A Time-EmbeddingNetwork Models the Ontogeny of 23Hepatic Drug Metabolizing Enzymes

Pediatric patients are at elevated risk of adverse drug reactions, and there is insufficient information on drug safety in children. Complicating risk assessment in children, there are numerous age-dependent changes in the absorption, distribution, metabolism, and elimination of drugs. A key contributor to age-dependent drug toxicity risk is the ontogenyof drug metabolismenzymes, the changes in both abundance and type throughout development from the fetal period through adulthood. Critically, these changes affect not just the overall clearance of drugs, but exposure to individual metabolites as well. In this study, we introduce time-embedding neural networks in order to model population-level variation in metabolism enzyme expression as a function of age. We use a time-embedding network to model the ontogenyof 23 drug metabolismenzymes. The time-embedding network recapitulates known demographic factors impacting 3A5 expression. The time-embedding network also effectively models the non-linear dynamics...