Successful Quantitative Structure-activity Relationships Research Articles

A non-conformational QSAR study for plant-derived larvicides against Zika Aedes aegypti L. vector

A set of 263 plant-derived compounds with larvicidal activity against Aedes aegypti L. (Diptera: Culicidae) vector is collected from the literature, and is studied by means of a non-conformational quantitative structure-activity relationships (QSAR) approach. The balanced subsets method (BSM) is employed to split the complete dataset into training, validation and test sets. From 26,775 freely available molecular descriptors, the most relevant structural features of compounds affecting the bioactivity are taken. The molecular descriptors are calculated through four different freewares, such as PaDEL, Mold2, EPI Suite and QuBiLs-MAS. The replacement method (RM) variable subset selection technique leads to the best linear regression models. A successful QSAR equation involves 7-conformation-independent molecular descriptors, fulfiling the evaluated internal (loo, l30%o, VIF and Y-randomization) and external (test set with Ntest = 65 compounds) validation criteria. The practical application of this QSAR model reveals promising predicted values for some natural compounds with unknown experimental larvicidal activity. Therefore, the present model constitutes the first one based on a large molecular set, being a useful computational tool for identifying and guiding the synthesis of new active molecules inspired by natural products.

Identification of novel inhibitors for Pim-1 kinase using pharmacophore modeling based on a novel method for selecting pharmacophore generation subsets.

Targeting Proviral integration-site of murine Moloney leukemia virus 1 kinase, hereafter called Pim-1 kinase, is a promising strategy for treating different kinds of human cancer. Headed for this a total list of 328 formerly reported Pim-1 kinase inhibitors has been explored and divided based on the pharmacophoric features of the most active molecules into 10 subsets projected to represent potential active binding manners accessible to ligands within the binding pocket of Pim-1 kinase. Discovery Studio 4.1 (DS 4.1) was employed to detect potential pharmacophoric active binding manners anticipated by Pim-1 Kinase inhibitors. The pharmacophoric models were then allowed to compete within Quantitative Structure Activity Relationship (QSAR) framework with other 2D descriptors. Accordingly Genetic algorithm and multiple linear regression investigation were engaged to find the finest QSAR equation that has the best predictive power r262(2) = 0.70, F = 119.14, rLOO(2) = 0.693, rPRESS(2) against 66 external test inhibitors = 0.71 q(2) = 0.55. Three different pharmacophores appeared in the successful QSAR equation this represents three different binding modes for inhibitors within the Pim-1 kinase binding pocket. Pharmacophoric models were later used to screen compounds within the National Cancer Institute database. Several low micromolar Pim-1 Kinase inhibitors were captured. The most potent hits show IC50 values of 0.77 and 1.03 µM. Also, upon analyzing the successful QSAR Equation we found that some polycyclic aromatic electron-rich structures namely 6-Chloro-2-methoxy-acridine can be considered as putative hits for Pim-1 kinase inhibition.

Predicting Anti HIV Activity of Quinolone Carboxylic Acids-Computation Approach Using Topological Indices

Background: Quantitative structure–activity relationships (QSAR) are a major factor in contemporary drug designing. Thus, it is quite clear that a large number of users of QSAR are located in industrial research units. Objectives: A Topological Index is a numeric quantity that is mathematically derived in a direct and uambiguous manner from the structural graph of a molecule. In structure-activity relationship studies, molecular topology quantifies chemical structure into characteristic numerical descriptors. All structural formulas of chemical compounds are molecular graphs where vertices represent the set of atoms and edges represent chemical bonds. The construction and investigation of topological indices that could be used to describe molecular structures is one of the important directions of mathematical chemistry Topological descriptors developed for predicting physicochemical properties and biological activities of chemical substances can be used for drug design. Matherials and Methods: A number of successful QSAR studies were made based on the Wiener index, Terminal Wiener Index and Platt Number. These indices are derived from matrices, like distance matrix and adjacency matrix which represents a molecular graph. Zagreb Index is based on degree connectivity indices. Results and Conclusion: In this paper we analyze, Quantitative structure activity relationship studies were performed on anti- HIV activity of Quinolone carboxylic acid for Wiener Index, Terminal Wiener Index, Platt Number and Zagreb Index

Ligand-based pharmacophore exploration and QSAR analysis of transition state analogues followed by in silico screening guide the discovery of new sub-micromolar β-secreatase inhibitors

Transition state analogues (TSA)-based inhibitors have particularly potent inhibitory profiles. The fact that β-secretase (BACE) inhibitors have potential as anti-Alzheimer’s treatments prompted us to explore the pharmacophoric space of 68 known TSA BACE inhibitors. Subsequently, quantitative structure–activity relationship (QSAR) analysis was employed to select optimal combination of binding model(s) and 2D physicochemical descriptors capable of explaining bioactivity variation. One pharmacophoric model emerged in the successful QSAR equation. However, to closely mimic the sterically demanding transition state of BACE we were obliged to complement the successful pharmacophore with strict shape-based query decorated with carefully positioned hydroxyl fragment that simulates the statin hydroxyl of known TSA BACE inhibitors. Both models, i.e., the hybrid shape–OH and optimal pharmacophore, were validated via receiver-operating characteristic curve analysis and were found to exhibit excellent abilities in discerning active compounds from decoys. Subsequent in silico screening against the National Cancer Institute structural database using the two models yielded two novel inhibitors of nanomolar and low micromolar IC50 values.

Qsar Analysis of [(Biphenyloxy)Propyl]Isoxazoles: Agents Against Coxsackievirus B3

Antiviral drugs are urgently needed for the treatment of acute and chronic diseases caused by enteroviruses such as coxsackievirus B3 (CVB3). The main goal of this study is quantitative structure-activity relationship (QSAR) analysis of anti-CVB3 activity (clinical CVB3 isolate 97927 [log IC50, µM]) and investigation of the selectivity of 25 ([biphenyloxy]propyl)isoxazoles, followed by computer-aided design and virtual screening of novel active compounds. The 2D QSAR obtained models are quite satisfactory (R(2) = 0.84-0.99, Q(2) = 0.76-0.92, R(2)(ext) = 0.62-0.79). Compounds with high antiviral activity and selectivity have to contain 5-trifluoromethyl-[1,2,4]oxadiazole or 2,4-difluorophenyl fragments. Insertion of 2,5-dimethylbenzene, napthyl and especially biphenyl substituents into investigated compounds substantially decreases both their antiviral activity and selectivity. Several compounds were proposed as a result of design and virtual screening. A high level of activity of 2-methoxy-1-phenyl-1H-imidazo[4,5-c]pyridine (sm428) was confirmed experimentally. Simplex representation of molecular structure allows successful QSAR analysis of anti-CVB3 activity of ([biphenyloxy]propyl)isoxazole derivatives. Two possible ways of battling CVB3 are considered as a future perspective.

QSAR in the Pharmaceutical Research Setting: QSAR Models for Broad, Large Problems

The field of quantitative structure activity relationships (QSAR) has evolved into an integral tool for pharmaceutical discovery. It is presently an accessible technology, as can be shown by the number papers which are easily found through PubMed literature searches. At one level, QSAR is used routinely and invisibly as an aid for the bench chemist for logP, logS, pK(a)/pK(b), metabolic stability and other such properties. Chemoinformaticians and computational chemists develop models from scratch for less routine purposes associated with lead optimization around a single target or library design around a target family such as kinase, ion channel or GPCR inhibitors. Regardless of the differences in frequency of use and the end user, any successful QSAR is successful because it rests on appropriate mathematics linking valid data and relevant descriptors. Though success is defined by the end user, the QSAR originator is well advised to validate his model and understand how it performs in different situations. The present review will cover QSAR from the ground up as it is used in pharmaceutical research environments. It will focus towards larger dataset methodologies (a minimum 100 of compounds) and by consequence will focus on 2D descriptors. It will start with the critical base of data, descriptors, equations and validation methods. It will review the broadly used and invisible QSARs for logP, pKa/pKb and metabolic stability. The review will then present progress in QSARs of broad interest which are under active development: 1) hERG liability models, 2) modeling for 2a) drug-likeness and related properties, 2b) kinase ligand likeness and 2c) GPCR ligand likeness.

SAR of Cu (II) Thiosemicarbazone Complexes as Hypoxic Imaging Agents: MM3 Analysis and Prediction of Biologic Properties

Copper(II) bis(thiosemicarbazone) are very useful for blood flow and hypoxic imaging. The aim of this study was to identify structure-activity relationships (SARs) within a series of analogues with different substitution patterns in the ligands, in order to design improved hypoxia imaging agents and elucidate hypoxia selectivity mechanisms. Genetic algorithms (GAs) were used to develop specific copper metal-ligand force field parameters for the MM3 force-field calculations. These new parameters produced results in good agreement with experiment and previously reported copper metal-ligand parameters. A successful quantitative SAR (QSAR) for predicting the several classes of Cu(II)-chelating ligands was built using a training set of 21 Cu(II) complexes. The QSAR exhibited a correlation between the predicted and experimental test set. The QSAR preformed with great accuracy; r(2) = 0.95 and q(2) = 0.90 utilizing a leave-one-out cross-validation with multiple linear regression analysis to find correlation between different calculated molecular descriptors of these complexes. The final QSAR mathematical models were found as the following: Log P = {3.01698 (+/-0.0590)} - LUMO {0.1248 (+/-0.068)} + MR {0.3219 (+/-0.086)} n = 21 |r| = 0.972 s = 0.188 F = 98.102 The resulting models could act as an efficient strategy for estimating the hypoxic conditions through imaging and provide some insights into the structural features related to the biological activity of these compounds.

Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling

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, comprised primarily of pharmaceutical, industrial and some natural products developed under an FDA-MDL cooperative research and development agreement (CRADA). The predictive performance for this group of dietary natural products and the control group was 97% sensitivity and 80% concordance. Specificity was marginal at 53%. This study finds that the in silico QSAR analysis employing this software's rodent carcinogenicity database is capable of identifying the rodent carcinogenic potential of naturally occurring organic molecules found in the human diet with a high degree of sensitivity. It is the first study to demonstrate successful QSAR predictive modeling of naturally occurring carcinogens found in the human diet using an external validation test. Further test validation of this software and expansion of the training data set for dietary chemicals will help to support the future use of such QSAR methods for screening and prioritizing the risk of dietary chemicals when actual animal data are inadequate, equivocal, or absent.

QSAR Studies of Copper Azamacrocycles and Thiosemicarbazones: MM3 Parameter Development and Prediction of Biological Properties

Genetic algorithms (GA) were used to develop specific copper metal-ligand force field parameters for the MM3 force field, from a combination of crystallographic structures and ab initio calculations. These new parameters produced results in good agreement with experiment and previously reported copper metal-ligand parameters for the AMBER force field. The MM3 parameters were then used to develop several quantitative structure-activity relationship (QSAR) models. A successful QSAR for predicting the lipophilicity (log P(ow)) of several classes of Cu(II)-chelating ligands was built using a training set of 32 Cu(II) radiometal complexes and 6 simple molecular descriptors. The QSAR exhibited a correlation between the predicted and experimental log P(ow) with an r(2) = 0.95, q(2) = 0.92. When applied to an external test set of 11 Cu(II) complexes, the QSAR preformed with great accuracy; r(2) = 0.93 and a q(2) = 0.91 utilizing a leave-one-out cross-validation analysis. Additional QSAR models were developed to predict the biodistribution of a smaller set of Cu(II) bis(thiosemicarbazone) complexes.

An exploratory study of the use of multivariate techniques to determine mechanisms of toxic action

The most successful quantitative structure–activity relationships have been developed by separating compounds by their mechanisms of toxic action (MOAs). However, to correctly determine the MOA of a compound is often not easy. We investigated the usefulness of discriminant analysis and logistic regression in determining MOAs. The discriminating variables used were the logarithm of octanol–water partition coefficients ( log K ow ) and the experimental toxicity data obtained from Pimephales promelas and Tetrahymena pyriformis assays. Small total error rates were obtained when separating nonpolar narcotic compounds from other compounds, however, relatively high total error rates were obtained when separating less reactive compounds (polar, ester, and amine narcotics) from more reactive compounds (electrophiles, proelectrophiles, and nucleophiles).

Identifying the mechanism of aquatic toxicity of selected compounds by hydrophobicity and electrophilicity descriptors

The most successful quantitative structure–activity relationships (QSARs) have been developed by separating toxicants by their mechanisms of action (MOAs). However, since the activity of a chemical compound on an organism is dependent upon several physical, chemical and biological factors, among which interactions may also exist, the MOA of a compound is not easily determined. In this study, the use of discriminant analysis and logistic regression in distinguishing between narcotic and reactive compounds was investigated. The discriminating variables included hydrophobicity (log( K ow)) and electrophilicity descriptors ( S av N, E HOMO, and E LUMO). Classification results showed that logistic regression gave a smaller total error rate compared to discriminant analysis. Since the value of the descriptors can be calculated, the classification methods can be used in predictive toxicology.

Determining the mechanisms of toxic action of phenols to Tetrahymena pyriformis.

Phenols are environmentally important compounds that are widely used in agriculture and industry. Thus, phenols are of interest to environmental toxicologists, which has led to the development of several quantitative structure-activity relationship (QSAR) models. The most successful QSARs have been established for compounds having a common mechanism of toxic action (MOA). However, correctly determining the MOA of a compound is not easy. Discriminant analysis was used in this study to separate phenols by their MOAs. The working hypothesis of this study was that phenols with different MOAs lie in different regions in the response surface described by the ionization-corrected hydrophobicity descriptor, log D, the electrophilicity descriptor, ELUMO, and the interaction between hydrophobicity and electrophilicity, D * ELUMO. Results of discriminant analysis showed a total error rate of 15%. In addition, it was difficult to separate phenols that had a polar narcosis MOA from phenols that had a proelectrophile MOA.

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

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.

Quantitative structure-activity relationships for chemical toxicity to environmental bacteria

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 r 2) 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 p K a compounds, and aldehydes. Nitro compounds and chlorinated aliphatic hydrocarbons and alcohols showed enhanced toxicity to the methanogens only. Chemicals with low IC 50 concentrations (log IC 50 μmol/liter < 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.

Structure activity relationships for the prediction of biodegradability of environmental pollutants

Abstract This paper reviews the development of Structure Activity Relationships (SARs) and Quantitative SARs (QSARs) for the prediction of biodegradability of environmental pollutants. The utility of QSARs for biodegradation has been evaluated in the light of various physicochemical, quantum, and molecular descriptors and biodegradation parameters. It has been suggested that further development of successful QSARs for biodegradation should involve the establishment of large and consistent biodegradation data bases and the identification descriptors that can relate well to the mechanisms and rate limiting processes in the biodegradation sequence. Among the descriptors studied, the quantum chemical‐type descriptors appear to be most promising for the development of QSARs for the prediction of biodegradability of environmental pollutants.