Abstract
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.
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This post is Copyright: Thomas Doherty,
Zhi Yao,
Ahmad A.l. Khleifat,
Hanz Tantiangco,
Stefano Tamburin,
Chris Albertyn,
Lokendra Thakur,
David J. Llewellyn,
Neil P. Oxtoby,
Ilianna Lourida,
for the Deep Dementia Phenotyping (DEMON) Network,
Janice M. Ranson,
James A. Duce | August 17, 2023