@article{16731fbeb43244e181a655b5ba7f792a,
title = "Artificial intelligence for dementia drug discovery and trials optimization",
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.",
author = "Thomas Doherty and Zhi Yao and {Al Khleifat}, Ahmad and Tantiangco, {Hanz M} and Stefano Tamburin and Chris Albertyn and Llewellyn, {David J.} and Oxtoby, {Neil P.} and Ranson, {Janice M.} and James Duce",
note = "Funding Information: With thanks to collaborators from the Deep Dementia Phenotyping (DEMON) Network State of the Science symposium participants (in alphabetical order): Peter BAGSHAW, Robin BORCHERT, Magda BUCHOLC, James DUCE, Charlotte JAMES, David LLEWELLYN, Donald LYALL, Sarah MARZI, Danielle NEWBY, Neil OXTOBY, Janice RANSON, Tim RITTMAN, Nathan SKENE, Eugene TANG, Michele VELDSMAN, Laura WINCHESTER, and Zhi YAO. This paper was the product of a DEMON Network State of the Science symposium entitled “Harnessing Data Science and AI in Dementia Research” funded by Alzheimer's Research UK. JMR is supported by Alzheimer's Research UK and the Alan Turing Institute/Engineering and Physical Sciences Research Council (EP/N510129/1). DJL is supported by Alzheimer's Research UK, National Institute for Health Research (NIHR) Applied Research Collaboration (ARC) South West Peninsula, National Health and Medical Research Council (NHMRC), National Institute on Aging/National Institutes of Health (RF1AG055654), and the Alan Turing Institute/Engineering and Physical Sciences Research Council (EP/N510129/1). NPO is a UKRI Future Leaders Fellow (MR/S03546X/1) and acknowledges funding from the National Institute for Health Research University College London Hospitals Biomedical Research Centre. AAK is funded by ALS Association Milton Safenowitz Research Fellowship (grant number 22‐ PDF‐609.DOI :10.52546/pc.gr.150909.), The Motor Neurone Disease Association (MNDA) Fellowship (Al Khleifat/Oct21/975‐799), The Darby Rimmer Foundation, and The NIHR Maudsley Biomedical Research Centre. Publisher Copyright: {\textcopyright} 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.",
year = "2023",
month = aug,
day = "17",
doi = "10.1002/alz.13428",
language = "English",
journal = "Alzheimer's Dementia: The Journal of the Alzheimer's Association",
publisher = "Elsevier",
}