TY - JOUR
T1 - A time-embedding network models the ontogeny of 23 hepatic drug metabolizing enzymes
AU - Matlock, Matthew K.
AU - Tambe, Abhik
AU - Elliott-Higgins, Jack
AU - Hines, Ronald N.
AU - Miller, Grover P.
AU - Swamidass, S. Joshua
N1 - Funding Information:
The authors declare that they have no competing financial conflicts of interests. Research reported in this publication was supported by the National Library of Medicine of the National Institutes of Health under award numbers R01LM012222 and R01LM012482 and by the National Institutes of Health under award number GM07200. The content is the sole responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Computations were performed using the facilities of the Washington University Center for High Performance Computing, which is partially funded by NIH grants nos. 1S10RR022984-01A1 and 1S10OD018091-01. We also thank both the Department of Immunology and Pathology at the Washington University School of Medicine, the Washington University Center for Biological Systems Engineering, and the Washington University Medical Scientist Training Program for their generous support of this work.
Publisher Copyright:
Copyright © 2019 American Chemical Society.
PY - 2019/8/19
Y1 - 2019/8/19
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85070597269&partnerID=8YFLogxK
U2 - 10.1021/acs.chemrestox.9b00223
DO - 10.1021/acs.chemrestox.9b00223
M3 - Article
C2 - 31304741
AN - SCOPUS:85070597269
SN - 0893-228X
VL - 32
SP - 1707
EP - 1721
JO - Chemical Research in Toxicology
JF - Chemical Research in Toxicology
IS - 8
ER -