Machine Learning Uncovers Food- and Excipient-Drug Interactions

Daniel Reker, Yunhua Shi, Ameya R. Kirtane, Kaitlyn Hess, Grace J. Zhong, Evan Crane, Chih Hsin Lin, Robert Langer, Giovanni Traverso

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)3710-3716.e4
JournalCell Reports
Issue number11
Publication statusPublished - Mar 17 2020


  • data science
  • drug delivery
  • excipient-drug interactions
  • food-drug interactions
  • inactive ingredients
  • machine learning
  • pharmacokinetics
  • pharmacology
  • virtual screening

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)


Dive into the research topics of 'Machine Learning Uncovers Food- and Excipient-Drug Interactions'. Together they form a unique fingerprint.

Cite this