Lead Investigator: Christian Dansereau, Perceiv AI
Title of Proposal Research: Validation of prognostic machine learning tools for forecasting clinical progression to enrich Alzheimer’s disease trials
Vivli Data Request: 9236
Funding Source: Perceiv Research Inc. will fund the proposed research.
Potential Conflicts of Interest: Christian Dansereau is a full-time employee and holds stocks/stock options with Perceiv Research Inc. We will declare all conflicts of interest in any subsequent publications. Angela Tam is a full-time employee and holds stocks/stock options with Perceiv Research Inc. We will declare all conflicts of interest in any subsequent publications. César Laurent is a full-time employee and holds stocks/stock options with Perceiv Research Inc. We will declare all conflicts of interest in any subsequent publications.
Summary of the Proposed Research:
It is estimated that around 50 million people currently live with dementia worldwide and this number will reach 152 million by 2050. Alzheimer’s disease is a neurodegenerative disorder and a leading cause of dementia as it is believed to account for 60-80% of cases. Over 90% of trials in Alzheimer’s disease have failed to demonstrate the effectiveness of the tested treatments. Key contributing factors to the failures of trials include individual heterogeneity (differences) in clinical progression and suboptimal patient selection. Clinical progression (progress of the disease) across Alzheimer’s disease patients is highly variable as some patients will decline very quickly and others will remain relatively stable for a long time. The inadvertent enrollment of patients who will remain stable into a trial is problematic because not only does it expose patients who are unlikely to benefit from treatment to unnecessary risks, but it also reduces a trial’s potential to detect treatment effects. Alzheimer’s disease trials therefore need enrichment strategies to select the most appropriate participants, individuals who will likely decline within the trials’ durations.
We propose to use prognostic machine learning models that forecast disease progression to select participants who will likely decline within the typical trial duration as an enrichment strategy. We have previously trained and validated models on individuals with mild cognitive impairment (decline in mental abilities) and Alzheimer’s disease dementia who participated in observational studies (i.e. research that did not test potential treatments). With these models, we demonstrated enrichment with likely decliners can allow for substantial reductions in the number of patients that need to be recruited compared to standard clinical trial recruitment while preserving the same potential to detect treatment effects. We would like to validate these models on clinical trial subject groups to assess their generalizability, to quantify the impact of enriching trial samples with likely decliners, and to compare treatment efficacy in subgroups of likely decliners against the total patient populations.
Better patient selection is important for accelerating therapeutic development in Alzheimer’s disease. Prognostic models for enrichment can give trials higher chances for success by enhancing their power to detect treatment effects in the most appropriate patient populations.
Requested Studies:
Effect of Passive Immunization on the Progression of Mild Alzheimer’s Disease: Solanezumab (LY2062430) Versus Placebo
Data Contributor: Lilly
Study ID: NCT01900665
Sponsor ID: 15136
Continued Efficacy and Safety Monitoring of Solanezumab, an Anti-Amyloid β Antibody in Patients With Alzheimer’s Disease
Data Contributor: Lilly
Study ID: NCT01127633
Sponsor ID: 11935
Effect of Passive Immunization on the Progression of Alzheimer’s Disease: LY2062430 Versus Placebo
Data Contributor: Lilly
Study ID: NCT00904683
Sponsor ID: 11934
Effect of LY2062430, an Anti-Amyloid Beta Monoclonal Antibody, on the Progression of Alzheimer’s Disease as Compared With Placebo
Data Contributor: Lilly
Study ID: NCT00905372
Sponsor ID: 6747
Effect of LY450139 a y-Secretase Inhibitor, on the Progression of Alzheimer’s Disease as Compared With Placebo
Data Contributor: Lilly
Study ID: NCT00762411
Sponsor ID: 11271
Effect of γ-Secretase Inhibition on the Progression of Alzheimer’s Disease: LY450139 Versus Placebo
Data Contributor: Lilly
Study ID: NCT00594568
Sponsor ID: 7666
A Phase 2 Multiple Dose, Multicenter, Randomized, Double-Blind, Placebo-Controlled Study to Evaluate the Efficacy and Safety of ABBV-8E12 in Subjects With Early Alzheimer’s Disease
Data Contributor: AbbVie
Study ID: NCT02880956
Sponsor ID: M15-566
A Double Blind, Randomized, Placebo Controlled, Parallel Group Study to Simultaneously Qualify a Biomarker Algorithm for Prognosis of Risk of Developing Mild Cognitive Impairment Due to Alzheimer’s Disease (MCI Due to AD) and to Test the Safety and Efficacy of Pioglitazone (AD-4833 SR 0.8 mg QD) to Delay the Onset of MCI Due to AD in Cognitively Normal Subjects
Data Contributor: Takeda
Study ID: NCT01931566
Sponsor ID: AD-4833/TOMM40_301