In Silico Prediction of Toxicological and Pharmacokinetic Characteristics of Medicinal Compounds

Scientific relevance. Studies of the toxicological and pharmacokinetic properties of medicinal compounds are a crucial stage of preclinical research; unsatisfactory results may invalidate further drug development. Therefore, the development of in silico methods for a preliminary pre-experimental assessment of toxicological and pharmacokinetic properties is a relevant and crucial task. Aim. The study aimed to review current approaches to in silico prediction of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters of pharmacologically active compounds, in particular, the most important toxicological and pharmacokinetic parameters, and to present the results of the authors’ own research in this area. Discussion. According to the review of models for predicting the toxicological properties of chemical compounds (acute toxicity, carcinogenicity, mutagenicity, genotoxicity, endocrine toxicity, cytotoxicity, cardiotoxicity, hepatotoxicity, and immunotoxicity), the accuracy of predictions ranged from 74.0% to 98.0%. According to the review of models for predicting the pharmacokinetic properties of chemical compounds (gastrointestinal absorption; oral bioavailability; volume of distribution; total, renal, and hepatic clearance; and half-life), the coefficient of determination for the predictions ranged from 0.265 to 0.920. The literature review showed that the most widely used methods for in silico assessment of the ADMET parameters of pharmacologically active compounds included the random forest method and the support vector machines method. The authors compared the literature data with the results they obtained by modelling 12 toxicological and pharmacokinetic properties of chemical compounds using the consensus method in the IT Microcosm system and artificial neural networks. IT Microcosm outperformed the models described in the literature in terms of predicting 2 toxicological properties, including carcinogenicity and blood–brain barrier penetration (the prediction accuracy reached 93.4%). Neural network models were superior in predicting 4 toxicological properties, including acute toxicity, carcinogenicity, genotoxicity, and blood–brain barrier penetration (the prediction accuracy reached 93.8%). In addition, neural network models were better in predicting 3 pharmacokinetic properties, including gastrointestinal absorption, volume of distribution, and hepatic clearance (the coefficient of determination reached 0.825). Conclusions. The data obtained suggest that artificial neural networks are the most promising and practically significant direction for the development of in silico systems for predicting the ADMET characteristics of new medicinal products.