Machine Learning Uncovers Food- and Excipient-Drug Interactions.

Published

Journal Article

Inactive ingredients and generally recognized as safe compounds are regarded by the US Food and Drug Administration (FDA) as benign for human consumption within specified dose ranges, but a growing body of research has revealed that many inactive ingredients might have unknown biological effects at these concentrations and might alter treatment outcomes. To speed up such discoveries, we apply state-of-the-art machine learning to delineate currently unknown biological effects of inactive ingredients-focusing on P-glycoprotein (P-gp) and uridine diphosphate-glucuronosyltransferase-2B7 (UGT2B7), two proteins that impact the pharmacokinetics of approximately 20% of FDA-approved drugs. Our platform identifies vitamin A palmitate and abietic acid as inhibitors of P-gp and UGT2B7, respectively; in silico, in vitro, ex vivo, and in vivo validations support these interactions. Our predictive framework can elucidate biological effects of commonly consumed chemical matter with implications on food- and excipient-drug interactions and functional drug formulation development.

Full Text

Duke Authors

Cited Authors

  • Reker, D; Shi, Y; Kirtane, AR; Hess, K; Zhong, GJ; Crane, E; Lin, C-H; Langer, R; Traverso, G

Published Date

  • March 2020

Published In

Volume / Issue

  • 30 / 11

Start / End Page

  • 3710 - 3716.e4

PubMed ID

  • 32187543

Pubmed Central ID

  • 32187543

Electronic International Standard Serial Number (EISSN)

  • 2211-1247

International Standard Serial Number (ISSN)

  • 2211-1247

Digital Object Identifier (DOI)

  • 10.1016/j.celrep.2020.02.094

Language

  • eng