BC CLC-Pred: a freely available web-application for quantitative and qualitative predictions of substance cytotoxicity in relation to human breast cancer cell lines.

In silico prediction of cell line cytotoxicity considerably decreases time and financial costs during drug development of new antineoplastic agents. (Q)SAR models for the prediction of drug-like compound cytotoxicity in relation to nine breast cancer cell lines (T47D, ZR-75-1, MX1, Hs-578T, MCF7-DOX, MCF7, Bcap37, MCF7R, BT-20) were created by GUSAR software based on the data from ChEMBL database (v. 30). The separate datasets related with IC50 and IG50 values were used for the creation of (Q)SAR models for each cell line. Based on leave-one-out and 5F CV procedures, 24 reasonable (Q)SAR models were selected for the creation of a freely available web-application (BC CLC-Pred: https://www.way2drug.com/bc/) to predict substance cytotoxicity in relation to human breast cancer cell lines. The mean accuracies of prediction r2, RMSE, Balance Accuracy for the selected (Q)SAR models calculated by 5F CV were 0.599, 0.679 and 0.875, respectively. As a result, BC CLC-Pred provides simultaneous quantitative and qualitative predictions of IC50 and IG50 values for most of the nine breast cancer cell lines, which may be helpful in selecting promising compounds and optimizing lead compounds during the development of new antineoplastic agents against breast cancer.

Quantitative Structure-Activity Relationship in the Series of 5-Ethyluridine, N2-Guanine, and 6-Oxopurine Derivatives with Pronounced Anti-Herpetic Activity.

A quantitative analysis of the relationship between the structure and inhibitory activity against the herpes simplex virus thymidine kinase (HSV-TK) was performed for the series of 5-ethyluridine, N2-guanine, and 6-oxopurines derivatives with pronounced anti-herpetic activity (IC50 = 0.09 ÷ 160,000 µmol/L) using the GUSAR 2019 software. On the basis of the MNA and QNA descriptors and whole-molecule descriptors using the self-consistent regression, 12 statistically significant consensus models for predicting numerical pIC50 values were constructed. These models demonstrated high predictive accuracy for the training and test sets. Molecular fragments of HSV-1 and HSV-2 TK inhibitors that enhance or diminish the anti-herpetic activity are considered. Virtual screening of the ChEMBL database using the developed QSAR models revealed 42 new effective HSV-1 and HSV-2 TK inhibitors. These compounds are promising for further research. The obtained data open up new opportunities for developing novel effective inhibitors of TK.

QSPR Modeling and Experimental Determination of the Antioxidant Activity of Some Polycyclic Compounds in the Radical-Chain Oxidation Reaction of Organic Substrates.

The present work addresses the quantitative structure−antioxidant activity relationship in a series of 148 sulfur-containing alkylphenols, natural phenols, chromane, betulonic and betulinic acids, and 20-hydroxyecdysone using GUSAR2019 software. Statistically significant valid models were constructed to predict the parameter logk7, where k7 is the rate constant for the oxidation chain termination by the antioxidant molecule. These results can be used to search for new potentially effective antioxidants in virtual libraries and databases and adequately predict logk7 for test samples. A combination of MNA- and QNA-descriptors with three whole molecule descriptors (topological length, topological volume, and lipophilicity) was used to develop six statistically significant valid consensus QSPR models, which have a satisfactory accuracy in predicting logk7 for training and test set structures: R2TR > 0.6; Q2TR > 0.5; R2TS > 0.5. Our theoretical prediction of logk7 for antioxidants AO1 and AO2, based on consensus models agrees well with the experimental value of the measure in this paper. Thus, the descriptor calculation algorithms implemented in the GUSAR2019 software allowed us to model the kinetic parameters of the reactions underlying the liquid-phase oxidation of organic hydrocarbons.

Computational Prediction of Inhibitors and Inducers of the Major Isoforms of Cytochrome P450.

Human cytochrome P450 enzymes (CYPs) are heme-containing monooxygenases. This superfamily of drug-metabolizing enzymes is responsible for the metabolism of most drugs and other xenobiotics. The inhibition of CYPs may lead to drug-drug interactions and impair the biotransformation of drugs. CYP inducers may decrease the bioavailability and increase the clearance of drugs. Based on the freely available databases ChEMBL and PubChem, we have collected over 70,000 records containing the structures of inhibitors and inducers together with the IC50 values for the inhibitors of the five major human CYPs: 1A2, 3A4, 2D6, 2C9, and 2C19. Based on the collected data, we developed (Q)SAR models for predicting inhibitors and inducers of these CYPs using GUSAR and PASS software. The developed (Q)SAR models could be applied for assessment of the interaction of novel drug-like substances with the major human CYPs. The created (Q)SAR models demonstrated reasonable accuracy of prediction. They have been implemented in the web application P450-Analyzer that is freely available via the Internet.

[Predict of metabolic stability of xenobiotics by the PASS and GUSAR programs]. / Prognoz metabolicheskoi stabil'nosti ksenobiotikov programmami PASS i GUSAR.

Metabolic stability refers to the susceptibility of compounds to the biotransformation; it is characterized by such pharmacokinetic parameters as half-life (T1/2) and clearance (CL). Generally, these parameters are estimated by in vitro assays, which are based on cells or subcellular fractions (mainly liver microsomal enzymes) and serve as models of the processes occurring in living organisms. Data obtained from the experiments are used to build QSAR (Quantitative Structure-Activity Relationship) models. More than 8000 compounds with known CL and/or T1/2 values obtained in vitro using human liver microsomes were selected from the freely available ChEMBL v.27 database. GUSAR (General Unrestricted Structure-Activity Relationships) and PASS (Prediction of Activity Spectra for Substances) softwares were used to make quantitative and classification models. The quality of the models was evaluated using 5-fold cross-validation. Compounds were subdivided into "stable" and "unstable" by means of the following threshold parameters: T1/2 = 30 minutes, CL = 20 ml/min/kg. The accuracy of the models ranged from 0.5 (calculated in 5-fold CV on the test set for the half-life prediction quantitative model) to 0.96 (calculated in 5-fold CV on the test set for the clearance prediction classification model).

QSAR Assessing the Efficiency of Antioxidants in the Termination of Radical-Chain Oxidation Processes of Organic Compounds.

Using the GUSAR 2013 program, the quantitative structure-antioxidant activity relationship has been studied for 74 phenols, aminophenols, aromatic amines and uracils having lgk7 = 0.01-6.65 (where k7 is the rate constant for the reaction of antioxidants with peroxyl radicals generated upon oxidation). Based on the atomic descriptors (Quantitative Neighborhood of Atoms (QNA) and Multilevel Neighborhoods of Atoms (MNA)) and molecular (topological length, topological volume and lipophilicity) descriptors, we have developed 9 statistically significant QSAR consensus models that demonstrate high accuracy in predicting the lgk7 values for the compounds of training sets and appropriately predict lgk7 for the test samples. Moderate predictive power of these models is demonstrated using metrics of two categories: (1) based on the determination coefficients R2 (R2TSi, R20, Q2(F1), Q2(F2), RmTSi2¯) and based on the concordance correlation coefficient (CCC)); or (2) based on the prediction lgk7 errors (root mean square error (RMSEP), mean absolute error (MAE) and standard deviation (S.D.)) The RBF-SCR method has been used for selecting the descriptors. Our theoretical prognosis of the lgk7 for 8-PPDA, a known antioxidant, based on the consensus models well agrees with the experimental value measure in the present work. Thus, the algorithms for calculating the descriptors implemented in the GUSAR 2013 program allow simulating kinetic parameters of the reactions underling the liquid-phase oxidation of hydrocarbons.

Antimicrobial Activity of Nitrogen-Containing 5-Alpha-androstane Derivatives: In Silico and Experimental Studies.

We evaluated the antimicrobial activity of thirty-one nitrogen-containing 5-alpha-androstane derivatives in silico using computer program PASS (Prediction of Activity Spectra for Substances) and freely available PASS-based web applications (www.way2drug.com). Antibacterial activity was predicted for 27 out of 31 molecules; antifungal activity was predicted for 25 out of 31 compounds. The results of experiments, which we conducted to study the antimicrobial activity, are in agreement with the predictions. All compounds were found to be active with MIC (Minimum Inhibitory Concentration) and MBC (Minimum Bactericidal Concentration) values in the range of 0.0005-0.6 mg/mL. The activity of all studied 5-alpha-androstane derivatives exceeded or was equal to those of Streptomycin and, except for the 3ß-hydroxy-17α-aza-d-homo-5α-androstane-17-one, all molecules were more active than Ampicillin. Activity against the resistant strains of E. coli, P. aeruginosa, and methicillin-resistant Staphylococcus aureus was also shown in experiments. Antifungal activity was determined with MIC and MFC (Minimum Fungicidal Concentration) values varying from 0.007 to 0.6 mg/mL. Most of the compounds were found to be more potent than the reference drugs Bifonazole and Ketoconazole. According to the results of docking studies, the putative targets for antibacterial and antifungal activity are UDP-N-acetylenolpyruvoylglucosamine reductase and 14-alpha demethylase, respectively. In silico assessments of the acute rodent toxicity and cytotoxicity obtained using GUSAR (General Unrestricted Structure-Activity Relationships) and CLC-Pred (Cell Line Cytotoxicity Predictor) web-services were low for the majority of compounds under study, which contributes to the chances for those compounds to advance in the development.

(Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds.

Despite the achievements of antiretroviral therapy, discovery of new anti-HIV medicines remains an essential task because the existing drugs do not provide a complete cure for the infected patients, exhibit severe adverse effects, and lead to the appearance of resistant strains. To predict the interaction of drug-like compounds with multiple targets for HIV treatment, ligand-based drug design approach is widely applied. In this study, we evaluated the possibilities and limitations of (Q)SAR analysis aimed at the discovery of novel antiretroviral agents inhibiting the vital HIV enzymes. Local (Q)SAR models are based on the analysis of structure-activity relationships for molecules from the same chemical class, which significantly restrict their applicability domain. In contrast, global (Q)SAR models exploit data from heterogeneous sets of drug-like compounds, which allows their application to databases containing diverse structures. We compared the information for HIV-1 integrase, protease and reverse transcriptase inhibitors available in the EBI ChEMBL, NIAID HIV/OI/TB Therapeutics, and Clarivate Analytics Integrity databases as the sources for (Q)SAR training sets. Using the PASS and GUSAR software, we developed and validated a variety of (Q)SAR models, which can be further used for virtual screening of new antiretrovirals in the SAVI library. The developed models are implemented in the freely available web resource AntiHIV-Pred.

[The prediction of the spectrum of biological activity and antimicrobial properties of diaminoazoles]. / Prognozirovanie spektra biologicheskoi aktivnosti i antimikrobnye svoistva diaminoazolov.

The work is devoted to predicting and studying biological properties of N-substituted analogs of 3,5-diamino-1,2,4-thiadiazole, which, in their turn, include in the composition of many drugs that exhibit a wide range of pharmacological actions. For searching of new alternative drugs with an antibacterial activity, but lacking resistance of microorganism strains to them, a computer screening of 2N-alkyl-substituted 5-amino-3-imino-1,2,4-thiadiazolines previously synthesized by us was carried out. The prediction of the spectrum of biological activity, as well as the determination of the probable toxicity of these compounds, was performed using the freely available computer programs PASS, Anti-Bac-Pred, and GUSAR. The study of the antibacterial activity in vitro against gram-positive (Staphylococcus aureus, Staphylococcus saprophyticus, Staphylococcus epidermidis) and gram-negative (Escherichia coli, Pseudomonas aeruginosae) bacterial strains was performed by the disco-diffusion method. Experimental data roughly correspond to the predictions.

[QSAR-modeling of desoxyuridine triphosphatase inhibitors in a series of some derivatives of uracil]. / QSAR-modelirovanie ingibitorov dezoksiuridintrifosfatazy v riadu nekotorykh proizvodnykh uratsila.

Due to the widespread prevalence, deoxyuridine triphosphatase (UTPase) is considered by modern biochemists and physicians as a promising target for the development of drugs with a wide range of activities. The therapeutic effect of these drugs will be due to suppression of DNA biosynthesis in various viruses, bacteria and protozoa. In order to rationalize the search for new dUTPase inhibitors, domestic and foreign researchers are actively using the QSAR methodology at the selection stage of hit compounds. However, the practical application of this methodology is impossible without existence of valid QSAR models. With the use of the GUSAR 2013 program, a quantitative analysis of the relationship between the structure and efficacy of 135 dUTPase inhibitors based on uracil derivatives was performed in the IC50 range of 30¸185000 nmol/L. Six statistically significant valid consensus models, characterized by high descriptive ability and moderate prognostic ability on the structures of training and test samples, are constructed. To build valid QSAR models for dUTPase inhibitors can use QNA or MNA descriptors and their combinations in a consensus approach.

Metatox - Web application for generation of metabolic pathways and toxicity estimation.

Xenobiotics biotransformation in humans is a process of the chemical modifications, which may lead to the formation of toxic metabolites. The prediction of such metabolites is very important for drug development and ecotoxicology studies. We created the web-application MetaTox ( http://way2drug.com/mg ) for the generation of xenobiotics metabolic pathways in the human organism. For each generated metabolite, the estimations of the acute toxicity (based on GUSAR software prediction), organ-specific carcinogenicity and adverse effects (based on PASS software prediction) are performed. Generation of metabolites by MetaTox is based on the fragments datasets, which describe transformations of substrates structures to a metabolites structure. We added three new classes of biotransformation reactions: Dehydrogenation, Glutathionation, and Hydrolysis, and now metabolite generation for 15 most frequent classes of xenobiotic's biotransformation reactions are available. MetaTox calculates the probability of formation of generated metabolite - it is the integrated assessment of the biotransformation reactions probabilities and their sites using the algorithm of PASS ( http://way2drug.com/passonline ). The prediction accuracy estimated by the leave-one-out cross-validation (LOO-CV) procedure calculated separately for the probabilities of biotransformation reactions and their sites is about 0.9 on the average for all reactions.

Quantitative structure-property relationship modeling of the C60 fullerene derivatives as electron acceptors of polymer solar cells: Elucidating the functional groups critical for device performance.

Using the GUSAR 2013 program, we have performed a quantitative analysis of the "structure-power conversion efficiency (PCE)" on the series of 100 methano[60]fullerenes previously tested as acceptor components of bulk-heterojunction polymer organic solar cells (PSCs) utilizing the same donor polymer, viz. poly(3-hexylthiophene). Based on the MNA and QNA descriptors and self-consistent regression implemented in the program, six statistically significant consensus models for predicting the PCE values of the methano[60]fullerene-based PSCs have been constructed. The structural fragments of the fullerene compounds leading to an increase in the device performances are determined. Based on these structural descriptors, we have designed the three methano[60]fullerenes included in the training sets and characterized by poor optoelectrical properties is performed. As a result, two new compounds with potentially moderate efficiency have been proposed. This result opens opportunities of using the GUSAR 2013 program for modeling of the "structure-PCE" relationship for diverse compounds (not only fullerene derivatives).

Comparison of Quantitative and Qualitative (Q)SAR Models Created for the Prediction of Ki and IC50 Values of Antitarget Inhibitors.

Estimation of interaction of drug-like compounds with antitargets is important for the assessment of possible toxic effects during drug development. Publicly available online databases provide data on the experimental results of chemical interactions with antitargets, which can be used for the creation of (Q)SAR models. The structures and experimental Ki and IC50 values for compounds tested on the inhibition of 30 antitargets from the ChEMBL 20 database were used. Data sets with Ki and IC50 values including more than 100 compounds were created for each antitarget. The (Q)SAR models were created by GUSAR software using quantitative neighborhoods of atoms (QNA), multilevel neighborhoods of atoms (MNA) descriptors, and self-consistent regression. The accuracy of (Q)SAR models was validated by the fivefold cross-validation procedure. The balanced accuracy was higher for qualitative SAR models (0.80 and 0.81 for Ki and IC50 values, respectively) than for quantitative QSAR models (0.73 and 0.76 for Ki and IC50 values, respectively). In most cases, sensitivity was higher for SAR models than for QSAR models, but specificity was higher for QSAR models. The mean R 2 and RMSE were 0.64 and 0.77 for Ki values and 0.59 and 0.73 for IC50 values, respectively. The number of compounds falling within the applicability domain was higher for SAR models than for the test sets.

Quantitative structure-activity relationship of the thymidylate synthase inhibitors of Mus musculus in the series of quinazolin-4-one and quinazolin-4-imine derivatives.

A quantitative structure-activity relationship analysis of the 2-methylquinazolin-4-one and quinazolin-4-imine derivatives, well-known antifolate thymidylate synthase (TYMS) inhibitors, has been performed in the range IC50 = 0.4÷380000.0 nmoL/L using the GUSAR 2013 program. Based on the MNA and QNA descriptors using the self-consistent regression, 6 statistically significant consensus models for predicting the IC50 numerical values have been constructed. These models demonstrate high and moderate prognostic accuracies for the training and external validation test sets, respectively. The molecular fragments of TYMS inhibitors regulating their antitumor activity are identified. The obtained data open opportunities for developing novel promising inhibitors of TYMS.

Molecular docking, synthesis and biological screening of mefenamic acid derivatives as anti-inflammatory agents.

Drug induced gastrointestinal ulceration, renal side effects and hepatotoxicity are the main causes of numerous Non-Steroidal Anti-inflammatory Drugs (NSAIDs). Cyclooxygenase-2 (COX-2) inhibitors discovered to decrease the gastrointestinal issues, but unfortunately, most of them are associated with major cardiovascular adverse effects. Along these lines, various new strategies and frameworks were developed wherein basic alterations of the present medications were accounted for. The aim of the study was to prepare derivatives of mefenamic acid to evaluate anti-inflammatory activity with fewer adverse reactions. In this study, molecular docking investigations of outlined derivatives were done utilizing Protein Data Bank (PDB ID-4PH9). Synthesis of heterocyclic compounds was carried out utilizing Dicyclohexylcarbodiimide/4-Dimethylaminopyridine (DCC/DMAP) coupling. Acute toxicity prediction was performed using free online GUSAR (General Unrestricted Structure-Activity Relationships) software. The study indicated most of the compounds under safe category. In-vitro pharmacological assessment of heterocyclic compounds was done for COX-1 and COX-2 enzymes for the determination of selectivity. In vivo pharmacological screening for anti-inflammatory activity and ED50 value were determined utilizing carrageenan induced rat paw edema. Gastro intestinal safety study was carried out on selected compounds and found to be devoid of any gastric ulcer toxicity. Most of the compounds indicated high scores as compared to standard during molecular modelling, analysis and displayed interactions with active amino acids of a COX-2 enzyme. The pharmacological screening uncovered that compound substituted with p-bromophenyl indicated maximum potency.

QSAR Modeling and Prediction of Drug-Drug Interactions.

Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.

Recent advances on antimony(III/V) compounds with potential activity against tumor cells.

Antimony one of the heavier pnictogens, has been in medical use against microbes and parasites as well. Antimony-based drugs have been prescribed against leishmaniasis since the parasitic transmission of the tropical disease was understood in the beginning of the 20th century. The activity of arsenic against visceral leishmaniasis led to the synthesis of an array of arsenic-containing parasitic agents, among them the less toxic pentavalent antimonials: Stibosan, Neostibosan, and Ureastibamine. Other antimony drugs followed: sodium stibogluconate (Pentostam) and melglumine antimoniate (Glucantim or Glucantime); both continue to be in use today despite their toxic side effects and increasing loss in potency due to the growing resistance of the parasite against antimony. Antimony compounds and their therapeutic potentials are under consideration from many research groups, while a number of early reviews recording advances of antimony biomedical applications are also available. However, there are only few reports on the screening for antitumor potential of antimony compounds. This review focuses upon results obtained on the anti-proliferative activity of antimony compounds in the past years. This survey shows that antimony(III/V) complexes containing various types of ligands such as thiones, thiosemicarbazones, dithiocarbamates, carboxylic acids, or ketones, nitrogen donor ligands, exhibit selectivity against a variety of cancer cells. The role of the ligand type of the complex is elucidated within this review. The complexes and their biological activity are already reported elsewhere. However quantitative structure-activity relationship (QSAR) modeling studies have been carried out and they are reported for the first time here.