Machine Learning Uncovers Excipient-Drug Interactions

Summary:

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.

Highlights:

Machine learning predicts biological effects of excipients and GRAS compounds
Abietic acid and gum rosin inhibit UGT2b7 metabolism ex vivo
Vitamin A palmitate inhibits P-glycoprotein transport in vivo
Such associations can cause unknown drug interactions

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Article Information: Daniel Reker, Yunhua Shi, Ameya R. Kirtane, Kaitlyn Hess, Grace J. Zhong, Evan Crane, Chih-Hsin Lin, Robert Langer, Giovanni Traverso; Cell Reports, 2020.