Targeted Screening for Alzheimer's Disease Clinical Trials Using Data-Driven Disease Progression Models

• Main Authors: Alexander, D.C (Author), Barkhof, F. (Author), Cash, D.M (Author), For the Alzheimer's Disease Neuroimaging Initiative and the Alzheimer's Disease Cooperative Study (Author), Oxtoby, N.P (Author), Shand, C. (Author)
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2022
• Subjects: Alzheimer's disease; biomarkers; clinical trials; dementia; disease progression modeling; donepezil; mild cognitive impairment; screening
• Online Access: View Fulltext in Publisher

 Heterogeneity in Alzheimer's disease progression contributes to the ongoing failure to demonstrate efficacy of putative disease-modifying therapeutics that have been trialed over the past two decades. Any treatment effect present in a subgroup of trial participants (responders) can be diluted by non-responders who ideally should have been screened out of the trial. How to identify (screen-in) the most likely potential responders is an important question that is still without an answer. Here, we pilot a computational screening tool that leverages recent advances in data-driven disease progression modeling to improve stratification. This aims to increase the sensitivity to treatment effect by screening out non-responders, which will ultimately reduce the size, duration, and cost of a clinical trial. We demonstrate the concept of such a computational screening tool by retrospectively analyzing a completed double-blind clinical trial of donepezil in people with amnestic mild cognitive impairment (clinicaltrials.gov: NCT00000173), identifying a data-driven subgroup having more severe cognitive impairment who showed clearer treatment response than observed for the full cohort. Copyright © 2022 Oxtoby, Shand, Cash, Alexander and Barkhof.