Leveraging AI to Evaluate Minimal Residual Disease Endpoint Surrogacy in Multiple Myeloma

Minimal residual disease (MRD) has been endorsed by FDA Oncology Drugs Advisory Committee as an endpoint for accelerated approval in Multiple Myeloma (MM) based on individual patient data collected from randomized trials. However, emerging data from recent trials were not included. A novel AI-assisted framework is proposed, which automates information identification and extraction, providing up-to-date analyses that confirm moderate trial-level and strong individual-patient-level association between MRD-CR and survival endpoints in MM. Specifically, this study utilized an AI-assisted framework that identifies relevant studies and filters critical information to analyze published data via two independent objectives. Firstly, we examined the trial-level association by the coefficients of determination (R^2) and its 95% confidence intervals (CIs) based on published statistics of treatment effects on MRD and various endpoints. Next, we generated synthetic IPD with covariates through AI-curated tools to estimate the individual-level association. The AI tool searched for eligible randomized clinical trials. A total of 20 two-arm comparisons from 19 RCTs were analyzed. Trial-level analysis showed an R² of 0.71 (95% CI 0.52 - 0.89) pooling disease subpopulations. Furthermore, AI techniques were applied to create synthetic individual data, combining information extracted from Kaplan-Meier curves and subgroup analyses from published literatures. Using generated synthetic data, we estimated the individual-level correlation between MRD-CR rates and PFS outcomes with a bivariate copula model and calculated a Global OR of 7.28 (95% CI 5.60-8.95).