Anatomy of a Setback: A Taxonomy of Clinical Trial Failures in Alzheimer's Disease and Strategic Lessons for the Future

Although Alzheimer's disease (AD) carries the highest clinical trial failure rate of any major therapeutic area, the strategic lessons from more than two decades of negative outcomes remain insufficiently integrated into drug development practice. To date, trial failures have been examined largely in isolation, with limited attention to the recurring patterns that may connect them across therapeutic classes and disease stages. In this perspective, we propose a taxonomy that assigns AD therapeutic failures to five categories according to the primary driver of each negative outcome-namely wrong target, wrong timing, wrong patient, insufficient target engagement, and wrong endpoint. We subsequently applied this framework to a representative set of drugs that entered clinical testing between 2000 and 2025, comprising anti-amyloid monoclonal antibodies, β-site amyloid precursor protein-cleaving enzyme 1 inhibitors, tau-directed immunotherapies, neuroimmune-targeting agents, and metabolic repurposing candidates. Analysis across classes showed that the distribution of failure categories shifted progressively over time, from predominantly target-related failures in the mid-to-late 2010s toward patient-selection and endpoint-related failures in the current decade. From these patterns, we formulated strategic recommendations for next-generation trial design-including biomarker-driven patient enrichment, mechanism-specific endpoint validation, and adaptive platform architectures. Because numerous failures examined here replicate the negative outcomes of earlier programs, we conclude that a substantial proportion of late-stage AD trial failures may be structurally predictable and, through systematic classification, at least in part preventable. Despite these promising findings, the proposed taxonomy should be considered a working heuristic requiring external calibration before prospective deployment.