Alexey A. Lagunin

Robustness of Biological Activity Spectra Predicting by Computer Program PASS for Noncongeneric Sets of Chemical Compounds

The computer system PASS provides simultaneous prediction of several hundreds of biological activity types for any drug-like compound. The prediction is based on the analysis of structure-activity relationships of the training set including more than 30000 known biologically active compounds. In this paper we investigate the influence on the accuracy of predicting the types of activity with PASS by (a) reduction of the number of structures in the training set and (b) reduction of the number of known activities in the training set. The compounds from the MDDR database are used to create heterogeneous training and evaluation sets. We demonstrate that predictions are robust despite the exclusion of up to 60% of information.

Prediction of Biological Activity Spectra via The Internet

The majority of biologically active compounds have both pharmacotherapeutic and side/toxic actions. To estimate general efficacy and safety of the molecules under study, their biological potential should be thoroughly evaluated. In an early stage of study, only information about structural formulae was available and was used as an input for computational prediction. Based on a structural formulae of compounds presented as SDF or MOL-files, computer program PASS predicts 900 pharmacological effects, mechanism of action, and specific toxicity. An average accuracy of prediction in leave-one-out cross-validation is about 85%. For evaluating new compounds, scientific community may use PASS via the Internet for free at URL: http://www.ibmh.msk.su/PASS. In the first 18 months of PASS Inets use, approximately 1000 researchers from 60 countries have obtained predicted biological activity spectra for about 23,000 different chemical compounds. More than 64 million PASS predictions for almost 250,000 compounds from Open NCI database are available on the web site http://cactus.nci.nih.gov/ncidb2/. These predictions are used for selecting compounds with desirable and without unwanted types of biological activities among the NCI samples available for screening.

Computer-aided prediction of QT-prolongation1

Drug-induced cardiac arrhythmia is acknowledged as a serious obstacle in successful development of new drugs. Several methods for in silico prediction of acquired long QT syndrome (LQTS) caused by the pharmacological blockade of human hERG K+ channels are discussed in literature. We propose to use the computer program PASS, which estimates the probabilities of about 3000 biological activities, not only for prediction of hERG blockade and QT-prolongation but also for the analysis of indirect mechanisms of these actions. After addition in the PASS training set of 163 compounds with data on QT-Prolongation and re-training, it was shown that accuracy of prediction was 87.1% and 81.8% for hERG blockade and QT-prolongation, respectively. Using computer program PharmaExpert we found that in the predicted biological activity spectra there was a certain correlation between the hERG blockade and some other molecular mechanisms of action. Possible role of 1-phosphatidylinositol-4-phospate 5-kinase, dimethylargininase and progesterone 11 alpha-monooxygenase inhibition in hERG blockade was discussed. 1Presented at CMTPI 2007: Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (Moscow, Russia, September 1–5, 2007).

SOMP: web-server for in silico prediction of sites of metabolism for drug-like compounds

UNLABELLEDnA new freely available web server site of metabolism predictor to predict the sites of metabolism (SOM) based on the structural formula of chemicals has been developed. It is based on the analyses of structure-SOM relationships using a Bayesian approach and labelled multilevel neighbourhoods of atoms descriptors to represent the structures of over 1000 metabolized xenobiotics. The server allows predicting SOMs that are catalysed by 1A2, 2C9, 2C19, 2D6 and 3A4 isoforms of cytochrome P450 and enzymes of the UDP-glucuronosyltransferase family. The average invariant accuracy of prediction that was calculated for the training sets (using leave-one-out cross-validation) and evaluation sets is 0.9 and 0.95, respectively.nnnAVAILABILITY AND IMPLEMENTATIONnFreely available on the web at http://www.way2drug.com/SOMP.

In silico assessment of adverse drug reactions and associated mechanisms.

During recent years, various in silico approaches have been developed to estimate chemical and biological drug features, for example chemical fragments, protein targets, pathways, among others, that correlate with adverse drug reactions (ADRs) and explain the associated mechanisms. These features have also been used for the creation of predictive models that enable estimation of ADRs during the early stages of drug development. In this review, we discuss various in silico approaches to predict these features for a certain drug, estimate correlations with ADRs, establish causal relationships between selected features and ADR mechanisms and create corresponding predictive models.

PASS Targets: Ligand-based multi-target computational system based on a public data and naïve Bayes approach

Estimation of interactions between drug-like compounds and drug targets is very important for drug discovery and toxicity assessment. Using data extracted from the 19th version of the ChEMBL database (https://www.ebi.ac.uk/chembl) as a training set and a Bayesian-like method realized in PASS software (http://www.way2drug.com/PASSOnline), we developed a computational tool for the prediction of interactions between protein targets and drug-like compounds. After training, PASS Targets became able to predict interactions of drug-like compounds with 2507 protein targets from different organisms based on analysis of structure–activity relationships for 589,107 different chemical compounds. The prediction accuracy, estimated as AUC ROC calculated by the leave-one-out cross-validation and 20-fold cross-validation procedures, was about 96%. Average AUC ROC value was about 90% for the external test set from approximately 700 known drugs interacting with 206 protein targets.

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.

Online resources for the prediction of biological activity of organic compounds

Online resources (PASS Online, SuperPred, SwissTargetPrediction and DRAR-CPI) for the prediction of biological activity of organic compounds from their structural formulas were considered. Based on a test set of drugs approved by 2014, the accuracies of predictions were compared. The four web resources can be arranged with respect to the quality of prediction (sensitivity, S) as follows: SwissTargetPrediction (S = 0.37) < DRAR-CPI (S = 0.41) < Super-Pred (S = 0.53) < PASS Online (S = 0.95). A conclusion was made that PASS Online employs superior machine learning algorithms based on MNA descriptors and Bayessian classifier in contrast to the similarity-based methods used in SuperPred and SwissTargetPrediction or the molecular docking methods used in DRAR-CPI. Possible reasons for the low prediction quality of SuperPred, SwissTargetPrediction, and DRAR-CPI are discussed and the development perspectives of this area of computational chemistry are given.

Identification of Drug Targets Related to the Induction of Ventricular Tachyarrhythmia Through a Systems Chemical Biology Approach

Ventricular tachyarrhythmia (VT) is one of the most serious adverse drug reactions leading to death. The in vitro assessment of the interaction of lead compounds with HERG potassium channels, which is one of the primary known causes of VT induction, is an obligatory test during drug development. However, experimental and clinical data support the hypothesis that the inhibition of ion channels is not the only mechanism of VT induction. Therefore, the identification of other drug targets contributing to the induction of VT is crucial. We developed a systems chemical biology approach for searching for such targets. This approach involves the following steps: (1) creation of special sets of VT-causing and non-VT-causing drugs, (2) statistical analysis of in silico predicted drug-target interaction profiles of studied drugs with 1738 human protein targets for the identification of potential VT-related targets, (3) gene ontology and pathway enrichment analysis of the revealed targets for the identification of biological processes underlying drug-induced VT etiology, (4) creation of a cardiomyocyte regulatory network (CRN) based on general and heart-specific signaling and regulatory pathways, and (5) simulation of changes in the behavior of the CRN caused by the inhibition of each node for the identification of potential VT-related targets. As a result, we revealed 312 potential VT-related targets and classified them into 3 confidence categories: high (36 proteins), medium (111 proteins), and low (165 proteins) classes. The most probable targets may serve as a basis for experimental confirmation and may be used for in vitro or in silico assessments of the relationships between drug candidates and drug-induced VT, the understanding of contraindications of drug application and dangerous drug combinations.

In Silico Identification of Proteins Associated with Drug-Induced Liver Injury Based on the Prediction of Drug-Target Interactions

Drug‐induced liver injury (DILI) is the leading cause of acute liver failure as well as one of the major reasons for drug withdrawal from clinical trials and the market. Elucidation of molecular interactions associated with DILI may help to detect potentially hazardous pharmacological agents at the early stages of drug development. The purpose of our study is to investigate which interactions with specific human protein targets may cause DILI. Prediction of interactions with 1534 human proteins was performed for the dataset with information about 699 drugs, which were divided into three categories of DILI: severe (178 drugs), moderate (310 drugs) and without DILI (211 drugs). Based on the comparison of drug‐target interactions predicted for different drugs’ categories and interpretation of those results using clustering, Gene Ontology, pathway and gene expression analysis, we identified 61 protein targets associated with DILI. Most of the revealed proteins were linked with hepatocytes’ death caused by disruption of vital cellular processes, as well as the emergence of inflammation in the liver. It was found that interaction of a drug with the identified targets is the essential molecular mechanism of the severe DILI for the most of the considered pharmaceuticals. Thus, pharmaceutical agents interacting with many of the identified targets may be considered as candidates for filtering out at the early stages of drug research.