A data-driven Alzheimer's disease progression simulator for retrospective validation and prospective Phase III power design

Anti-amyloid immunotherapies have recently demonstrated the first significant slowing of cognitive decline in Alzheimers disease (AD), yet clinical benefit varies markedly across drugs and scales with the completeness of amyloid clearance. Pharmacokinetic/pharmacodynamic (PK/PD) models are currently the standard tool for trial simulation, but they typically operate on single biomarkers and rely on drug-concentration assumptions, leaving the multi-scale cascade from amyloid clearance through tau, neurodegeneration, and cognition largely unmodelled. No existing framework has been jointly validated against the quantitative outcomes of multiple real-world phase III trials, spanning clearance kinetics, multi-modal biomarker trajectories, and statistical power. We present a trial simulation platform based on SimulAD, a disease progression model trained exclusively on longitudinal observational data from ADNI, with no access to trial-arm labels or drug-specific outcomes. SimulAD encodes intervention as piecewise amyloid clearance terms within a latent ordinary differential equation system that jointly governs amyloid, tau, structural MRI, and cognitive trajectories under the amyloid cascade hypothesis. We retrospectively simulated six landmark phase III anti-amyloid trials (TRAILBLAZER-ALZ2, CLARITY AD, EMERGE and ENGAGE, GRADUATE I and GRADUATE II) using a single trained model with trial-specific calibration limited to amyloid clearance kinetics. SimulAD reproduced published mean centiloid reductions within 5% error across all six trials and generated CDR-SB distributions broadly consistent with reported placebo and treated-arm outcomes. In a retrospective power analysis, calibrated simulations separated the three positive from the three null trials, with EMERGE near the decision boundary and ENGAGE and both GRADUATE trials below it. Across trials, higher amyloid-clearance rates were associated with larger calibrated clinical effects and lower estimated sample sizes. These results establish SimulAD as a valid disease-progression-centric trial simulator providing quantitative guidance on sample size planning and treatment kinetics optimisation that is grounded in the full multi-modal biomarker cascade of AD.