Modeling of Human Cytochrome P450-Mediated Drug Metabolism Using Unsupervised Machine Learning Approach

• Published in: Journal of medicinal chemistry 2003-08, Vol.46 (17), p.3631-3643
• Main Authors: Korolev, Dmitry, Balakin, Konstantin V, Nikolsky, Yuri, Kirillov, Eugene, Ivanenkov, Yan A, Savchuk, Nikolay P, Ivashchenko, Andrey A, Nikolskaya, Tatiana
• Format: Article
• Language: English
• Subjects: Algorithms; Biological and medical sciences; Cytochrome P-450 Enzyme System - chemistry; Cytochrome P-450 Enzyme System - metabolism; Databases, Factual; General aspects. Methods; General pharmacology; Humans; Isoenzymes - chemistry; Isoenzymes - metabolism; Medical sciences; Neural Networks (Computer); Pharmaceutical Preparations - chemistry; Pharmaceutical Preparations - metabolism; Pharmacokinetics. Pharmacogenetics. Drug-receptor interactions; Pharmacology. Drug treatments; Quantitative Structure-Activity Relationship; Toxicology; Xenobiotics - chemistry; Xenobiotics - metabolism
• ISSN: 0022-2623; 1520-4804
• DOI: 10.1021/jm030102a

We developed a computational algorithm for evaluating the possibility of cytochrome P450-mediated metabolic transformations that xenobiotics molecules undergo in the human body. First, we compiled a database of known human cytochrome P-450 substrates, products, and nonsubstrates for 38 enzyme-specific groups (total of 2200 compounds). Second, we determined the cytochrome-mediated metabolic reactions most typical for each group and examined the substrates and products of these reactions. To assess the probability of P450 transformations of novel compounds, we built a nonlinear quantitative structure−metabolism relationships (QSMR) model based on Kohonen self-organizing maps (SOM). This neural network QSMR model incorporated a predefined set of physicochemical descriptors encoding the key molecular properties that define the metabolic fate of individual molecules. Isozyme-specific groups of substrate molecules were visualized, thus facilitating prediction of tissue-specific metabolism. The developed algorithm can be used in early stages of drug discovery as an efficient tool for the assessment of human metabolism and toxicity of novel compounds in designing discovery libraries and in lead optimization.