Alexey Zakharov

PASS: identification of probable targets and mechanisms of toxicity†

Toxicity of chemical compound is a complex phenomenon that may be caused by its interaction with different targets in the organism. Two distinct types of toxicity can be broadly specified: the first one is caused by the strong compounds interaction with a single target (e.g. AChE inhibition); while the second one is caused by the moderate compounds interaction with many various targets. Computer program PASS predicts about 2500 kinds of biological activities based on the structural formula of chemical compounds. Prediction is based on the robust analysis of structure-activity relationships for about 60,000 biologically active compounds. Mean accuracy exceeds 90% in leave-one-out cross-validation. In addition to some kinds of adverse effects and specific toxicity (e.g. carcinogenicity, mutagenicity, etc.), PASS predicts ∼2000 kinds of biological activities at the molecular level, that providing an estimated profile of compounds action in biological space. Such profiles can be used to recognize the most probable targets, interaction with which might be a reason of compounds toxicity. Applications of PASS predictions for analysis of probable targets and mechanisms of toxicity are discussed. †Presented at the 12th International Workshop on Quantitative Structure-Activity Relationships in Environmental Toxicology (QSAR2006), 8–12 May 2006, Lyon, France.

QSAR Modelling of Rat Acute Toxicity on the Basis of PASS Prediction

The method for QSAR modelling of rat acute toxicity based on the combination of QNA (Quantitative Neighbourhoods of Atoms) descriptors, PASS (Prediction of Activity Spectra for Substances) predictions and self‐consistent regression (SCR) is presented. PASS predicted biological activity profiles are used as independent input variables for QSAR modelling with SCR. QSAR models were developed using LD50 values for compounds tested on rats with four types of administration (oral, intravenous, intraperitoneal, subcutaneous). The proposed method was evaluated on the set of compounds tested for acute rat toxicity with oral administration (7286 compounds) used for testing the known QSAR methods in T.E.S.T. 3.0 program (U.S. EPA). The several other sets of compounds tested for acute rat toxicity by different routes of administration selected from SYMYX MDL Toxicity Database were used too. The method was compared with the results of prediction of acute rodent toxicity for noncongeneric sets obtained by ACD/Labs Inc. The test sets were predicted with regards to the applicability domain. Comparison of accuracy for QSAR models obtained separately using QNA descriptors, PASS predictions, nearest neighbours’ assessment with consensus models clearly demonstrated the benefits of consensus prediction. Free available web‐service for prediction of LD50 values of rat acute toxicity was developed: http://www.pharmaexpert.ru/GUSAR/AcuToxPredict/

QNA-based ‘Star Track’ QSAR approach

In the existing quantitative structure–activity relationship (QSAR) methods any molecule is represented as a single point in a many-dimensional space of molecular descriptors. We propose a new QSAR approach based on Quantitative Neighbourhoods of Atoms (QNA) descriptors, which characterize each atom of a molecule and depend on the whole molecule structure. In the ‘Star Track’ methodology any molecule is represented as a set of points in a two-dimensional space of QNA descriptors. With our new method the estimate of the target property of a chemical compound is calculated as the average value of the function of QNA descriptors in the points of the atoms of a molecule in QNA descriptor space. Substantially, we propose the use of only two descriptors rather than more than 3000 molecular descriptors that apply in the QSAR method. On the basis of this approach we have developed the computer program GUSAR and compared it with several widely used QSAR methods including CoMFA, CoMSIA, Golpe/GRID, HQSAR and others, using ten data sets representing various chemical series and diverse types of biological activity. We show that in the majority of cases the accuracy and predictivity of GUSAR models appears to be better than those for the reference QSAR methods. High predictive ability and robustness of GUSAR are also shown in the leave-20%-out cross-validation procedure.

Computer-aided prediction for medicinal chemistry via the Internet1

Some computational tools for medicinal chemistry freely available on the Internet were compared to examine whether the results of prediction obtained with different methods coincided or not. It was shown that the correlation coefficients varied from 0.65 to 0.90 for log P (seven methods), from 0.01 to 0.73 for aqueous solubility (four methods), and from 0.19 to 0.73 for drug-likeness (three methods). While for log P estimates, reasonable average pairwise correlation was found, for aqueous solubility and drug-likeness it was rather poor. Therefore, using computational tools freely available via the Internet, medicinal chemists should evaluate their accuracy versus experimental data for particular series of compounds. In contrast to prediction of above mentioned properties, which can be done with several Internet tools, wide profiling of biological activity can be obtained only with PASS Inet (http://www.ibmc.msk.ru/PASS). PASS Inet was tested by a dozen medicinal chemists for compounds from different chemical series with various kinds of biological activity, and in the majority of cases the results of prediction coincided with the experiments. New anxiolytics, antiarrhythmics, antileishmanials, and other biologically active agents have been identified on this basis. The advantages and limitations of computer-aided predictions for medicinal chemistry via the Internet are discussed. 1Presented at CMTPI 2007: Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (Moscow, Russia, September 1–5, 2007).

A new approach to QSAR modelling of acute toxicity

A new QSAR approach based on a Quantitative Neighbourhoods of Atoms description of molecular structures and self-consistent regression was developed. Its prediction accuracy, advantages and limitations were analysed from three sets of published experimental data on acute toxicity: 56 phenylsulfonyl carboxylates for Vibrio fischeri; 65 aromatic compounds for the alga Chlorella vulgaris and 200 phenols for the ciliated protozoan Tetrahymena pyriformis. According to our findings, the proposed approach provides a good correlation and prediction accuracy (r 2 = 0.908 and Q 2 = 0.866) for the set of 56 phenylsulfonyl carboxylates and the 65 aromatic compounds tested on C. vulgaris (r 2 = 0.885, Q 2 = 0.849). For the 200 phenols tested on T. pyriformis, the prediction accuracy was r 2 = 0.685 and Q 2 = 0.651. This is at least as good as the best results obtained with the other QSAR methods originally used on the same data sets. †Presented at the 12th International Workshop on Quantitative Structure-Activity Relationships in Environmental Toxicology (QSAR2006), 8–12 May 2006, Lyon, France.

Synthesis, antifungal activity and QSAR study of 2-arylhydroxynitroindoles

A series of 2-arylhydroxynitroindoles were prepared and tested for antifungal activity in vitro. The preliminary bioassays indicated that some compounds are comparable to the commercial fungicide (triadimefon). To further explore the structure-activity relationships, the data set of the seventeen structures and their quantitative values of antifungal activities were used for QSAR modeling. Based on the obtained QSAR models four new chemical compounds were designed, synthesized and tested in fungicidal assays. Reasonable correspondence between the experimental and predicted values of antifungal activity was observed.

CYCLONET—an integrated database on cell cycle regulation and carcinogenesis

Computational modelling of mammalian cell cycle regulation is a challenging task, which requires comprehensive knowledge on many interrelated processes in the cell. We have developed a web-based integrated database on cell cycle regulation in mammals in normal and pathological states (Cyclonet database). It integrates data obtained by ‘omics’ sciences and chemoinformatics on the basis of systems biology approach. Cyclonet is a specialized resource, which enables researchers working in the field of anticancer drug discovery to analyze the wealth of currently available information in a systematic way. Cyclonet contains information on relevant genes and molecules; diagrams and models of cell cycle regulation and results of their simulation; microarray data on cell cycle and on various types of cancer, information on drug targets and their ligands, as well as extensive bibliography on modelling of cell cycle and cancer-related gene expression data. The Cyclonet database is also accessible through the BioUML workbench, which allows flexible querying, analyzing and editing the data by means of visual modelling. Cyclonet aims to predict promising anticancer targets and their agents by application of Prediction of Activity Spectra for Substances. The Cyclonet database is available at .

Functional classification of proteins based on projection of amino acid sequences: application for prediction of protein kinase substrates

BackgroundThe knowledge about proteins with specific interaction capacity to the protein partners is very important for the modeling of cell signaling networks. However, the experimentally-derived data are sufficiently not complete for the reconstruction of signaling pathways. This problem can be solved by the network enrichment with predicted protein interactions. The previously published in silico method PAAS was applied for prediction of interactions between protein kinases and their substrates.ResultsWe used the method for recognition of the protein classes defined by the interaction with the same protein partners. 1021 protein kinase substrates classified by 45 kinases were extracted from the Phospho.ELM database and used as a training set. The reasonable accuracy of prediction calculated by leave-one-out cross validation procedure was observed in the majority of kinase-specificity classes. The random multiple splitting of the studied set onto the test and training set had also led to satisfactory results. The kinase substrate specificity for 186 proteins extracted from TRANSPATH® database was predicted by PAAS method. Several kinase-substrate interactions described in this database were correctly predicted. Using the previously developed ExPlain™ system for the reconstruction of signal transduction pathways, we showed that addition of the newly predicted interactions enabled us to find the possible path between signal trigger, TNF-alpha, and its target genes in the cell.ConclusionsIt was shown that the predictions of protein kinase substrates by PAAS were suitable for the enrichment of signaling pathway networks and identification of the novel signaling pathways. The on-line version of PAAS for prediction of protein kinase substrates is freely available at http://www.ibmc.msk.ru/PAAS/.

In silico method for identification of promising anticancer drug targets.

In recent years, the accumulation of the genomics, proteomics, transcriptomics data for topological and functional organization of regulatory networks in a cell has provided the possibility of identifying the potential targets involved in pathological processes and of selecting the most promising targets for future drug development. We propose an approach for anticancer drug target identification, which, using microarray data, allows discrete modelling of regulatory network behaviour. The effect of drugs inhibiting a particular protein or a combination of proteins in a regulatory network is analysed by simulation of a blockade of single nodes or their combinations. The method was applied to the four groups of breast cancer, HER2/neu-positive breast carcinomas, ductal carcinoma, invasive ductal carcinoma and/or a nodal metastasis, and to generalized breast cancer. As a result, some promising specific molecular targets and their combinations were identified. Inhibitors of some identified targets are known as potential drugs for therapy of malignant diseases; for some other targets we identified hits in the commercially available sample databases.

Computer search for molecular mechanisms of ulcerogenic action of non-steroidal anti-inflammatory drugs

Peptic ulcers are the most frequent side effect of therapy with non-steroidal anti-inflammatory drugs (NSAIDs). Good experimental evidence exists that pathogenesis of peptic ulcers cannot be attributed only to inhibition of cyclooxygenases. The knowledge about other molecular mechanisms of drug action associated with development of peptic ulcers could be useful for design of new safer NSAIDs. However, considerable time and material resources are needed for corresponding experimental studies. For simplification of the experimental search, we have developed an approach for in silico identification of putative molecular mechanisms of drug actions associated with their side effects. We have generated a data set of 85 NSAIDs, which includes information about their structures and side effects. Unknown molecular mechanisms of action of these NSAIDs were evaluated by the computer program PASS (Prediction of Activity Spectra for Substances) predicting more than 3000 molecular mechanisms of action based on structural formula of sub-stances. Statistically significant associations have been found between predicted molecular mechanisms of action and development of peptic ulcers. Twenty six molecular mechanisms of action probably associated with development of peptic ulcers have been found: two of them were known previously and 24 were quite new. Analyzing Gene Ontology data, data on signal and metabolic pathways, and available MEDLINE publication data, we proposed hypotheses on the role of 10 molecular mechanisms of action in the pathogenesis of peptic ulcer.