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Machine Learning Uncovers Food- and Excipient-Drug Interactions
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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7179333/ https://www.ncbi.nlm.nih.gov/pubmed/32187543 http://dx.doi.org/10.1016/j.celrep.2020.02.094 |
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author | Reker, Daniel Shi, Yunhua Kirtane, Ameya R. Hess, Kaitlyn Zhong, Grace J. Crane, Evan Lin, Chih-Hsin Langer, Robert Traverso, Giovanni |
author_facet | Reker, Daniel Shi, Yunhua Kirtane, Ameya R. Hess, Kaitlyn Zhong, Grace J. Crane, Evan Lin, Chih-Hsin Langer, Robert Traverso, Giovanni |
author_sort | Reker, Daniel |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7179333 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-71793332020-04-23 Machine Learning Uncovers Food- and Excipient-Drug Interactions Reker, Daniel Shi, Yunhua Kirtane, Ameya R. Hess, Kaitlyn Zhong, Grace J. Crane, Evan Lin, Chih-Hsin Langer, Robert Traverso, Giovanni Cell Rep 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. 2020-03-17 /pmc/articles/PMC7179333/ /pubmed/32187543 http://dx.doi.org/10.1016/j.celrep.2020.02.094 Text en This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Reker, Daniel Shi, Yunhua Kirtane, Ameya R. Hess, Kaitlyn Zhong, Grace J. Crane, Evan Lin, Chih-Hsin Langer, Robert Traverso, Giovanni Machine Learning Uncovers Food- and Excipient-Drug Interactions |
title | Machine Learning Uncovers Food- and Excipient-Drug Interactions |
title_full | Machine Learning Uncovers Food- and Excipient-Drug Interactions |
title_fullStr | Machine Learning Uncovers Food- and Excipient-Drug Interactions |
title_full_unstemmed | Machine Learning Uncovers Food- and Excipient-Drug Interactions |
title_short | Machine Learning Uncovers Food- and Excipient-Drug Interactions |
title_sort | machine learning uncovers food- and excipient-drug interactions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7179333/ https://www.ncbi.nlm.nih.gov/pubmed/32187543 http://dx.doi.org/10.1016/j.celrep.2020.02.094 |
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