人工智能结合生理药代动力学模型的研究进展  

Research progress of artificial intelligence combined with physiologically based pharmacokinetic models

作　　者：李龙杰 计佩影 郑澳乐 穆耶赛尔·阿里甫 相小强[1] LI Long-jie;JI Pei-ying;ZHENG Ao-le;ALIFU Muyesaier;XIANG Xiao-qiang(School of Pharmacy,Fudan University,Shanghai 200120,China;Kong Jiang Hospital of Yangpu District,Shanghai 200000,China)

机构地区：[1]复旦大学药学院,上海200120 [2]上海市杨浦区控江医院,上海200000

出　　处：《药学学报》2024年第9期2491-2498,共8页Acta Pharmaceutica Sinica

基　　金：国家自然科学基金面上项目(82373952).

摘　　要：生理药代动力学(physiologically based pharmacokinetic,PBPK)模型已经被广泛用于预测药物的吸收、分布、代谢和排泄等特性,而基于机器学习(machine learning,ML)和人工智能(artificial intelligenceAI)可以和PBPK模型进行深度融合,从而加快PBPK的预测速度和提高其预测质量,进一步加快药物研发进展。本文介绍了机器学习和人工智能在药代动力学中的应用,对基于机器学习和人工智能的生理药代动力学模型研究进展进行综述,并分析了机器学习和人工智能应用的局限性以及其应用前景和展望。Physiologically based pharmacokinetic(PBPK)models have been widely used to predict various stages of drug absorption,distribution,metabolism and excretion.Models based on machine learning(ML)and artificial intelligence(AI)can provide better ideas for the construction of PBPK models,which can accelerate the prediction speed and improve the prediction quality of PBPK.ML and AL can complement the advantages of PBPK model to accelerate the progress of drug research and development.This review introduces the application of machine learning and artificial intelligence in pharmacokinetics,summarizes the research progress of physiological pharmacokinetic models based on machine learning and artificial intelligence,and analyzes the limitations of machine learning and artificial intelligence applications and their application prospects and prospects.

关 键 词：生理药代动力学模型 人工智能 机器学习 药代动力学 药物毒理学 药物相互作用 

分 类 号：R917[医药卫生—药物分析学]