i.e., topostructural, topochemical, geometrical, and quantum theoretical descriptors, in property estimation. This study examines the contribution of chemodescriptors ranging from topostructural to quantum theoretic calculations up to the Gaussian STO-3G level in the prediction of the toxicity of a set of twenty halocarbons. We also report the results of experimental cell-level toxicity studies on these twenty halocarbons to validate our models.

  3. Comparison of fate profiles of PAHs in soil, sediments and mangrove leaves after oil spills by QSAR and QSPR.

    PubMed

    Tansel, Berrin; Lee, Mengshan; Tansel, Derya Z

    2013-08-15

    First order removal rates for 15 polyaromatic hydrocarbons (PAHs) in soil, sediments and mangrove leaves were compared in relation to the parameters used in fate transport analyses (i.e., octanol-water partition coefficient, organic carbon-water partition coefficient, solubility, diffusivity in water, HOMO-LUMO gap, molecular size, molecular aspect ratio). The quantitative structure activity relationships (QSAR) and quantitative structure property relationships (QSPR) showed that the rate of disappearance of PAHs is correlated with their diffusivities in water as well as molecular volumes in different media. Strong correlations for the rate of disappearance of PAHs in sediments could not be obtained in relation to most of the parameters evaluated. The analyses showed that the QSAR and QSPR correlations developed for removal rates of PAHs in soils would not be adequate for sediments and plant tissues.

  4. Comparison of fate profiles of PAHs in soil, sediments and mangrove leaves after oil spills by QSAR and QSPR.

    PubMed

    Tansel, Berrin; Lee, Mengshan; Tansel, Derya Z

    2013-08-15

    First order removal rates for 15 polyaromatic hydrocarbons (PAHs) in soil, sediments and mangrove leaves were compared in relation to the parameters used in fate transport analyses (i.e., octanol-water partition coefficient, organic carbon-water partition coefficient, solubility, diffusivity in water, HOMO-LUMO gap, molecular size, molecular aspect ratio). The quantitative structure activity relationships (QSAR) and quantitative structure property relationships (QSPR) showed that the rate of disappearance of PAHs is correlated with their diffusivities in water as well as molecular volumes in different media. Strong correlations for the rate of disappearance of PAHs in sediments could not be obtained in relation to most of the parameters evaluated. The analyses showed that the QSAR and QSPR correlations developed for removal rates of PAHs in soils would not be adequate for sediments and plant tissues. PMID:23756470

  5. QSAR study of some pyrazolo[3,4-d]pyrimidine derivatives as the c-Src inhibitors

    NASA Astrophysics Data System (ADS)

    Shukla, Bindesh Kumar; Yadava, Umesh

    2016-05-01

    Two dimensional quantitative structure activity relationship (QSAR) studies have been carried out on a series of 42 pyrazolo[3,4-d]pyrimidine derivatives to find out the structural requirements for the inhibition of c-SRC phosphorilation. The best predictions were obtained using Heuristic and Best MLR methods from the model where 33 compounds were considered in the training set and the remaining 9 in the test set. Both Best MLR and Heuristic methods indicate that squared correlation coefficient for training and test sets are very close to observed biological activities which designate the good correlation between the experimental and predicted activity. The results that are obtained from 2D-QSAR studies may provide useful insights into the roles of various substitution patterns on the pyrazolo[3,4-d]pyrimidine core and may also help to design more potent compounds.

  6. Interpretable, probability-based confidence metric for continuous quantitative structure-activity relationship models.

    PubMed

    Keefer, Christopher E; Kauffman, Gregory W; Gupta, Rishi Raj

    2013-02-25

    A great deal of research has gone into the development of robust confidence in prediction and applicability domain (AD) measures for quantitative structure-activity relationship (QSAR) models in recent years. Much of the attention has historically focused on structural similarity, which can be defined in many forms and flavors. A concept that is frequently overlooked in the realm of the QSAR applicability domain is how the local activity landscape plays a role in how accurate a prediction is or is not. In this work, we describe an approach that pairs information about both the chemical similarity and activity landscape of a test compound's neighborhood into a single calculated confidence value. We also present an approach for converting this value into an interpretable confidence metric that has a simple and informative meaning across data sets. The approach will be introduced to the reader in the context of models built upon four diverse literature data sets. The steps we will outline include the definition of similarity used to determine nearest neighbors (NN), how we incorporate the NN activity landscape with a similarity-weighted root-mean-square distance (wRMSD) value, and how that value is then calibrated to generate an intuitive confidence metric for prospective application. Finally, we will illustrate the prospective performance of the approach on five proprietary models whose predictions and confidence metrics have been tracked for more than a year.

  7. Quantitative structure-activity relationship model for the fetal-maternal blood concentration ratio of chemicals in humans.

    PubMed

    Takaku, Tomoyuki; Nagahori, Hirohisa; Sogame, Yoshihisa; Takagi, Tatsuya

    2015-01-01

    A quantitative structure-activity relationship (QSAR) model of the fetal-maternal blood concentration ratio (F/M ratio) of chemicals was developed to predict the placental transfer in humans. Data on F/M ratio of 55 compounds found in the literature were separated into training (75%, 41 compounds) and testing sets (25%, 14 compounds). The training sets were then subjected to multiple linear regression analysis using the descriptors of molecular weight (MW), topological polar surface area (TopoPSA), and maximum E-state of hydrogen atom (Hmax). Multiple linear regression analysis and a cross-validation showed a relatively high adjusted coefficient of determination (Ra(2)) (0.73) and cross-validated coefficient of determination (Q(2)) (0.71), after removing three outliers. In the external validation, R(2) for external validation (R(2)pred) was calculated to be 0.51. These results suggested that the QSAR model developed in this study can be considered reliable in terms of its robustness and predictive performance. Since it is difficult to examine the F/M ratio in humans experimentally, this QSAR model for prediction of the placental transfer of chemicals in humans could be useful in risk assessment of chemicals in humans.

  8. QSAR modeling and prediction of the endocrine-disrupting potencies of brominated flame retardants.

    PubMed

    Papa, Ester; Kovarich, Simona; Gramatica, Paola

    2010-05-17

    In the European Union REACH regulation, the chemicals with particularly harmful behaviors, such as endocrine disruptors (EDs), are subject to authorization, and the identification of safer alternatives to these chemicals is required. In this context, the use of quantitative structure-activity relationships (QSAR) becomes particularly useful to fill the data gap due to the very small number of experimental data available to characterize the environmental and toxicological profiles of new and emerging pollutants with ED behavior such as brominated flame retardants (BFRs). In this study, different QSAR models were developed on different responses of endocrine disruption measured for several BFRs. The multiple linear regression approach was applied to a variety of theoretical molecular descriptors, and the best models, which were identified from all of the possible combinations of the structural variables, were internally validated for their performance using the leave-one-out (Q(LOO)(2) = 73-91%) procedure and scrambling of the responses. External validation was provided, when possible, by splitting the data sets in training and test sets (range of Q(EXT)(2) = 76-90%), which confirmed the predictive ability of the proposed equations. These models, which were developed according to the principles defined by the Organization for Economic Co-operation and Development to improve the regulatory acceptance of QSARs, represent a simple tool for the screening and characterization of BFRs.

  9. QSAR analysis of nitroaromatics' toxicity in Tetrahymena pyriformis: structural factors and possible modes of action

    PubMed Central

    Artemenko, A.G.; Muratov, E. N.; Kuz’min, V.E.; Muratov, N.N.; Varlamova, E.V.; Kuz'mina, A.V.; Gorb, L. G.; Golius, A.; Hill, F.C.; Leszczynski, J.; Tropsha, A.

    2012-01-01

    The Hierarchical Technology for Quantitative Structure - Activity Relationships (HiT QSAR) was applied to 95 diverse nitroaromatic compounds (including some widely known explosives) tested for their toxicity (50% inhibition growth concentration, IGC50) against the ciliate Tetrahymena pyriformis. The dataset was divided into subsets according to putative mechanisms of toxicity. Classification and Regression Trees (CART) approach implemented within HiT QSAR has been used for prediction of mechanism of toxicity for new compounds. The resulting models were shown to have ~80% accuracy for external datasets indicating that the mechanistic dataset division was sensible. Then, Partial Least Squares (PLS) statistical approach was used for the development of 2D QSAR models. Validated PLS models were explored to (i) elucidate the effects of different substituents in nitroaromatic compounds on toxicity; (ii) differentiate compounds by probable mechanisms of toxicity based on their structural descriptors; (iii) analyze the role of various physical-chemical factors responsible for compounds’ toxicity. Models were interpreted in terms of molecular fragments promoting or interfering with toxicity. It was also shown that mutual influence of substituents in benzene ring plays the determining role in toxicity variation. Although chemical mechanism based models were statistically significant and externally predictive (R2ext=0.64 for the external set of 63 nitroaromatics identified after all calculations have been completed), they were also shown to have limited coverage (57% for modeling and 76% for external set). PMID:21714735

  10. QSAR models for reproductive toxicity and endocrine disruption in regulatory use – a preliminary investigation†

    PubMed Central

    Jensen, G.E.; Niemelä, J.R.; Wedebye, E.B.; Nikolov, N.G.

    2008-01-01

    A special challenge in the new European Union chemicals legislation, Registration, Evaluation and Authorisation of Chemicals, will be the toxicological evaluation of chemicals for reproductive toxicity. Use of valid quantitative structure–activity relationships (QSARs) is a possibility under the new legislation. This article focuses on a screening exercise by use of our own and commercial QSAR models for identification of possible reproductive toxicants. Three QSAR models were used for reproductive toxicity for the endpoints teratogenic risk to humans (based on animal tests, clinical data and epidemiological human studies), dominant lethal effect in rodents (in vivo) and Drosophila melanogaster sex-linked recessive lethal effect. A structure set of 57,014 European Inventory of Existing Chemical Substances (EINECS) chemicals was screened. A total of 5240 EINECS chemicals, corresponding to 9.2%, were predicted as reproductive toxicants by one or more of the models. The chemicals predicted positive for reproductive toxicity will be submitted to the Danish Environmental Protection Agency as scientific input for a future updated advisory classification list with advisory classifications for concern for humans owing to possible developmental toxic effects: Xn (Harmful) and R63 (Possible risk of harm to the unborn child). The chemicals were also screened in three models for endocrine disruption. PMID:19061080

  11. QSAR study for the soybean 15-lipoxygenase inhibitory activity of organosulfur compounds derived from the essential oil of garlic.

    PubMed

    Camargo, Alejandra B; Marchevsky, Eduardo; Luco, Juan M

    2007-04-18

    In this study, multiple linear regression (MLR) and partial least-squares (PLS) techniques were used for modeling the soybean 15-lipoxygenase inhibitory activity of a varied group of mono-, di-, and trisulfides derived from the essential oil of garlic. The structures of the compounds under study were characterized by means of calculated physicochemical parameters and several nonempirical descriptors, such as topological, geometrical, and quantum chemical indices. The results obtained indicate that the inhibitory activity is strongly dependent on the ability of the compounds to participate in dispersive interactions with the enzyme, as expressed by the solvent-accessible surface area (SASA) and the average distance/distance degree descriptor (ADDD) index. On the other hand, the high contribution of the lowest unoccupied molecular orbit term (LUMO) in the PLS models derived for the di- and trisulfides suggests that the solute's electron-acceptor capacity plays a fundamental role in the inhibitory activity exhibited for these compounds. Finally, the geometric features as expressed by the shape parameters included in the models indicate a low but not negligible positive contribution of molecular linearity in the enzyme-inhibitor binding. In summary, the developed quantitative structure-activity relationship approach successfully accounts for the potencies of organosulfur compounds acting on soybean 15-lipoxygenase and thereby offers both a guide for the synthesis of new compounds and a hypothesis for the molecular basis of their activity. PMID:17367159

  12. Cholesteryl ester transfer protein inhibitors in coronary heart disease: Validated comparative QSAR modeling of N, N-disubstituted trifluoro-3-amino-2-propanols.

    PubMed

    Mondal, Chanchal; Halder, Amit Kumar; Adhikari, Nilanjan; Jha, Tarun

    2013-10-01

    Cholesteryl ester transfer protein (CETP) converts high density lipoprotein cholesterol to low density lipoproteins. It is a promising target for treatment of coronary heart disease. Two dimensional quantitative structure activity relationship (2D-QSAR), hologram QSAR (HQSAR) studies and comparative molecular field analysis (CoMFA) as well as comparative molecular similarity analysis (CoMSIA) were performed on 104 CETP inhibitors. The statistical qualities of generated models were justified by internal and external validation, i.e., q(2) and R(2)pred respectively. The best 2D-QSAR model was obtained with q(2) and R(2)pred values of 0.794 and 0.796 respectively. The 2D-QSAR study suggests that unsaturation, branching and van der Waals volumes may play important roles. The HQSAR model showed q(2) and R(2)pred values of 0.628 and 0.550 respectively. Similarly, CoMFA model showed q(2) and R(2)pred values of 0.707 and 0.755 respectively whereas CoMSIA model was obtained with q(2) and R(2)pred values of 0.696 and 0.703 respectively. CoMFA and CoMSIA studies indicate that steric factors are important at substituted phenoxy and tetrafluoroethoxy groups whereas electropositive factors play important role at difluoromethyl group. The results of 3D-QSAR studies validate those of 2D-QSAR and HQSAR studies as well as the earlier observed SAR data. Current work may help to develop better CETP inhibitors.

  13. Structure-Activity Relationship Analysis of the Thermal Stabilities of Nitroaromatic Compounds Following Different Decomposition Mechanisms.

    PubMed

    Li, Jiazhong; Liu, Huanxiang; Huo, Xing; Gramatica, Paola

    2013-02-01

    The decomposition behavior of energetic materials is very important for the safety problems concerning their production, transportation, use and storage, because molecular decomposition is intimately connected to their explosive properties. Nitroaromatic compounds, particularly nitrobenzene derivatives, are often considered as prototypical energetic molecules, and some of them are commonly used as high explosives. Quantitative structure-activity relationship (QSAR) represents a potential tool for predicting the thermal stability properties of energetic materials. But it is reported that constructing general reliable models to predict their stability and their potential explosive properties is a very difficult task. In this work, we make our efforts to investigate the relationship between the molecular structures and corresponding thermal stabilities of 77 nitrobenzene derivatives with various substituent functional groups (in ortho, meta and/or para positions). The proposed best MLR model, developed by the new software QSARINS, based on Genetic Algorithm for variable selection and with various validation tools, is robust, stable and predictive with R(2) of 0.86, QLOO (2) of 0.79 and CCC of 0.90. The results indicated that, though difficult, it is possible to build predictive, externally validated QSAR models to estimate the thermal stability of nitroaromatic compounds.

  14. Critically Assessing the Predictive Power of QSAR Models for Human Liver Microsomal Stability.

    PubMed

    Liu, Ruifeng; Schyman, Patric; Wallqvist, Anders

    2015-08-24

    To lower the possibility of late-stage failures in the drug development process, an up-front assessment of absorption, distribution, metabolism, elimination, and toxicity is commonly implemented through a battery of in silico and in vitro assays. As in vitro data is accumulated, in silico quantitative structure-activity relationship (QSAR) models can be trained and used to assess compounds even before they are synthesized. Even though it is generally recognized that QSAR model performance deteriorates over time, rigorous independent studies of model performance deterioration is typically hindered by the lack of publicly available large data sets of structurally diverse compounds. Here, we investigated predictive properties of QSAR models derived from an assembly of publicly available human liver microsomal (HLM) stability data using variable nearest neighbor (v-NN) and random forest (RF) methods. In particular, we evaluated the degree of time-dependent model performance deterioration. Our results show that when evaluated by 10-fold cross-validation with all available HLM data randomly distributed among 10 equal-sized validation groups, we achieved high-quality model performance from both machine-learning methods. However, when we developed HLM models based on when the data appeared and tried to predict data published later, we found that neither method produced predictive models and that their applicability was dramatically reduced. On the other hand, when a small percentage of randomly selected compounds from data published later were included in the training set, performance of both machine-learning methods improved significantly. The implication is that 1) QSAR model quality should be analyzed in a time-dependent manner to assess their true predictive power and 2) it is imperative to retrain models with any up-to-date experimental data to ensure maximum applicability. PMID:26170251

  15. Does rational selection of training and test sets improve the outcome of QSAR modeling?

    PubMed

    Martin, Todd M; Harten, Paul; Young, Douglas M; Muratov, Eugene N; Golbraikh, Alexander; Zhu, Hao; Tropsha, Alexander

    2012-10-22

    Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external data set, the best way to validate the predictive ability of a model is to perform its statistical external validation. In statistical external validation, the overall data set is divided into training and test sets. Commonly, this splitting is performed using random division. Rational splitting methods can divide data sets into training and test sets in an intelligent fashion. The purpose of this study was to determine whether rational division methods lead to more predictive models compared to random division. A special data splitting procedure was used to facilitate the comparison between random and rational division methods. For each toxicity end point, the overall data set was divided into a modeling set (80% of the overall set) and an external evaluation set (20% of the overall set) using random division. The modeling set was then subdivided into a training set (80% of the modeling set) and a test set (20% of the modeling set) using rational division methods and by using random division. The Kennard-Stone, minimal test set dissimilarity, and sphere exclusion algorithms were used as the rational division methods. The hierarchical clustering, random forest, and k-nearest neighbor (kNN) methods were used to develop QSAR models based on the training sets. For kNN QSAR, multiple training and test sets were generated, and multiple QSAR models were built. The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than models based on random division, but the predictive power of both types of models are comparable.

  16. QSAR models for the removal of organic micropollutants in four different river water matrices.

    PubMed

    Sudhakaran, Sairam; Calvin, James; Amy, Gary L

    2012-04-01

    Ozonation is an advanced water treatment process used to remove organic micropollutants (OMPs) such as pharmaceuticals and personal care products (PPCPs). In this study, Quantitative Structure Activity Relationship (QSAR) models, for ozonation and advanced oxidation process (AOP), were developed with percent-removal of OMPs by ozonation as the criterion variable. The models focused on PPCPs and pesticides elimination in bench-scale studies done within natural water matrices: Colorado River, Passaic River, Ohio River and Suwannee synthetic water. The OMPs removal for the different water matrices varied depending on the water quality conditions such as pH, DOC, alkalinity. The molecular descriptors used to define the OMPs physico-chemical properties range from one-dimensional (atom counts) to three-dimensional (quantum-chemical). Based on a statistical modeling approach using more than 40 molecular descriptors as predictors, descriptors influencing ozonation/AOP were chosen for inclusion in the QSAR models. The modeling approach was based on multiple linear regression (MLR). Also, a global model based on neural networks was created, compiling OMPs from all the four river water matrices. The chemically relevant molecular descriptors involved in the QSAR models were: energy difference between lowest unoccupied and highest occupied molecular orbital (E(LUMO)-E(HOMO)), electron-affinity (EA), number of halogen atoms (#X), number of ring atoms (#ring atoms), weakly polar component of the solvent accessible surface area (WPSA) and oxygen to carbon ratio (O/C). All the QSAR models resulted in a goodness-of-fit, R(2), greater than 0.8. Internal and external validations were performed on the models. PMID:22245076

  17. Critically Assessing the Predictive Power of QSAR Models for Human Liver Microsomal Stability.

    PubMed

    Liu, Ruifeng; Schyman, Patric; Wallqvist, Anders

    2015-08-24

    To lower the possibility of late-stage failures in the drug development process, an up-front assessment of absorption, distribution, metabolism, elimination, and toxicity is commonly implemented through a battery of in silico and in vitro assays. As in vitro data is accumulated, in silico quantitative structure-activity relationship (QSAR) models can be trained and used to assess compounds even before they are synthesized. Even though it is generally recognized that QSAR model performance deteriorates over time, rigorous independent studies of model performance deterioration is typically hindered by the lack of publicly available large data sets of structurally diverse compounds. Here, we investigated predictive properties of QSAR models derived from an assembly of publicly available human liver microsomal (HLM) stability data using variable nearest neighbor (v-NN) and random forest (RF) methods. In particular, we evaluated the degree of time-dependent model performance deterioration. Our results show that when evaluated by 10-fold cross-validation with all available HLM data randomly distributed among 10 equal-sized validation groups, we achieved high-quality model performance from both machine-learning methods. However, when we developed HLM models based on when the data appeared and tried to predict data published later, we found that neither method produced predictive models and that their applicability was dramatically reduced. On the other hand, when a small percentage of randomly selected compounds from data published later were included in the training set, performance of both machine-learning methods improved significantly. The implication is that 1) QSAR model quality should be analyzed in a time-dependent manner to assess their true predictive power and 2) it is imperative to retrain models with any up-to-date experimental data to ensure maximum applicability.

  18. Toxicity of aryl- and benzylhalides to Daphnia magna and classification of their mode of action based on quantitative structure-activity relationship

    SciTech Connect

    Marchini, S.; Passerini, L.; Hoglund, M.D.; Pino, A.; Nendza, M.

    1999-12-01

    The acute toxicity of aryl- and benzylhalides to Daphnia magna was investigated to test the validity of existing classification schemes for chemicals by mode of action, mainly based on fish studies, and the applicability of predictive quantitative structure-activity relationship (QSAR) models. Halobenzenes and halotoluenes are generally agreed to be unambiguous baseline toxicants (class 1) with the major exception of the benzylic structures, which are reactive in fish tests (class 3). Eighty-nine percent of the arylhalides tested in this study match a log P{sub ow}-dependent QSAR, including fluorinated, chlorinated, brominated, and iodinated derivatives, thereby confirming the validity of the baseline models also for variously halogenated compounds (other than only-chloro compounds). The toxicities of the benzylhalides relative to baseline QSARs clearly indicate that these compounds belong to two classes of mode of action, i.e., they either act as narcotic toxicants (class 1) or reveal excess toxicity due to unspecific reactivity (class 3). On some occasions, the assignment to the two classes of F. magna deviates from the structural rules derived from fish, i.e., iodinated compounds as well as {alpha},{alpha}-Cl{sub 2}-toluene's lack reactive excess toxicity but behave as nonpolar nonspecific toxicants. The QSARs derived during this study reveal lower slopes and higher intercepts than typical baseline models and, together with the analysis of mixture toxicity studies, behavioral studies, and critical body burden, advocate the hypothesis that there are several different ways to produce baseline toxicity. Most halobenzenes and halotoluenes are actually baseline chemicals with some extra reactivity and as such form a subgroup, whose limits still have to be defined. Different primary sites of action could explain why the chemicals are discriminated by different classification systems, but still they must have some rate-limiting interaction in common as they fit the

  19. Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action

    PubMed Central

    Papamokos, George; Silins, Ilona

    2016-01-01

    There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens. PMID:27625608

  20. Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action.

    PubMed

    Papamokos, George; Silins, Ilona

    2016-01-01

    There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens. PMID:27625608

  1. Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action

    PubMed Central

    Papamokos, George; Silins, Ilona

    2016-01-01

    There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens.

  2. QSAR in predictive models for ecological risk assessment

    SciTech Connect

    Passino-Reader, D.R.; Hickey, J.P.

    1994-12-31

    The end use of toxicity and exposure data is risk assessment to determine the probability that receptors experience harmful effects from exposure to environmental contaminants at a site. Determination of processes and development of predictive models precede the collection of data for risk assessment. The presence of hundreds of contaminants at a site and absence of data for many contaminants lead to the use of QSAR to implement the models. Examples of the use of linear salvation energy relationships (LSER) to provide estimates of aquatic toxicity and exposure endpoints will be provided. Integration of QSAR estimates and measured data must be addressed in the uncertainty analysis accompanying ecological risk assessment.

  3. Study of the Differential Activity of Thrombin Inhibitors Using Docking, QSAR, Molecular Dynamics, and MM-GBSA

    PubMed Central

    Mena-Ulecia, Karel; Tiznado, William; Caballero, Julio

    2015-01-01

    Non-peptidic thrombin inhibitors (TIs; 177 compounds) with diverse groups at motifs P1 (such as oxyguanidine, amidinohydrazone, amidine, amidinopiperidine), P2 (such as cyanofluorophenylacetamide, 2-(2-chloro-6-fluorophenyl)acetamide), and P3 (such as phenylethyl, arylsulfonate groups) were studied using molecular modeling to analyze their interactions with S1, S2, and S3 subsites of the thrombin binding site. Firstly, a protocol combining docking and three dimensional quantitative structure–activity relationship was performed. We described the orientations and preferred active conformations of the studied inhibitors, and derived a predictive CoMSIA model including steric, donor hydrogen bond, and acceptor hydrogen bond fields. Secondly, the dynamic behaviors of some selected TIs (compounds 26, 133, 147, 149, 162, and 177 in this manuscript) that contain different molecular features and different activities were analyzed by creating the solvated models and using molecular dynamics (MD) simulations. We used the conformational structures derived from MD to accomplish binding free energetic calculations using MM-GBSA. With this analysis, we theorized about the effect of van der Waals contacts, electrostatic interactions and solvation in the potency of TIs. In general, the contents reported in this article help to understand the physical and chemical characteristics of thrombin-inhibitor complexes. PMID:26599107

  4. Study of the Differential Activity of Thrombin Inhibitors Using Docking, QSAR, Molecular Dynamics, and MM-GBSA.

    PubMed

    Mena-Ulecia, Karel; Tiznado, William; Caballero, Julio

    2015-01-01

    Non-peptidic thrombin inhibitors (TIs; 177 compounds) with diverse groups at motifs P1 (such as oxyguanidine, amidinohydrazone, amidine, amidinopiperidine), P2 (such as cyanofluorophenylacetamide, 2-(2-chloro-6-fluorophenyl)acetamide), and P3 (such as phenylethyl, arylsulfonate groups) were studied using molecular modeling to analyze their interactions with S1, S2, and S3 subsites of the thrombin binding site. Firstly, a protocol combining docking and three dimensional quantitative structure-activity relationship was performed. We described the orientations and preferred active conformations of the studied inhibitors, and derived a predictive CoMSIA model including steric, donor hydrogen bond, and acceptor hydrogen bond fields. Secondly, the dynamic behaviors of some selected TIs (compounds 26, 133, 147, 149, 162, and 177 in this manuscript) that contain different molecular features and different activities were analyzed by creating the solvated models and using molecular dynamics (MD) simulations. We used the conformational structures derived from MD to accomplish binding free energetic calculations using MM-GBSA. With this analysis, we theorized about the effect of van der Waals contacts, electrostatic interactions and solvation in the potency of TIs. In general, the contents reported in this article help to understand the physical and chemical characteristics of thrombin-inhibitor complexes. PMID:26599107

  5. Some methods of obtaining quantitative structure-activity relationships for quantities of environmental interest

    SciTech Connect

    Charton, M.

    1985-09-01

    Methods are described for obtaining quantitative structure-activity relationships (QSAR) for the estimation of quantities of environmental interest. Toxicities of alkylamines and of alkyl alkanoates are well correlated by the alkyl bioactivity branching equation (ABB). Narcotic activities of 1,1-disubstituted ethylenes are correlated by the intermolecular forces bioactivity (IMF) equation. When the data set has a limited number of substituents in equivalent positions the group number (GN) equation, derivable from the IMF equation, can be used for correlation. It has been successfully applied to aqueous solubilities, 1-octanol-water partition coefficients, and bioaccumulation factors and ecological magnifications for organochlorine compounds. A combination of the omega method for combining data sets for different organisms with the GN equation has been used to correlate toxicities of organochlorine insecticides in two species of fish. Toxicities of carbamates have been correlated by a combination of the zeta method and the IMFB equation. The ABB and the GN equations are particularly useful in that they generally do not require parameter tables, and that the parameters they use are error-free. The methods presented here, as shown by the examples given, should make it possible to establish a collection of QSAR for toxicities, bioaccumulation factors, aqueous solubilities, partition coefficients, and other properties of sets of compounds of environmental interest. 29 references.

  6. Modeling structure-activity relationships of prodiginines with antimalarial activity using GA/MLR and OPS/PLS.

    PubMed

    de Campos, Luana Janaína; de Melo, Eduardo Borges

    2014-11-01

    In the present study, we performed a multivariate quantitative structure-activity relationship (QSAR) analysis of 52 prodiginines with antimalarial activity. Variable selection was based on the genetic algorithm (GA) and ordered predictor selection (OPS) approaches, and the models were built using the multiple linear regression (MLR) and partial least squares (PLS) regression methods. The leave-N-out crossvalidation and y-randomization tests showed that the models were robust and free from chance correlation. The mechanistic interpretation of the results was supported by earlier findings. In addition, the comparison of our models with those previously described indicated that the OPS/PLS-based model had a higher quality of external prediction. Thus, this study provides a comprehensive approach to the evaluation of the antimalarial activity of prodiginines, which may be used as a support tool in designing new therapeutic agents for malaria.

  7. Determining cleanup levels in bioremediation: Quantitative structure activity relationship techniques

    SciTech Connect

    Arulgnanendran, V.R.J.; Nirmalakhandan, N.

    1995-12-31

    An important feature in the process of planning and initiating bioremediation is the quantification of the toxicity of either an individual chemical or a group of chemicals when multiple chemicals are involved. A laboratory protocol was developed to test the toxicity of single chemicals and mixtures of organic chemicals in a soil medium. Portions of these chemicals are used as a training set to develop Quantitative Structure Activity Relationship (QSAR) models. These predictive models are tested using the chemicals in the testing set, i.e., the remaining chemicals. Moreover mixtures with 10 contaminants in each mixture are tested experimentally to determine joint toxicity for mixtures of chemicals. Using the concepts of Toxic Units, Additivity Index, and Mixture Toxicity Index, the laboratory results are tested for additive, synergistic, or antagonistic effects of the contaminants. These concepts are further validated on mixtures containing eight chemicals that are tested in the laboratory. In addition to the use of the predictive models in evaluating cleanup levels for hazardous waste locations, they are useful to predict microbial toxicity in soils of new chemicals from a congeneric group acting by the same mode of toxicity. These models are applicable when the contaminants act singly or jointly in a mixture.

  8. Structure activity relationships to assess new chemicals under TSCA

    SciTech Connect

    Auletta, A.E.

    1990-12-31

    Under Section 5 of the Toxic Substances Control Act (TSCA), manufacturers must notify the US Environmental Protection Agency (EPA) 90 days before manufacturing, processing, or importing a new chemical substance. This is referred to as a premanufacture notice (PMN). The PMN must contain certain information including chemical identity, production volume, proposed uses, estimates of exposure and release, and any health or environmental test data that are available to the submitter. Because there is no explicit statutory authority that requires testing of new chemicals prior to their entry into the market, most PMNs are submitted with little or no data. As a result, EPA has developed special techniques for hazard assessment of PMN chemicals. These include (1) evaluation of available data on the chemical itself, (2) evaluation of data on analogues of the PMN, or evaluation of data on metabolites or analogues of metabolites of the PMN, (3) use of quantitative structure activity relationships (QSARs), and (4) knowledge and judgement of scientific assessors in the interpretation and integration of the information developed in the course of the assessment. This approach to evaluating potential hazards of new chemicals is used to identify those that are most in need of addition review of further testing. It should not be viewed as a replacement for testing. 4 tabs.

  9. QSAR of Tryptanthrin Analogs via Tunneling Barrier Height Imaging

    NASA Astrophysics Data System (ADS)

    Sriraman, Krishnan

    A new class of potential therapeutic agents, namely indolo[2,1-b]quinazolin-6,12- dione (tryptanthrin), and its analogues, have generated interest due to their broad spectrum of activity against a variety of pathogenic organisms. Little is known about the mechanism of action of tryptanthrins at the cellular and molecular levels. One method that has been employed to understand mechanisms of action and predict biological activities is quantitative structure activity relationship (QSAR). Since previous tryptanthrin studies could not clearly identify the pharmacophore, it was proposed to measure barrier height (BH) energy values for preparing a QSAR vs. IC50 values from the literature. The BH energy values were measured using barrier height spectroscopy which is performed using a scanning tunneling microscope (STM). Topography images (7 x 7 nm size) for tryptanthrin and three of its analogs namely 8-fluorotryptanthrin, 4-aza-8-fluorotryptanthrin, and 4-aza-8-chlorotryptanthrin were collected. Both HOMO and LUMO were collected at an applied bias of +/- 0.8 V and 0.1 nA. Excellent positive bias images (LUMO) of tryptanthrin were collected in which individual molecules and their lobes could be clearly recognized. A comparison with the density functional theory (DFT) calculated image of the LUMO resulted in an excellent match. An interesting outcome of the tryptanthrin LUMO imaging was the arrangement of molecules (parallel alignments) in the image which was explained by considering the intermolecular forces. Excellent BH images with sub-molecular resolution for 4-aza-8-fluorotryptanthrin were observed. BH values were calculated for each of the various lobes in the molecule from the BH image. Preliminary QSAR training sets were constructed using literature values of IC50 for W-2 and D-6 strains of Plasmodium falciparum as well as Leishmanai donovani versus average measured molecular barrier heights. The correlations were found to be fair for all the three pathogens. The

  10. Critical body residues linked to octanol-water partitioning, organism composition, and LC50 QSARs: meta-analysis and model.

    PubMed

    Hendriks, A Jan; Traas, Theo P; Huijbregts, Mark A J

    2005-05-01

    To protect thousands of species from thousands of chemicals released in the environment, various risk assessment tools have been developed. Here, we link quantitative structure-activity relationships (QSARs) for response concentrations in water (LC50) to critical concentrations in organisms (C50) by a model for accumulation in lipid or non-lipid phases versus water Kpw. The model indicates that affinity for neutral body components such as storage fat yields steep Kpw-Kow relationships, whereas slopes for accumulation in polar phases such as proteins are gentle. This pattern is confirmed by LC50 QSARs for different modes of action, such as neutral versus polar narcotics and organochlorine versus organophosphor insecticides. LC50 QSARs were all between 0.00002 and 0.2Kow(-1). After calibrating the model with the intercepts and, for the first time also, with the slopes of the LC50 QSARs, critical concentrations in organisms C50 are calculated and compared to an independent validation data set. About 60% of the variability in lethal body burdens C50 is explained by the model. Explanations for differences between estimated and measured levels for 11 modes of action are discussed. In particular, relationships between the critical concentrations in organisms C50 and chemical (Kow) or species (lipid content) characteristics are specified and tested. The analysis combines different models proposed before and provides a substantial extension of the data set in comparison to previous work. Moreover, the concept is applied to species (e.g., plants, lean animals) and substances (e.g., specific modes of action) that were scarcely studied quantitatively so far.

  11. Deep neural nets as a method for quantitative structure-activity relationships.

    PubMed

    Ma, Junshui; Sheridan, Robert P; Liaw, Andy; Dahl, George E; Svetnik, Vladimir

    2015-02-23

    Neural networks were widely used for quantitative structure-activity relationships (QSAR) in the 1990s. Because of various practical issues (e.g., slow on large problems, difficult to train, prone to overfitting, etc.), they were superseded by more robust methods like support vector machine (SVM) and random forest (RF), which arose in the early 2000s. The last 10 years has witnessed a revival of neural networks in the machine learning community thanks to new methods for preventing overfitting, more efficient training algorithms, and advancements in computer hardware. In particular, deep neural nets (DNNs), i.e. neural nets with more than one hidden layer, have found great successes in many applications, such as computer vision and natural language processing. Here we show that DNNs can routinely make better prospective predictions than RF on a set of large diverse QSAR data sets that are taken from Merck's drug discovery effort. The number of adjustable parameters needed for DNNs is fairly large, but our results show that it is not necessary to optimize them for individual data sets, and a single set of recommended parameters can achieve better performance than RF for most of the data sets we studied. The usefulness of the parameters is demonstrated on additional data sets not used in the calibration. Although training DNNs is still computationally intensive, using graphical processing units (GPUs) can make this issue manageable.

  12. Deep neural nets as a method for quantitative structure-activity relationships.

    PubMed

    Ma, Junshui; Sheridan, Robert P; Liaw, Andy; Dahl, George E; Svetnik, Vladimir

    2015-02-23

    Neural networks were widely used for quantitative structure-activity relationships (QSAR) in the 1990s. Because of various practical issues (e.g., slow on large problems, difficult to train, prone to overfitting, etc.), they were superseded by more robust methods like support vector machine (SVM) and random forest (RF), which arose in the early 2000s. The last 10 years has witnessed a revival of neural networks in the machine learning community thanks to new methods for preventing overfitting, more efficient training algorithms, and advancements in computer hardware. In particular, deep neural nets (DNNs), i.e. neural nets with more than one hidden layer, have found great successes in many applications, such as computer vision and natural language processing. Here we show that DNNs can routinely make better prospective predictions than RF on a set of large diverse QSAR data sets that are taken from Merck's drug discovery effort. The number of adjustable parameters needed for DNNs is fairly large, but our results show that it is not necessary to optimize them for individual data sets, and a single set of recommended parameters can achieve better performance than RF for most of the data sets we studied. The usefulness of the parameters is demonstrated on additional data sets not used in the calibration. Although training DNNs is still computationally intensive, using graphical processing units (GPUs) can make this issue manageable. PMID:25635324

  13. Using quantitative structure activity relationship models to predict an appropriate solvent system from a common solvent system family for countercurrent chromatography separation.

    PubMed

    Marsden-Jones, Siân; Colclough, Nicola; Garrard, Ian; Sumner, Neil; Ignatova, Svetlana

    2015-06-12

    Countercurrent chromatography (CCC) is a form of liquid-liquid chromatography. It works by running one immiscible solvent (mobile phase) over another solvent (stationary phase) being held in a CCC column using centrifugal force. The concentration of compound in each phase is characterised by the partition coefficient (Kd), which is the concentration in the stationary phase divided by the concentration in the mobile phase. When Kd is between approximately 0.2 and 2, it is most likely that optimal separation will be achieved. Having the Kd in this range allows the compound enough time in the column to be separated without resulting in a broad peak and long run time. In this paper we report the development of quantitative structure activity relationship (QSAR) models to predict logKd. The QSAR models use only the molecule's 2D structure to predict the molecular property logKd.

  14. Prediction of Intracellular Localization of Fluorescent Dyes Using QSAR Models.

    PubMed

    Uchinomiya, Shohei; Horobin, Richard W; Alvarado-Martínez, Enrique; Peña-Cabrera, Eduardo; Chang, Young-Tae

    2016-01-01

    Control of fluorescent dye localization in live cells is crucial for fluorescence imaging. Here, we describe quantitative structure activity relation (QSAR) models for predicting intracellular localization of fluorescent dyes. For generating the QSAR models, electric charge (Z) calculated by pKa, conjugated bond number (CBN), the largest conjugated fragment (LCF), molecular weight (MW) and log P were used as parameters. We identified the intracellular localization of 119 BODIPY dyes in live NIH3T3 cells, and assessed the accuracy of our models by comparing their predictions with the observed dye localizations. As predicted by the models, no BODIPY dyes localized in nuclei or plasma membranes. The accuracy of the model for localization in fat droplets was 92%, with the models for cytosol and lysosomes showing poorer agreement with observed dye localization, albeit well above chance levels. Overall therefore the utility of QSAR models for predicting dye localization in live cells was clearly demonstrated. PMID:27055752

  15. Quantitative structure-activity relationships of imidazolium oximes as nerve agent antidotes

    SciTech Connect

    Musallam, H.A.; Foye, W.O.; Hansch, C.; Harris, R.N.; Engle, R.R.

    1993-05-13

    Organophosphorus-containing pesticides and chemical warfare agents are potent inhibitors of synaptic acetylcholinesterase, a key regulator of cholinergic neurotransmission. These nerve agents have for many years constituted a serious threat to military personnel. These threats stimulated considerable efforts to develop effective medical countermeasures. Several potential drugs have been found recently which are capable of protecting animals from lethal levels of nerve agents. A recent U. S. Army Medical Research and Development Command drug development project synthesized a large number of imidazolium oximes. These compounds were found to possess strong antidotal activity against one of the most lethal nerve agents, soman. The Army's approach, like most conventional drug discovery approaches, depended primarily on the trial and error method. This research was carried out to determine if these potential nerve agent antidotes could have been discovered through the use of Quantitative Structure Activity-Relationships (QSAR) technique.

  16. Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methods

    SciTech Connect

    Politi, Regina; Rusyn, Ivan; Tropsha, Alexander

    2014-10-01

    The thyroid hormone receptor (THR) is an important member of the nuclear receptor family that can be activated by endocrine disrupting chemicals (EDC). Quantitative Structure–Activity Relationship (QSAR) models have been developed to facilitate the prioritization of THR-mediated EDC for the experimental validation. The largest database of binding affinities available at the time of the study for ligand binding domain (LBD) of THRβ was assembled to generate both continuous and classification QSAR models with an external accuracy of R{sup 2} = 0.55 and CCR = 0.76, respectively. In addition, for the first time a QSAR model was developed to predict binding affinities of antagonists inhibiting the interaction of coactivators with the AF-2 domain of THRβ (R{sup 2} = 0.70). Furthermore, molecular docking studies were performed for a set of THRβ ligands (57 agonists and 15 antagonists of LBD, 210 antagonists of the AF-2 domain, supplemented by putative decoys/non-binders) using several THRβ structures retrieved from the Protein Data Bank. We found that two agonist-bound THRβ conformations could effectively discriminate their corresponding ligands from presumed non-binders. Moreover, one of the agonist conformations could discriminate agonists from antagonists. Finally, we have conducted virtual screening of a chemical library compiled by the EPA as part of the Tox21 program to identify potential THRβ-mediated EDCs using both QSAR models and docking. We concluded that the library is unlikely to have any EDC that would bind to the THRβ. Models developed in this study can be employed either to identify environmental chemicals interacting with the THR or, conversely, to eliminate the THR-mediated mechanism of action for chemicals of concern. - Highlights: • This is the largest curated dataset for ligand binding domain (LBD) of the THRβ. • We report the first QSAR model for antagonists of AF-2 domain of THRβ. • A combination of QSAR and docking enables

  17. Fish acute toxicity syndromes and their use in the QSAR approach to hazard assessment

    SciTech Connect

    McKim, J.M.; Bradbury, S.P.; Niemi, G.J.

    1987-04-01

    Implementation of the Toxic Substances Control Act of 1977 creates the need to reliably establish testing priorities because laboratory resources are limited and the number of industrial chemicals requiring evaluation is overwhelming. The use of quantitative structure activity relationship (QSAR) models as rapid and predictive screening tools to select more potentially hazardous chemicals for in-depth laboratory evaluation has been proposed. Further implementation and refinement of quantitative structure-toxicity relationships in aqueous toxicology and hazard assessment requires the development of a mode-of-action database. With such a database, a qualitative structure-activity relationship can be formulated to assign the proper mode of action, and respective QSAR, to a given chemical structure. In this review, the development of fish acute toxicity syndromes (FATS), which are toxic-response sets based on various behavioral and physiological-biochemical measurements, and their projected use in the mode-of-action database are outlined. Using behavioral parameters monitored in the fathead minnow during acute toxicity testing, FATS associated with acetylcholinesterase (AChE) inhibitors and narcotics could be reliably predicted. However, compounds classified as oxidative phosphorylation uncouplers or stimulants could not be resolved. Refinement of this approach by using respiratory-cardiovascular responses in the rainbow trout, enabled FATS associated with AChE inhibitors, convulsants, narcotics, respiratory blockers, respiratory membrane irritants, and uncouplers to be correctly predicted.

  18. Insights on Cytochrome P450 Enzymes and Inhibitors Obtained Through QSAR Studies

    PubMed Central

    Sridhar, Jayalakshmi; Liu, Jiawang; Foroozesh, Maryam; Stevens, Cheryl L. Klein

    2013-01-01

    The cytochrome P450 (CYP) superfamily of heme enzymes play an important role in the metabolism of a large number of endogenous and exogenous compounds, including most of the drugs currently on the market. Inhibitors of CYP enzymes have important roles in the treatment of several disease conditions such as numerous cancers and fungal infections in addition to their critical role in drug-drug interactions. Structure activity relationships (SAR), and three-dimensional quantitative structure activity relationships (3D-QSAR) represent important tools in understanding the interactions of the inhibitors with the active sites of the CYP enzymes. A comprehensive account of the QSAR studies on the major human CYPs 1A1, 1A2, 1B1, 2A6, 2B6, 2C9, 2C19, 2D6, 2E1, 3A4 and a few other CYPs are detailed in this review which will provide us with an insight into the individual/common characteristics of the active sites of these enzymes and the enzyme-inhibitor interactions. PMID:22864238

  19. 2D- and 3D-QSAR of tocainide and mexiletine analogues acting as Na(v)1.4 channel blockers.

    PubMed

    Carrieri, Antonio; Muraglia, Marilena; Corbo, Filomena; Pacifico, Concetta

    2009-04-01

    Enantiomeric forms of Tocainide, Mexiletine, and structurally related local anaesthetic compounds, were analyzed with respect to their potency in blocking Na(v)1.4 channel. Structure-activity relationships based on in vitro pharmacological assays, suggested that an increase in terms of lipophilicity and/or molecular surface as well as the presence of specific polar spacers might be determinant for receptor interactions. QSAR and pharmacophore models were then used to support at 3D level this hypothesis. PMID:19027197

  20. Improving quantitative structure-activity relationships through multiobjective optimization.

    PubMed

    Nicolotti, Orazio; Giangreco, Ilenia; Miscioscia, Teresa Fabiola; Carotti, Angelo

    2009-10-01

    A multiobjective optimization algorithm was proposed for the automated integration of structure- and ligand-based molecular design. Driven by a genetic algorithm, the herein proposed approach enabled the detection of a number of trade-off QSAR models accounting simultaneously for two independent objectives. The first was biased toward best regressions among docking scores and biological affinities; the second minimized the atom displacements from a properly established crystal-based binding topology. Based on the concept of dominance, 3D QSAR equivalent models profiled the Pareto frontier and were, thus, designated as nondominated solutions of the search space. K-means clustering was, then, operated to select a representative subset of the available trade-off models. These were effectively subjected to GRID/GOLPE analyses for quantitatively featuring molecular determinants of ligand binding affinity. More specifically, it was demonstrated that a) diverse binding conformations occurred on the basis of the ligand ability to profitably contact different part of protein binding site; b) enzyme selectivity was better approached and interpreted by combining diverse equivalent models; and c) trade-off models were successful and even better than docking virtual screening, in retrieving at high sensitivity active hits from a large pool of chemically similar decoys. The approach was tested on a large series, very well-known to QSAR practitioners, of 3-amidinophenylalanine inhibitors of thrombin and trypsin, two serine proteases having rather different biological actions despite a high sequence similarity. PMID:19785453

  1. Evolution of graph theory-based QSAR methods and their applications to the search for new antibacterial agents.

    PubMed

    Speck-Planche, Alejandro; Cordeiro, M N D S

    2013-01-01

    Resistance of bacteria to current antibiotics has increased worldwide, being one of the leading unresolved situations in public health. Due to negligence regarding the treatment of community-acquired diseases, even healthcare facilities have been highly impacted by an emerging problem: nosocomial infections. Moreover, infectious diseases, including nosocomial infections, have been found to depend on multiple pathogenicity factors, confirming the need to discover of multi-target antibacterial agents. Drug discovery is a very complex, expensive, and time-consuming process. In this sense, Quantitative Structure-Activity Relationships (QSAR) methods have become complementary tools for medicinal chemistry, permitting the efficient screening of potential drugs, and consequently, rationalizing the organic synthesis as well as the biological evaluation of compounds. In the consolidation of QSAR methods as important components of chemoinformatics, the use of mathematical chemistry, and more specifically, the use of graph-theoretical approaches has played a vital role. Here, we focus our attention on the evolution of QSAR methods, citing the most relevant works devoted to the development of promising graph-theoretical approaches in the last 8 years, and their applications to the prediction of antibacterial activities of chemicals against pathogens causing both community-acquired and nosocomial infections.

  2. Identification of 3-Nitro-2,4,6-trihydroxybenzamide Derivatives as Photosynthetic Electron Transport Inhibitors by QSAR and Pharmacophore Studies.

    PubMed

    Sharma, Mukesh C

    2016-06-01

    In the present investigation, quantitative structure-activity relationship (QSAR) analysis was performed on a data set consisting of structurally diverse compounds in order to investigate the role of their structural features on their photosynthetic electron transport Inhibitors. The best 2D-QSAR model was selected, having correlation coefficient r (2) = 0.8544 and cross-validated squared correlation coefficient q (2) = 0.7139 with external predictive ability of pred_r (2) = 0.7753. The results obtained in this study indicate that the presence of hydroxy and nitro groups, expressed by the SsOHcount and SddsN (nitro) count, is the most relevant molecular property determining efficiency of photosynthetic inhibitory. Molecular field analysis was used to construct the best k-nearest neighbor (kNN-MFA)-based 3D-QSAR model using SA-PLS method, showing good correlative and predictive capabilities in terms of [Formula: see text] and [Formula: see text]. The pharmacophore model includes three features viz. hydrogen bond donor, hydrogen bond acceptor, and one aromatic feature. The developed model was found to be predictive and can be used to design potent photosynthetic electron transport activities prior to their synthesis for further lead modification.

  3. Evolution of graph theory-based QSAR methods and their applications to the search for new antibacterial agents.

    PubMed

    Speck-Planche, Alejandro; Cordeiro, M N D S

    2013-01-01

    Resistance of bacteria to current antibiotics has increased worldwide, being one of the leading unresolved situations in public health. Due to negligence regarding the treatment of community-acquired diseases, even healthcare facilities have been highly impacted by an emerging problem: nosocomial infections. Moreover, infectious diseases, including nosocomial infections, have been found to depend on multiple pathogenicity factors, confirming the need to discover of multi-target antibacterial agents. Drug discovery is a very complex, expensive, and time-consuming process. In this sense, Quantitative Structure-Activity Relationships (QSAR) methods have become complementary tools for medicinal chemistry, permitting the efficient screening of potential drugs, and consequently, rationalizing the organic synthesis as well as the biological evaluation of compounds. In the consolidation of QSAR methods as important components of chemoinformatics, the use of mathematical chemistry, and more specifically, the use of graph-theoretical approaches has played a vital role. Here, we focus our attention on the evolution of QSAR methods, citing the most relevant works devoted to the development of promising graph-theoretical approaches in the last 8 years, and their applications to the prediction of antibacterial activities of chemicals against pathogens causing both community-acquired and nosocomial infections. PMID:24200354

  4. QSAR modeling for quinoxaline derivatives using genetic algorithm and simulated annealing based feature selection.

    PubMed

    Ghosh, P; Bagchi, M C

    2009-01-01

    With a view to the rational design of selective quinoxaline derivatives, 2D and 3D-QSAR models have been developed for the prediction of anti-tubercular activities. Successful implementation of a predictive QSAR model largely depends on the selection of a preferred set of molecular descriptors that can signify the chemico-biological interaction. Genetic algorithm (GA) and simulated annealing (SA) are applied as variable selection methods for model development. 2D-QSAR modeling using GA or SA based partial least squares (GA-PLS and SA-PLS) methods identified some important topological and electrostatic descriptors as important factor for tubercular activity. Kohonen network and counter propagation artificial neural network (CP-ANN) considering GA and SA based feature selection methods have been applied for such QSAR modeling of Quinoxaline compounds. Out of a variable pool of 380 molecular descriptors, predictive QSAR models are developed for the training set and validated on the test set compounds and a comparative study of the relative effectiveness of linear and non-linear approaches has been investigated. Further analysis using 3D-QSAR technique identifies two models obtained by GA-PLS and SA-PLS methods leading to anti-tubercular activity prediction. The influences of steric and electrostatic field effects generated by the contribution plots are discussed. The results indicate that SA is a very effective variable selection approach for such 3D-QSAR modeling.

  5. Relative toxicity of para-substituted phenols: log K/sub ow/ and pKa-dependent structure-activity relationships

    SciTech Connect

    Schultz, T.W.

    1987-06-01

    The toxic response of the majority of industrial chemicals which are nonreactive and non -ionic can be quantitatively modeled by the 1-octanol/water partition coefficient (K/sub ow/) in a linear fashion following a log-log transformation of the data. The toxicity of phenols, however does not model by these same quantitative structure-activity relationships (QSAR). Earlier studies have shown that the addition of pKa as a second molecular descriptor improves the predictive capability of log K/sub ow/ QSAR. The predicted toxicity of phenol derivatives, which ionize poorly under the test conditions, is not significantly altered by using models in which pKa is not included. In an effort to systematically explore the extent to which such log K/sub ow/ and, log K/sub ow/ and pKa-dependent QSAR can be used to predict the biological activity of phenols, the first in a series of investigations was conducted using the rapid and inexpensive Tetrahymena population growth impairment assay to determine relative toxicity of 30 para-substituted derivatives.

  6. Quantitative Structure-Activity Relationship Analysis and a Combined Ligand-Based/Structure-Based Virtual Screening Study for Glycogen Synthase Kinase-3.

    PubMed

    Fu, Gang; Liu, Sheng; Nan, Xiaofei; Dale, Olivia R; Zhao, Zhendong; Chen, Yixin; Wilkins, Dawn E; Manly, Susan P; Cutler, Stephen J; Doerksen, Robert J

    2014-09-01

    Glycogen synthase kinase-3 (GSK-3) is a multifunctional serine/threonine protein kinase which regulates a wide range of cellular processes, involving various signalling pathways. GSK-3β has emerged as an important therapeutic target for diabetes and Alzheimer's disease. To identify structurally novel GSK-3β inhibitors, we performed virtual screening by implementing a combined ligand-based/structure-based approach, which included quantitative structure-activity relationship (QSAR) analysis and docking prediction. To integrate and analyze complex data sets from multiple experimental sources, we drafted and validated a hierarchical QSAR method, which adopts a two-level structure to take data heterogeneity into account. A collection of 728 GSK-3 inhibitors with diverse structural scaffolds was obtained from published papers that used different experimental assay protocols. Support vector machines and random forests were implemented with wrapper-based feature selection algorithms to construct predictive learning models. The best models for each single group of compounds were then used to build the final hierarchical QSAR model, with an overall R(2) of 0.752 for the 141 compounds in the test set. The compounds obtained from the virtual screening experiment were tested for GSK-3β inhibition. The bioassay results confirmed that 2 hit compounds are indeed GSK-3β inhibitors exhibiting sub-micromolar inhibitory activity, and therefore validated our combined ligand-based/structure-based approach as effective for virtual screening experiments. PMID:27486081

  7. Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure based modeling methods

    PubMed Central

    Politi, Regina; Rusyn, Ivan; Tropsha, Alexander

    2016-01-01

    The thyroid hormone receptor (THR) is an important member of the nuclear receptor family that can be activated by endocrine disrupting chemicals (EDC). Quantitative Structure-Activity Relationship (QSAR) models have been developed to facilitate the prioritization of THR-mediated EDC for the experimental validation. The largest database of binding affinities available at the time of the study for ligand binding domain (LBD) of THRβ was assembled to generate both continuous and classification QSAR models with an external accuracy of R2=0.55 and CCR=0.76, respectively. In addition, for the first time a QSAR model was developed to predict binding affinities of antagonists inhibiting the interaction of coactivators with the AF-2 domain of THRβ (R2=0.70). Furthermore, molecular docking studies were performed for a set of THRβ ligands (57 agonists and 15 antagonists of LBD, 210 antagonists of the AF-2 domain, supplemented by putative decoys/non-binders) using several THRβ structures retrieved from the Protein Data Bank. We found that two agonist-bound THRβ conformations could effectively discriminate their corresponding ligands from presumed non-binders. Moreover, one of the agonist conformations could discriminate agonists from antagonists. Finally, we have conducted virtual screening of a chemical library compiled by the EPA as part of the Tox21 program to identify potential THRβ-mediated EDCs using both QSAR models and docking. We concluded that the library is unlikely to have any EDC that would bind to the THRβ. Models developed in this study can be employed either to identify environmental chemicals interacting with the THR or, conversely, to eliminate the THR-mediated mechanism of action for chemicals of concern. PMID:25058446

  8. Chemometric QSAR Modeling and In Silico Design of Antioxidant NO Donor Phenols

    PubMed Central

    Mitra, Indrani; Saha, Achintya; Roy, Kunal

    2011-01-01

    An acceleration of free radical formation within human system exacerbates the incidence of several life-threatening diseases. The systemic antioxidants often fall short for neutralizing the free radicals thereby demanding external antioxidant supplementation. Therein arises the need for development of new antioxidants with improved potency. In order to search for efficient antioxidant molecules, the present work deals with quantitative structure-activity relationship (QSAR) studies of a series of antioxidants belonging to the class of phenolic derivatives bearing NO donor groups. In this study, several QSAR models with appreciable statistical significance have been reported. Models were built using various chemometric tools and validated both internally and externally. These models chiefly infer that presence of substituted aromatic carbons, long chain branched substituents, an oxadiazole-N-oxide ring with an electronegative atom containing group substituted at the 5 position and high degree of methyl substitutions of the parent moiety are conducive to the antioxidant activity profile of these molecules. The novelty of this work is not only that the structural attributes of NO donor phenolic compounds required for potent antioxidant activity have been explored in this study, but new compounds with possible antioxidant activity have also been designed and their antioxidant activity has been predicted in silico. PMID:21617771

  9. Chemometric QSAR modeling and in silico design of antioxidant NO donor phenols.

    PubMed

    Mitra, Indrani; Saha, Achintya; Roy, Kunal

    2011-03-01

    An acceleration of free radical formation within human system exacerbates the incidence of several life-threatening diseases. The systemic antioxidants often fall short for neutralizing the free radicals thereby demanding external antioxidant supplementation. Therein arises the need for development of new antioxidants with improved potency. In order to search for efficient antioxidant molecules, the present work deals with quantitative structure-activity relationship (QSAR) studies of a series of antioxidants belonging to the class of phenolic derivatives bearing NO donor groups. In this study, several QSAR models with appreciable statistical significance have been reported. Models were built using various chemometric tools and validated both internally and externally. These models chiefly infer that presence of substituted aromatic carbons, long chain branched substituents, an oxadiazole-N-oxide ring with an electronegative atom containing group substituted at the 5 position and high degree of methyl substitutions of the parent moiety are conducive to the antioxidant activity profile of these molecules. The novelty of this work is not only that the structural attributes of NO donor phenolic compounds required for potent antioxidant activity have been explored in this study, but new compounds with possible antioxidant activity have also been designed and their antioxidant activity has been predicted in silico.

  10. Parameters for Pyrethroid Insecticide QSAR and PBPK/PD Models for Human Risk Assessment

    EPA Science Inventory

    This pyrethroid insecticide parameter review is an extension of our interest in developing quantitative structure–activity relationship–physiologically based pharmacokinetic/pharmacodynamic (QSAR-PBPK/PD) models for assessing health risks, which interest started with the organoph...

  11. QSARS for predicting biotic and abiotic reductive transformation rate constants of halogenated hydrocarbons in anoxic sediment systems

    SciTech Connect

    Peijnenburg, W.J.G.M.; 't Hart, M.J.; den Hollander, H.A.; van de Meent, D.; Verboom, H.H.

    1991-01-01

    Quantitative structure-activity relationships (QSARs) are developed relating biotic and abiotic pseudo-first-order disappearance rate constants of halogenated hydrocarbons in anoxic sediments to a number of readily available molecular descriptors. Based upon knowledge of the underlying reaction mechanisms, four descriptors were selected: carbon halogen bond strength, the summation of the Hammett (aromatics) and Taft (aliphatics) sigma constants and the inductive constants (aromatics) of the additional substituents, carbon-carbon bond dissociation energy (aliphatics), and steric factors of the additional substituents. Comparison of the abiotic and biotic QSARs clearly showed the close similarities between both processes. By correlating the rate constants for reduction of a number of halocarbons obtained in a number of distinct sediment samples to the organic carbon content of the samples, the QSARs were made operative for predicting rates of reduction of given halocarbons in given sediment-water systems. The correlations were enhanced by taking into account the fraction of the compounds sorbed to the solid phase. (Copyright (c) 1991 Elsevier Science Publishers B.V.)

  12. A QSAR study on the inhibition mechanism of matrix metalloproteinase-12 by arylsulfone analogs based on molecular orbital calculations.

    PubMed

    Hitaoka, Seiji; Chuman, Hiroshi; Yoshizawa, Kazunari

    2015-01-21

    A binding mechanism between human matrix metalloproteinase-12 (MMP-12) and eight arylsulfone analogs having two types of carboxylic and hydroxamic acids as the most representative zinc binding group is investigated using a quantitative structure-activity relationship (QSAR) analysis based on a linear expression by representative energy terms (LERE). The LERE-QSAR analysis quantitatively reveals that the variation in the observed (experimental) inhibitory potency among the arylsulfone analogs is decisively governed by those in the intrinsic binding and dispersion interaction energies. The results show that the LERE-QSAR analysis not only can excellently reproduce the observed overall free-energy change but also can determine the contributions of representative free-energy changes. An inter-fragment interaction energy difference (IFIED) analysis based on the fragment molecular orbital (FMO) method (FMO-IFIED) leads to the identification of key residues governing the variation in the inhibitory potency as well as to the understanding of the difference between the interactions of the carboxylic and hydroxamic acid zinc binding groups. The current results that have led to the optimization of the inhibitory potency of arylsulfone analogs toward MMP-12 to be used in the treatment of chronic obstructive pulmonary disease may be useful for the development of a new potent MMP-12 inhibitor.

  13. ASTER: An integration of the AQUIRE database and the QSAR system for use in ecological risk assessments

    SciTech Connect

    Russom, C.L.; Anderson, E.B.; Greenwood, B.E.; Pilli, A.

    1990-01-01

    Ecological risk assessments are used by the U.S. Environmental Protection Agency (USEPA) and other governmental agencies to assist in determining the probability and magnitude of deleterious effects of hazardous chemicals on plants and animals. These assessments are important steps in formulating regulatory decisions. The completion of an ecological risk assessment requires the gathering of ecotoxicological hazard and environmental exposure information. The information is evaluated in the risk characterization section to assist in making the final risk assessment. ASTER (Assessment Tools for the Evaluation of Risk) was designed by the USEPA Environmental Research Laboratory-Duluth (ERL-D) to assist regulators in producing risk assessments. ASTER is an integration of the AQUIRE (AQUatic toxicity Information Retrieval System) and QSAR (Quantitative Structure Activity Relationships) systems. AQUIRE is a database of aquatic toxicity tests and QSAR is comprised of a database of measured physicochemical properties, and various QSAR models that estimate physicochemical and ecotoxicological endpoints. ASTER will be available to international governmental agencies through the USEPA National Computing Center.

  14. Comparison of Performance of Docking, LIE, Metadynamics and QSAR in Predicting Binding Affinity of Benzenesulfonamides.

    PubMed

    Raškevičius, Vytautas; Kairys, Visvaldas

    2015-01-01

    The design of inhibitors specific for one relevant carbonic anhydrase isozyme is the major challenge in the new therapeutic agents development. Comparative computational chemical structure and biological activity relationship studies on a series of carbonic anhydrase II inhibitors, benzenesulfonamide derivatives, bearing pyrimidine moieties are reported in this paper using docking, Linear Interaction Energy (LIE), Metadynamics and Quantitative Structure Activity Relationship (QSAR) methods. The computed binding affinities were compared with the experimental data with the goal to explore strengths and weaknesses of various approaches applied to the investigated carbonic anhydrase/inhibitor system. From the tested methods initially only QSAR showed promising results (R2=0.83-0.89 between experimentally determined versus predicted pKd values.). Possible reasons for this performance were discussed. A modification of the LIE method was suggested which used an alternative LIE-like equation yielding significantly improved results (R2 between the experimentally determined versus the predicted ΔG(bind) improved from 0.24 to 0.50).

  15. An integrated QSAR-PBK/D modelling approach for predicting detoxification and DNA adduct formation of 18 acyclic food-borne α,β-unsaturated aldehydes

    SciTech Connect

    Kiwamoto, R. Spenkelink, A.; Rietjens, I.M.C.M.; Punt, A.

    2015-01-01

    Acyclic α,β-unsaturated aldehydes present in food raise a concern because the α,β-unsaturated aldehyde moiety is considered a structural alert for genotoxicity. However, controversy remains on whether in vivo at realistic dietary exposure DNA adduct formation is significant. The aim of the present study was to develop physiologically based kinetic/dynamic (PBK/D) models to examine dose-dependent detoxification and DNA adduct formation of a group of 18 food-borne acyclic α,β-unsaturated aldehydes without 2- or 3-alkylation, and with no more than one conjugated double bond. Parameters for the PBK/D models were obtained using quantitative structure–activity relationships (QSARs) defined with a training set of six selected aldehydes. Using the QSARs, PBK/D models for the other 12 aldehydes were defined. Results revealed that DNA adduct formation in the liver increases with decreasing bulkiness of the molecule especially due to less efficient detoxification. 2-Propenal (acrolein) was identified to induce the highest DNA adduct levels. At realistic dietary intake, the predicted DNA adduct levels for all aldehydes were two orders of magnitude lower than endogenous background levels observed in disease free human liver, suggesting that for all 18 aldehydes DNA adduct formation is negligible at the relevant levels of dietary intake. The present study provides a proof of principle for the use of QSAR-based PBK/D modelling to facilitate group evaluations and read-across in risk assessment. - Highlights: • Physiologically based in silico models were made for 18 α,β-unsaturated aldehydes. • Kinetic parameters were determined by in vitro incubations and a QSAR approach. • DNA adduct formation was negligible at levels relevant for dietary intake. • The use of QSAR-based PBK/D modelling facilitates group evaluations and read-across.

  16. QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors.

    PubMed

    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

  17. 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.

  18. 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

  19. QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors.

    PubMed

    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.

  20. Compatibility of functional groups in K[sup ow]-based QSARs: Application to nitro compounds

    SciTech Connect

    Banerjee, S.; Williams, C.L. )

    1993-10-01

    Nitro compounds are particular difficult to handle in simple K[sup ow]-based QSARs, owing to differences in their lipid-phase activity coefficients. These differences can be corrected, in part, through inclusion of a term in octanol solubility. A procedure for identifying potentially incompatible groups in a given QSAR is suggested. The quality of a QSAR is best if the interactions of the functional groups involved with octanol fall within a narrow range. These interactions are easily calculated by the UNIFAC method.

  1. Acute toxicity of benzophenone-type UV filters for Photobacterium phosphoreum and Daphnia magna: QSAR analysis, interspecies relationship and integrated assessment.

    PubMed

    Liu, Hui; Sun, Ping; Liu, Hongxia; Yang, Shaogui; Wang, Liansheng; Wang, Zunyao

    2015-09-01

    The hazardous potential of benzophenone (BP)-type UV filters is becoming an issue of great concern due to the wide application of these compounds in many personal care products. In the present study, the toxicities of BPs to Photobacterium phosphoreum and Daphnia magna were determined. Next, density functional theory (DFT) and comparative molecular field analysis (CoMFA) descriptors were used to obtain more detailed insight into the structure - activity relationships and to preliminarily discuss the toxicity mechanism. Additionally, the sensitivities of the two organisms to BPs and the interspecies toxicity relationship were compared. Moreover, an approach for providing a global index of the environmental risk of BPs to aquatic organisms is proposed. The results demonstrated that the mechanism underlying the toxicity of BPs to P. phosphoreum is primarily related to their electronic properties, and the mechanism of toxicity to D. magna is hydrophobicity. Additionally, D. magna was more sensitive than P. phosphoreum to most of the BPs, with the exceptions of the polyhydric BPs. Moreover, comparisons with published data revealed a high interspecies correlation coefficient among the experimental toxicity values for D. magna and Dugesia japonica. Furthermore, hydrophobicity was also found to be the most important descriptor of integrated toxicity. This investigation will provide insight into the toxicity mechanisms and useful information for assessing the potential ecological risk of BP-type UV filters.

  2. Predicting Toxicities of Diverse Chemical Pesticides in Multiple Avian Species Using Tree-Based QSAR Approaches for Regulatory Purposes.

    PubMed

    Basant, Nikita; Gupta, Shikha; Singh, Kunwar P

    2015-07-27

    A comprehensive safety evaluation of chemicals should require toxicity assessment in both the aquatic and terrestrial test species. Due to the application practices and nature of chemical pesticides, the avian toxicity testing is considered as an essential requirement in the risk assessment process. In this study, tree-based multispecies QSAR (quantitative-structure activity relationship) models were constructed for predicting the avian toxicity of pesticides using a set of nine descriptors derived directly from the chemical structures and following the OECD guidelines. Accordingly, the Bobwhite quail toxicity data was used to construct the QSAR models (SDT, DTF, DTB) and were externally validated using the toxicity data in four other test species (Mallard duck, Ring-necked pheasant, Japanese quail, House sparrow). Prior to the model development, the diversity in the chemical structures and end-point were verified. The external predictive power of the QSAR models was tested through rigorous validation deriving a wide series of statistical checks. Intercorrelation analysis and PCA methods provided information on the association of the molecular descriptors related to MW and topology. The S36 and MW were the most influential descriptors identified by DTF and DTB models. The DTF and DTB performed better than the SDT model and yielded a correlation (R(2)) of 0.945 and 0.966 between the measured and predicted toxicity values in test data array. Both these models also performed well in four other test species (R(2) > 0.918). ChemoTyper was used to identify the substructure alerts responsible for the avian toxicity. The results suggest for the appropriateness of the developed QSAR models to reliably predict the toxicity of pesticides in multiple avian test species and can be useful tools in screening the new chemical pesticides for regulatory purposes.

  3. 3D-QSAR and docking studies of flavonoids as potent Escherichia coli inhibitors

    PubMed Central

    Fang, Yajing; Lu, Yulin; Zang, Xixi; Wu, Ting; Qi, XiaoJuan; Pan, Siyi; Xu, Xiaoyun

    2016-01-01

    Flavonoids are potential antibacterial agents. However, key substituents and mechanism for their antibacterial activity have not been fully investigated. The quantitative structure-activity relationship (QSAR) and molecular docking of flavonoids relating to potent anti-Escherichia coli agents were investigated. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were developed by using the pIC50 values of flavonoids. The cross-validated coefficient (q2) values for CoMFA (0.743) and for CoMSIA (0.708) were achieved, illustrating high predictive capabilities. Selected descriptors for the CoMFA model were ClogP (logarithm of the octanol/water partition coefficient), steric and electrostatic fields, while, ClogP, electrostatic and hydrogen bond donor fields were used for the CoMSIA model. Molecular docking results confirmed that half of the tested flavonoids inhibited DNA gyrase B (GyrB) by interacting with adenosine-triphosphate (ATP) pocket in a same orientation. Polymethoxyl flavones, flavonoid glycosides, isoflavonoids changed their orientation, resulting in a decrease of inhibitory activity. Moreover, docking results showed that 3-hydroxyl, 5-hydroxyl, 7-hydroxyl and 4-carbonyl groups were found to be crucial active substituents of flavonoids by interacting with key residues of GyrB, which were in agreement with the QSAR study results. These results provide valuable information for structure requirements of flavonoids as antibacterial agents. PMID:27049530

  4. 3D-QSAR and docking studies of flavonoids as potent Escherichia coli inhibitors.

    PubMed

    Fang, Yajing; Lu, Yulin; Zang, Xixi; Wu, Ting; Qi, XiaoJuan; Pan, Siyi; Xu, Xiaoyun

    2016-01-01

    Flavonoids are potential antibacterial agents. However, key substituents and mechanism for their antibacterial activity have not been fully investigated. The quantitative structure-activity relationship (QSAR) and molecular docking of flavonoids relating to potent anti-Escherichia coli agents were investigated. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were developed by using the pIC50 values of flavonoids. The cross-validated coefficient (q(2)) values for CoMFA (0.743) and for CoMSIA (0.708) were achieved, illustrating high predictive capabilities. Selected descriptors for the CoMFA model were ClogP (logarithm of the octanol/water partition coefficient), steric and electrostatic fields, while, ClogP, electrostatic and hydrogen bond donor fields were used for the CoMSIA model. Molecular docking results confirmed that half of the tested flavonoids inhibited DNA gyrase B (GyrB) by interacting with adenosine-triphosphate (ATP) pocket in a same orientation. Polymethoxyl flavones, flavonoid glycosides, isoflavonoids changed their orientation, resulting in a decrease of inhibitory activity. Moreover, docking results showed that 3-hydroxyl, 5-hydroxyl, 7-hydroxyl and 4-carbonyl groups were found to be crucial active substituents of flavonoids by interacting with key residues of GyrB, which were in agreement with the QSAR study results. These results provide valuable information for structure requirements of flavonoids as antibacterial agents. PMID:27049530

  5. The QSAR and docking calculations of fullerene derivatives as HIV-1 protease inhibitors

    NASA Astrophysics Data System (ADS)

    Saleh, Noha A.

    2015-02-01

    The inhibition of HIV-1 protease is considered as one of the most important targets for drug design and the deactivation of HIV-1. In the present work, the fullerene surface (C60) is modified by adding oxygen atoms as well as hydroxymethylcarbonyl (HMC) groups to form 6 investigated fullerene derivative compounds. These compounds have one, two, three, four or five O atoms + HMC groups at different positions on phenyl ring. The effect of the repeating of these groups on the ability of suggested compounds to inhibit the HIV protease is studied by calculating both Quantitative Structure Activity Relationship (QSAR) properties and docking simulation. Based on the QSAR descriptors, the solubility and the hydrophilicity of studied fullerene derivatives increased with increasing the number of oxygen atoms + HMC groups in the compound. While docking calculations indicate that, the compound with two oxygen atoms + HMC groups could interact and binds with HIV-1 protease active site. This is could be attributed to the active site residues of HIV-1 protease are hydrophobic except the two aspartic acids. So that, the increase in the hydrophilicity and polarity of the compound is preventing and/or decreasing the hydrophobic interaction between the compound and HIV-1 protease active site.

  6. The QSAR study of flavonoid-metal complexes scavenging rad OH free radical

    NASA Astrophysics Data System (ADS)

    Wang, Bo-chu; Qian, Jun-zhen; Fan, Ying; Tan, Jun

    2014-10-01

    Flavonoid-metal complexes have antioxidant activities. However, quantitative structure-activity relationships (QSAR) of flavonoid-metal complexes and their antioxidant activities has still not been tackled. On the basis of 21 structures of flavonoid-metal complexes and their antioxidant activities for scavenging rad OH free radical, we optimised their structures using Gaussian 03 software package and we subsequently calculated and chose 18 quantum chemistry descriptors such as dipole, charge and energy. Then we chose several quantum chemistry descriptors that are very important to the IC50 of flavonoid-metal complexes for scavenging rad OH free radical through method of stepwise linear regression, Meanwhile we obtained 4 new variables through the principal component analysis. Finally, we built the QSAR models based on those important quantum chemistry descriptors and the 4 new variables as the independent variables and the IC50 as the dependent variable using an Artificial Neural Network (ANN), and we validated the two models using experimental data. These results show that the two models in this paper are reliable and predictable.

  7. Quantitative structure-activity relationships for polychlorinated hydroxybiphenyl estrogen receptor binding affinity: An assessment of conformer flexibility

    SciTech Connect

    Bradbury, S.P.; Ankley, G.T.; Mekenyan, O.G.

    1996-11-01

    A diverse group of xenobiotics has a high binding affinity to the estrogen receptor (ER), suggesting that it can accommodate large variability in ligand structure. Relationships between xenobiotic surface, binding affinity, and estrogenic response have been suggested to be dependent on the conformational structures of the ligands. To explore the influence of conformational flexibility on ER binding affinity, a quantitative structure-activity relationship (QSAR) study was undertaken with estradiol, diethylstilbestrol, and a set of polychlorinated hydroxybiphenyls (PCHBs) of environmental concern. Although the low-energy minima of the PCHB congeners suggested that interconversions among conformers were likely, the electronic parameters associated with the conformer geometries for a specific PCHB congener could vary significantly. The results of the QSAR analysis suggested that among the PCHBs studied, the most polarizable conformers (lower absolute volume polarizability values) were most closely associated with ER binding affinity. Across the set of polarizable conformers, which did not include the low-energy gas-phase conformers, the electron donating properties of the hydroxy moiety and the aromatic component of the estradiol A ring analogue in the PCHBs were found to be correlated with higher ER binding affinity.

  8. Synthesis, Herbicidal Activity, and QSAR of Novel N-Benzothiazolyl- pyrimidine-2,4-diones as Protoporphyrinogen Oxidase Inhibitors.

    PubMed

    Zuo, Yang; Wu, Qiongyou; Su, Sun-Wen; Niu, Cong-Wei; Xi, Zhen; Yang, Guang-Fu

    2016-01-27

    Protoporphyrinogen oxidase (PPO, E.C. 1.3.3.4) is known as a key action target for several structurally diverse herbicides. As a continuation of our research work on the development of new PPO-inhibiting herbicides, a series of novel 3-(2'-halo-5'-substituted-benzothiazol-1'-yl)-1-methyl-6-(trifluoromethyl)pyrimidine-2,4-diones 9 were designed and synthesized. The bioassay results indicated that a number of the newly synthesized compounds exhibited higher inhibition activity against tobacco PPO (mtPPO) than the controls, saflufenacil and sulfentrazone. Compound 9F-5 was identified as the most potent inhibitor with a Ki value of 0.0072 μM against mtPPO, showing about 4.2-fold and 1.4-fold higher potency than sulfentrazone (Ki = 0.03 μM) and saflufenacil (Ki = 0.01 μM), respectively. An additional green house assay demonstrated that compound 9F-6 (Ki = 0.012 μM) displayed the most promising postemergence herbicidal activity with a broad spectrum even at a concentration as low as 37.5 g of active ingredient (ai)/ha. Maize exhibits relative tolerance against compound 9F-6 at the dosage of 150 g ai/ha, but it is susceptible to saflufenacil even at 75 g ai/ha. Thus, compound 9F-6 exhibits the potential to be a new herbicide for weed control in maize fields. PMID:26728549

  9. The Interplay between QSAR/QSPR Studies and Partial Order Ranking and Formal Concept Analyses

    PubMed Central

    Carlsen, Lars

    2009-01-01

    The often observed scarcity of physical-chemical and well as toxicological data hampers the assessment of potentially hazardous chemicals released to the environment. In such cases Quantitative Structure-Activity Relationships/Quantitative Structure-Property Relationships (QSAR/QSPR) constitute an obvious alternative for rapidly, effectively and inexpensively generatng missing experimental values. However, typically further treatment of the data appears necessary, e.g., to elucidate the possible relations between the single compounds as well as implications and associations between the various parameters used for the combined characterization of the compounds under investigation. In the present paper the application of QSAR/QSPR in combination with Partial Order Ranking (POR) methodologies will be reviewed and new aspects using Formal Concept Analysis (FCA) will be introduced. Where POR constitutes an attractive method for, e.g., prioritizing a series of chemical substances based on a simultaneous inclusion of a range of parameters, FCA gives important information on the implications associations between the parameters. The combined approach thus constitutes an attractive method to a preliminary assessment of the impact on environmental and human health by primary pollutants or possibly by a primary pollutant well as a possible suite of transformation subsequent products that may be both persistent in and bioaccumulating and toxic. The present review focus on the environmental – and human health impact by residuals of the rocket fuel 1,1-dimethylhydrazine (heptyl) and its transformation products as an illustrative example. PMID:19468330

  10. Synthesis, biological evaluation, QSAR study and molecular docking of novel N-(4-amino carbonylpiperazinyl) (thio)phosphoramide derivatives as cholinesterase inhibitors.

    PubMed

    Gholivand, Khodayar; Ebrahimi Valmoozi, Ali Asghar; Bonsaii, Mahyar

    2014-06-01

    Novel (thio)phosphoramidate derivatives based on piperidincarboxamide with the general formula of (NH2-C(O)-C5H9N)-P(X=O,S)R1R2 (1-5) and (NH2-C(O)-C5H9N)2-P(O)R (6-9) were synthesized and characterized by (31)P, (13)C, (1)H NMR, IR spectroscopy. Furthermore, the crystal structure of compound (NH2-C(O)-C5H9N)2-P(O)(OC6H5) (6) was investigated. The activities of derivatives on cholinesterases (ChE) were determined using a modified Ellman's method. Also the mixed-type mechanisms of these compounds were evaluated by Lineweaver-Burk plots. Molecular docking and quantitative structure-activity relationship (QSAR) were used to understand the relationship between molecular structural features and anti-ChE activity, and to predict the binding affinity of phosphoramido-piperidinecarboxamides (PAPCAs) to ChE receptors. From molecular docking analysis, noncovalent interactions especially hydrogen bonding as well as hydrophobic was found between PAPCAs and ChE. Based on the docking results, appropriate molecular structural parameters were adopted to develop a QSAR model. DFT-QSAR models for ChE enzymes demonstrated the importance of electrophilicity parameter in describing the anti-AChE and anti-BChE activities of the synthesized compounds. The correlation matrix of QSAR models and docking analysis confirmed that electrophilicity descriptor can control the influence of the hydrophobic properties of P=(O, S) and CO functional groups of PAPCA derivatives in the inhibition of human ChE enzymes.

  11. Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors

    PubMed Central

    Zhou, Nannan; Xu, Yuan; Liu, Xian; Wang, Yulan; Peng, Jianlong; Luo, Xiaomin; Zheng, Mingyue; Chen, Kaixian; Jiang, Hualiang

    2015-01-01

    The fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) signaling pathway plays crucial roles in cell proliferation, angiogenesis, migration, and survival. Aberration in FGFRs correlates with several malignancies and disorders. FGFRs have proved to be attractive targets for therapeutic intervention in cancer, and it is of high interest to find FGFR inhibitors with novel scaffolds. In this study, a combinatorial three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed based on previously reported FGFR1 inhibitors with diverse structural skeletons. This model was evaluated for its prediction performance on a diverse test set containing 232 FGFR inhibitors, and it yielded a SD value of 0.75 pIC50 units from measured inhibition affinities and a Pearson’s correlation coefficient R2 of 0.53. This result suggests that the combinatorial 3D-QSAR model could be used to search for new FGFR1 hit structures and predict their potential activity. To further evaluate the performance of the model, a decoy set validation was used to measure the efficiency of the model by calculating EF (enrichment factor). Based on the combinatorial pharmacophore model, a virtual screening against SPECS database was performed. Nineteen novel active compounds were successfully identified, which provide new chemical starting points for further structural optimization of FGFR1 inhibitors. PMID:26110383

  12. Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors.

    PubMed

    Zhou, Nannan; Xu, Yuan; Liu, Xian; Wang, Yulan; Peng, Jianlong; Luo, Xiaomin; Zheng, Mingyue; Chen, Kaixian; Jiang, Hualiang

    2015-06-11

    The fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) signaling pathway plays crucial roles in cell proliferation, angiogenesis, migration, and survival. Aberration in FGFRs correlates with several malignancies and disorders. FGFRs have proved to be attractive targets for therapeutic intervention in cancer, and it is of high interest to find FGFR inhibitors with novel scaffolds. In this study, a combinatorial three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed based on previously reported FGFR1 inhibitors with diverse structural skeletons. This model was evaluated for its prediction performance on a diverse test set containing 232 FGFR inhibitors, and it yielded a SD value of 0.75 pIC50 units from measured inhibition affinities and a Pearson's correlation coefficient R2 of 0.53. This result suggests that the combinatorial 3D-QSAR model could be used to search for new FGFR1 hit structures and predict their potential activity. To further evaluate the performance of the model, a decoy set validation was used to measure the efficiency of the model by calculating EF (enrichment factor). Based on the combinatorial pharmacophore model, a virtual screening against SPECS database was performed. Nineteen novel active compounds were successfully identified, which provide new chemical starting points for further structural optimization of FGFR1 inhibitors.

  13. Structure-guided unravelling: Phenolic hydroxyls contribute to reduction of acrylamide using multiplex quantitative structure-activity relationship modelling.

    PubMed

    Zhang, Yu; Huang, Mengmeng; Wang, Qiao; Cheng, Jun

    2016-05-15

    We reported a structure-activity relationship study on unravelling phenolic hydroxyls instead of alcoholic hydroxyls contribute to the reduction of acrylamide formation by flavonoids. The dose-dependent study shows a close correlation between the number of phenolic hydroxyls of flavonoids and their reduction effects. In view of positions of hydroxyls, the 3',4'(ortho)-dihydroxyls in B cycle, 3-hydroxyl or hydroxyls of 3-gallate in C cycle, and 5,7(meta)-dihydroxyls in A cycle of flavonoid structures play an important role in the reduction of acrylamide. Flavone C-glycosides are more effective at reducing the formation of acrylamide than flavone O-glycosides when sharing the same aglycone. The current multiplex quantitative structure-activity relationship (QSAR) equations effectively predict the inhibitory rates of acrylamide using selected chemometric parameters (R(2): 0.835-0.938). This pioneer study opens a broad understanding on the chemoprevention of acrylamide contaminants on a structural basis.

  14. Structural interpretation of activity cliffs revealed by systematic analysis of structure-activity relationships in analog series.

    PubMed

    Sisay, Mihiret T; Peltason, Lisa; Bajorath, Jürgen

    2009-10-01

    Discontinuity in structure-activity relationships (SARs) is caused by so-called activity cliffs and represents one of the major caveats in SAR modeling and lead optimization. At activity cliffs, small structural modifications of compounds lead to substantial differences in potency that are essentially unpredictable using quantitative structure-activity relationship (QSAR) methods. In order to better understand SAR discontinuity at the molecular level of detail, we have analyzed different compound series in combinatorial analog graphs and determined substitution patterns that introduce activity cliffs of varying magnitude. So identified SAR determinants were then analyzed on the basis of complex crystal structures to enable a structural interpretation of SAR discontinuity and underlying activity cliffs. In some instances, SAR discontinuity detected within analog series could be well rationalized on the basis of structural data, whereas in others a structural explanation was not possible. This reflects the intrinsic complexity of small molecule SARs and suggests that the analysis of short-range receptor-ligand interactions seen in X-ray structures is insufficient to comprehensively account for SAR discontinuity. However, in other cases, SAR information extracted from ligands was incomplete but could be deduced taking X-ray data into account. Thus, taken together, these findings illustrate the complementarity of ligand-based SAR analysis and structural information. PMID:19761254

  15. Assessment of quantitative structure-activity relationship of toxicity prediction models for Korean chemical substance control legislation

    PubMed Central

    Kim, Kwang-Yon; Shin, Seong Eun; No, Kyoung Tai

    2015-01-01

    Objectives For successful adoption of legislation controlling registration and assessment of chemical substances, it is important to obtain sufficient toxicological experimental evidence and other related information. It is also essential to obtain a sufficient number of predicted risk and toxicity results. Particularly, methods used in predicting toxicities of chemical substances during acquisition of required data, ultimately become an economic method for future dealings with new substances. Although the need for such methods is gradually increasing, the-required information about reliability and applicability range has not been systematically provided. Methods There are various representative environmental and human toxicity models based on quantitative structure-activity relationships (QSAR). Here, we secured the 10 representative QSAR-based prediction models and its information that can make predictions about substances that are expected to be regulated. We used models that predict and confirm usability of the information expected to be collected and submitted according to the legislation. After collecting and evaluating each predictive model and relevant data, we prepared methods quantifying the scientific validity and reliability, which are essential conditions for using predictive models. Results We calculated predicted values for the models. Furthermore, we deduced and compared adequacies of the models using the Alternative non-testing method assessed for Registration, Evaluation, Authorization, and Restriction of Chemicals Substances scoring system, and deduced the applicability domains for each model. Additionally, we calculated and compared inclusion rates of substances expected to be regulated, to confirm the applicability. Conclusions We evaluated and compared the data, adequacy, and applicability of our selected QSAR-based toxicity prediction models, and included them in a database. Based on this data, we aimed to construct a system that can be used

  16. Physicochemical and biological data for the development of predictive organophosphorus pesticide QSARs and PBPK/PD models for human risk assessment.

    PubMed

    Knaak, James B; Dary, Curt C; Power, Fred; Thompson, Carol B; Blancato, Jerry N

    2004-01-01

    A search of the scientific literature was carried out for physiochemical and biological data [i.e., IC50, LD50, Kp (cm/h) for percutaneous absorption, skin/water and tissue/blood partition coefficients, inhibition ki values, and metabolic parameters such as Vmax and Km] on 31 organophosphorus pesticides (OPs) to support the development of predictive quantitative structure-activity relationship (QSAR) and physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) models for human risk assessment. Except for work on parathion, chlorpyrifos, and isofenphos, very few modeling data were found on the 31 OPs of interest. The available percutaneous absorption, partition coefficients and metabolic parameters were insufficient in number to develop predictive QSAR models. Metabolic kinetic parameters (Vmax, Km) varied according to enzyme source and the manner in which the enzymes were characterized. The metabolic activity of microsomes should be based on the kinetic activity of purified or cDNA-expressed cytochrome P450s (CYPs) and the specific content of each active CYP in tissue microsomes. Similar requirements are needed to assess the activity of tissue A- and B-esterases metabolizing OPs. A limited amount of acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and carboxylesterase (CaE) inhibition and recovery data were found in the literature on the 31 OPs. A program is needed to require the development of physicochemical and biological data to support risk assessment methodologies involving QSAR and PBPK/PD models.

  17. Development of QSARs for the toxicity of chlorobenzenes to the soil dwelling springtail Folsomia candida.

    PubMed

    Giesen, Daniel; Jonker, Michiel T O; van Gestel, Cornelis A M

    2012-05-01

    To meet the goals of Registration, Evaluation, Authorisation, and Restriction of Chemicals (REACH) as formulated by the European Commission, fast and resource-effective tools are needed to predict the toxicity of compounds in the environment. We developed quantitative structure-activity relationships (QSARs) for the toxicity of nine chlorinated benzenes to the soil-dwelling collembolan Folsomia candida in natural LUFA2.2 (Landwirtschaftliche Untersuchungs und Forschungsanstalt [LUFA]) standard soil and in Organisation for Economic Co-operation and Development artificial soil. Toxicity endpoints used were the effect concentrations causing 10% (EC10) and 50% (EC50) reduction in the reproduction of the test organism over 28 d, while lethal effects on survival (LC50) were used for comparisons with earlier studies. Chlorobenzene toxicity was based on concentrations in interstitial water as estimated using nominal concentrations in soil and literature soil-water partition coefficients. Additionally, for LUFA2.2 soil the estimated concentrations in interstitial water were experimentally determined by solid-phase microextraction measurements. Measured and estimated concentrations showed the same general trend, but significant differences were observed. With the exception of hexachlorobenzene, estimated EC10 and EC50 values were all negatively correlated with their logK(ow) and QSARs were developed. However, no correlation for the LC50 could be derived and 1,2,4,5-tetrachlorobenzene and hexachlorobenzene had no effect on adult survival at all. The derived QSARs may contribute to the development of better ecotoxicity-based models serving the REACH program. PMID:22431168

  18. Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes.

    PubMed

    Toropov, Andrey A; Toropova, Alla P

    2015-04-01

    Available on the Internet, the CORAL software (http://www.insilico.eu/coral) has been used to build up quasi-quantitative structure-activity relationships (quasi-QSAR) for prediction of mutagenic potential of multi-walled carbon-nanotubes (MWCNTs). In contrast with the previous models built up by CORAL which were based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) the quasi-QSARs based on the representation of conditions (not on the molecular structure) such as concentration, presence (absence) S9 mix, the using (or without the using) of preincubation were encoded by so-called quasi-SMILES. The statistical characteristics of these models (quasi-QSARs) for three random splits into the visible training set and test set and invisible validation set are the following: (i) split 1: n=13, r(2)=0.8037, q(2)=0.7260, s=0.033, F=45 (training set); n=5, r(2)=0.9102, s=0.071 (test set); n=6, r(2)=0.7627, s=0.044 (validation set); (ii) split 2: n=13, r(2)=0.6446, q(2)=0.4733, s=0.045, F=20 (training set); n=5, r(2)=0.6785, s=0.054 (test set); n=6, r(2)=0.9593, s=0.032 (validation set); and (iii) n=14, r(2)=0.8087, q(2)=0.6975, s=0.026, F=51 (training set); n=5, r(2)=0.9453, s=0.074 (test set); n=5, r(2)=0.8951, s=0.052 (validation set). PMID:25465947

  19. Computational Study of Quinolone Derivatives to Improve their Therapeutic Index as Anti-malaria Agents: QSAR and QSTR

    PubMed Central

    Iman, Maryam; Davood, Asghar; Khamesipour, Ali

    2015-01-01

    Malaria is a parasitic disease caused by five different species of Plasmodium. More than 40% of the world’s population is at risk and malaria annual incidence is estimated to be more than two hundred million, malaria is one of the most important public health problems especially in children of the poorest parts of the world, annual mortality is about 1 million. The epidemiological status of the disease justifies to search for control measures, new therapeutic options and development of an effective vaccine. Chemotherapy options in malaria are limited, moreover, drug resistant rate is high. In spite of global efforts to develop an effective vaccine yet there is no vaccine available. In the current study, a series of quinolone derivatives were subjected to quantitative structure activity relationship (QSAR) and quantitative structure toxicity relationship (QSTR) analyses to identify the ideal physicochemical characteristics of potential anti-malaria activity and less cytotoxicity. Quinolone with desirable properties was built using HyperChem program, and conformational studies were performed through the semi-empirical method followed by the PM3 force field. Multi linear regression (MLR) was used as a chemo metric tool for quantitative structure activity relationship modeling and the developed models were shown to be statistically significant according to the validation parameters. The obtained QSAR model reveals that the descriptors PJI2, Mv, PCR, nBM, and VAR mainly affect the anti-malaria activity and descriptors MSD, MAXDP, and X1sol affect the cytotoxicity of the series of ligands. PMID:26330866

  20. Synthetic cannabinoids: In silico prediction of the cannabinoid receptor 1 affinity by a quantitative structure-activity relationship model.

    PubMed

    Paulke, Alexander; Proschak, Ewgenij; Sommer, Kai; Achenbach, Janosch; Wunder, Cora; Toennes, Stefan W

    2016-03-14

    The number of new synthetic psychoactive compounds increase steadily. Among the group of these psychoactive compounds, the synthetic cannabinoids (SCBs) are most popular and serve as a substitute of herbal cannabis. More than 600 of these substances already exist. For some SCBs the in vitro cannabinoid receptor 1 (CB1) affinity is known, but for the majority it is unknown. A quantitative structure-activity relationship (QSAR) model was developed, which allows the determination of the SCBs affinity to CB1 (expressed as binding constant (Ki)) without reference substances. The chemically advance template search descriptor was used for vector representation of the compound structures. The similarity between two molecules was calculated using the Feature-Pair Distribution Similarity. The Ki values were calculated using the Inverse Distance Weighting method. The prediction model was validated using a cross validation procedure. The predicted Ki values of some new SCBs were in a range between 20 (considerably higher affinity to CB1 than THC) to 468 (considerably lower affinity to CB1 than THC). The present QSAR model can serve as a simple, fast and cheap tool to get a first hint of the biological activity of new synthetic cannabinoids or of other new psychoactive compounds.

  1. Using three-dimensional quantitative structure-activity relationships to examine estrogen receptor binding affinities of polychlorinated hydroxybiphenyls

    SciTech Connect

    Waller, C.L.; Minor, D.L.; McKinney, J.D.

    1995-07-01

    Certain phenyl-substituted hydrocarbons of environmental concern have the potential to disrupt the endocrine system of animals, apparently in association with their estrogenic properties. Competition with natural estrogens for the estrogen receptor is a possible mechanism by which such effects could occur. We used comparative molecular field analysis (CoMFA), a three-dimensional quantitative structure-activity relationship (QSAR) paradigm, to examine the underlying structural properties of ortho-chlorinated hydroxybiphenyl analogs known to bind to the estrogen receptor. The cross-validated and conventional statistical results indicate a high degree of internal predictability for the molecules included in the training data set. In addition to the phenolic (A) ring system, conformational restriction of the overall structure appears to play an important role in estrogen receptor binding affinity. Hydrophobic character as assessed using hydropathic interaction fields also contributes in a positive way to binding affinity. The CoMFA-derived QSARs may be useful in examining the estrogenic activity of a wider range of phenyl-substituted hydrocarbons of environmental concern. 37 refs., 2 figs., 2 tabs.

  2. Biological Evaluation and 3D-QSAR Studies of Curcumin Analogues as Aldehyde Dehydrogenase 1 Inhibitors

    PubMed Central

    Wang, Hui; Du, Zhiyun; Zhang, Changyuan; Tang, Zhikai; He, Yan; Zhang, Qiuyan; Zhao, Jun; Zheng, Xi

    2014-01-01

    Aldehyde dehydrogenase 1 (ALDH1) is reported as a biomarker for identifying some cancer stem cells, and down-regulation or inhibition of the enzyme can be effective in anti-drug resistance and a potent therapeutic for some tumours. In this paper, the inhibitory activity, mechanism mode, molecular docking and 3D-QSAR (three-dimensional quantitative structure activity relationship) of curcumin analogues (CAs) against ALDH1 were studied. Results demonstrated that curcumin and CAs possessed potent inhibitory activity against ALDH1, and the CAs compound with ortho di-hydroxyl groups showed the most potent inhibitory activity. This study indicates that CAs may represent a new class of ALDH1 inhibitor. PMID:24840575

  3. QSAR, molecular docking studies of thiophene and imidazopyridine derivatives as polo-like kinase 1 inhibitors

    NASA Astrophysics Data System (ADS)

    Cao, Shandong

    2012-08-01

    The purpose of the present study was to develop in silico models allowing for a reliable prediction of polo-like kinase inhibitors based on a large diverse dataset of 136 compounds. As an effective method, quantitative structure activity relationship (QSAR) was applied using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The proposed QSAR models showed reasonable predictivity of thiophene analogs (Rcv2=0.533, Rpred2=0.845) and included four molecular descriptors, namely IC3, RDF075m, Mor02m and R4e+. The optimal model for imidazopyridine derivatives (Rcv2=0.776, Rpred2=0.876) was shown to perform good in prediction accuracy, using GATS2m and BEHe1 descriptors. Analysis of the contour maps helped to identify structural requirements for the inhibitors and served as a basis for the design of the next generation of the inhibitor analogues. Docking studies were also employed to position the inhibitors into the polo-like kinase active site to determine the most probable binding mode. These studies may help to understand the factors influencing the binding affinity of chemicals and to develop alternative methods for prescreening and designing of polo-like kinase inhibitors.

  4. Molecular docking and 3D-QSAR studies on triazolinone and pyridazinone, non-nucleoside inhibitor of HIV-1 reverse transcriptase.

    PubMed

    Sivan, Sree Kanth; Manga, Vijjulatha

    2010-06-01

    Nonnucleoside reverse transcriptase inhibitors (NNRTIs) are allosteric inhibitors of the HIV-1 reverse transcriptase. Recently a series of Triazolinone and Pyridazinone were reported as potent inhibitors of HIV-1 wild type reverse transcriptase. In the present study, docking and 3D quantitative structure activity relationship (3D QSAR) studies involving comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 31 molecules. Ligands were built and minimized using Tripos force field and applying Gasteiger-Hückel charges. These ligands were docked into protein active site using GLIDE 4.0. The docked poses were analyzed; the best docked poses were selected and aligned. CoMFA and CoMSIA fields were calculated using SYBYL6.9. The molecules were divided into training set and test set, a PLS analysis was performed and QSAR models were generated. The model showed good statistical reliability which is evident from the r2 nv, q2 loo and r2 pred values. The CoMFA model provides the most significant correlation of steric and electrostatic fields with biological activities. The CoMSIA model provides a correlation of steric, electrostatic, acceptor and hydrophobic fields with biological activities. The information rendered by 3D QSAR model initiated us to optimize the lead and design new potential inhibitors.

  5. CheS-Mapper 2.0 for visual validation of (Q)SAR models

    PubMed Central

    2014-01-01

    Background Sound statistical validation is important to evaluate and compare the overall performance of (Q)SAR models. However, classical validation does not support the user in better understanding the properties of the model or the underlying data. Even though, a number of visualization tools for analyzing (Q)SAR information in small molecule datasets exist, integrated visualization methods that allow the investigation of model validation results are still lacking. Results We propose visual validation, as an approach for the graphical inspection of (Q)SAR model validation results. The approach applies the 3D viewer CheS-Mapper, an open-source application for the exploration of small molecules in virtual 3D space. The present work describes the new functionalities in CheS-Mapper 2.0, that facilitate the analysis of (Q)SAR information and allows the visual validation of (Q)SAR models. The tool enables the comparison of model predictions to the actual activity in feature space. The approach is generic: It is model-independent and can handle physico-chemical and structural input features as well as quantitative and qualitative endpoints. Conclusions Visual validation with CheS-Mapper enables analyzing (Q)SAR information in the data and indicates how this information is employed by the (Q)SAR model. It reveals, if the endpoint is modeled too specific or too generic and highlights common properties of misclassified compounds. Moreover, the researcher can use CheS-Mapper to inspect how the (Q)SAR model predicts activity cliffs. The CheS-Mapper software is freely available at http://ches-mapper.org. Graphical abstract Comparing actual and predicted activity values with CheS-Mapper.

  6. Insight into the Interactions between Novel Isoquinolin-1,3-Dione Derivatives and Cyclin-Dependent Kinase 4 Combining QSAR and Molecular Docking

    PubMed Central

    Zheng, Junxia; Kong, Hao; Wilson, James M.; Guo, Jialiang; Chang, Yiqun; Yang, Mengjia; Xiao, Gaokeng; Sun, Pinghua

    2014-01-01

    Several small-molecule CDK inhibitors have been identified, but none have been approved for clinical use in the past few years. A new series of 4-[(3-hydroxybenzylamino)-methylene]-4H-isoquinoline-1,3-diones were reported as highly potent and selective CDK4 inhibitors. In order to find more potent CDK4 inhibitors, the interactions between these novel isoquinoline-1,3-diones and cyclin-dependent kinase 4 was explored via in silico methodologies such as 3D-QSAR and docking on eighty-one compounds displaying potent selective activities against cyclin-dependent kinase 4. Internal and external cross-validation techniques were investigated as well as region focusing, bootstraping and leave-group-out. A training set of 66 compounds gave the satisfactory CoMFA model (q2 = 0.695, r2 = 0.947) and CoMSIA model (q2 = 0.641, r2 = 0.933). The remaining 15 compounds as a test set also gave good external predictive abilities with r2pred values of 0.875 and 0.769 for CoMFA and CoMSIA, respectively. The 3D-QSAR models generated here predicted that all five parameters are important for activity toward CDK4. Surflex-dock results, coincident with CoMFA/CoMSIA contour maps, gave the path for binding mode exploration between the inhibitors and CDK4 protein. Based on the QSAR and docking models, twenty new potent molecules have been designed and predicted better than the most active compound 12 in the literatures. The QSAR, docking and interactions analysis expand the structure-activity relationships of constrained isoquinoline-1,3-diones and contribute towards the development of more active CDK4 subtype-selective inhibitors. PMID:24722522

  7. Structure-activity relationship for Fe(III)-salen-like complexes as potent anticancer agents.

    PubMed

    Ghanbari, Zahra; Housaindokht, Mohammad R; Izadyar, Mohammad; Bozorgmehr, Mohammad R; Eshtiagh-Hosseini, Hossein; Bahrami, Ahmad R; Matin, Maryam M; Khoshkholgh, Maliheh Javan

    2014-01-01

    Quantitative structure activity relationship (QSAR) for the anticancer activity of Fe(III)-salen and salen-like complexes was studied. The methods of density function theory (B3LYP/LANL2DZ) were used to optimize the structures. A pool of descriptors was calculated: 1497 theoretical descriptors and quantum-chemical parameters, shielding NMR, and electronic descriptors. The study of structure and activity relationship was performed with multiple linear regression (MLR) and artificial neural network (ANN). In nonlinear method, the adaptive neuro-fuzzy inference system (ANFIS) was applied in order to choose the most effective descriptors. The ANN-ANFIS model with high statistical significance (R (2) train = 0.99, RMSE = 0.138, and Q (2) LOO = 0.82) has better capability to predict the anticancer activity of the new compounds series of this family. Based on this study, anticancer activity of this compound is mainly dependent on the geometrical parameters, position, and the nature of the substituent of salen ligand. PMID:24955417

  8. Structure-Activity Relationship for Fe(III)-Salen-Like Complexes as Potent Anticancer Agents

    PubMed Central

    Ghanbari, Zahra; Housaindokht, Mohammad R.; Izadyar, Mohammad; Bozorgmehr, Mohammad R.; Eshtiagh-Hosseini, Hossein; Bahrami, Ahmad R.; Matin, Maryam M.; Khoshkholgh, Maliheh Javan

    2014-01-01

    Quantitative structure activity relationship (QSAR) for the anticancer activity of Fe(III)-salen and salen-like complexes was studied. The methods of density function theory (B3LYP/LANL2DZ) were used to optimize the structures. A pool of descriptors was calculated: 1497 theoretical descriptors and quantum-chemical parameters, shielding NMR, and electronic descriptors. The study of structure and activity relationship was performed with multiple linear regression (MLR) and artificial neural network (ANN). In nonlinear method, the adaptive neuro-fuzzy inference system (ANFIS) was applied in order to choose the most effective descriptors. The ANN-ANFIS model with high statistical significance (R2train = 0.99, RMSE = 0.138, and Q2LOO = 0.82) has better capability to predict the anticancer activity of the new compounds series of this family. Based on this study, anticancer activity of this compound is mainly dependent on the geometrical parameters, position, and the nature of the substituent of salen ligand. PMID:24955417

  9. Amino substituted benzimidazo[1,2-a]quinolines: Antiproliferative potency, 3D QSAR study and DNA binding properties.

    PubMed

    Perin, Nataša; Nhili, Raja; Cindrić, Maja; Bertoša, Branimir; Vušak, Darko; Martin-Kleiner, Irena; Laine, William; Karminski-Zamola, Grace; Kralj, Marijeta; David-Cordonnier, Marie-Hélène; Hranjec, Marijana

    2016-10-21

    We describe the synthesis, 3D-derived quantitative structure-activity relationship (QSAR), antiproliferative activity and DNA binding properties of a series of 2-amino, 5-amino and 2,5-diamino substituted benzimidazo[1,2-a]quinolines prepared by environmentally friendly uncatalyzed microwave assisted amination. The antiproliferative activities were assessed in vitro against colon, lung and breast carcinoma cell lines; activities ranged from submicromolar to micromolar. The strongest antiproliferative activity was demonstrated by 2-amino-substituted analogues, whereas 5-amino and or 2,5-diamino substituted derivatives resulted in much less activity. Derivatives bearing 4-methyl- or 3,5-dimethyl-1-piperazinyl substituents emerged as the most active. DNA binding properties and the mode of interaction of chosen substituted benzimidazo[1,2-a]quinolines prepared herein were studied using melting temperature studies, a series of spectroscopic studies (UV/Visible, fluorescence, and circular dichroism), and biochemical experiments (topoisomerase I-mediated DNA relaxation and DNase I footprinting experiments). Both compound 36 and its bis-quaternary iodide salt 37 intercalate between adjacent base pairs of the DNA helix while compound 33 presented a very weak topoisomerase I poisoning activity. A 3D-QSAR analysis was performed to identify hydrogen bonding properties, hydrophobicity, molecular flexibility and distribution of hydrophobic regions as these molecular properties had the highest impact on the antiproliferative activity against the three cell lines. PMID:27448912

  10. Amino substituted benzimidazo[1,2-a]quinolines: Antiproliferative potency, 3D QSAR study and DNA binding properties.

    PubMed

    Perin, Nataša; Nhili, Raja; Cindrić, Maja; Bertoša, Branimir; Vušak, Darko; Martin-Kleiner, Irena; Laine, William; Karminski-Zamola, Grace; Kralj, Marijeta; David-Cordonnier, Marie-Hélène; Hranjec, Marijana

    2016-10-21

    We describe the synthesis, 3D-derived quantitative structure-activity relationship (QSAR), antiproliferative activity and DNA binding properties of a series of 2-amino, 5-amino and 2,5-diamino substituted benzimidazo[1,2-a]quinolines prepared by environmentally friendly uncatalyzed microwave assisted amination. The antiproliferative activities were assessed in vitro against colon, lung and breast carcinoma cell lines; activities ranged from submicromolar to micromolar. The strongest antiproliferative activity was demonstrated by 2-amino-substituted analogues, whereas 5-amino and or 2,5-diamino substituted derivatives resulted in much less activity. Derivatives bearing 4-methyl- or 3,5-dimethyl-1-piperazinyl substituents emerged as the most active. DNA binding properties and the mode of interaction of chosen substituted benzimidazo[1,2-a]quinolines prepared herein were studied using melting temperature studies, a series of spectroscopic studies (UV/Visible, fluorescence, and circular dichroism), and biochemical experiments (topoisomerase I-mediated DNA relaxation and DNase I footprinting experiments). Both compound 36 and its bis-quaternary iodide salt 37 intercalate between adjacent base pairs of the DNA helix while compound 33 presented a very weak topoisomerase I poisoning activity. A 3D-QSAR analysis was performed to identify hydrogen bonding properties, hydrophobicity, molecular flexibility and distribution of hydrophobic regions as these molecular properties had the highest impact on the antiproliferative activity against the three cell lines.

  11. Quantitative structure-activity relationships for organophosphates binding to trypsin and chymotrypsin.

    PubMed

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

    2011-01-01

    Organophosphate (OP) nerve agents such as sarin, soman, tabun, and O-ethyl S-[2-(diisopropylamino) ethyl] methylphosphonothioate (VX) do not react solely with acetylcholinesterase (AChE). Evidence suggests that cholinergic-independent pathways over a wide range are also targeted, including serine proteases. These proteases comprise nearly one-third of all known proteases and play major roles in synaptic plasticity, learning, memory, neuroprotection, wound healing, cell signaling, inflammation, blood coagulation, and protein processing. Inhibition of these proteases by OP was found to exert a wide range of noncholinergic effects depending on the type of OP, the dose, and the duration of exposure. Consequently, in order to understand these differences, in silico biologically based dose-response and quantitative structure-activity relationship (QSAR) methodologies need to be integrated. Here, QSAR were used to predict OP bimolecular rate constants for trypsin and α-chymotrypsin. A heuristic regression of over 500 topological/constitutional, geometric, thermodynamic, electrostatic, and quantum mechanical descriptors, using the software Ampac 8.0 and Codessa 2.51 (SemiChem, Inc., Shawnee, KS), was developed to obtain statistically verified equations for the models. General models, using all data subsets, resulted in R(2) values of .94 and .92 and leave-one-out Q(2) values of 0.9 and 0.87 for trypsin and α-chymotrypsin. To validate the general model, training sets were split into independent subsets for test set evaluation. A y-randomization procedure, used to estimate chance correlation, was performed 10,000 times, resulting in mean R(2) values of .24 and .3 for trypsin and α-chymotrypsin. The results show that these models are highly predictive and capable of delineating the complex mechanism of action between OP and serine proteases, and ultimately, by applying this approach to other OP enzyme reactions such as AChE, facilitate the development of biologically based

  12. Deciphering the Structural Requirements of Nucleoside Bisubstrate Analogues for Inhibition of MbtA in Mycobacterium tuberculosis: A FB-QSAR Study and Combinatorial Library Generation for Identifying Potential Hits.

    PubMed

    Maganti, Lakshmi; Das, Sanjit Kumar; Mascarenhas, Nahren Manuel; Ghoshal, Nanda

    2011-10-01

    The re-emergence of tuberculosis infections, which are resistant to conventional drug therapy, has steadily risen in the last decade. Inhibitors of aryl acid adenylating enzyme known as MbtA, involved in siderophore biosynthesis in Mycobacterium tuberculosis, are being explored as potential antitubercular agents. The ability to identify fragments that interact with a biological target is a key step in fragment based drug design (FBDD). To expand the boundaries of quantitative structure activity relationship (QSAR) paradigm, we have proposed a Fragment Based QSAR methodology, referred here in as FB-QSAR, for deciphering the structural requirements of a series of nucleoside bisubstrate analogs for inhibition of MbtA, a key enzyme involved in siderophore biosynthetic pathway. For the development of FB-QSAR models, statistical techniques such as stepwise multiple linear regression (SMLR), genetic function approximation (GFA) and GFAspline were used. The predictive ability of the generated models was validated using different statistical metrics, and similarity-based coverage estimation was carried out to define applicability boundaries. To aid the creation of novel antituberculosis compounds, a bioisosteric database was enumerated using the combichem approach endorsed mining in a lead-like chemical space. The generated library was screened using an integrated in-silico approach and potential hits identified. PMID:27468106

  13. Estimating the Potential Toxicity of Chemicals Associated with Hydraulic Fracturing Operations Using Quantitative Structure-Activity Relationship Modeling.

    PubMed

    Yost, Erin E; Stanek, John; DeWoskin, Robert S; Burgoon, Lyle D

    2016-07-19

    The United States Environmental Protection Agency (EPA) identified 1173 chemicals associated with hydraulic fracturing fluids, flowback, or produced water, of which 1026 (87%) lack chronic oral toxicity values for human health assessments. To facilitate the ranking and prioritization of chemicals that lack toxicity values, it may be useful to employ toxicity estimates from quantitative structure-activity relationship (QSAR) models. Here we describe an approach for applying the results of a QSAR model from the TOPKAT program suite, which provides estimates of the rat chronic oral lowest-observed-adverse-effect level (LOAEL). Of the 1173 chemicals, TOPKAT was able to generate LOAEL estimates for 515 (44%). To address the uncertainty associated with these estimates, we assigned qualitative confidence scores (high, medium, or low) to each TOPKAT LOAEL estimate, and found 481 to be high-confidence. For 48 chemicals that had both a high-confidence TOPKAT LOAEL estimate and a chronic oral reference dose from EPA's Integrated Risk Information System (IRIS) database, Spearman rank correlation identified 68% agreement between the two values (permutation p-value =1 × 10(-11)). These results provide support for the use of TOPKAT LOAEL estimates in identifying and prioritizing potentially hazardous chemicals. High-confidence TOPKAT LOAEL estimates were available for 389 of 1026 hydraulic fracturing-related chemicals that lack chronic oral RfVs and OSFs from EPA-identified sources, including a subset of chemicals that are frequently used in hydraulic fracturing fluids. PMID:27172125

  14. Estimating the Potential Toxicity of Chemicals Associated with Hydraulic Fracturing Operations Using Quantitative Structure-Activity Relationship Modeling.

    PubMed

    Yost, Erin E; Stanek, John; DeWoskin, Robert S; Burgoon, Lyle D

    2016-07-19

    The United States Environmental Protection Agency (EPA) identified 1173 chemicals associated with hydraulic fracturing fluids, flowback, or produced water, of which 1026 (87%) lack chronic oral toxicity values for human health assessments. To facilitate the ranking and prioritization of chemicals that lack toxicity values, it may be useful to employ toxicity estimates from quantitative structure-activity relationship (QSAR) models. Here we describe an approach for applying the results of a QSAR model from the TOPKAT program suite, which provides estimates of the rat chronic oral lowest-observed-adverse-effect level (LOAEL). Of the 1173 chemicals, TOPKAT was able to generate LOAEL estimates for 515 (44%). To address the uncertainty associated with these estimates, we assigned qualitative confidence scores (high, medium, or low) to each TOPKAT LOAEL estimate, and found 481 to be high-confidence. For 48 chemicals that had both a high-confidence TOPKAT LOAEL estimate and a chronic oral reference dose from EPA's Integrated Risk Information System (IRIS) database, Spearman rank correlation identified 68% agreement between the two values (permutation p-value =1 × 10(-11)). These results provide support for the use of TOPKAT LOAEL estimates in identifying and prioritizing potentially hazardous chemicals. High-confidence TOPKAT LOAEL estimates were available for 389 of 1026 hydraulic fracturing-related chemicals that lack chronic oral RfVs and OSFs from EPA-identified sources, including a subset of chemicals that are frequently used in hydraulic fracturing fluids.

  15. Quantitative structure-activity relationships for evaluating the influence of sorbate structure on sorption of organic compounds by soil

    SciTech Connect

    Hu, Q.; Wang, X.; Brusseau, M.L.

    1995-07-01

    The purpose of this work was to investigate the effect of sorbate structure, by using the quantitative structure-activity relationship (QSAR) approach, on sorption of organic compounds by two soils with different amounts of organic matter. Miscible displacement experiments were performed with several organic contaminants representing six classes of nonpolar, nonionizable organic chemicals, including chlorinated aliphatics, chlorobenzenes, polycyclic aromatic hydrocarbons (PAHs), n-alkylbenzenes, methylated benzenes, and polychlorinated biphenyls (PCBs). The breakthrough curves were analyzed by using a bicontinuum model where in sorption is assumed to be instantaneous for a fraction of the sorbent and rate limited for the remainder. The QSAR approach was used to investigate the dependency of both equilibrium and nonequilibrium sorption coefficients on topological descriptor representing structural properties of the solutes. For both equilibrium and nonequilibrium parameters, the first-order valence molecular connectivity ({sup 1}{chi}{sup v}) was found to be the best topological descriptor. Most of the rate-limited sorption behavior could be explained by accounting for the size and structure of the solute molecule, as indicated by the good correlation between the rate coefficient and {sup 1}{chi}{sup v}. This supports the contention that rate-limited sorption in these systems is controlled by a physical diffusion mechanism, consistent with the polymer diffusion model. Based on this model, the calculated diffusion-length ratios for the Borden and Mt. Lemmon soils, which have a large difference in organic-matter contents, compare favorably to the values determined from the measured rate data.

  16. Designing quantitative structure activity relationships to predict specific toxic endpoints for polybrominated diphenyl ethers in mammalian cells.

    PubMed

    Rawat, S; Bruce, E D

    2014-01-01

    Polybrominated diphenyl ethers (PBDEs) are known as effective flame retardants and have vast industrial application in products like plastics, building materials and textiles. They are found to be structurally similar to thyroid hormones that are responsible for regulating metabolism in the body. Structural similarity with the hormones poses a threat to human health because, once in the system, PBDEs have the potential to affect thyroid hormone transport and metabolism. This study was aimed at designing quantitative structure-activity relationship (QSAR) models for predicting toxic endpoints, namely cell viability and apoptosis, elicited by PBDEs in mammalian cells. Cell viability was evaluated quantitatively using a general cytotoxicity bioassay using Janus Green dye and apoptosis was evaluated using a caspase assay. This study has thus modelled the overall cytotoxic influence of PBDEs at an early and a late endpoint by the Genetic Function Approximation method. This research was a twofold process including running in vitro bioassays to collect data on the toxic endpoints and modeling the evaluated endpoints using QSARs. Cell viability and apoptosis responses for Hep G2 cells exposed to PBDEs were successfully modelled with an r(2) of 0.97 and 0.94, respectively. PMID:24738916

  17. Quantitative Structure-Activity Relationships Study on the Rate Constants of Polychlorinated Dibenzo-p-Dioxins with OH Radical

    PubMed Central

    Qi, Chuansong; Zhang, Chenxi; Sun, Xiaomin

    2015-01-01

    The OH-initiated reaction rate constants (kOH) are of great importance to measure atmospheric behaviors of polychlorinated dibenzo-p-dioxins (PCDDs) in the environment. The rate constants of 75 PCDDs with the OH radical at 298.15 K have been calculated using high level molecular orbital theory, and the rate constants (kα, kβ, kγ and kOH) were further analyzed by the quantitative structure-activity relationships (QSAR) study. According to the QSAR models, the relations between rate constants and the numbers and positions of Cl atoms, the energy of the highest occupied molecular orbital (EHOMO), the energy of the lowest unoccupied molecular orbital (ELUMO), the difference ΔEHOMO-LUMO between EHOMO and ELUMO, and the dipole of oxidizing agents (D) were discussed. It was found that EHOMO is the main factor in the kOH. The number of Cl atoms is more effective than the number of relative position of these Cl atoms in the kOH. The kOH decreases with the increase of the substitute number of Cl atoms. PMID:26274950

  18. Approaches to developing alternative and predictive toxicology based on PBPK/PD and QSAR modeling.

    PubMed Central

    Yang, R S; Thomas, R S; Gustafson, D L; Campain, J; Benjamin, S A; Verhaar, H J; Mumtaz, M M

    1998-01-01

    Systematic toxicity testing, using conventional toxicology methodologies, of single chemicals and chemical mixtures is highly impractical because of the immense numbers of chemicals and chemical mixtures involved and the limited scientific resources. Therefore, the development of unconventional, efficient, and predictive toxicology methods is imperative. Using carcinogenicity as an end point, we present approaches for developing predictive tools for toxicologic evaluation of chemicals and chemical mixtures relevant to environmental contamination. Central to the approaches presented is the integration of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) and quantitative structure--activity relationship (QSAR) modeling with focused mechanistically based experimental toxicology. In this development, molecular and cellular biomarkers critical to the carcinogenesis process are evaluated quantitatively between different chemicals and/or chemical mixtures. Examples presented include the integration of PBPK/PD and QSAR modeling with a time-course medium-term liver foci assay, molecular biology and cell proliferation studies. Fourier transform infrared spectroscopic analyses of DNA changes, and cancer modeling to assess and attempt to predict the carcinogenicity of the series of 12 chlorobenzene isomers. Also presented is an ongoing effort to develop and apply a similar approach to chemical mixtures using in vitro cell culture (Syrian hamster embryo cell transformation assay and human keratinocytes) methodologies and in vivo studies. The promise and pitfalls of these developments are elaborated. When successfully applied, these approaches may greatly reduce animal usage, personnel, resources, and time required to evaluate the carcinogenicity of chemicals and chemical mixtures. Images Figure 6 PMID:9860897

  19. Quantification of contributions of molecular fragments for eye irritation of organic chemicals using QSAR study.

    PubMed

    Kar, Supratik; Roy, Kunal

    2014-05-01

    The eye irritation potential of chemicals has largely been evaluated using the Draize rabbit-eye test for a very long time. The Draize eye-irritation data on 38 compounds established by the European Center for Ecotoxicology and Toxicology of Chemicals (ECETOC) has been used in the present quantitative structure-activity relationship (QSAR) analysis in order to predict molar-adjusted eye scores (MES) and determine possible structural requisites and attributes that are primarily responsible for the eye irritation caused by the studied solutes. The developed model was rigorously validated internally as well as externally by applying principles of the Organization for Economic Cooperation and Development (OECD). The test for applicability domain was also carried out in order to check the reliability of the predictions. Important fragments contributing to higher MES values of the solutes were identified through critical analysis and interpretation of the developed model. Considering all the identified structural attributes, one can choose or design safe solutes with low eye irritant properties. The presented approach suggests a model for use in the context of virtual screening of relevant solute libraries. The developed QSAR model can be used to predict existing as well as future chemicals falling within the applicability domain of the model in order to reduce the use of animals.

  20. Monte carlo method-based QSAR modeling of penicillins binding to human serum proteins.

    PubMed

    Veselinović, Jovana B; Toropov, Andrey A; Toropova, Alla P; Nikolić, Goran M; Veselinović, Aleksandar M

    2015-01-01

    The binding of penicillins to human serum proteins was modeled with optimal descriptors based on the Simplified Molecular Input-Line Entry System (SMILES). The concentrations of protein-bound drug for 87 penicillins expressed as percentage of the total plasma concentration were used as experimental data. The Monte Carlo method was used as a computational tool to build up the quantitative structure-activity relationship (QSAR) model for penicillins binding to plasma proteins. One random data split into training, test and validation set was examined. The calculated QSAR model had the following statistical parameters: r(2)  = 0.8760, q(2)  = 0.8665, s = 8.94 for the training set and r(2)  = 0.9812, q(2)  = 0.9753, s = 7.31 for the test set. For the validation set, the statistical parameters were r(2)  = 0.727 and s = 12.52, but after removing the three worst outliers, the statistical parameters improved to r(2)  = 0.921 and s = 7.18. SMILES-based molecular fragments (structural indicators) responsible for the increase and decrease of penicillins binding to plasma proteins were identified. The possibility of using these results for the computer-aided design of new penicillins with desired binding properties is presented.

  1. Evaluation of a statistics-based Ames mutagenicity QSAR model and interpretation of the results obtained.

    PubMed

    Barber, Chris; Cayley, Alex; Hanser, Thierry; Harding, Alex; Heghes, Crina; Vessey, Jonathan D; Werner, Stephane; Weiner, Sandy K; Wichard, Joerg; Giddings, Amanda; Glowienke, Susanne; Parenty, Alexis; Brigo, Alessandro; Spirkl, Hans-Peter; Amberg, Alexander; Kemper, Ray; Greene, Nigel

    2016-04-01

    The relative wealth of bacterial mutagenicity data available in the public literature means that in silico quantitative/qualitative structure activity relationship (QSAR) systems can readily be built for this endpoint. A good means of evaluating the performance of such systems is to use private unpublished data sets, which generally represent a more distinct chemical space than publicly available test sets and, as a result, provide a greater challenge to the model. However, raw performance metrics should not be the only factor considered when judging this type of software since expert interpretation of the results obtained may allow for further improvements in predictivity. Enough information should be provided by a QSAR to allow the user to make general, scientifically-based arguments in order to assess and overrule predictions when necessary. With all this in mind, we sought to validate the performance of the statistics-based in vitro bacterial mutagenicity prediction system Sarah Nexus (version 1.1) against private test data sets supplied by nine different pharmaceutical companies. The results of these evaluations were then analysed in order to identify findings presented by the model which would be useful for the user to take into consideration when interpreting the results and making their final decision about the mutagenic potential of a given compound. PMID:26708083

  2. Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling

    SciTech Connect

    Valerio, Luis G. . E-mail: luis.valerio@FDA.HHS.gov; Arvidson, Kirk B.; Chanderbhan, Ronald F.; Contrera, Joseph F.

    2007-07-01

    Consistent with the U.S. Food and Drug Administration (FDA) Critical Path Initiative, predictive toxicology software programs employing quantitative structure-activity relationship (QSAR) models are currently under evaluation for regulatory risk assessment and scientific decision support for highly sensitive endpoints such as carcinogenicity, mutagenicity and reproductive toxicity. At the FDA's Center for Food Safety and Applied Nutrition's Office of Food Additive Safety and the Center for Drug Evaluation and Research's Informatics and Computational Safety Analysis Staff (ICSAS), the use of computational SAR tools for both qualitative and quantitative risk assessment applications are being developed and evaluated. One tool of current interest is MDL-QSAR predictive discriminant analysis modeling of rodent carcinogenicity, which has been previously evaluated for pharmaceutical applications by the FDA ICSAS. The study described in this paper aims to evaluate the utility of this software to estimate the carcinogenic potential of small, organic, naturally occurring chemicals found in the human diet. In addition, a group of 19 known synthetic dietary constituents that were positive in rodent carcinogenicity studies served as a control group. In the test group of naturally occurring chemicals, 101 were found to be suitable for predictive modeling using this software's discriminant analysis modeling approach. Predictions performed on these compounds were compared to published experimental evidence of each compound's carcinogenic potential. Experimental evidence included relevant toxicological studies such as rodent cancer bioassays, rodent anti-carcinogenicity studies, genotoxic studies, and the presence of chemical structural alerts. Statistical indices of predictive performance were calculated to assess the utility of the predictive modeling method. Results revealed good predictive performance using this software's rodent carcinogenicity module of over 1200 chemicals

  3. The importance of molecular structures, endpoints' values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders.

    PubMed

    Li, Jiazhong; Gramatica, Paola

    2010-11-01

    Quantitative structure-activity relationship (QSAR) methodology aims to explore the relationship between molecular structures and experimental endpoints, producing a model for the prediction of new data; the predictive performance of the model must be checked by external validation. Clearly, the qualities of chemical structure information and experimental endpoints, as well as the statistical parameters used to verify the external predictivity have a strong influence on QSAR model reliability. Here, we emphasize the importance of these three aspects by analyzing our models on estrogen receptor binders (Endocrine disruptor knowledge base (EDKB) database). Endocrine disrupting chemicals, which mimic or antagonize the endogenous hormones such as estrogens, are a hot topic in environmental and toxicological sciences. QSAR shows great values in predicting the estrogenic activity and exploring the interactions between the estrogen receptor and ligands. We have verified our previously published model for additional external validation on new EDKB chemicals. Having found some errors in the used 3D molecular conformations, we redevelop a new model using the same data set with corrected structures, the same method (ordinary least-square regression, OLS) and DRAGON descriptors. The new model, based on some different descriptors, is more predictive on external prediction sets. Three different formulas to calculate correlation coefficient for the external prediction set (Q2 EXT) were compared, and the results indicated that the new proposal of Consonni et al. had more reasonable results, consistent with the conclusions from regression line, Williams plot and root mean square error (RMSE) values. Finally, the importance of reliable endpoints values has been highlighted by comparing the classification assignments of EDKB with those of another estrogen receptor binders database (METI): we found that 16.1% assignments of the common compounds were opposite (20 among 124 common

  4. Quantitative structure-activity relationship modeling on in vitro endocrine effects and metabolic stability involving 26 selected brominated flame retardants.

    PubMed

    Harju, Mikael; Hamers, Timo; Kamstra, Jorke H; Sonneveld, Edwin; Boon, Jan P; Tysklind, Mats; Andersson, Patrik L

    2007-04-01

    In this work, quantitative structure-activity relationships (QSARs) were developed to aid human and environmental risk assessment processes for brominated flame retardants (BFRs). Brominated flame retardants, such as the high-production-volume chemicals polybrominated diphenyl ethers (PBDEs), tetrabromobisphenol A, and hexabromocyclododecane, have been identified as potential endocrine disruptors. Quantitative structure-activity relationship models were built based on the in vitro potencies of 26 selected BFRs. The in vitro assays included interactions with, for example, androgen, progesterone, estrogen, and dioxin (aryl hydrocarbon) receptor, plus competition with thyroxine for its plasma carrier protein (transthyretin), inhibition of estradiol sulfation via sulfotransferase, and finally, rate of metabolization. The QSAR modeling, a number of physicochemical parameters were calculated describing the electronic, lipophilic, and structural characteristics of the molecules. These include frontier molecular orbitals, molecular charges, polarities, log octanol/water partitioning coefficient, and two- and three-dimensional molecularproperties. Experimental properties were included and measured for PBDEs, such as their individual ultraviolet spectra (200-320 nm) and retention times on three different high-performance liquid chromatography columns and one nonpolar gas chromatography column. Quantitative structure-activity relationship models based on androgen antagonism and metabolic degradation rates generally gave similar results, suggesting that lower-brominated PBDEs with bromine substitutions in ortho positions and bromine-free meta- and para positions had the highest potencies and metabolic degradation rates. Predictions made for the constituents of the technical flame retardant Bromkal 70-5DE found BDE 17 to be a potent androgen antagonist and BDE 66, which is a relevant PBDE in environmental samples, to be only a weak antagonist.

  5. Multi-target QSAR and docking study of steroids binding to corticosteroid-binding globulin and sex hormone-binding globulin.

    PubMed

    Nikolic, Katarina; Filipic, Slavica; Agbaba, Danica

    2012-12-01

    The QSAR and docking studies were performed on fifty seven steroids with binding affinities for corticosteroid-binding globulin (CBG) and eighty four steroids with binding affinities for sex hormone-binding globulin (SHBG). Since the steroidal compounds have binding affinity for both CBG and SHBG, multi-target QSAR approach was employed to establish a unique QSAR method for simultaneous evaluation of the CBG and SHBG binding affinities. The constitutional, geometrical, physico-chemical and electronic descriptors were computed for the examined structures by use of the Chem3D Ultra 7.0.0, the Dragon 6.0, the MOPAC2009, and the Chemical Descriptors Library (CDL) program. Partial least squares regression (PLSR) has been applied for selection of the most relevant molecular descriptors and QSAR models building. The QSAR (SHGB) model, QSAR model (CBG), and multi-target QSAR model (CBG, SHBG) were created. The multi-target QSAR model (CBG and SHBG) was found to be more effective in describing the CBG and SHBG affinity of steroids in comparison to the one target models (QSAR (SHGB) model, QSAR model (CBG)). The multi-target QSAR study indicated the importance of the electronic descriptor (Mor16v), steric/symmetry descriptors (Eig06_EA(ed)), 2D autocorrelation descriptor (GATS4m), distance distribution descriptor (RDF045m), and atom type fingerprint descriptor (CDL-ATFP 253) in describing the CBG and SHBG affinity of steroidal compounds. Results of the created multi-target QSAR model were in accordance with the performed docking studies. The theoretical study defined physicochemical, electronic and structural requirements for selective and effective binding of steroids to the CBG and SHBG active sites.

  6. A new strategy to improve the predictive ability of the local lazy regression and its application to the QSAR study of melanin-concentrating hormone receptor 1 antagonists.

    PubMed

    Li, Jiazhong; Li, Shuyan; Lei, Beilei; Liu, Huanxiang; Yao, Xiaojun; Liu, Mancang; Gramatica, Paola

    2010-04-15

    In the quantitative structure-activity relationship (QSAR) study, local lazy regression (LLR) can predict the activity of a query molecule by using the information of its local neighborhood without need to produce QSAR models a priori. When a prediction is required for a query compound, a set of local models including different number of nearest neighbors are identified. The leave-one-out cross-validation (LOO-CV) procedure is usually used to assess the prediction ability of each model, and the model giving the lowest LOO-CV error or highest LOO-CV correlation coefficient is chosen as the best model. However, it has been proved that the good statistical value from LOO cross-validation appears to be the necessary, but not the sufficient condition for the model to have a high predictive power. In this work, a new strategy is proposed to improve the predictive ability of LLR models and to access the accuracy of a query prediction. The bandwidth of k neighbor value for LLR is optimized by considering the predictive ability of local models using an external validation set. This approach was applied to the QSAR study of a series of thienopyrimidinone antagonists of melanin-concentrating hormone receptor 1. The obtained results from the new strategy shows evident improvement compared with the commonly used LOO-CV LLR methods and the traditional global linear model.

  7. Prediction of drug-related cardiac adverse effects in humans--B: use of QSAR programs for early detection of drug-induced cardiac toxicities.

    PubMed

    Frid, Anna A; Matthews, Edwin J

    2010-04-01

    This report describes the use of three quantitative structure-activity relationship (QSAR) programs to predict drug-related cardiac adverse effects (AEs), BioEpisteme, MC4PC, and Leadscope Predictive Data Miner. QSAR models were constructed for 9 cardiac AE clusters affecting Purkinje nerve fibers (arrhythmia, bradycardia, conduction disorder, electrocardiogram, palpitations, QT prolongation, rate rhythm composite, tachycardia, and Torsades de pointes) and 5 clusters affecting the heart muscle (coronary artery disorders, heart failure, myocardial disorders, myocardial infarction, and valve disorders). The models were based on a database of post-marketing AEs linked to 1632 chemical structures, and identical training data sets were configured for three QSAR programs. Model performance was optimized and shown to be affected by the ratio of the number of active to inactive drugs. Results revealed that the three programs were complementary and predictive performances using any single positive, consensus two positives, or consensus three positives were as follows, respectively: 70.7%, 91.7%, and 98.0% specificity; 74.7%, 47.2%, and 21.0% sensitivity; and 138.2, 206.3, and 144.2 chi(2). In addition, a prospective study using AE data from the U.S. Food and Drug Administration's (FDA's) MedWatch Program showed 82.4% specificity and 94.3% sensitivity. Furthermore, an external validation study of 18 drugs with serious cardiotoxicity not considered in the models had 88.9% sensitivity. PMID:19941924

  8. Prediction of PAH mutagenicity in human cells by QSAR classification.

    PubMed

    Papa, E; Pilutti, P; Gramatica, P

    2008-01-01

    Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants of high environmental concern. The experimental data of a mutagenicity test on human B-lymphoblastoid cells (alternative to the Ames bacterial test) for a set of 70 oxo-, nitro- and unsubstituted PAHs, detected in particulate matter (PM), were modelled by Quantitative Structure-Activity Relationships (QSAR) classification methods (k-NN, k-Nearest Neighbour, and CART, Classification and Regression Tree) based on different theoretical molecular descriptors selected by Genetic Algorithms. The best models were validated for predictivity both externally and internally. For external validation, Self Organizing Maps (SOM) were applied to split the original data set. The best models, developed on the training set alone, show good predictive performance also on the prediction set chemicals (sensitivity 69.2-87.1%, specificity 62.5-87.5%). The classification of PAHs according to their mutagenicity, based only on a few theoretical molecular descriptors, allows a preliminary assessment of the human health risk, and the prioritisation of these compounds.

  9. QSAR on antiproliferative naphthoquinones based on a conformation-independent approach.

    PubMed

    Duchowicz, Pablo R; Bennardi, Daniel O; Bacelo, Daniel E; Bonifazi, Evelyn L; Rios-Luci, Carla; Padrón, José M; Burton, Gerardo; Misico, Rosana I

    2014-04-22

    The antiproliferative activities of a series of 36 naphthoquinone derivatives were subjected to a Quantitative Structure-Activity Relationships (QSAR) study. For this purpose a panel of four human cancer cell lines was used, namely HBL-100 (breast), HeLa (cervix), SW-1573 (non-small cell lung) and WiDr (colon). A conformation-independent representation of the chemical structure was established in order to avoid leading with the scarce experimental information on X-ray crystal structure of the drug interaction. The 1179 theoretical descriptors derived with E-Dragon and Recon software were simultaneously analyzed through linear regression models based on the Replacement Method variable subset selection technique. The established models were validated and tested through the use of external test sets of compounds, the Leave-One-Out Cross Validation method, Y-Randomization and Applicability Domain analysis.

  10. QSAR on antiproliferative naphthoquinones based on a conformation-independent approach.

    PubMed

    Duchowicz, Pablo R; Bennardi, Daniel O; Bacelo, Daniel E; Bonifazi, Evelyn L; Rios-Luci, Carla; Padrón, José M; Burton, Gerardo; Misico, Rosana I

    2014-04-22

    The antiproliferative activities of a series of 36 naphthoquinone derivatives were subjected to a Quantitative Structure-Activity Relationships (QSAR) study. For this purpose a panel of four human cancer cell lines was used, namely HBL-100 (breast), HeLa (cervix), SW-1573 (non-small cell lung) and WiDr (colon). A conformation-independent representation of the chemical structure was established in order to avoid leading with the scarce experimental information on X-ray crystal structure of the drug interaction. The 1179 theoretical descriptors derived with E-Dragon and Recon software were simultaneously analyzed through linear regression models based on the Replacement Method variable subset selection technique. The established models were validated and tested through the use of external test sets of compounds, the Leave-One-Out Cross Validation method, Y-Randomization and Applicability Domain analysis. PMID:24631897

  11. 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.

  12. From QSAR models of drugs to complex networks: state-of-art review and introduction of new Markov-spectral moments indices.

    PubMed

    Riera-Fernández, Pablo; Martín-Romalde, Raquel; Prado-Prado, Francisco J; Escobar, Manuel; Munteanu, Cristian R; Concu, Riccardo; Duardo-Sanchez, Aliuska; González-Díaz, Humberto

    2012-01-01

    Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models have been largely used for different kind of problems in Medicinal Chemistry and other Biosciences as well. Nevertheless, the applications of QSAR models have been restricted to the study of small molecules in the past. In this context, many authors use molecular graphs, atoms (nodes) connected by chemical bonds (links) to represent and numerically characterize the molecular structure. On the other hand, Complex Networks are useful in solving problems in drug research and industry, developing mathematical representations of different systems. These systems move in a wide range from relatively simple graph representations of drug molecular structures (molecular graphs used in classic QSAR) to large systems. We can cite for instance, drug-target interaction networks, protein structure networks, protein interaction networks (PINs), or drug treatment in large geographical disease spreading networks. In any case, all complex networks have essentially the same components: nodes (atoms, drugs, proteins, microorganisms and/or parasites, geographical areas, drug policy legislations, etc.) and links (chemical bonds, drug-target interactions, drug-parasite treatment, drug use, etc.). Consequently, we can use the same type of numeric parameters called Topological Indices (TIs) to describe the connectivity patterns in all these kinds of Complex Networks irrespective the nature of the object they represent and use these TIs to develop QSAR/QSPR models beyond the classic frontiers of drugs small-sized molecules. The goal of this work, in first instance, is to offer a common background to all the manuscripts presented in this special issue. In so doing, we make a review of the most used software and databases, common types of QSAR/QSPR models, and complex networks involving drugs or their targets. In addition, we review both classic TIs that have been used to describe the molecular structure of drugs and

  13. HP-Lattice QSAR for dynein proteins: experimental proteomics (2D-electrophoresis, mass spectrometry) and theoretic study of a Leishmania infantum sequence.

    PubMed

    Dea-Ayuela, María Auxiliadora; Pérez-Castillo, Yunierkis; Meneses-Marcel, Alfredo; Ubeira, Florencio M; Bolas-Fernández, Francisco; Chou, Kuo-Chen; González-Díaz, Humberto

    2008-08-15

    The toxicity and inefficacy of actual organic drugs against Leishmaniosis justify research projects to find new molecular targets in Leishmania species including Leishmania infantum (L. infantum) and Leishmaniamajor (L. major), both important pathogens. In this sense, quantitative structure-activity relationship (QSAR) methods, which are very useful in Bioorganic and Medicinal Chemistry to discover small-sized drugs, may help to identify not only new drugs but also new drug targets, if we apply them to proteins. Dyneins are important proteins of these parasites governing fundamental processes such as cilia and flagella motion, nuclear migration, organization of the mitotic splinde, and chromosome separation during mitosis. However, despite the interest for them as potential drug targets, so far there has been no report whatsoever on dyneins with QSAR techniques. To the best of our knowledge, we report here the first QSAR for dynein proteins. We used as input the Spectral Moments of a Markov matrix associated to the HP-Lattice Network of the protein sequence. The data contain 411 protein sequences of different species selected by ClustalX to develop a QSAR that correctly discriminates on average between 92.75% and 92.51% of dyneins and other proteins in four different train and cross-validation datasets. We also report a combined experimental and theoretic study of a new dynein sequence in order to illustrate the utility of the model to search for potential drug targets with a practical example. First, we carried out a 2D-electrophoresis analysis of L. infantum biological samples. Next, we excised from 2D-E gels one spot of interest belonging to an unknown protein or protein fragment in the region M<20,200 and pI<4. We used MASCOT search engine to find proteins in the L. major data base with the highest similarity score to the MS of the protein isolated from L. infantum. We used the QSAR model to predict the new sequence as dynein with probability of 99.99% without

  14. From QSAR models of drugs to complex networks: state-of-art review and introduction of new Markov-spectral moments indices.

    PubMed

    Riera-Fernández, Pablo; Martín-Romalde, Raquel; Prado-Prado, Francisco J; Escobar, Manuel; Munteanu, Cristian R; Concu, Riccardo; Duardo-Sanchez, Aliuska; González-Díaz, Humberto

    2012-01-01

    Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models have been largely used for different kind of problems in Medicinal Chemistry and other Biosciences as well. Nevertheless, the applications of QSAR models have been restricted to the study of small molecules in the past. In this context, many authors use molecular graphs, atoms (nodes) connected by chemical bonds (links) to represent and numerically characterize the molecular structure. On the other hand, Complex Networks are useful in solving problems in drug research and industry, developing mathematical representations of different systems. These systems move in a wide range from relatively simple graph representations of drug molecular structures (molecular graphs used in classic QSAR) to large systems. We can cite for instance, drug-target interaction networks, protein structure networks, protein interaction networks (PINs), or drug treatment in large geographical disease spreading networks. In any case, all complex networks have essentially the same components: nodes (atoms, drugs, proteins, microorganisms and/or parasites, geographical areas, drug policy legislations, etc.) and links (chemical bonds, drug-target interactions, drug-parasite treatment, drug use, etc.). Consequently, we can use the same type of numeric parameters called Topological Indices (TIs) to describe the connectivity patterns in all these kinds of Complex Networks irrespective the nature of the object they represent and use these TIs to develop QSAR/QSPR models beyond the classic frontiers of drugs small-sized molecules. The goal of this work, in first instance, is to offer a common background to all the manuscripts presented in this special issue. In so doing, we make a review of the most used software and databases, common types of QSAR/QSPR models, and complex networks involving drugs or their targets. In addition, we review both classic TIs that have been used to describe the molecular structure of drugs and

  15. An in vivo quantitative structure-activity relationship for a congeneric series of pyropheophorbide derivatives as photosensitizers for photodynamic therapy.

    PubMed

    Henderson, B W; Bellnier, D A; Greco, W R; Sharma, A; Pandey, R K; Vaughan, L A; Weishaupt, K R; Dougherty, T J

    1997-09-15

    An in vivo quantitative structure-activity relationship (QSAR) study was carried out on a congeneric series of pyropheophorbide photosensitizers to identify structural features critical for their antitumor activity in photodynamic therapy (PDT). The structural elements evaluated in this study include the length and shape (alkyl, alkenyl, cyclic, and secondary analogs) of the ether side chain. C3H mice, harboring the radiation-induced fibrosarcoma tumor model, were used to study three biological response endpoints: tumor growth delay, tumor cell lethality, and vascular perfusion. All three endpoints revealed highly similar QSAR patterns that constituted a function of the alkyl ether chain length and drug lipophilicity, which is defined as the log of the octanol:water partition coefficient (log P). When the illumination of tumor, tumor cells, or cutaneous vasculature occurred 24 h after sensitizer administration, activities were minimal with analogs of log P < or = 5, increased dramatically between log P of 5-6, and peaked between log P of 5.6-6.6. Activities declined gradually with higher log P. The lack of activity of the least-lipophilic analogs was explained in large part by their poor biodistribution characteristics, which yielded negligible tumor and plasma drug levels at the time of treatment with light. The progressively lower potencies of the most lipophilic analogs cannot be explained through the overall tumor and plasma pharmacokinetics of photosensitizer because tumor and plasma concentrations progressively increased with lipophilicity. When compensated for differences in tumor photosensitizer concentration, the 1-hexyl derivative (optimal lipophilicity) was 5-fold more potent than the 1-dodecyl derivative (more lipophilic) and 3-fold more potent than the 1-pentyl analog (less lipophilic), indicating that, in addition to the overall tumor pharmacokinetics, pharmacodynamic factors may influence PDT activity. Drug lipophilicity was highly predictive for

  16. Mode of action and the assessment of chemical hazards in the presence of limited data: use of structure-activity relationships (SAR) under TSCA, Section 5.

    PubMed Central

    Auer, C M; Nabholz, J V; Baetcke, K P

    1990-01-01

    Section 5 of the Toxic Substances Control Act (TSCA) requires that manufacturers and importers of new chemicals must submit a Premanufacture Notification (PMN) to the U.S. Environmental Protection Agency 90 days before they intend to commence manufacture or import. Certain information such as chemical identity, uses, etc., must be included in the notification. The submission of test data on the new substance, however, is not required, although any available health and environmental information must be provided. Nonetheless, over half of all PMNs submitted to the agency do not contain any test data; because PMN chemicals are new, no test data is generally available in the scientific literature. Given this situation, EPA has had to develop techniques for hazard assessment that can be used in the presence of limited test data. EPA's approach has been termed "structure-activity relationships" (SAR) and involves three major components: the first is critical evaluation and interpretation of available toxicity data on the chemical; the second component involves evaluation of test data available on analogous substances and/or potential metabolites; and the third component involves the use of mathematical expressions for biological activity known as "quantitative structure-activity relationships" (QSARs). At present, the use of QSARs is limited to estimating physical chemical properties, environmental toxicity, and bioconcentration factors. An important overarching element in EPA's approach is the experience and judgment of scientific assessors in interpreting and integrating the available data and information. Examples are provided that illustrate EPA's approach to hazard assessment for PMN chemicals. PMID:2269224

  17. QSAR models for oxidation of organic micropollutants in water based on ozone and hydroxyl radical rate constants and their chemical classification.

    PubMed

    Sudhakaran, Sairam; Amy, Gary L

    2013-03-01

    Ozonation is an oxidation process for the removal of organic micropollutants (OMPs) from water and the chemical reaction is governed by second-order kinetics. An advanced oxidation process (AOP), wherein the hydroxyl radicals (OH radicals) are generated, is more effective in removing a wider range of OMPs from water than direct ozonation. Second-order rate constants (k(OH) and k(O3) are good indices to estimate the oxidation efficiency, where higher rate constants indicate more rapid oxidation. In this study, quantitative structure activity relationships (QSAR) models for O(3) and AOP processes were developed, and rate constants, k(OH) and [Formula: see text] , were predicted based on target compound properties. The k(O3) and k(OH) values ranged from 5 * 10(-4) to 10(5) M(-1)s(-1) and 0.04 to 18 * (10(9)) M(-1) s(-1), respectively. Several molecular descriptors which potentially influence O(3) and OH radical oxidation were identified and studied. The QSAR-defining descriptors were double bond equivalence (DBE), ionisation potential (IP), electron-affinity (EA) and weakly-polar component of solvent accessible surface area (WPSA), and the chemical and statistical significance of these descriptors was discussed. Multiple linear regression was used to build the QSAR models, resulting in high goodness-of-fit, r(2) (>0.75). The models were validated by internal and external validation along with residual plots. PMID:23260175

  18. Are Mechanistic and Statistical QSAR Approaches Really Different? MLR Studies on 158 Cycloalkyl-Pyranones.

    PubMed

    Bhhatarai, Barun; Garg, Rajni; Gramatica, Paola

    2010-07-12

    Two parallel approaches for quantitative structure-activity relationships (QSAR) are predominant in literature, one guided by mechanistic methods (including read-across) and another by the use of statistical methods. To bridge the gap between these two approaches and to verify their main differences, a comparative study of mechanistically relevant and statistically relevant QSAR models, developed on a case study of 158 cycloalkyl-pyranones, biologically active on inhibition (Ki ) of HIV protease, was performed. Firstly, Multiple Linear Regression (MLR) based models were developed starting from a limited amount of molecular descriptors which were widely proven to have mechanistic interpretation. Then robust and predictive MLR models were developed on the same set using two different statistical approaches unbiased of input descriptors. Development of models based on Statistical I method was guided by stepwise addition of descriptors while Genetic Algorithm based selection of descriptors was used for the Statistical II. Internal validation, the standard error of the estimate, and Fisher's significance test were performed for both the statistical models. In addition, external validation was performed for Statistical II model, and Applicability Domain was verified as normally practiced in this approach. The relationships between the activity and the important descriptors selected in all the models were analyzed and compared. It is concluded that, despite the different type and number of input descriptors, and the applied descriptor selection tools or the algorithms used for developing the final model, the mechanistical and statistical approach are comparable to each other in terms of quality and also for mechanistic interpretability of modelling descriptors. Agreement can be observed between these two approaches and the better result could be a consensus prediction from both the models.

  19. Application of Quantitative Structure–Activity Relationship Models of 5-HT1A Receptor Binding to Virtual Screening Identifies Novel and Potent 5-HT1A Ligands

    PubMed Central

    2015-01-01

    The 5-hydroxytryptamine 1A (5-HT1A) serotonin receptor has been an attractive target for treating mood and anxiety disorders such as schizophrenia. We have developed binary classification quantitative structure–activity relationship (QSAR) models of 5-HT1A receptor binding activity using data retrieved from the PDSP Ki database. The prediction accuracy of these models was estimated by external 5-fold cross-validation as well as using an additional validation set comprising 66 structurally distinct compounds from the World of Molecular Bioactivity database. These validated models were then used to mine three major types of chemical screening libraries, i.e., drug-like libraries, GPCR targeted libraries, and diversity libraries, to identify novel computational hits. The five best hits from each class of libraries were chosen for further experimental testing in radioligand binding assays, and nine of the 15 hits were confirmed to be active experimentally with binding affinity better than 10 μM. The most active compound, Lysergol, from the diversity library showed very high binding affinity (Ki) of 2.3 nM against 5-HT1A receptor. The novel 5-HT1A actives identified with the QSAR-based virtual screening approach could be potentially developed as novel anxiolytics or potential antischizophrenic drugs. PMID:24410373

  20. Application of quantitative structure-activity relationship models of 5-HT1A receptor binding to virtual screening identifies novel and potent 5-HT1A ligands.

    PubMed

    Luo, Man; Wang, Xiang Simon; Roth, Bryan L; Golbraikh, Alexander; Tropsha, Alexander

    2014-02-24

    The 5-hydroxytryptamine 1A (5-HT1A) serotonin receptor has been an attractive target for treating mood and anxiety disorders such as schizophrenia. We have developed binary classification quantitative structure-activity relationship (QSAR) models of 5-HT1A receptor binding activity using data retrieved from the PDSP Ki database. The prediction accuracy of these models was estimated by external 5-fold cross-validation as well as using an additional validation set comprising 66 structurally distinct compounds from the World of Molecular Bioactivity database. These validated models were then used to mine three major types of chemical screening libraries, i.e., drug-like libraries, GPCR targeted libraries, and diversity libraries, to identify novel computational hits. The five best hits from each class of libraries were chosen for further experimental testing in radioligand binding assays, and nine of the 15 hits were confirmed to be active experimentally with binding affinity better than 10 μM. The most active compound, Lysergol, from the diversity library showed very high binding affinity (Ki) of 2.3 nM against 5-HT1A receptor. The novel 5-HT1A actives identified with the QSAR-based virtual screening approach could be potentially developed as novel anxiolytics or potential antischizophrenic drugs.

  1. A Combined Quantitative Structure-Activity Relationship Research of Quinolinone Derivatives as Androgen Receptor Antagonists.

    PubMed

    Wang, Yuwei; Bai, Fang; Cao, Hong; Li, Jiazhong; Liu, Huanxiang; Gramatica, Paola

    2015-01-01

    Antiandrogens bicalutamide, flutamide and enzalutamide etc. have been used in clinical trials to treat prostate cancer by binding to and antagonizing androgen receptor (AR). Although initially effective, the drug resistance problem will emerge eventually, which results in a high medical need for novel AR antagonist exploitation. Here in this work, to facilitate the rational design of novel AR antagonists, we studied the structure-activity relationships of a series of 2-quinolinone derivatives and investigated the structural requirements for their antiandrogenic activities. Different modeling methods, including 2D MLR, 3D CoMFA and CoMSIA, were implemented to evolve QSAR models. All these models, thoroughly validated, demonstrated satisfactory results especially for the good predictive abilities. The contour maps from 3D CoMFA and CoMSIA models provide visualized explanation of key structural characteristics relevant to the antiandrogenic activities, which is summarized to a position-specific conclusion at the end. The obtained results from this research are practically useful for rational design and screening of promising chemicals with high antiandrogenic activities.

  2. Topological study on the toxicity of ionic liquids on Vibrio fischeri by the quantitative structure-activity relationship method.

    PubMed

    Yan, Fangyou; Shang, Qiaoyan; Xia, Shuqian; Wang, Qiang; Ma, Peisheng

    2015-04-01

    As environmentally friendly solvents, ionic liquids (ILs) are unlikely to act as air contaminants or inhalation toxins resulting from their negligible vapor pressure and excellent thermal stability. However, they can be potential water contaminants because of their considerable solubility in water; therefore, a proper toxicological assessment of ILs is essential. The environmental fate of ILs is studied by quantitative structure-activity relationship (QSAR) method. A multiple linear regression (MLR) model is obtained by topological method using toxicity data of 157 ILs on Vibrio fischeri, which are composed of 74 cations and 22 anions. The topological index developed in our research group is used for predicting the V. fischeri toxicity for the first time. The MLR model is precise for estimating LogEC50 of ILs on V. fischeri with square of correlation coefficient (R(2)) = 0.908 and the average absolute error (AAE) = 0.278.

  3. Toxicity of substituted anilines to Pseudokirchneriella subcapitata and quantitative structure-activity relationship analysis for polar narcotics.

    PubMed

    Chen, Chung-Yuan; Ko, Chia-Wen; Lee, Po-I

    2007-06-01

    This study evaluated the toxic effects of substituted anilines on Pseudokirchneriella subcapitata with the use of a closed algal toxicity testing technique with no headspace. Two response endpoints (i.e., dissolved oxygen production [DO] and algal growth rate) were used to evaluate the toxicity of anilines. Both DO and growth rate endpoints revealed similar sensitivity to the effects of anilines. However, trichloroanilines showed stronger inhibitory effects on microalgal photosynthetic reactions than that on algal growth. For various aquatic organisms, the relative sensitivity relationship for anilines is Daphnia magna > luminescent bacteria (Microtox) > or = Pocelia reticulata > or = Pseudokirchneriella subcapitata > or = fathead minnow > Tetrahymena pyriformis. The susceptibility of P. subcapitata to anilines is similar to fish, but P. subcapitata is apparently less sensitive than the water flea. The lack of correlation between the toxicity revealed by different aquatic organisms (microalgae, D. magna, luminescent bacteria, and P. reticulata) suggests that anilines might have different metabolic routes in these organisms. Both hydrogen bonding donor capacity (the lowest unoccupied molecular orbital energy, Elumo) and hydrophobicity (1-octanol:water partition coefficient, Kow) were found to provide satisfactory descriptions for the toxicity of polar narcotics (substituted anilines and chlorophenols). Quantitative structure-activity relationships (QSARs) based on Elumo, log Kow, or both values were established with r2 values varying from 0.75 to 0.92. The predictive power for the QSAR models were found to be satisfactory through leave-one-out cross-validation. Such relationships could provide useful information for the estimation of toxicity for other polar narcotic compounds.

  4. Rate constants of hydroxyl radical oxidation of polychlorinated biphenyls in the gas phase: A single-descriptor based QSAR and DFT study.

    PubMed

    Yang, Zhihui; Luo, Shuang; Wei, Zongsu; Ye, Tiantian; Spinney, Richard; Chen, Dong; Xiao, Ruiyang

    2016-04-01

    The second-order rate constants (k) of hydroxyl radical (·OH) with polychlorinated biphenyls (PCBs) in the gas phase are of scientific and regulatory importance for assessing their global distribution and fate in the atmosphere. Due to the limited number of measured k values, there is a need to model the k values for unknown PCBs congeners. In the present study, we developed a quantitative structure-activity relationship (QSAR) model with quantum chemical descriptors using a sequential approach, including correlation analysis, principal component analysis, multi-linear regression, validation, and estimation of applicability domain. The result indicates that the single descriptor, polarizability (α), plays an important role in determining the reactivity with a global standardized function of lnk = -0.054 × α ‒ 19.49 at 298 K. In order to validate the QSAR predicted k values and expand the current k value database for PCBs congeners, an independent method, density functional theory (DFT), was employed to calculate the kinetics and thermodynamics of the gas-phase ·OH oxidation of 2,4',5-trichlorobiphenyl (PCB31), 2,2',4,4'-tetrachlorobiphenyl (PCB47), 2,3,4,5,6-pentachlorobiphenyl (PCB116), 3,3',4,4',5,5'-hexachlorobiphenyl (PCB169), and 2,3,3',4,5,5',6-heptachlorobiphenyl (PCB192) at 298 K at B3LYP/6-311++G**//B3LYP/6-31 + G** level of theory. The QSAR predicted and DFT calculated k values for ·OH oxidation of these PCB congeners exhibit excellent agreement with the experimental k values, indicating the robustness and predictive power of the single-descriptor based QSAR model we developed.

  5. Rate constants of hydroxyl radical oxidation of polychlorinated biphenyls in the gas phase: A single-descriptor based QSAR and DFT study.

    PubMed

    Yang, Zhihui; Luo, Shuang; Wei, Zongsu; Ye, Tiantian; Spinney, Richard; Chen, Dong; Xiao, Ruiyang

    2016-04-01

    The second-order rate constants (k) of hydroxyl radical (·OH) with polychlorinated biphenyls (PCBs) in the gas phase are of scientific and regulatory importance for assessing their global distribution and fate in the atmosphere. Due to the limited number of measured k values, there is a need to model the k values for unknown PCBs congeners. In the present study, we developed a quantitative structure-activity relationship (QSAR) model with quantum chemical descriptors using a sequential approach, including correlation analysis, principal component analysis, multi-linear regression, validation, and estimation of applicability domain. The result indicates that the single descriptor, polarizability (α), plays an important role in determining the reactivity with a global standardized function of lnk = -0.054 × α ‒ 19.49 at 298 K. In order to validate the QSAR predicted k values and expand the current k value database for PCBs congeners, an independent method, density functional theory (DFT), was employed to calculate the kinetics and thermodynamics of the gas-phase ·OH oxidation of 2,4',5-trichlorobiphenyl (PCB31), 2,2',4,4'-tetrachlorobiphenyl (PCB47), 2,3,4,5,6-pentachlorobiphenyl (PCB116), 3,3',4,4',5,5'-hexachlorobiphenyl (PCB169), and 2,3,3',4,5,5',6-heptachlorobiphenyl (PCB192) at 298 K at B3LYP/6-311++G**//B3LYP/6-31 + G** level of theory. The QSAR predicted and DFT calculated k values for ·OH oxidation of these PCB congeners exhibit excellent agreement with the experimental k values, indicating the robustness and predictive power of the single-descriptor based QSAR model we developed. PMID:26748251

  6. 3D-QSAR, molecular docking and molecular dynamics studies of a series of RORγt inhibitors.

    PubMed

    Wang, Fangfang; Yang, Wei; Shi, Yonghui; Le, Guowei

    2015-09-01

    The discovery of clinically relevant inhibitors of retinoic acid receptor-related orphan receptor-gamma-t (RORγt) for autoimmune diseases therapy has proven to be a challenging task. In the present work, to find out the structural features required for the inhibitory activity, we show for the first time a three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics (MD) simulations for a series of novel thiazole/thiophene ketone amides with inhibitory activity at the RORγt receptor. The optimum CoMFA and CoMSIA models, derived from ligand-based superimposition I, exhibit leave-one-out cross-validated correlation coefficient (R(2)cv) of .859 and .805, respectively. Furthermore, the external predictive abilities of the models were evaluated by a test set, producing the predicted correlation coefficient (R(2)pred) of .7317 and .7097, respectively. In addition, molecular docking analysis was applied to explore the binding modes between the inhibitors and the receptor. MD simulation and MM/PBSA method were also employed to study the stability and rationality of the derived conformations, and the binding free energies in detail. The QSAR models and the results of molecular docking, MD simulation, binding free energies corroborate well with each other and further provide insights regarding the development of novel RORγt inhibitors with better activity.

  7. Use of Quasi-SMILES and Monte Carlo Optimization to Develop Quantitative Feature Property/Activity Relationships (QFPR/QFAR) for Nanomaterials.

    PubMed

    Toropov, Andrey A; Rallo, Robert; Toropova, Alla P

    2015-01-01

    The CORAL software (http://www.insilico.eu/coral) has been used to develop quantitative feature-property/activity relationships (QFPRs/QFARs) for the prediction of endpoints related to different categories of nanomaterials. In contrast to previous models built up by using CORAL from a representation of the molecular structure by using simplified molecular input-line entry system (SMILES), the current QFPR/QFARs are based on an integrated representation of acting conditions (i.e., a combination of physicochemical and/or biochemical factors) of nanomaterials via the so-called quasi-SMILES notation. In contrast to traditional quantitative structure - property / activity relationships (QSPRs/QSARs), the new models are able to provide new insight on the conditions of acting of substances (e.g., chemicals and nanomaterials) independently of their molecular structure. The development and validation of the QFPR/QFAR models was carried out following the OECD principles. The statistical quality of models developed from quasi-SMILES is acceptable, with values for the determination coefficient in the range of 0.70 to 0.85 for various endpoints of environmental and human health relevance. Perspectives of the QFPR/QFAR and their interaction and overlapping with traditional QSPR/QSAR are also discussed. PMID:25961527

  8. Potential antitumor agents. 36. Quantitative relationships between experimental antitumor activity, toxicity, and structure for the general class of 9-anilinoacridine antitumor agents

    SciTech Connect

    Denny, W.A.; Cain, B.F.; Atwell, G.J.; Hansch, C.; Panthananickal, A.; Leo, A.

    1982-03-01

    Quantitative relationships (QSAR) have been derived between antileukemic (L1210) activity and agent physicochemical properties for 509 tumor-active members of the general class of 9-anilinoacridines. One member of this class is the clinical agent m-AMSA (NSC 249992). Agent hydrophobicity proved a significant but not a dominant influence on in vivo potency. The electronic properties of substituent groups proved important, but the most significant effects on drug potency were shown by the steric influence of groups placed at various positions on the 9-anilinoacridine skeleton. The results are entirely consistent with the physiologically important step in the action of these compounds being their binding to double-stranded DNA by intercalation of the acridine chromophore between the base pairs and positioning of the anilino group in the minor groove, as previously suggested. An equation was also derived for the acute toxicities of 643 derivatives of 9-anilinoacridine. This equation took a somewhat similar form to the one modeling antileukemia potency, emphasizing the usual fairly close relationship between potency and acute toxicity for antitumor agents in general. This study demonstrated the power of QSAR techniques to structure very large amounts of biological data and to allow the extraction of useful information from them bearing on the possible site of action of the compounds concerned.

  9. Mining Discriminative Patterns from Graph Data with Multiple Labels and Its Application to Quantitative Structure-Activity Relationship (QSAR) Models.

    PubMed

    Shao, Zheng; Hirayama, Yuya; Yamanishi, Yoshihiro; Saigo, Hiroto

    2015-12-28

    Graph data are becoming increasingly common in machine learning and data mining, and its application field pervades to bioinformatics and cheminformatics. Accordingly, as a method to extract patterns from graph data, graph mining recently has been studied and developed rapidly. Since the number of patterns in graph data is huge, a central issue is how to efficiently collect informative patterns suitable for subsequent tasks such as classification or regression. In this paper, we consider mining discriminative subgraphs from graph data with multiple labels. The resulting task has important applications in cheminformatics, such as finding common functional groups that trigger multiple drug side effects, or identifying ligand functional groups that hit multiple targets. In computational experiments, we first verify the effectiveness of the proposed approach in synthetic data, then we apply it to drug adverse effect prediction problem. In the latter dataset, we compared the proposed method with L1-norm logistic regression in combination with the PubChem/Open Babel fingerprint, in that the proposed method showed superior performance with a much smaller number of subgraph patterns. Software is available from https://github.com/axot/GLP.

  10. DEVELOPMENT OF QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS (QSARS) TO PREDICT TOXICITY FOR A VARIETY OF HUMAN AND ECOLOGICAL ENDPOINTS

    EPA Science Inventory

    A web accessible software tool is being developed to predict the toxicity of unknown chemicals for a wide variety of endpoints. The tool will enable a user to easily predict the toxicity of a query compound by simply entering its structure in a 2-dimensional (2-D) chemical sketc...

  11. Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles.

    PubMed

    Puzyn, Tomasz; Rasulev, Bakhtiyor; Gajewicz, Agnieszka; Hu, Xiaoke; Dasari, Thabitha P; Michalkova, Andrea; Hwang, Huey-Min; Toropov, Andrey; Leszczynska, Danuta; Leszczynski, Jerzy

    2011-03-01

    It is expected that the number and variety of engineered nanoparticles will increase rapidly over the next few years, and there is a need for new methods to quickly test the potential toxicity of these materials. Because experimental evaluation of the safety of chemicals is expensive and time-consuming, computational methods have been found to be efficient alternatives for predicting the potential toxicity and environmental impact of new nanomaterials before mass production. Here, we show that the quantitative structure-activity relationship (QSAR) method commonly used to predict the physicochemical properties of chemical compounds can be applied to predict the toxicity of various metal oxides. Based on experimental testing, we have developed a model to describe the cytotoxicity of 17 different types of metal oxide nanoparticles to bacteria Escherichia coli. The model reliably predicts the toxicity of all considered compounds, and the methodology is expected to provide guidance for the future design of safe nanomaterials.

  12. Expert QSAR system for predicting the bioconcentration factor under the REACH regulation.

    PubMed

    Grisoni, Francesca; Consonni, Viviana; Vighi, Marco; Villa, Sara; Todeschini, Roberto

    2016-07-01

    Expert systems are a rational integration of several models that generally aim to exploit their advantages and overcome their drawbacks. This work is founded on our previously published Quantitative Structure-Activity Relationship (QSAR) classification scheme, which detects compounds whose Bioconcentration Factor (BCF) is (1) well predicted by the octanol-water partition coefficient (KOW), (2) underestimated by KOW or (3) overestimated by KOW. The classification scheme served as the starting point to identify and combine the best BCF model for each class among three VEGA models and one KOW-based equation. The rationalized model integration showed stability and surprising performance on unknown data when compared with benchmark BCF models. Model simplicity, transparency and mechanistic interpretation were fostered in order to allow for its application and acceptance within the REACH framework.

  13. QSAR Study and Molecular Design of Open-Chain Enaminones as Anticonvulsant Agents

    PubMed Central

    Garro Martinez, Juan C.; Duchowicz, Pablo R.; Estrada, Mario R.; Zamarbide, Graciela N.; Castro, Eduardo A.

    2011-01-01

    Present work employs the QSAR formalism to predict the ED50 anticonvulsant activity of ringed-enaminones, in order to apply these relationships for the prediction of unknown open-chain compounds containing the same types of functional groups in their molecular structure. Two different modeling approaches are applied with the purpose of comparing the consistency of our results: (a) the search of molecular descriptors via multivariable linear regressions; and (b) the calculation of flexible descriptors with the CORAL (CORrelation And Logic) program. Among the results found, we propose some potent candidate open-chain enaminones having ED50 values lower than 10 mg·kg−1 for corresponding pharmacological studies. These compounds are classified as Class 1 and Class 2 according to the Anticonvulsant Selection Project. PMID:22272137

  14. Expert QSAR system for predicting the bioconcentration factor under the REACH regulation.

    PubMed

    Grisoni, Francesca; Consonni, Viviana; Vighi, Marco; Villa, Sara; Todeschini, Roberto

    2016-07-01

    Expert systems are a rational integration of several models that generally aim to exploit their advantages and overcome their drawbacks. This work is founded on our previously published Quantitative Structure-Activity Relationship (QSAR) classification scheme, which detects compounds whose Bioconcentration Factor (BCF) is (1) well predicted by the octanol-water partition coefficient (KOW), (2) underestimated by KOW or (3) overestimated by KOW. The classification scheme served as the starting point to identify and combine the best BCF model for each class among three VEGA models and one KOW-based equation. The rationalized model integration showed stability and surprising performance on unknown data when compared with benchmark BCF models. Model simplicity, transparency and mechanistic interpretation were fostered in order to allow for its application and acceptance within the REACH framework. PMID:27152714

  15. QSAR OF DISTRIBUTION COEFFICIENTS FOR PU (NO3)062-COMPLEXES USING MOLECULAR MECHANICS

    SciTech Connect

    M. BARR; G. JARVINEN; E. MOODY

    2000-08-01

    Computer-aided modeling has been very successful in the design of chelating ligands for the formation of selective metal complexes. We report herein preliminary efforts to extend the principles developed for ion-specific chelating ligands to the weaker, more diffuse electrostatic interactions between complex anions and dicationic sites of anion-exchange resins. Calculated electrostatic affinity between plutonium (IV) hexanitrato dianions and analogue of dicationic anion-exchange sites correlate well with empirically-determined distribution coefficients. This Quantitative Structure Activity Relationship (QSAR) is useful in the determination of the overall trend within a select series of bifunctional resins and which structural modifications are most likely to be advantageous. Ultimately, we hope to refine this methodology to allow the a priori determination of ion-exchange behavior for abroad class of materials.

  16. Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles

    NASA Astrophysics Data System (ADS)

    Puzyn, Tomasz; Rasulev, Bakhtiyor; Gajewicz, Agnieszka; Hu, Xiaoke; Dasari, Thabitha P.; Michalkova, Andrea; Hwang, Huey-Min; Toropov, Andrey; Leszczynska, Danuta; Leszczynski, Jerzy

    2011-03-01

    It is expected that the number and variety of engineered nanoparticles will increase rapidly over the next few years, and there is a need for new methods to quickly test the potential toxicity of these materials. Because experimental evaluation of the safety of chemicals is expensive and time-consuming, computational methods have been found to be efficient alternatives for predicting the potential toxicity and environmental impact of new nanomaterials before mass production. Here, we show that the quantitative structure-activity relationship (QSAR) method commonly used to predict the physicochemical properties of chemical compounds can be applied to predict the toxicity of various metal oxides. Based on experimental testing, we have developed a model to describe the cytotoxicity of 17 different types of metal oxide nanoparticles to bacteria Escherichia coli. The model reliably predicts the toxicity of all considered compounds, and the methodology is expected to provide guidance for the future design of safe nanomaterials.

  17. 2D QSAR Study for Gemfibrozil Glucuronide as the Mechanism-based Inhibitor of CYP2C8

    PubMed Central

    Taxak, N.; Bharatam, P. V.

    2013-01-01

    Mechanism-based inhibition of cytochrome P450 involves the bioactivation of the drug to a reactive metabolite, which leads to cytochrome inhibition via various mechanisms. This is generally seen in the Phase I of drug metabolism. However, gemfibrozil (hypolipidemic drug) leads to mechanism-based inhibition after generating glucuronide conjugate (gemfibrozil acyl-β-glucuronide) in the Phase II metabolism reaction. The mechanism involves the covalent binding of the benzyl radical (generated from the oxidation of aromatic methyl group in conjugate) to the heme of CYP2C8. This article deals with the development of a 2D QSAR model based on the inhibitory potential of gemfibrozil, its analogues and corresponding glucuronide conjugates in inhibiting the CYP2C8-catalysed amodiaquine N-deethylation. The 2D QSAR model was developed using multiple linear regression analysis in Accelrys Discovery Studio 2.5 and helps in identifying the descriptors, which are actually contributing to the inhibitory potency of the molecules studied. The built model was further validated using leave one out method. The best quantitative structure activity relationship model was selected having a correlation coefficient (r) of 0.814 and cross-validated correlation coefficient (q2) of 0.799. 2D QSAR revealed the importance of volume descriptor (Mor15v), shape descriptor (SP09) and 3D matrix-based descriptor (SpMax_RG) in defining the activity for this series of molecules. It was observed that volume and 3D matrix-based descriptors were crucial in imparting higher potency to gemfibrozil glucuronide conjugate, as compared with other molecules. The results obtained from the present study may be useful in predicting the inhibitory potential (IC50 for CYP2C8 inhibition) of the glucuronide conjugates of new molecules and compare with the standard gemfibrozil acyl-β-glucuronide (in terms of pIC50 values) in early stages of drug discovery and development. PMID:24591743

  18. Design, Synthesis, Antifungal Activities and 3D-QSAR of New N,N′-Diacylhydrazines Containing 2,4-Dichlorophenoxy Moiety

    PubMed Central

    Sun, Na-Bo; Shi, Yan-Xia; Liu, Xing-Hai; Ma, Yi; Tan, Cheng-Xia; Weng, Jian-Quan; Jin, Jian-Zhong; Li, Bao-Ju

    2013-01-01

    A series of new N,N′-diacylhydrazine derivatives were designed and synthesized. Their structures were verified by 1H-NMR, mass spectra (MS) and elemental analysis. The antifungal activities of these N,N′-diacylhydrazines were evaluated. The bioassay results showed that most of these N,N′-diacylhydrazines showed excellent antifungal activities against Cladosporium cucumerinum, Corynespora cassiicola, Sclerotinia sclerotiorum, Erysiphe cichoracearum, and Colletotrichum orbiculare in vivo. The half maximal effective concentration (EC50) of one of the compounds was also determined, and found to be comparable with a commercial drug. To further investigate the structure–activity relationship, comparative molecular field analysis (CoMFA) was performed on the basis of antifungal activity data. Both the steric and electronic field distributions of CoMFA are in good agreement in this study. PMID:24189221

  19. Comparative QSAR- and fragments distribution analysis of drugs, druglikes, metabolic substances, and antimicrobial compounds.

    PubMed

    Karakoc, Emre; Sahinalp, S Cenk; Cherkasov, Artem

    2006-01-01

    A number of binary QSAR models have been developed using methods of artificial neural networks, k-nearest neighbors, linear discriminative analysis, and multiple linear regression and have been compared for their ability to recognize five types of chemical compounds that include conventional drugs, inactive druglikes, antimicrobial substituents, and bacterial and human metabolites. Thus, 20 binary classifiers have been created using a variety of 'inductive' and traditional 2D QSAR descriptors which allowed up to 99% accurate separation of the studied groups of activities. The comparison of the performance by four computational approaches demonstrated that the neural nets result in generally more accurate predictions, followed closely by k-nearest neighbors methods. It has also been demonstrated that complementation of 'inductive' descriptors with conventional QSAR parameters does not generally improve the quality of resulting solutions, conforming high predictive ability of 'inductive' variables. The conducted comparative QSAR analysis based on a novel linear optimization approach has helped to identify the extent of overlapping between the studied groups of compounds, such as cross-recognition of bacterial metabolites and antimicrobial compounds reflecting their immanent resemblance and similar origin. Human metabolites have been characterized as a very distinctive class of substances, separated from all other groups in the descriptors space and exhibiting different QSAR behavior. The analysis of unique structural fragments and substituents revealed inhomogeneous scale-free organization of human metabolites illustrating the fact that certain molecular scaffolds (such as sugars and nucleotides) may be strongly favored by natural evolution. The established scale-free organization of human metabolites has been contemplated as a factor of their unique positioning in the descriptors space and their distinctive QSAR properties. It is anticipated that the study may bring

  20. Descriptors and their selection methods in QSAR analysis: paradigm for drug design.

    PubMed

    Danishuddin; Khan, Asad U

    2016-08-01

    The screening of chemical libraries with traditional methods, such as high-throughput screening (HTS), is expensive and time consuming. Quantitative structure-activity relation (QSAR) modeling is an alternative method that can assist in the selection of lead molecules by using the information from reference active and inactive compounds. This approach requires good molecular descriptors that are representative of the molecular features responsible for the relevant molecular activity. The usefulness of these descriptors in QSAR studies has been extensively demonstrated, and they have also been used as a measure of structural similarity or diversity. In this review, we provide a brief explanation of descriptors and the selection approaches most commonly used in QSAR experiments. In addition, some studies have also demonstrated the positive influence of features selection for any drug development model. PMID:27326911

  1. Synthesis, antimicrobial evaluation and QSAR studies of stearic acid derivatives.

    PubMed

    Tahlan, S; Kumar, P; Narasimhan, B

    2014-02-01

    A series of Schiff bases (1-17) and esters (18-28) of stearic acid was synthesized and characterized by physicochemical as well as spectral means. The synthesized compounds were evaluated in vitro for their antimicrobial activity by tube dilution method. The antimicrobial screening results indicated that the compounds having electron releasing groups on benzylidene nucleus were found to be more active against bacterial strains and compounds having electron withdrawing groups on benzylidene nucleus were found to be more active against fungal strains. QSAR studies demonstrated that electronic parameters dipole moment (µ) and total energy (Te) were the most important descriptors in describing the antimicrobial activity of synthesized stearic acid derivatives.

  2. Quantitative Structure-Cytotoxic Activity Relationship 1-(Benzoyloxy)urea and Its Derivative.

    PubMed

    Hardjono, Suko; Siswodihardjo, Siswandono; Pramono, Purwanto; Darmanto, Win

    2016-01-01

    Drug development is originally carried out on a trial and error basis and it is cost-prohibitive. To minimize the trial and error risks, drug design is needed. One of the compound development processes to get a new drug is by designing a structure modification of the mother compound whose activities are recognized. A substitution of the mother compounds alters the physicochemical properties: lipophilic, electronic and steric properties. In Indonesia, one of medical treatments to cure cancer is through chemotherapy and hydroxyurea. Some derivatives, phenylthiourea, phenylurea, benzoylurea, thiourea and benzoylphenylurea, have been found to be anticancer drug candidates. To predict the activity of the drug compound before it is synthesized, the in-silico test is required. From the test, Rerank Score which is the energy of interaction between the receptor and the ligand molecule is then obtained. Hydroxyurea derivatives were synthesized by modifying Schotten-Baumann's method by the addition of benzoyl group and its homologs resulted in the increase of lipophilic, electronic and steric properties, and cytotoxic activity. Synthesized compounds were 1-(benzoyloxy)urea and its derivatives. Structure characterization was obtained by the spectrum of UV, IR, H NMR, C NMR and Mass Spectrometer. Anticancer activity was carried out using MTT method on HeLa cells. The Quantitative Structure-Cytotoxic Activity Relationships of 1-(benzoyloxy)urea compound and its derivatives was calculated using SPSS. The chemical structure was described, namely: ClogP, π, σ, RS, CMR and Es; while, the cytotoxic activity was indicated by log (1 / IC50). The results show that the best equation of Quantitative Structure-Cytotoxic Activity Relationships (QSAR) of 1- (benzoyloxy)urea compound and its derivatives is Log 1/IC50 = - 0.205 (+ 0.068) σ - 0.051 (+ 0.022) Es - 1.911 (+ 0.020). PMID:27222144

  3. A Combined Pharmacophore Modeling, 3D QSAR and Virtual Screening Studies on Imidazopyridines as B-Raf Inhibitors

    PubMed Central

    Xie, Huiding; Chen, Lijun; Zhang, Jianqiang; Xie, Xiaoguang; Qiu, Kaixiong; Fu, Jijun

    2015-01-01

    B-Raf kinase is an important target in treatment of cancers. In order to design and find potent B-Raf inhibitors (BRIs), 3D pharmacophore models were created using the Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database (GALAHAD). The best pharmacophore model obtained which was used in effective alignment of the data set contains two acceptor atoms, three donor atoms and three hydrophobes. In succession, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 39 imidazopyridine BRIs to build three dimensional quantitative structure-activity relationship (3D QSAR) models based on both pharmacophore and docking alignments. The CoMSIA model based on the pharmacophore alignment shows the best result (q2 = 0.621, r2pred = 0.885). This 3D QSAR approach provides significant insights that are useful for designing potent BRIs. In addition, the obtained best pharmacophore model was used for virtual screening against the NCI2000 database. The hit compounds were further filtered with molecular docking, and their biological activities were predicted using the CoMSIA model, and three potential BRIs with new skeletons were obtained. PMID:26035757

  4. Quantitative structure-activity relationship models for predicting drug-induced liver injury based on FDA-approved drug labeling annotation and using a large collection of drugs.

    PubMed

    Chen, Minjun; Hong, Huixiao; Fang, Hong; Kelly, Reagan; Zhou, Guangxu; Borlak, Jürgen; Tong, Weida

    2013-11-01

    Drug-induced liver injury (DILI) is one of the leading causes of the termination of drug development programs. Consequently, identifying the risk of DILI in humans for drug candidates during the early stages of the development process would greatly reduce the drug attrition rate in the pharmaceutical industry but would require the implementation of new research and development strategies. In this regard, several in silico models have been proposed as alternative means in prioritizing drug candidates. Because the accuracy and utility of a predictive model rests largely on how to annotate the potential of a drug to cause DILI in a reliable and consistent way, the Food and Drug Administration-approved drug labeling was given prominence. Out of 387 drugs annotated, 197 drugs were used to develop a quantitative structure-activity relationship (QSAR) model and the model was subsequently challenged by the left of drugs serving as an external validation set with an overall prediction accuracy of 68.9%. The performance of the model was further assessed by the use of 2 additional independent validation sets, and the 3 validation data sets have a total of 483 unique drugs. We observed that the QSAR model's performance varied for drugs with different therapeutic uses; however, it achieved a better estimated accuracy (73.6%) as well as negative predictive value (77.0%) when focusing only on these therapeutic categories with high prediction confidence. Thus, the model's applicability domain was defined. Taken collectively, the developed QSAR model has the potential utility to prioritize compound's risk for DILI in humans, particularly for the high-confidence therapeutic subgroups like analgesics, antibacterial agents, and antihistamines.

  5. 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

  6. 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

  7. Molecular modeling, quantum polarized ligand docking and structure-based 3D-QSAR analysis of the imidazole series as dual AT1 and ETA receptor antagonists

    PubMed Central

    Singh, Khuraijam Dhanachandra; Muthusamy, Karthikeyan

    2013-01-01

    Aim: Both endothelin ETA receptor antagonists and angiotensin AT1 receptor antagonists lower blood pressure in hypertensive patients. A dual AT1 and ETA receptor antagonist may be more efficacious antihypertensive drug. In this study we identified the mode and mechanism of binding of imidazole series of compounds as dual AT1 and ETA receptor antagonists. Methods: Molecular modeling approach combining quantum-polarized ligand docking (QPLD), MM/GBSA free-energy calculation and 3D-QSAR analysis was used to evaluate 24 compounds as dual AT1 and ETA receptor antagonists and to reveal their binding modes and structural basis of the inhibitory activity. Pharmacophore-based virtual screening and docking studies were performed to identify more potent dual antagonists. Results: 3D-QSAR models of the imidazole compounds were developed from the conformer generated by QPLD, and the resulting models showed a good correlation between the predicted and experimental activity. The visualization of the 3D-QSAR model in the context of the compounds under study revealed the details of the structure-activity relationship: substitution of methoxymethyl and cyclooctanone might increase the activity against AT1 receptor, while substitution of cyclohexone and trimethylpyrrolidinone was important for the activity against ETA receptor; addition of a trimethylpyrrolidinone to compound 9 significantly reduced its activity against AT1 receptor but significantly increased its activity against ETA receptor, which was likely due to the larger size and higher intensities of the H-bond donor and acceptor regions in the active site of ETA receptor. Pharmacophore-based virtual screening followed by subsequent Glide SP, XP, QPLD and MM/GBSA calculation identified 5 potential lead compounds that might act as dual AT1 and ETA receptor antagonists. Conclusion: This study may provide some insights into the development of novel potent dual ETA and AT1 receptor antagonists. As a result, five compounds are

  8. Synthesis, quantitative structure-activity relationship and biological evaluation of 1,3,4-oxadiazole derivatives possessing diphenylamine moiety as potential anticancer agents.

    PubMed

    Abdel Rahman, Doaa Ezzat

    2013-01-01

    Synthesis of 2,5-disubstituted-1,3,4-oxadiazole (2a-c), 3-substituted aminomethyl-5-substituted-1,3,4-oxadiazole-2(3H)-thione (4a-m) and 2-substituted thio-5-substituted-1,3,4-oxadiazole (5a, b) had been described. All the synthesized derivatives were screened for anticancer activity against HT29 and MCF7 cancer cell lines using Sulfo-Rodamine B (SRB) standard method. Most of the tested compounds exploited potent antiproliferative activity against HT29 cancer cell line rather than MCF7 cancer cell line. Compounds 2a-c, 4f and 5a exhibited potent cytotoxicity (IC(50) 1.3-2.0 µM) and selectivity against HT29 cancer cell line. Quantitative structure-activity relationship (QSAR) study was applied to find a correlation between the experimental antiproliferative activities of the newly synthesized oxadiazole derivatives with their physicochemical parameter and topological index.

  9. Regression methods for developing QSAR and QSPR models to predict compounds of specific pharmacodynamic, pharmacokinetic and toxicological properties.

    PubMed

    Yap, C W; Li, H; Ji, Z L; Chen, Y Z

    2007-11-01

    Quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) models have been extensively used for predicting compounds of specific pharmacodynamic, pharmacokinetic, or toxicological property from structure-derived physicochemical and structural features. These models can be developed by using various regression methods including conventional approaches (multiple linear regression and partial least squares) and more recently explored genetic (genetic function approximation) and machine learning (k-nearest neighbour, neural networks, and support vector regression) approaches. This article describes the algorithms of these methods, evaluates their advantages and disadvantages, and discusses the application potential of the recently explored methods. Freely available online and commercial software for these regression methods and the areas of their applications are also presented. PMID:18045213

  10. QSAR Analysis for Some 1, 2-Benzisothiazol-3-one Derivatives as Caspase-3 Inhibitors by Stepwise MLR Method

    PubMed Central

    Hajimahdi, Zahra; Safizadeh, Fatemeh; Zarghi, Afshin

    2016-01-01

    Caspase-3 inhibitory activities of some 1, 2-benzisothiazol-3-one derivatives were modeled by quantitative structure–activity relationship (QSAR) using stepwise-multiple linear regression (SW-MLR) method. The built model was robust and predictive with correlation coefficient (R2) of 0.91 and 0.59 for training and test groups, respectively. The quality of the model was evaluated by leave-one out (LOO) cross validation (LOO correlation coefficient, Q2) of 0.80). The results indicate that the descriptors related to the electronegativity, the atomic masses, the atomic van der Waals volumes and R--CX--R Atom-centered fragments play a more significant role in caspase-3 inhibitory activity. PMID:27642314

  11. QSAR Analysis for Some 1, 2-Benzisothiazol-3-one Derivatives as Caspase-3 Inhibitors by Stepwise MLR Method.

    PubMed

    Hajimahdi, Zahra; Safizadeh, Fatemeh; Zarghi, Afshin

    2016-01-01

    Caspase-3 inhibitory activities of some 1, 2-benzisothiazol-3-one derivatives were modeled by quantitative structure-activity relationship (QSAR) using stepwise-multiple linear regression (SW-MLR) method. The built model was robust and predictive with correlation coefficient (R(2)) of 0.91 and 0.59 for training and test groups, respectively. The quality of the model was evaluated by leave-one out (LOO) cross validation (LOO correlation coefficient, Q(2)) of 0.80). The results indicate that the descriptors related to the electronegativity, the atomic masses, the atomic van der Waals volumes and R--CX--R Atom-centered fragments play a more significant role in caspase-3 inhibitory activity. PMID:27642314

  12. Quantitative structure-activity relationship for toxicity of ionic liquids to Daphnia magna: aromaticity vs. lipophilicity.

    PubMed

    Roy, Kunal; Das, Rudra Narayan; Popelier, Paul L A

    2014-10-01

    Water solubility of ionic liquids (ILs) allows their dispersion into aquatic systems and raises concerns on their pollutant potential. Again, lipophilicity can contribute to the toxicity of ILs due to increased ability of the compounds to cross lipoidal bio-membranes. In the present work, we have performed statistical model development for toxicity of a set of ionic liquids to Daphnia magna, a widely accepted model organism for toxicity testing, using computed lipophilicity, atom-type fragment, quantum topological molecular similarity (QTMS) and extended topochemical atom (ETA) descriptors. The models have been developed and validated in accordance with the Organization for Economic Co-operation and Development (OECD) guidelines for quantitative structure-activity relationships (QSARs). The best partial least squares (PLS) model outperforms the previously reported multiple linear regression (MLR) model in statistical quality and predictive ability (R(2)=0.955, Q(2)=0.917, Rpred(2)=0.848). In this work, the ETA descriptors show importance of branching and aromaticity while the QTMS descriptor ellipticity efficiently shows which compounds are influential in the data set, with reference to the model. While obvious importance of lipophilicity is evident from the models, the best model clearly shows the importance of aromaticity suggesting that more lipophilic ILs with less toxicity may be designed by avoiding aromaticity, nitrogen atoms and increasing branching in the cationic structure. The developed quantitative models are in consonance with the recent hypothesis of importance of aromaticity for toxicity of ILs.

  13. Structure-Activity Relationships for Rates of Aromatic Amine Oxidation by Manganese Dioxide.

    PubMed

    Salter-Blanc, Alexandra J; Bylaska, Eric J; Lyon, Molly A; Ness, Stuart C; Tratnyek, Paul G

    2016-05-17

    New energetic compounds are designed to minimize their potential environmental impacts, which includes their transformation and the fate and effects of their transformation products. The nitro groups of energetic compounds are readily reduced to amines, and the resulting aromatic amines are subject to oxidation and coupling reactions. Manganese dioxide (MnO2) is a common environmental oxidant and model system for kinetic studies of aromatic amine oxidation. In this study, a training set of new and previously reported kinetic data for the oxidation of model and energetic-derived aromatic amines was assembled and subjected to correlation analysis against descriptor variables that ranged from general purpose [Hammett σ constants (σ(-)), pKas of the amines, and energies of the highest occupied molecular orbital (EHOMO)] to specific for the likely rate-limiting step [one-electron oxidation potentials (Eox)]. The selection of calculated descriptors (pKa, EHOMO, and Eox) was based on validation with experimental data. All of the correlations gave satisfactory quantitative structure-activity relationships (QSARs), but they improved with the specificity of the descriptor. The scope of correlation analysis was extended beyond MnO2 to include literature data on aromatic amine oxidation by other environmentally relevant oxidants (ozone, chlorine dioxide, and phosphate and carbonate radicals) by correlating relative rate constants (normalized to 4-chloroaniline) to EHOMO (calculated with a modest level of theory). PMID:27074054

  14. 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. PMID:20814717

  15. Exploration of Novel Inhibitors for Class I Histone Deacetylase Isoforms by QSAR Modeling and Molecular Dynamics Simulation Assays

    PubMed Central

    Noor, Zainab; Afzal, Noreen; Rashid, Sajid

    2015-01-01

    Histone deacetylases (HDAC) are metal-dependent enzymes and considered as important targets for cell functioning. Particularly, higher expression of class I HDACs is common in the onset of multiple malignancies which results in deregulation of many target genes involved in cell growth, differentiation and survival. Although substantial attempts have been made to control the irregular functioning of HDACs by employing various inhibitors with high sensitivity towards transformed cells, limited success has been achieved in epigenetic cancer therapy. Here in this study, we used ligand-based pharmacophore and 2-dimensional quantitative structure activity relationship (QSAR) modeling approaches for targeting class I HDAC isoforms. Pharmacophore models were generated by taking into account the known IC50 values and experimental energy scores with extensive validations. The QSAR model having an external R2 value of 0.93 was employed for virtual screening of compound libraries. 10 potential lead compounds (C1-C10) were short-listed having strong binding affinities for HDACs, out of which 2 compounds (C8 and C9) were able to interact with all members of class I HDACs. The potential binding modes of HDAC2 and HDAC8 to C8 were explored through molecular dynamics simulations. Overall, bioactivity and ligand efficiency (binding energy/non-hydrogen atoms) profiles suggested that proposed hits may be more effective inhibitors for cancer therapy. PMID:26431201

  16. Mixed-model QSAR at the human mineralocorticoid receptor: predicting binding mode and affinity of anabolic steroids.

    PubMed

    Peristera, Ourania; Spreafico, Morena; Smiesko, Martin; Ernst, Beat; Vedani, Angelo

    2009-09-28

    We present a computational study on the human mineralocorticoid receptor (hMR) that is based on multi-dimensional quantitative structure-activity relationships (mQSAR). Therein, we identified the binding mode of 48 steroid and non-steroid homologues by flexible docking to the crystal structure (software Yeti) and quantified it using 6D-QSAR (software Quasar). The receptor surrogate, evolved using a genetic algorithm, converged at a cross-validated r2 of 0.810, and yielded a predictive r2 of 0.661. The model was challenged by a series of scramble tests and by consensus scoring (software Raptor: r2=0.844, predictive r(2)=0.620). The model was then employed to predict the binding affinity of 26 anabolic steroids, demonstrating to which extent they might disrupt the endocrine system via binding to the hMR. The model for the hMR was added to the VirtualToxLab, a technology developed by the Biographics Laboratory 3R, allows the identification of the endocrine-disrupting potential of drugs, chemicals and natural products in silico.

  17. Exploration of Novel Inhibitors for Class I Histone Deacetylase Isoforms by QSAR Modeling and Molecular Dynamics Simulation Assays.

    PubMed

    Noor, Zainab; Afzal, Noreen; Rashid, Sajid

    2015-01-01

    Histone deacetylases (HDAC) are metal-dependent enzymes and considered as important targets for cell functioning. Particularly, higher expression of class I HDACs is common in the onset of multiple malignancies which results in deregulation of many target genes involved in cell growth, differentiation and survival. Although substantial attempts have been made to control the irregular functioning of HDACs by employing various inhibitors with high sensitivity towards transformed cells, limited success has been achieved in epigenetic cancer therapy. Here in this study, we used ligand-based pharmacophore and 2-dimensional quantitative structure activity relationship (QSAR) modeling approaches for targeting class I HDAC isoforms. Pharmacophore models were generated by taking into account the known IC50 values and experimental energy scores with extensive validations. The QSAR model having an external R2 value of 0.93 was employed for virtual screening of compound libraries. 10 potential lead compounds (C1-C10) were short-listed having strong binding affinities for HDACs, out of which 2 compounds (C8 and C9) were able to interact with all members of class I HDACs. The potential binding modes of HDAC2 and HDAC8 to C8 were explored through molecular dynamics simulations. Overall, bioactivity and ligand efficiency (binding energy/non-hydrogen atoms) profiles suggested that proposed hits may be more effective inhibitors for cancer therapy.

  18. Tuning HERG out: antitarget QSAR models for drug development.

    PubMed

    Braga, Rodolpho C; Alves, Vinicius M; Silva, Meryck F B; Muratov, Eugene; Fourches, Denis; Tropsha, Alexander; Andrade, Carolina H

    2014-01-01

    Several non-cardiovascular drugs have been withdrawn from the market due to their inhibition of hERG K+ channels that can potentially lead to severe heart arrhythmia and death. As hERG safety testing is a mandatory FDArequired procedure, there is a considerable interest for developing predictive computational tools to identify and filter out potential hERG blockers early in the drug discovery process. In this study, we aimed to generate predictive and well-characterized quantitative structure-activity relationship (QSAR) models for hERG blockage using the largest publicly available dataset of 11,958 compounds from the ChEMBL database. The models have been developed and validated according to OECD guidelines using four types of descriptors and four different machine-learning techniques. The classification accuracies discriminating blockers from non-blockers were as high as 0.83-0.93 on external set. Model interpretation revealed several SAR rules, which can guide structural optimization of some hERG blockers into non-blockers. We have also applied the generated models for screening the World Drug Index (WDI) database and identify putative hERG blockers and non-blockers among currently marketed drugs. The developed models can reliably identify blockers and non-blockers, which could be useful for the scientific community. A freely accessible web server has been developed allowing users to identify putative hERG blockers and non-blockers in chemical libraries of their interest (http://labmol.farmacia.ufg.br/predherg).

  19. Multi-conformation 3D QSAR study of benzenesulfonyl-pyrazol-ester compounds and their analogs as cathepsin B inhibitors

    PubMed Central

    Zhou, Zhigang; Wang, Yanli; Bryant, Stephen H.

    2011-01-01

    Cathepsin B has been found being responsible for many human diseases. Inhibitors of cathepsin B, a ubiquitous lysosomal cysteine protease, have been developed as a promising treatment for human diseases resulting from malfunction and over-expression of this enzyme. Through a high throughput screening assay, a set of compounds were found able to inhibit the enzymatic activity of cathepsin B. The binding structures of these active compounds were modeled through docking simulation. Three-dimensional (3D) quantitative structure-activity relationship (QSAR) models were constructed using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on the docked structures of the compounds. Strong correlations were obtained for both CoMFA and CoMSIA models with cross-validated correlation coefficients (q2) of 0.605 and 0.605 and the regression correlation coefficients (r2) of 0.999 and 0.997, respectively. The robustness of these models was further validated using leave-one-out (LOO) method and training-test set method. The activities of eight (8) randomly selected compounds were predicted using models built from training set of compounds with prediction errors of less than 1 unit for most compounds in CoMFA and CoMSIA models. Structural features for compounds with improved activity are suggested based on the analysis of the CoMFA and CoMSIA contour maps and the property map of the protein ligand binding site. These results may help to provide better understanding of the structure-activity relationship of cathepsin B inhibitors and to facilitate lead optimization and novel inhibitor design. The multi-conformation method to build 3D QSAR is very effective approach to obtain satisfactory models with high correlation with experimental results and high prediction power for unknown compounds. PMID:21798778

  20. Relationships between solar activity and climate change

    NASA Technical Reports Server (NTRS)

    Roberts, W. O.

    1975-01-01

    The relationship between recurrent droughts in the High Plains of the United States and the double sunspot cycle is discussed in detail. It is suggested that high solar activity is generally related to an increase in meridional circulation and blocking patterns at high and intermediate latitudes, especially in winter, and the effect is related to the sudden formation of cirrus clouds during strong geomagnetic activity that originates in the solar corpuscular emission.

  1. The use of quantitative structure-activity relationship models to develop optimized processes for the removal of tobacco host cell proteins during biopharmaceutical production.

    PubMed

    Buyel, J F; Woo, J A; Cramer, S M; Fischer, R

    2013-12-27

    The production of recombinant pharmaceutical proteins in plants benefits from the low cost of upstream production and the greater scalability of plants compared to fermenter-based systems. Now that manufacturing processes that comply with current good manufacturing practices have been developed, plants can compete with established platforms on equal terms. However, the costs of downstream processing remain high, in part because of the dedicated process steps required to remove plant-specific process-related impurities. We therefore investigated whether the ideal strategy for the chromatographic removal of tobacco host cell proteins can be predicted by quantitative structure-activity relationship (QSAR) modeling to reduce the process development time and overall costs. We identified more than 100 tobacco proteins by mass spectrometry and their structures were reconstructed from X-ray crystallography, nuclear magnetic resonance spectroscopy and/or homology modeling data. The resulting three-dimensional models were used to calculate protein descriptors, and significant descriptors were selected based on recently-published retention data for model proteins to develop QSAR models for protein retention on anion, cation and mixed-mode resins. The predicted protein retention profiles were compared with experimental results using crude tobacco protein extracts. Because of the generic nature of the method, it can easily be transferred to other expression systems such as mammalian cells. The quality of the models and potential improvements are discussed.

  2. The use of quantitative structure-activity relationship models to develop optimized processes for the removal of tobacco host cell proteins during biopharmaceutical production.

    PubMed

    Buyel, J F; Woo, J A; Cramer, S M; Fischer, R

    2013-12-27

    The production of recombinant pharmaceutical proteins in plants benefits from the low cost of upstream production and the greater scalability of plants compared to fermenter-based systems. Now that manufacturing processes that comply with current good manufacturing practices have been developed, plants can compete with established platforms on equal terms. However, the costs of downstream processing remain high, in part because of the dedicated process steps required to remove plant-specific process-related impurities. We therefore investigated whether the ideal strategy for the chromatographic removal of tobacco host cell proteins can be predicted by quantitative structure-activity relationship (QSAR) modeling to reduce the process development time and overall costs. We identified more than 100 tobacco proteins by mass spectrometry and their structures were reconstructed from X-ray crystallography, nuclear magnetic resonance spectroscopy and/or homology modeling data. The resulting three-dimensional models were used to calculate protein descriptors, and significant descriptors were selected based on recently-published retention data for model proteins to develop QSAR models for protein retention on anion, cation and mixed-mode resins. The predicted protein retention profiles were compared with experimental results using crude tobacco protein extracts. Because of the generic nature of the method, it can easily be transferred to other expression systems such as mammalian cells. The quality of the models and potential improvements are discussed. PMID:24268820

  3. Per- and polyfluoro toxicity (LC(50) inhalation) study in rat and mouse using QSAR modeling.

    PubMed

    Bhhatarai, Barun; Gramatica, Paola

    2010-03-15

    Fully or partially fluorinated compounds, known as per- and polyfluorinated chemicals are widely distributed in the environment and released because of their use in different household and industrial products. Few of these long chain per- and polyfluorinated chemicals are classified as emerging pollutants, and their environmental and toxicological effects are unveiled in the literature. This has diverted the production of long chain compounds, considered as more toxic, to short chains, but concerns regarding the toxicity of both types of per- and polyfluorinated chemicals are alarming. There are few experimental data available on the environmental behavior and toxicity of these compounds, and moreover, toxicity profiles are found to be different for the types of animals and species used. Quantitative structure-activity relationship (QSAR) is applied to a combination of short and long chain per- and polyfluorinated chemicals, for the first time, to model and predict the toxicity on two species of rodents, rat (Rattus) and mouse (Mus), by modeling inhalation (LC(50)) data. Multiple linear regression (MLR) models using the ordinary-least-squares (OLS) method, based on theoretical molecular descriptors selected by genetic algorithm (GA), were used for QSAR studies. Training and prediction sets were prepared a priori, and these sets were used to derive statistically robust and predictive (both internally and externally) models. The structural applicability domain (AD) of the model was verified on a larger set of per- and polyfluorinated chemicals retrieved from different databases and journals. The descriptors involved, the similarities, and the differences observed between models pertaining to the toxicity related to the two species are discussed. Chemometric methods such as principal component analysis (PCA) and multidimensional scaling (MDS) were used to select most toxic compounds from those within the AD of both models, which will be subjected to experimental tests

  4. Development of 3D-QSAR Model for Acetylcholinesterase Inhibitors Using a Combination of Fingerprint, Molecular Docking, and Structure-Based Pharmacophore Approaches.

    PubMed

    Lee, Sehan; Barron, Mace G

    2015-11-01

    Acetylcholinesterase (AChE), a serine hydrolase vital for regulating the neurotransmitter acetylcholine in animals, has been used as a target for drugs and pesticides. With the increasing availability of AChE crystal structures, with or without ligands bound, structure-based approaches have been successfully applied to AChE inhibitors (AChEIs). The major limitation of these approaches has been the small applicability domain due to the lack of structural diversity in the training set. In this study, we developed a 3 dimensional quantitative structure-activity relationship (3D-QSAR) for inhibitory activity of 89 reversible and irreversible AChEIs including drugs and insecticides. A 3D-fingerprint descriptor encoding protein-ligand interactions was developed using molecular docking and structure-based pharmacophore to rationalize the structural requirements responsible for the activity of these compounds. The obtained 3D-QSAR model exhibited high correlation value (R(2) = 0.93) and low mean absolute error (MAE = 0.32 log units) for the training set (n = 63). The model was predictive across a range of structures as shown by the leave-one-out cross-validated correlation coefficient (Q(2) = 0.89) and external validation results (n = 26, R(2) = 0.89, and MAE = 0.38 log units). The model revealed that the compounds with high inhibition potency had proper conformation in the active site gorge and interacted with key amino acid residues, in particular Trp84 and Phe330 at the catalytic anionic site, Trp279 at the peripheral anionic site, and Gly118, Gly119, and Ala201 at the oxyanion hole. The resulting universal 3D-QSAR model provides insight into the multiple molecular interactions determining AChEI potency that may guide future chemical design and regulation of toxic AChEIs.

  5. Development of 3D-QSAR Model for Acetylcholinesterase Inhibitors Using a Combination of Fingerprint, Molecular Docking, and Structure-Based Pharmacophore Approaches.

    PubMed

    Lee, Sehan; Barron, Mace G

    2015-11-01

    Acetylcholinesterase (AChE), a serine hydrolase vital for regulating the neurotransmitter acetylcholine in animals, has been used as a target for drugs and pesticides. With the increasing availability of AChE crystal structures, with or without ligands bound, structure-based approaches have been successfully applied to AChE inhibitors (AChEIs). The major limitation of these approaches has been the small applicability domain due to the lack of structural diversity in the training set. In this study, we developed a 3 dimensional quantitative structure-activity relationship (3D-QSAR) for inhibitory activity of 89 reversible and irreversible AChEIs including drugs and insecticides. A 3D-fingerprint descriptor encoding protein-ligand interactions was developed using molecular docking and structure-based pharmacophore to rationalize the structural requirements responsible for the activity of these compounds. The obtained 3D-QSAR model exhibited high correlation value (R(2) = 0.93) and low mean absolute error (MAE = 0.32 log units) for the training set (n = 63). The model was predictive across a range of structures as shown by the leave-one-out cross-validated correlation coefficient (Q(2) = 0.89) and external validation results (n = 26, R(2) = 0.89, and MAE = 0.38 log units). The model revealed that the compounds with high inhibition potency had proper conformation in the active site gorge and interacted with key amino acid residues, in particular Trp84 and Phe330 at the catalytic anionic site, Trp279 at the peripheral anionic site, and Gly118, Gly119, and Ala201 at the oxyanion hole. The resulting universal 3D-QSAR model provides insight into the multiple molecular interactions determining AChEI potency that may guide future chemical design and regulation of toxic AChEIs. PMID:26202430

  6. Insights into the interactions between maleimide derivates and GSK3β combining molecular docking and QSAR.

    PubMed

    Quesada-Romero, Luisa; Mena-Ulecia, Karel; Tiznado, William; Caballero, Julio

    2014-01-01

    Many protein kinase (PK) inhibitors have been reported in recent years, but only a few have been approved for clinical use. The understanding of the available molecular information using computational tools is an alternative to contribute to this process. With this in mind, we studied the binding modes of 77 maleimide derivates inside the PK glycogen synthase kinase 3 beta (GSK3β) using docking experiments. We found that the orientations that these compounds adopt inside GSK3β binding site prioritize the formation of hydrogen bond (HB) interactions between the maleimide group and the residues at the hinge region (residues Val135 and Asp133), and adopt propeller-like conformations (where the maleimide is the propeller axis and the heterocyclic substituents are two slanted blades). In addition, quantitative structure-activity relationship (QSAR) models using CoMSIA methodology were constructed to explain the trend of the GSK3β inhibitory activities for the studied compounds. We found a model to explain the structure-activity relationship of non-cyclic maleimide (NCM) derivatives (54 compounds). The best CoMSIA model (training set included 44 compounds) included steric, hydrophobic, and HB donor fields and had a good Q(2) value of 0.539. It also predicted adequately the most active compounds contained in the test set. Furthermore, the analysis of the plots of the steric CoMSIA field describes the elements involved in the differential potency of the inhibitors that can be considered for the selection of suitable inhibitors. PMID:25010341

  7. Insights into the Interactions between Maleimide Derivates and GSK3β Combining Molecular Docking and QSAR

    PubMed Central

    Quesada-Romero, Luisa; Mena-Ulecia, Karel; Tiznado, William; Caballero, Julio

    2014-01-01

    Many protein kinase (PK) inhibitors have been reported in recent years, but only a few have been approved for clinical use. The understanding of the available molecular information using computational tools is an alternative to contribute to this process. With this in mind, we studied the binding modes of 77 maleimide derivates inside the PK glycogen synthase kinase 3 beta (GSK3β) using docking experiments. We found that the orientations that these compounds adopt inside GSK3β binding site prioritize the formation of hydrogen bond (HB) interactions between the maleimide group and the residues at the hinge region (residues Val135 and Asp133), and adopt propeller-like conformations (where the maleimide is the propeller axis and the heterocyclic substituents are two slanted blades). In addition, quantitative structure–activity relationship (QSAR) models using CoMSIA methodology were constructed to explain the trend of the GSK3β inhibitory activities for the studied compounds. We found a model to explain the structure–activity relationship of non-cyclic maleimide (NCM) derivatives (54 compounds). The best CoMSIA model (training set included 44 compounds) included steric, hydrophobic, and HB donor fields and had a good Q2 value of 0.539. It also predicted adequately the most active compounds contained in the test set. Furthermore, the analysis of the plots of the steric CoMSIA field describes the elements involved in the differential potency of the inhibitors that can be considered for the selection of suitable inhibitors. PMID:25010341

  8. GTM-Based QSAR Models and Their Applicability Domains.

    PubMed

    Gaspar, H A; Baskin, I I; Marcou, G; Horvath, D; Varnek, A

    2015-06-01

    In this paper we demonstrate that Generative Topographic Mapping (GTM), a machine learning method traditionally used for data visualisation, can be efficiently applied to QSAR modelling using probability distribution functions (PDF) computed in the latent 2-dimensional space. Several different scenarios of the activity assessment were considered: (i) the "activity landscape" approach based on direct use of PDF, (ii) QSAR models involving GTM-generated on descriptors derived from PDF, and, (iii) the k-Nearest Neighbours approach in 2D latent space. Benchmarking calculations were performed on five different datasets: stability constants of metal cations Ca(2+) , Gd(3+) and Lu(3+) complexes with organic ligands in water, aqueous solubility and activity of thrombin inhibitors. It has been shown that the performance of GTM-based regression models is similar to that obtained with some popular machine-learning methods (random forest, k-NN, M5P regression tree and PLS) and ISIDA fragment descriptors. By comparing GTM activity landscapes built both on predicted and experimental activities, we may visually assess the model's performance and identify the areas in the chemical space corresponding to reliable predictions. The applicability domain used in this work is based on data likelihood. Its application has significantly improved the model performances for 4 out of 5 datasets.

  9. GTM-Based QSAR Models and Their Applicability Domains.

    PubMed

    Gaspar, H A; Baskin, I I; Marcou, G; Horvath, D; Varnek, A

    2015-06-01

    In this paper we demonstrate that Generative Topographic Mapping (GTM), a machine learning method traditionally used for data visualisation, can be efficiently applied to QSAR modelling using probability distribution functions (PDF) computed in the latent 2-dimensional space. Several different scenarios of the activity assessment were considered: (i) the "activity landscape" approach based on direct use of PDF, (ii) QSAR models involving GTM-generated on descriptors derived from PDF, and, (iii) the k-Nearest Neighbours approach in 2D latent space. Benchmarking calculations were performed on five different datasets: stability constants of metal cations Ca(2+) , Gd(3+) and Lu(3+) complexes with organic ligands in water, aqueous solubility and activity of thrombin inhibitors. It has been shown that the performance of GTM-based regression models is similar to that obtained with some popular machine-learning methods (random forest, k-NN, M5P regression tree and PLS) and ISIDA fragment descriptors. By comparing GTM activity landscapes built both on predicted and experimental activities, we may visually assess the model's performance and identify the areas in the chemical space corresponding to reliable predictions. The applicability domain used in this work is based on data likelihood. Its application has significantly improved the model performances for 4 out of 5 datasets. PMID:27490381

  10. QSAR study of selective ligands for the thyroid hormone receptor beta.

    PubMed

    Liu, Huanxiang; Gramatica, Paola

    2007-08-01

    In this paper, an accurate and reliable QSAR model of 87 selective ligands for the thyroid hormone receptor beta 1 (TRbeta1) was developed, based on theoretical molecular descriptors to predict the binding affinity of compounds with receptor. The structural characteristics of compounds were described wholly by a large amount of molecular structural descriptors calculated by DRAGON. Six most relevant structural descriptors to the studied activity were selected as the inputs of QSAR model by a robust optimization algorithm Genetic Algorithm. The built model was fully assessed by various validation methods, including internal and external validation, Y-randomization test, chemical applicability domain, and all the validations indicate that the QSAR model we proposed is robust and satisfactory. Thus, the built QSAR model can be used to fast and accurately predict the binding affinity of compounds (in the defined applicability domain) to TRbeta1. At the same time, the model proposed could also identify and provide some insight into what structural features are related to the biological activity of these compounds and provide some instruction for further designing the new selective ligands for TRbeta1 with high activity.

  11. Random sampling or 'random' model in skin flux measurements? [Commentary on "Investigation of the mechanism of flux across human skin in vitro by quantitative structure-permeability relationships"].

    PubMed

    Poda, G I; Landsittel, D P; Brumbaugh, K; Sharp, D S; Frasch, H F; Demchuk, E

    2001-10-01

    Transdermal therapy receives increasing attention as an attractive alternative to traditional drug delivery. Unfortunately the exact algorithm of transdermal permeation that could guide medicinal chemists towards delivery optimization at an early stage of the drug design process still remains to be decoded. This paper discusses some major hurdles on the way to full understanding of Quantitative Structure-Activity Relationships (QSAR) of skin permeation. From the statistical perspective, a recently published combined data set is found to be inappropriate with respect to the distribution of major molecular descriptors, and therefore should be approached cautiously as a source for QSAR model training and in modelling of occupational and environmental skin exposures.

  12. Toxicity of organic chemicals and their mixtures to activated sludge microorganisms

    SciTech Connect

    Hall, E.; Sun, B.; Prakash, J.; Nirmalakhandan, N.

    1996-05-01

    Toxicity of eight- and 10-component mixtures of several organic chemicals to activated sludge (A/S) microorganisms was analyzed. The joint toxic effects of the tested chemicals were found to be simply additive. The quantitative structure-activity relationship (QSAR) technique using molecular connectivity indices was used to develop single variable models to fit single chemical toxicity data. A QSAR-based approach is proposed to predict the concentrations of components in equitoxic mixtures that would jointly cause 50% inhibition of the A/S microorganisms. The validity of this predictive approach was demonstrated by comparing the predicted concentrations against those found experimentally.

  13. Synthesis of α, β-unsaturated carbonyl based compounds as acetylcholinesterase and butyrylcholinesterase inhibitors: characterization, molecular modeling, QSAR studies and effect against amyloid β-induced cytotoxicity.

    PubMed

    Bukhari, Syed Nasir Abbas; Jantan, Ibrahim; Masand, Vijay H; Mahajan, Devidas T; Sher, Muhammad; Naeem-ul-Hassan, M; Amjad, Muhammad Wahab

    2014-08-18

    A series of novel carbonyl compounds was synthesized by a simple, eco-friendly and efficient method. These compounds were screened for anti-oxidant activity, in vitro cytotoxicity and for inhibitory activity for acetylcholinesterase and butyrylcholinesterase. The effect of these compounds against amyloid β-induced cytotoxicity was also investigated. Among them, compound 14 exhibited strong free radical scavenging activity (18.39 μM) while six compounds (1, 3, 4, 13, 14, and 19) were found to be the most protective against Aβ-induced neuronal cell death in PC12 cells. Compounds 4 and 14, containing N-methyl-4-piperidone linker, showed high acetylcholinesterase inhibitory activity as compared to reference drug donepezil. Molecular docking and QSAR (Quantitative Structure-Activity Relationship) studies were also carried out to determine the structural features that are responsible for the acetylcholinesterase and butyrylcholinesterase inhibitory activity.

  14. Integrating (Q)SAR models, expert systems and read-across approaches for the prediction of developmental toxicity.

    PubMed

    Hewitt, M; Ellison, C M; Enoch, S J; Madden, J C; Cronin, M T D

    2010-08-01

    It has been estimated that reproductive and developmental toxicity tests will account for a significant proportion of the testing costs associated with REACH compliance. Consequently, the use of alternative methods to predict developmental toxicity is an attractive prospect. The present study evaluates a number of computational models and tools which can be used to aid assessment of developmental toxicity potential. The performance and limitations of traditional (quantitative) structure-activity relationship ((Q)SARs) modelling, structural alert-based expert system prediction and chemical profiling approaches are discussed. In addition, the use of category formation and read-across is also addressed. This study demonstrates the limited success of current modelling methods when used in isolation. However, the study also indicates that when used in combination, in a weight-of-evidence approach, better use may be made of the limited toxicity data available and predictivity improved. Recommendations are provided as to how this area could be further developed in the future.

  15. Building up a QSAR model for toxicity toward Tetrahymena pyriformis by the Monte Carlo method: A case of benzene derivatives.

    PubMed

    Toropova, Alla P; Schultz, Terry W; Toropov, Andrey A

    2016-03-01

    Data on toxicity toward Tetrahymena pyriformis is indicator of applicability of a substance in ecologic and pharmaceutical aspects. Quantitative structure-activity relationships (QSARs) between the molecular structure of benzene derivatives and toxicity toward T. pyriformis (expressed as the negative logarithms of the population growth inhibition dose, mmol/L) are established. The available data were randomly distributed three times into the visible training and calibration sets, and invisible validation sets. The statistical characteristics for the validation set are the following: r(2)=0.8179 and s=0.338 (first distribution); r(2)=0.8682 and s=0.341 (second distribution); r(2)=0.8435 and s=0.323 (third distribution). These models are built up using only information on the molecular structure: no data on physicochemical parameters, 3D features of the molecular structure and quantum mechanics descriptors are involved in the modeling process.

  16. Virtual Screening and Molecular Design Based on Hierarchical Qsar Technology

    NASA Astrophysics Data System (ADS)

    Kuz'min, Victor E.; Artemenko, A. G.; Muratov, Eugene N.; Polischuk, P. G.; Ognichenko, L. N.; Liahovsky, A. V.; Hromov, A. I.; Varlamova, E. V.

    This chapter is devoted to the hierarchical QSAR technology (HiT QSAR) based on simplex representation of molecular structure (SiRMS) and its application to different QSAR/QSPR tasks. The essence of this technology is a sequential solution (with the use of the information obtained on the previous steps) of the QSAR paradigm by a series of enhanced models based on molecular structure description (in a specific order from 1D to 4D). Actually, it's a system of permanently improved solutions. Different approaches for domain applicability estimation are implemented in HiT QSAR. In the SiRMS approach every molecule is represented as a system of different simplexes (tetratomic fragments with fixed composition, structure, chirality, and symmetry). The level of simplex descriptors detailed increases consecutively from the 1D to 4D representation of the molecular structure. The advantages of the approach presented are an ability to solve QSAR/QSPR tasks for mixtures of compounds, the absence of the "molecular alignment" problem, consideration of different physical-chemical properties of atoms (e.g., charge, lipophilicity), and the high adequacy and good interpretability of obtained models and clear ways for molecular design. The efficiency of HiT QSAR was demonstrated by its comparison with the most popular modern QSAR approaches on two representative examination sets. The examples of successful application of the HiT QSAR for various QSAR/QSPR investigations on the different levels (1D-4D) of the molecular structure description are also highlighted. The reliability of developed QSAR models as the predictive virtual screening tools and their ability to serve as the basis of directed drug design was validated by subsequent synthetic, biological, etc. experiments. The HiT QSAR is realized as the suite of computer programs termed the "HiT QSAR" software that so includes powerful statistical capabilities and a number of useful utilities.

  17. Synthesis and Quantitative Structure–Activity Relationship of Imidazotetrazine Prodrugs with Activity Independent of O6-Methylguanine-DNA-methyltransferase, DNA Mismatch Repair and p53

    PubMed Central

    Pletsas, Dimitrios; Garelnabi, Elrashied A.E.; Li, Li; Phillips, Roger M.; Wheelhouse, Richard T.

    2014-