A Time-Embedding Network Models the Ontogeny of 23 Hepatic 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 ontogeny of drug metabolism enzymes, the changes in both abundance
and type throughout development from the fetal period through adulthood.
Critically, these changes affect not only the overall clearance of
drugs but also exposure to individual metabolites. 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 ontogeny of 23 drug metabolism
enzymes. The time-embedding network recapitulates known demographic
factors impacting 3A5 expression. The time-embedding network also
effectively models the nonlinear dynamics of 2D6 expression, enabling
a better fit to clinical data than prior work. In contrast, a standard
neural network fails to model these features of 3A5 and 2D6 expression.
Finally, we combine the time-embedding model of ontogeny with additional
information to estimate age-dependent changes in reactive metabolite
exposure. This simple approach identifies age-dependent changes in
exposure to valproic acid and dextromethorphan metabolites and suggests
potential mechanisms of valproic acid toxicity. This approach may
help researchers evaluate the risk of drug toxicity in pediatric populations.