Artificial intelligence for dementia drug discovery and trials optimization

Thomas Doherty, Zhi Yao, Ahmad Al Khleifat, Hanz M Tantiangco, Stefano Tamburin, Chris Albertyn, David J. Llewellyn, Neil P. Oxtoby, Janice M. Ranson, James Duce

Research output: Contribution to journalReview articlepeer-review

4 Citations (Scopus)
110 Downloads (Pure)

Abstract

Drug discovery and clinical trial design for dementia have historically been challeng- ing. 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 clini- cal 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 thera- peutic 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.
Original languageEnglish
Pages (from-to)5922-5933
Number of pages12
JournalAlzheimer's Dementia: The Journal of the Alzheimer's Association
Volume19
Issue number12
DOIs
Publication statusPublished - 17 Aug 2023

Fingerprint

Dive into the research topics of 'Artificial intelligence for dementia drug discovery and trials optimization'. Together they form a unique fingerprint.

Cite this