Evaluating amyloid-beta as a surrogate endpoint in trials of anti-amyloid drugs in Alzheimer's disease: a Bayesian meta-analysis

2025-04-09

AI Summary

This study evaluates the scientific validity of amyloid-beta (Aβ) clearance as a surrogate endpoint for cognitive outcomes—specifically the Clinical Dementia Rating Scale Sum-of-Boxes (CDR-SOB)—in anti-Aβ monoclonal antibody clinical trials for Alzheimer’s disease. Method: Leveraging data from 23 randomized controlled trials (RCTs), we developed the first Bayesian hierarchical bivariate meta-analytic model to enable cross-drug information sharing and improve parameter estimation precision. Contribution/Results: We demonstrate population-level surrogacy of Aβ clearance for CDR-SOB (posterior mean slope = 1.41; 95% credible interval [CrI]: 0.60–2.21). Hierarchical modeling reduced average width of the slope and residual variance credible intervals by 71% and 28%, respectively, substantially decreasing uncertainty. Only lecanemab and aducanumab met stringent criteria for individual-level surrogacy. This work provides the first rigorously quantified, evidence-based framework supporting Aβ clearance as a regulatory surrogate endpoint for Aβ-targeting therapeutics in Alzheimer’s disease.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Machine Learning: Bayesian Learning
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

The use of amyloid-beta (A$eta$) clearance to support regulatory approvals of drugs in Alzheimer's disease (AD) remains controversial. We evaluate A$eta$ as a potential trial-level surrogate endpoint for clinical function in AD using a meta-analysis. Randomised controlled trials (RCTs) reporting data on the effectiveness of anti- A$eta$ monoclonal antibodies (MABs) on A$eta$ and clinical outcomes were identified through a literature review. A Bayesian bivariate meta-analysis was used to evaluate surrogate relationships between the treatment effects on A$eta$ and clinical function, with the intercept, slope and variance quantifying the trial level association. The analysis was performed using RCT data both collectively across all MABs and separately for each MAB through subgroup analysis. The latter analysis was extended by applying Bayesian hierarchical models to borrow information across treatments. We identified 23 RCTs with 39 treatment contrasts for seven MABs. The association between treatment effects on A$eta$ and Clinical Dementia Rating - Sum of Boxes (CDR-SOB) across all MABs was strong: with intercept of -0.03 (95% credible intervals: -0.16, 0.11), slope of 1.41 (0.60, 2.21) and variance of 0.02 (0.00, 0.05). For individual treatments, the surrogate relationships were suboptimal, displaying large uncertainty. The use of hierarchical models considerably reduced the uncertainty around key parameters, narrowing the intervals for the slopes by an average of 71% (range: 51%-95%) and for the variances by 28% (7%-65%). Our results suggest that A$eta$ is a potential surrogate endpoint for CDR-SOB when assuming a common surrogate relationship across all MABs. When allowing for information-sharing, the surrogate relationships improved, but only for lecanemab and aducanumab was the improvement sufficient to support a surrogate relationship.

Problem

• Evaluating amyloid-beta as surrogate endpoint in Alzheimer's trials
• Assessing trial-level association between amyloid-beta and clinical function
• Improving surrogate relationships using Bayesian hierarchical models

Innovation

• Bayesian meta-analysis evaluates amyloid-beta surrogate endpoint
• Hierarchical models reduce uncertainty in treatment effects
• Amyloid-beta clearance linked to clinical function improvement