Machine Learning Uncovers Food- and Excipient-Drug Interactions
Name
1-s2.0-S2211124720302680-main.pdf
Description
Published version
Size
3.63 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Reker, Daniel
Shi, Yunhua
Kirtane, Ameya R
Hess, Kaitlyn
Zhong, Grace J
Crane, Evan
Lin, Chih-Hsin
Langer, Robert
Traverso, Giovanni
Date Issued
2020
Journal
Cell Reports
Publisher
Elsevier BV
Version
Final published version
Abstract
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.
MIT Department
Koch Institute for Integrative Cancer Research at MIT
MIT-IBM Watson AI Lab
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.CELREP.2020.02.094
