Anastasia V. Rudik

PREDICTION OF THE BIOLOGICAL ACTIVITY SPECTRA OF ORGANIC COMPOUNDS USING THE PASS ONLINE WEB RESOURCE

The freely accessible web resource PASS Online is presented. This resource is designed for the prediction of the biological activity spectra of organic compounds based on their structural formulas for more than 4000 types of biological activity with average accuracy above 95% (http://www.way2drug.com/passonline). The prediction is based on an analysis of the structure-activity relationships in the training set containing information on the structure and biological activity of more than 300000 organic compounds. The possibilities and limitations of this approach are described. Recommendations are given for interpreting the prediction results. Examples are given for the practical use of the PASS Online web resource in order to establish priorities for chemical synthesis and biological testing of substances on the basis of prediction results. The further trends are considered for the using PASS Online as an Internet platform for joint projects of academic researchers for the search and development of new pharmaceutical agents.

Metabolism Site Prediction Based on Xenobiotic Structural Formulas and PASS Prediction Algorithm

A new ligand-based method for the prediction of sites of metabolism (SOMs) for xenobiotics has been developed on the basis of the LMNA (labeled multilevel neighborhoods of atom) descriptors and the PASS (prediction of activity spectra for substances) algorithm and applied to predict the SOMs of the 1A2, 2C9, 2C19, 2D6, and 3A4 isoforms of cytochrome P450. An average IAP (invariant accuracy of prediction) of SOMs calculated by the leave-one-out cross-validation procedure was 0.89 for the developed method. The external validation was made with evaluation sets containing data on biotransformations for 57 cardiovascular drugs. An average IAP of regioselectivity for evaluation sets was 0.83. It was shown that the proposed method exceeds accuracy of SOM prediction by RS-Predictor for CYP 1A2, 2D6, 2C9, 2C19, and 3A4 and is comparable to or better than SMARTCyp for CYP 2C9 and 2D6.

Prediction of reacting atoms for the major biotransformation reactions of organic xenobiotics

BackgroundThe knowledge of drug metabolite structures is essential at the early stage of drug discovery to understand the potential liabilities and risks connected with biotransformation. The determination of the site of a molecule at which a particular metabolic reaction occurs could be used as a starting point for metabolite identification. The prediction of the site of metabolism does not always correspond to the particular atom that is modified by the enzyme but rather is often associated with a group of atoms. To overcome this problem, we propose to operate with the term “reacting atom”, corresponding to a single atom in the substrate that is modified during the biotransformation reaction. The prediction of the reacting atom(s) in a molecule for the major classes of biotransformation reactions is necessary to generate drug metabolites.ResultsSubstrates of the major human cytochromes P450 and UDP-glucuronosyltransferases from the Biovia Metabolite database were divided into nine groups according to their reaction classes, which are aliphatic and aromatic hydroxylation, N- and O-glucuronidation, N-, S- and C-oxidation, and N- and O-dealkylation. Each training set consists of positive and negative examples of structures with one labelled atom. In the positive examples, the labelled atom is the reacting atom of a particular reaction that changed adjacency. Negative examples represent non-reacting atoms of a particular reaction. We used Labelled Multilevel Neighbourhoods of Atoms descriptors for the designation of reacting atoms. A Bayesian-like algorithm was applied to estimate the structure–activity relationships. The average invariant accuracy of prediction obtained in leave-one-out and 20-fold cross-validation procedures for five human isoforms of cytochrome P450 and all isoforms of UDP-glucuronosyltransferase varies from 0.86 to 0.99 (0.96 on average).ConclusionsWe report that reacting atoms may be predicted with reasonable accuracy for the major classes of metabolic reactions—aliphatic and aromatic hydroxylation, N- and O-glucuronidation, N-, S- and C-oxidation, and N- and O-dealkylation. The proposed method is implemented as a freely available web service at http://www.way2drug.com/RA and may be used for the prediction of the most probable biotransformation reaction(s) and the appropriate reacting atoms in drug-like compounds.Graphical abstract.

DIGEP-Pred: web service for in silico prediction of drug-induced gene expression profiles based on structural formula

SUMMARY Experimentally found gene expression profiles are used to solve different problems in pharmaceutical studies, such as drug repositioning, resistance, toxicity and drug-drug interactions. A special web service, DIGEP-Pred, for prediction of drug-induced changes of gene expression profiles based on structural formulae of chemicals has been developed. Structure-activity relationships for prediction of drug-induced gene expression profiles were determined by Prediction of Activity Spectra for Substances (PASS) software. Comparative Toxicogenomics Database with data on the known drug-induced gene expression profiles of chemicals was used to create mRNA- and protein-based training sets. An average prediction accuracy for the training sets (ROC AUC) calculated by leave-one-out cross-validation on the basis of mRNA data (1385 compounds, 952 genes, 500 up- and 475 down-regulations) and protein data (1451 compounds, 139 genes, 93 up- and 55 down-regulations) exceeded 0.85. AVAILABILITY Freely available on the web at http://www.way2drug.com/GE.

CLC-Pred: A freely available web-service for in silico prediction of human cell line cytotoxicity for drug-like compounds

In silico methods of phenotypic screening are necessary to reduce the time and cost of the experimental in vivo screening of anticancer agents through dozens of millions of natural and synthetic chemical compounds. We used the previously developed PASS (Prediction of Activity Spectra for Substances) algorithm to create and validate the classification SAR models for predicting the cytotoxicity of chemicals against different types of human cell lines using ChEMBL experimental data. A training set from 59,882 structures of compounds was created based on the experimental data (IG50, IC50, and % inhibition values) from ChEMBL. The average accuracy of prediction (AUC) calculated by leave-one-out and a 20-fold cross-validation procedure during the training was 0.930 and 0.927 for 278 cancer cell lines, respectively, and 0.948 and 0.947 for cytotoxicity prediction for 27 normal cell lines, respectively. Using the given SAR models, we developed a freely available web-service for cell-line cytotoxicity profile prediction (CLC-Pred: Cell-Line Cytotoxicity Predictor) based on the following structural formula: http://way2drug.com/Cell-line/.

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

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

ROSC-Pred: web-service for rodent organ-specific carcinogenicity prediction

Motivation Identification of rodent carcinogens is an important task in risk assessment of chemicals. SAR methods were proposed to reduce the number of animal experiments. Most of these methods ignore information about organ-specificity of tumorigenesis. Our study was aimed at the creation of classification models and a freely available online service for prediction of rodent carcinogens considering the species (rats, mice), sex and tissue-specificity from structural formula of compounds. Results The data from Carcinogenic Potency Database for 1011 organic compounds evaluated on the standard two-year rodent carcinogenicity bioassay was used for the creation of training sets. Structure-activity relationships models for prediction of rodent organ-specific carcinogenicity were created by PASS software, which was based on Bayesian-like approach and Multilevel Neighborhoods of Atoms descriptors. The average prediction accuracy for training sets calculated by leave-one-out and 10-fold cross-validation was 79 and 78.2%, respectively. Availability and implementation Freely available on the web at http://www.way2drug.com/ROSC. Contact alexey.lagunin@ibmc.msk.ru. Supplementary information Supplementary data are available at Bioinformatics online.

Integral estimation of xenobiotics’ toxicity with regard to their metabolism in human organism

Abstract Toxicity and severe adverse effects are the primary cause of drug-candidate failures at the late stages of preclinical and clinical trials. Since most xenobiotics undergo biotransformations, their interaction with human organism reveals the effects produced by parent compounds and all metabolites. To increase the chances of successful drug development, estimation of the entire toxicity for drug substance and its metabolites is necessary for filtering out the potentially toxic compounds. We proposed the computational approach to the integral evaluation of xenobiotics’ toxicity based on the structural formula of the drug-like compound. In the framework of this study, the consensus QSAR model was developed based on the analysis of over 3000 compounds with information about their rat acute toxicity for intravenous route of administration. Four different numerical methods, estimating the integral toxicity, were proposed, and their comparative performance was studied using the external evaluation set consisting of 37 structures of drugs and 200 their metabolites. It was shown that, on the average, the best correspondence between the predicted and published data is obtained using the method that takes into account the estimated characteristics for both the parent compound and its most toxic metabolite.