In Vitro Bioactivation of 3-(N-Phenylamino)propane-1,2-diol by Human and Rat Liver Microsomes and Recombinant P450 Enzymes. Implications for Toxic Oil Syndrome

Human Family 1-4 cytochrome P450 enzymes involved in the metabolic activation of xenobiotic and physiological chemicals: an update

This is an overview of the metabolic activation of drugs, natural products, physiological compounds, and general chemicals by the catalytic activity of cytochrome P450 enzymes belonging to Families 1-4. The data were collected from > 5152 references. The total number of data entries of reactions catalyzed by P450s Families 1-4 was 7696 of which 1121 (~ 15%) were defined as bioactivation reactions of different degrees. The data were divided into groups of General Chemicals, Drugs, Natural Products, and Physiological Compounds, presented in tabular form. The metabolism and bioactivation of selected examples of each group are discussed. In most of the cases, the metabolites are directly toxic chemicals reacting with cell macromolecules, but in some cases the metabolites formed are not direct toxicants but participate as substrates in succeeding metabolic reactions (e.g., conjugation reactions), the products of which are final toxicants. We identified a high level of activation for three groups of compounds (General Chemicals, Drugs, and Natural Products) yielding activated metabolites and the generally low participation of Physiological Compounds in bioactivation reactions. In the group of General Chemicals, P450 enzymes 1A1, 1A2, and 1B1 dominate in the formation of activated metabolites. Drugs are mostly activated by the enzyme P450 3A4, and Natural Products by P450s 1A2, 2E1, and 3A4. Physiological Compounds showed no clearly dominant enzyme, but the highest numbers of activations are attributed to P450 1A, 1B1, and 3A enzymes. The results thus show, perhaps not surprisingly, that Physiological Compounds are infrequent substrates in bioactivation reactions catalyzed by P450 enzyme Families 1-4, with the exception of estrogens and arachidonic acid. The results thus provide information on the enzymes that activate specific groups of chemicals to toxic metabolites.

Chemical and in vitro biological information to predict mouse liver toxicity using recursive random forests

In this study, recursive random forests were used to build classification models for mouse liver toxicity. The mouse liver toxicity endpoint (67 toxic and 166 non-toxic) was a composition of four in vivo chronic systemic and carcinogenic toxicity endpoints (non-proliferative, neoplastic, proliferative and gross pathology). A multiple under-sampling approach and a shifted classification threshold of 0.288 (non-toxic < 0.288 and toxic ≥ 0.288) were used to cope with the unbalanced data. Our study showed that recursive random forests are very efficient in variable selection and for the development of predictive in silico models. Generally, over 95% redundant descriptors could be reduced from modelling for all the chemical, biological and hybrid models in this study. The predictive performance of chemical models (CCR of 0.73) is comparable with hybrid model performance (CCR of 0.74). Descriptors related to the octanol-water partition coefficient are vital for model performance. The in vitro endpoint of CYP2A2 played a key role in the development and interpretation of hybrid models. Identifying high-throughput screening assays relevant to liver toxicity would be key for improving in silico models of liver toxicity.