Using AI-generated digital twins to boost clinical trial efficiency in Alzheimer's disease

Machine learning models leverage baseline data to create artificial intelligence (AI)-generated digital twins (DTs)-individualized predictions of each participant's clinical outcomes if they had received placebo. Incorporating DTs may increase statistical power or reduce required sample sizes in Phase 2 or 3 trials, and therefore improve efficiency in Alzheimer's disease (AD) trials. Here we demonstrate these properties using data from an AD Phase 2 clinical trial (AWARE, NCT02880956).

A conditional restricted Boltzmann machine (CRBM) model was trained on historical clinical trials and observational data from 6736 unique subjects after data harmonization to generate DTs of participants from the AWARE trial. The AWARE trial enrolled 453 subjects with mild cognitive impairment (MCI) or mild AD. DTs were assessed as prognostic covariates to evaluate gains in variance and sample size reduction.

Positive partial correlation coefficients were found between DTs and change score from baseline in key cognitive assessments ranging from 0.30 to 0.39 at Week 96 in the AWARE trial. These correlations were consistent with validation results from three independent trials, which ranged from 0.30 to 0.46. Total residual variance was reduced by ~9% to 15% with DTs. While maintaining statistical power, DTs could reduce total sample size by ~9% to 15%, and control arm sample size by 17% to 26% in future AD trials.

Efficiency was improved in AD clinical trials using machine learning models to generate prognostic DTs by including them in statistical analysis modeling. This methodology aligns with regulatory guidance and represents an application of machine learning models suitable for the analysis of pivotal trial data. Validated DTs have the potential to improve clinical development efficiency in AD and in other neurological indications.

Digital twins (DTs) were generated by artificial intelligence (AI) models trained on historical datasets.Use of digital twin (DT) as a covariate in the analysis model can reduce treatment effect variability.By coupling DT with the analysis model, trial sample size can be reduced.DT technology was accepted by the U.S. Food and Drug Administration and European Medicines Agency for applications in clinical trials.