More efficient screening for preclinical Alzheimer's clinical trials using mixture of experts

Blood plasma biomarkers identifying Alzheimer's disease (AD) neuropathology offer accessible and scalable alternatives to lumbar puncture and positron emission tomography (PET) scans, with potential efficiency gains in clinical trial recruitment.

We evaluated the impact of a blood-based screening algorithm on recruitment for the AHEAD 3-45 trial testing lecanemab in preclinical AD. The algorithm was developed during initial screening without blood plasma and subsequently deployed using a Mixture of Experts prediction model to estimate amyloid PET positivity; analyses reflect prospective enrichment and model characterization.

The algorithm incorporating amyloid beta A $\beta 42/$ A $\beta 40$ and subsequently adding percent phosphorylated tau 217 (%p-tau217), reduced ineligibility on amyloid PET from 71% to 31%. Latent class analysis identified low, intermediate, and high amyloid groups. Model-based analyses indicated %p-tau217 predicts High amyloid group, whereas A $\beta 42/$ A $\beta 40$ was more specific for the low group.

Blood plasma screening reduced participant and site burden, while preserving enrichment for amyloid positivity for a preclinical AD trial.