Machine Learning Uncovers Food- and Excipient-Drug Interactions.
Cell Rep
; 30(11): 3710-3716.e4, 2020 03 17.
Article
em En
| MEDLINE
| ID: mdl-32187543
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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Interações Medicamentosas
/
Excipientes
/
Aprendizado de Máquina
/
Alimentos
Tipo de estudo:
Prognostic_studies
Limite:
Animals
/
Female
/
Humans
País como assunto:
America do norte
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article