Eisai Europe Ltd, Hatfield, UK.
University of Westminster, London, UK.
Alzheimers Dement. 2023 Dec;19(12):5922-5933. doi: 10.1002/alz.13428. Epub 2023 Aug 16.
Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation.
药物研发和痴呆症临床试验设计历来具有挑战性。部分挑战源于患者异质性、疾病病程长短以及大脑靶标的可及性。应用大数据分析和机器学习工具进行药物研发，并利用这些工具为成功的临床试验设计提供信息，有可能加速进展。在治疗管道的多个阶段都存在机会，而且大量医疗数据集的日益普及为大数据分析提供了可能性，使其能够回答临床和治疗挑战中的关键问题。然而，在实现这一目标之前，需要克服几个挑战，只有多学科方法才能充分发挥数据驱动决策的潜力。本文综述了机器学习在临床试验设计和药物研发中的应用现状，同时提出了克服实施障碍的机会和建议。
