McComb Mason, Bies Robert, Ramanathan Murali
Department of Pharmaceutical Sciences, University at Buffalo, University at Buffalo, State University of New York, Buffalo, NY, USA.
Institute for Computational Data Science, University at Buffalo, NY, USA.
Br J Clin Pharmacol. 2022 Feb;88(4):1482-1499. doi: 10.1111/bcp.14801. Epub 2021 Mar 17.
The explosive growth in medical devices, imaging and diagnostics, computing, and communication and information technologies in drug development and healthcare has created an ever-expanding data landscape that the pharmacometrics (PMX) research community must now traverse. The tools of machine learning (ML) have emerged as a powerful computational approach in other data-rich disciplines but its effective utilization in the pharmaceutical sciences and PMX modelling is in its infancy. ML-based methods can complement PMX modelling by enabling the information in diverse sources of big data, e.g. population-based public databases and disease-specific clinical registries, to be harnessed because they are capable of efficiently identifying salient variables associated with outcomes and delineating their interdependencies. ML algorithms are computationally efficient, have strong predictive capabilities and can enable learning in the big data setting. ML algorithms can be viewed as providing a computational bridge from big data to complement PMX modelling. This review provides an overview of the strengths and weaknesses of ML approaches vis-à-vis population methods, assesses current research into ML applications in the pharmaceutical sciences and provides perspective for potential opportunities and strategies for the successful integration and utilization of ML in PMX.
医疗器械、成像与诊断、计算以及药物研发与医疗保健领域的通信和信息技术呈爆发式增长，创造了一个不断扩展的数据格局，如今药物计量学（PMX）研究群体必须应对这一格局。机器学习（ML）工具已成为其他数据丰富学科中一种强大的计算方法，但其在制药科学和PMX建模中的有效应用尚处于起步阶段。基于ML的方法能够利用来自各种大数据源（如基于人群的公共数据库和特定疾病的临床登记库）中的信息，从而补充PMX建模，因为它们能够有效地识别与结果相关的显著变量并描绘其相互依赖性。ML算法计算效率高，具有强大的预测能力，并且能够在大数据环境中进行学习。ML算法可被视为搭建了一座从大数据到补充PMX建模的计算桥梁。本综述概述了ML方法相对于群体方法的优缺点，评估了目前在制药科学中ML应用的研究情况，并为ML在PMX中成功整合与应用的潜在机会和策略提供了展望。
