Sharma Sheena, Kogan Clark, Varma Manthena V S, Prasad Bhagwat
Department of Pharmaceutical Sciences, Washington State University, Spokane, WA, USA.
Center for Interdisciplinary Statistical Education and Research (CISER), Washington State University, Pullman, WA, USA.
Drug Metab Pharmacokinet. 2023 Dec;53:100518. doi: 10.1016/j.dmpk.2023.100518. Epub 2023 Jun 12.
The effect of food on oral drug absorption is determined by the complex interplay among gut physiological factors and drug properties. The currently used dissolution testing and classification systems (biopharmaceutics classification system, BCS or biopharmaceutics drug disposition classification system, BDDCS) do not account for dynamic changes in gastrointestinal physiology caused by food intake. This study aimed to identify key drug properties that influence food effect (FE) using supervised machine learning approaches. The analysis showed that drugs with high logP, dose number, and extraction ratio have a higher probability of positive FE, while drugs with low permeability and high efflux saturation index have a greater likelihood of negative FE. Weakly acidic drugs also showed a greater probability of positive FE, particularly at pKa >4.3. The importance of drug properties in predicting FE was ranked as logP, dose number, extraction ratio, pKa, and permeability. The accuracy of FE prediction using the models was compared with BCS and extended clearance classification system (ECCS). Overall, the likelihood or magnitude of FE depends on physiological changes to food intake such as altered bile acid secretion rate, intestinal metabolism, transport kinetics, and gastric emptying time, which should be considered along with drug properties (e.g., solubility, logP, and ionization) in predicting FE of orally administered drugs.
食物对口服药物吸收的影响取决于肠道生理因素和药物性质之间复杂的相互作用。目前使用的溶出度测试和分类系统（生物药剂学分类系统，BCS或生物药剂学药物处置分类系统，BDDCS）并未考虑食物摄入引起的胃肠道生理动态变化。本研究旨在使用监督机器学习方法识别影响食物效应（FE）的关键药物性质。分析表明，具有高logP、剂量数和提取率的药物具有更高的阳性FE概率，而具有低渗透性和高外排饱和指数的药物具有更大的阴性FE可能性。弱酸性药物也显示出更高的阳性FE概率，尤其是在pKa>4.3时。预测FE时药物性质的重要性排序为logP、剂量数、提取率、pKa和渗透性。使用这些模型预测FE的准确性与BCS和扩展清除率分类系统（ECCS）进行了比较。总体而言，FE的可能性或程度取决于食物摄入引起的生理变化，如胆汁酸分泌率改变、肠道代谢、转运动力学和胃排空时间，在预测口服药物的FE时，应将这些因素与药物性质（如溶解度、logP和离子化）一并考虑。
