Publications

PUBLICATIONS

Prediction of cognitive decline for enrichment of Alzheimer's disease clinical trials

May 5, 2022

A key issue to Alzheimer's disease clinical trial failures is poor participant selection. Participants have heterogeneous cognitive trajectories and many do not decline during trials, which reduces a study's power to detect treatment effects. Trials need enrichment strategies to enroll individuals who will decline. We developed machine learning models to predict cognitive trajectories in participants with early Alzheimer's disease (n=1342) and presymptomatic individuals (n=756) over 24 and 48 months respectively. Baseline magnetic resonance imaging, cognitive tests, demographics, and APOE genotype were used to classify decliners, measured by an increase in CDR-Sum of Boxes, and non-decliners with up to 79% area under the curve (cross-validated and out-of-sample). Using these prognostic models to recruit enriched cohorts of decliners can reduce required sample sizes by as much as 51%, while maintaining the same detection power, and thus may improve trial quality, derisk endpoint failures, and accelerate therapeutic development in Alzheimer's disease.

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PUBLICATIONS

A machine learning tool to enrich Alzheimer’s disease clinical trials in presymptomatic cohorts (CTAD 2021)

November 4, 2021

We present a new algorithmic solution to enrich presymptomatic cohorts in Alzheimer's disease.

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PUBLICATIONS

A machine learning tool to enrich early Alzheimer's disease clinical trial cohorts (CTAD 2021)

November 4, 2021

A significant proportion of trial participants will not exhibit cognitive decline during a trial. This can negatively impact a trial's ability to detect differences between placebo and treatment arms, leading to failure to meet its endpoints. We present a new algorithmic enrichment solution for early AD clinical trials.

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PUBLICATIONS

A machine learning enrichment strategy for presymptomatic cohorts in Alzheimer's disease clinical trials. (AAIC 2021)

July 26, 2021

Presenting a new multimodal subtype approach to address challenges related of Alzheimer’s disease trials to enroll presymptomatic individuals who are likely to decline cognitively.

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PUBLICATIONS

Validation and Replication of a Prognostic Machine Learning Model for Enrichment of Cognitive Decliners in Clinical Trials (AAIC 2020)

July 27, 2020

We propose to use highly specific neuroimaging and genetic signatures that are indicative of the risk of cognitive decline in individuals with mild Alzheimer’s disease and mild cognitive impairment (MCI).

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PUBLICATIONS

PRE-SCREENING MACHINE LEARNING MODEL FOR β-AMYLOID POSITIVE ENRICHMENT (AAIC 2019)

July 14, 2019

Multimodal predictive model of Amyloid positiveness to pre-screen candidates for AD clinical trials.

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PUBLICATIONS

IDENTIFYING RISK OF COGNITIVE DECLINE IN MILD COGNITIVE IMPAIRMENT FOR POPULATION ENRICHMENT OF CLINICAL TRIALS (CTAD 2018)

October 25, 2018

Symposia. J Prev Alzheimers Dis 5, 1–45 (2018).

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