David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Division of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; MIT-IBM Watson AI Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Cell Rep. 2020 Mar 17;30(11):3710-3716.e4. doi: 10.1016/j.celrep.2020.02.094.
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.
美国食品和药物管理局（FDA）将赋形剂和一般公认安全的化合物视为在特定剂量范围内对人类食用是良性的，但越来越多的研究表明，许多赋形剂在这些浓度下可能具有未知的生物学效应，并可能改变治疗结果。为了加速这些发现，我们应用最先进的机器学习来描绘赋形剂目前未知的生物学效应——重点是 P-糖蛋白（P-gp）和尿苷二磷酸-葡萄糖醛酸转移酶 2B7（UGT2B7），这两种蛋白质影响大约 20%的 FDA 批准药物的药代动力学。我们的平台分别将维生素 A 棕榈酸酯和松香酸鉴定为 P-gp 和 UGT2B7 的抑制剂；在计算机模拟、体外、离体和体内验证中都支持这些相互作用。我们的预测框架可以阐明常用化学物质的生物学效应，这些效应对食物和赋形剂-药物相互作用以及功能性药物制剂的开发有影响。
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