Vladimir V. Poroikov

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.

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).

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.

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 fragment-based drug design using a PASS approach

Fragment-based drug design integrates different methods to create novel ligands using fragment libraries focused on particular biological activities. Experimental approaches to the preparation of fragment libraries have some drawbacks caused by the need for target crystallization (X-ray and nuclear magnetic resonance) and careful immobilization (surface plasmon resonance). Molecular modelling (docking) requires accurate data on protein–ligand interactions, which are difficult to obtain for some proteins. The main drawbacks of QSAR application are associated with the need to collect large homogeneous datasets of chemical structures with experimentally determined self-consistent quantitative values (potency). We propose a ligand-based approach to the selection of fragments with positive contribution to biological activity, developed on the basis of the PASS algorithm. The robustness of the PASS algorithm for heterogeneous datasets has been shown earlier. PASS estimates qualitative (yes/no) prediction of biological activity spectra for over 4000 biological activities and, therefore, provides the basis for the preparation of a fragment library corresponding to multiple criteria. The algorithm for fragment selection has been validated using the fractions of intermolecular interactions calculated for known inhibitors of nine enzymes extracted from the Protein Data Bank database. The statistical significance of differences between fractions of intermolecular interactions corresponds, for several enzymes, to the estimated positive and negative contribution of fragments in enzyme inhibition.

Pharmacological repositioning of Achyranthes aspera as an antidepressant using pharmacoinformatic tools PASS and PharmaExpert: a case study with wet lab validation

Abstract Traditional knowledge guides the use of plants for restricted therapeutic indications, but their pharmacological actions may be found beyond their ethnic therapeutic indications employing emerging computational tools. In this context, the present study was envisaged to explore the novel pharmacological effect of Achyranthes aspera (A. aspera) using PASS and PharmaExpert software tools. Based on the predicted mechanisms of the antidepressant effect for all analysed phytoconstituents of A. aspera, one may suggest its significant antidepressant action. The possible mechanism of this novel pharmacological effect is the enhancement of serotonin release, in particular caused by hexatriacontane. Therefore, pharmacological validation of the methanolic extract, hexatriacontane rich (HRF) and hexatriacontane lacking fraction (HLF) of A. aspera was carried out using the Forced Swimming Test and Tail suspension test in mice. The cortical and hippocampal monoamine and their metabolite levels were measured using high performance liquid chromatography (HPLC). A. aspera methanolic extract, HRF treatments showed a significant antidepressant effect comparable to imipramine. Further, the corresponding surge in cortical and hippocampal monoamine and their metabolite levels was also observed with these treatments. In conclusion, A. aspera has shown a significant antidepressant effect, possibly due to hexatriacontane, by raising monoamine levels.

Computer evaluation of hidden potential of phytochemicals of medicinal plants of the traditional Indian ayurvedic medicine

Applicability of our computer programs PASS and PharmaExpert to prediction of biological activity spectra of rather complex and structurally diverse phytocomponents of medicinal plants, both separately and in combinations has been evaluated. The web-resource on phytochemicals of 50 medicinal plants used in Ayurveda was created for the study of hidden therapeutic potential of Traditional Indian Medicine (TIM) (http://ayurveda.pharmaexpert.ru). It contains information on 50 medicinal plants, their using in TIM and their pharmacology activities, also as 1906 phytocomponents. PASS training set was updated by addition of information about 946 natural compounds; then the training procedure and validation were performed, to estimate the quality of PASS prediction. It was shown that the difference between the average accuracy of prediction obtained in leave-5%-out cross-validation (94,467%) and in leave-one-out cross-validation (94,605%) is very small. These results showed high predictive ability of the program. Results of biological activity spectra prediction for all phytocomponents included in our database are in good correspondence with the experimental data. Additional kinds of biological activity predicted with high probability provide the information about most promising directions of further studies. The analysis of prediction results of sets of phytocomponents in each of 50 medicinal plants was made by PharmaExpert software. Based on this analysis, we found that the combination of phytocomponents from Passiflora incarnata may exhibit nootropic, anticonvulsant and antidepressant effects. Experiments carried out in mice models confirmed the predicted effects of Passiflora incarnata extracts.

Prediction of metabolites of epoxidation reaction in MetaTox

Abstract Biotransformation is a process of the chemical modifications which may lead to the reactive metabolites, in particular the epoxides. Epoxide reactive metabolites may cause the toxic effects. The prediction of such metabolites is important for drug development and ecotoxicology studies. Epoxides are formed by some oxidation reactions, usually catalysed by cytochromes P450, and represent a large class of three-membered cyclic ethers. Identification of molecules, which may be epoxidized, and indication of the specific location of epoxide functional group (which is called SOE - site of epoxidation) are important for prediction of epoxide metabolites. Datasets from 355 molecules and 615 reactions were created for training and validation. The prediction of SOE is based on a combination of LMNA (Labelled Multilevel Neighbourhood of Atom) descriptors and Bayesian-like algorithm implemented in PASS software and MetaTox web-service. The average invariant accuracy of prediction (AUC) calculated in leave-one-out and 20-fold cross-validation procedures is 0.9. Prediction of epoxide formation based on the created SAR model is included as the component of MetaTox web-service (http://www.way2drug.com/mg).