Turning failure into success: how artificial intelligence can help personalize therapies and re-use patient data.

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not quantified

• Primary Outcome: Identification of patient subgroup showing slower Alzheimer's disease progression with Lanabecestat treatment

• Nnt Or Sensitivity: Not calculated

• Confidence Interval: Not provided

Clinical Application

Requires significant computational infrastructure, regulatory framework development, and validation studies before clinical implementation Would require TGA guidance on AI-based patient stratification methods. Potential application in Australian dementia research networks and PBS decision-making for targeted therapies Alzheimer's disease patients in clinical trials, potentially applicable to other therapeutic areas with heterogeneous patient populations

Abstract

Despite robust preclinical evidence, many clinical trials, including several that targeted the purinergic system, fail to demonstrate efficacy in humans. Failure may stem from inability to accurately identify patient subgroups responding similarly to treatments. Here, we explore the potential of artificial intelligence to revolutionize how we group and classify patients in clinical studies. We introduce a new framework using Large Language Models-generated embeddings of detailed patient data, to create a semantic-aware latent space, enabling us to identify truly meaningful patients' clusters. Large Language Models can provide explainable groupings, giving clear reasons why certain patients respond similarly to treatments. We present an example of successful application of this approach through the re-analysis of the AMARANTH clinical trial (NCT02245737, involving ~ 2200 patients and completed in 2018) testing Lanabecestat, a BACE1 inhibitor decreasing β-amyloid production in Alzheimer's disease, for which traditional analysis reported no efficacy. As in the original trial, our simulation showed no overall benefit. However, re-analysis per patients' clusters and subjects' re-stratification by semantic similarities (shared symptom profiles, progression patterns) identified a patients' subgroup in one of the clusters showing Lanabecestat-associated slower disease worsening, thus succeeding where the full trial had failed. By making a new therapy available to at least a subset of patients with a defined disease, this new approach may help maximize the return on drug development and reduce the burden on healthcare. Moreover, it will significantly improve the precision, efficiency and interpretability of clinical trials, paving the way for a new era of personalized medical treatments.

References

1. 1.Abbracchio, M. P., & Damiani, E. (2026). Turning failure into success: how artificial intelligence can help personalize therapies and re-use patient data. Purinergic Signalling. https://doi.org/10.1038/s41467-025-61355-3