近年来,我们利用多维生物和临床数据从实验环境到现实环境的能力呈指数级增长,改变了药物研究和开发。随着人工智能(AI)和机器学习(ML)应用的增加。以患者为中心的迭代正向和反向翻译是精准医学发现和发展的核心,从目标验证到药物治疗的优化。将高级分析集成到转化医学实践中,现在是充分利用各种大数据集中包含的信息的基本推动者,例如“组学”数据。如基因组的深层特征所示,转录组,蛋白质组,代谢组,微生物组,和曝光。在这篇评论中,我们概述了ML在药物发现和开发中的应用,与转化医学的三大战略支柱(目标,病人,剂量),并提供有关其改变学科科学和实践的潜力的观点。讨论了将ML方法整合到药物计量学学科中的机会,并将彻底改变基于模型的药物发现和开发的实践。最后,我们认为临床药理学的共同努力,生物信息学,和生物标记技术专家在跨职能团队设置中至关重要,以实现支持AI/ML的转化和精准医学的承诺。
The exponential increase in our ability to harness multi-dimensional biological and clinical data from experimental to real-world settings has transformed pharmaceutical research and development in recent years, with increasing applications of artificial intelligence (AI) and machine learning (ML). Patient-centered iterative forward and reverse translation is at the heart of precision medicine discovery and development across the continuum from target validation to optimization of pharmacotherapy. Integration of advanced analytics into the practice of Translational Medicine is now a fundamental enabler to fully exploit information contained in diverse sources of big data sets such as \"omics\" data, as illustrated by deep characterizations of the genome, transcriptome, proteome, metabolome, microbiome, and exposome. In this commentary, we provide an overview of ML applications in drug discovery and development, aligned with the three strategic pillars of Translational Medicine (target, patient, dose) and offer perspectives on their potential to transform the science and practice of the discipline. Opportunities for integrating ML approaches into the discipline of Pharmacometrics are discussed and will revolutionize the practice of model-informed drug discovery and development. Finally, we posit that joint efforts of Clinical Pharmacology, Bioinformatics, and Biomarker Technology experts are vital in cross-functional team settings to realize the promise of AI/ML-enabled Translational and Precision Medicine.