Predicting routes of phase I and II metabolism based on quantum mechanics and machine learning
Creators: name: Öeren, Mario
name: Hunt, Peter A.
name: Wharrick, Charlotte E.
name: Ghomi, Hamed Tabatabaei
name: Segall, Matthew D.
Publisher: name: Taylor & Francis
Year: 2024
Subjects: subject: Biochemistry
subject: Genetics
subject: FOS: Biological sciences
subject: Physiology
subject: Pharmacology
subject: Astronomical and Space Sciences not elsewhere classified
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Mathematical Sciences not elsewhere classified
subject: Virology
subject: Computational Biology

description: Unexpected metabolism could lead to the failure of many late-stage drug candidates or even the withdrawal of approved drugs. Thus, it is critical to predict and study the dominant routes of metabolism in the early stages of research.We describe the development and validation of a ‘WhichEnzyme’ model that accurately predicts the enzyme families most likely to be responsible for a drug-like molecule’s metabolism. Furthermore, we combine this model with our previously published regioselectivity models for Cytochromes P450, Aldehyde Oxidases, Flavin-containing Monooxygenases, UDP-glucuronosyltransferases and Sulfotransferases – the most important Phase I and Phase II drug metabolising enzymes – and a ‘WhichP450’ model that predicts the Cytochrome P450 isoform(s) responsible for a compound’s metabolism.The regioselectivity models are based on a mechanistic understanding of these enzymes’ actions and use quantum mechanical simulations with machine learning methods to accurately predict sites of metabolism and the resulting metabolites. We train heuristics based on the outputs of the ‘WhichEnzyme’, ‘WhichP450’, and regioselectivity models to determine the most likely routes of metabolism and metabolites to be observed experimentally.Finally, we demonstrate that this combination delivers high sensitivity in identifying experimentally reported metabolites and higher precision than other methods for predicting in vivo metabolite profiles. Unexpected metabolism could lead to the failure of many late-stage drug candidates or even the withdrawal of approved drugs. Thus, it is critical to predict and study the dominant routes of metabolism in the early stages of research. We describe the development and validation of a ‘WhichEnzyme’ model that accurately predicts the enzyme families most likely to be responsible for a drug-like molecule’s metabolism. Furthermore, we combine this model with our previously published regioselectivity models for Cytochromes P450, Aldehyde Oxidases, Flavin-containing Monooxygenases, UDP-glucuronosyltransferases and Sulfotransferases – the most important Phase I and Phase II drug metabolising enzymes – and a ‘WhichP450’ model that predicts the Cytochrome P450 isoform(s) responsible for a compound’s metabolism. The regioselectivity models are based on a mechanistic understanding of these enzymes’ actions and use quantum mechanical simulations with machine learning methods to accurately predict sites of metabolism and the resulting metabolites. We train heuristics based on the outputs of the ‘WhichEnzyme’, ‘WhichP450’, and regioselectivity models to determine the most likely routes of metabolism and metabolites to be observed experimentally. Finally, we demonstrate that this combination delivers high sensitivity in identifying experimentally reported metabolites and higher precision than other methods for predicting in vivo metabolite profiles.