Zhao, Zixuan

e19554 Background: Adding daratumumab to standard multiple myeloma (MM) regimens improves response rates and clinical outcomes. Systematic reviews (meta-analyses) allow further evaluation of its effectiveness in MM patients with high-risk cytogenetic factors but are limited by model assumptions. his study addresses these limitations using a novel synthetic individual patient data (SynIPD) generation algorithm to enable a more robust analysis. Methods: Advances in AI have enabled the creation of synthetic data that mimics the statistical properties of real patient data, empowering aggregated analyses of clinical trials. This study focuses on newly diagnosed MM (NDMM) and relapsed/refractory MM (RRMM) patients with high-risk cytogenetic profiles. Six studies were included: ALCYONE, CASSIOPEIA, and MAIA for NDMM, and CANDOR, CASTOR, and POLLUX for RRMM. Large language models extracted clinical evidence, and SynIPD was generated, incorporating cytogenetic profiles. Random-effects meta-analyses were conducted, and results were compared with pooled analyses using synthetic data. Results: Synthetic data were generated for three studies for NDMM (ALCYONE, CASSIOPEIA, MAIA) and three studies for RRMM (CANDOR, CASTOR, POLLUX). The generated hazard ratios (HRs) for each study closely aligned with reported HRs for high-risk patients, with a maximum relative error of less than 2%. By pooling these datasets, we obtained synthetic datasets for NDMM and RRMM patients. Stratified Cox regression analyses yielded HRs (95% confidence interval) of 0.764 (0.529-1.104) for NDMM and 0.431 (0.294-0.613) for RRMM. Random-effects meta-analyses showed HRs (95% confidence interval) of 0.764 (0.529-1.104) for NDMM and 0.425 (0.287-0.621) for RRMM. Compared with traditional meta-analysis techniques, the synthetic data approach provides additional clinical insights that were not available in the original publications. For instance, the clinical results for the three NDMM studies are summarized as KM curves in Figure 1, which were not reported in the original studies. Conclusions: Our results suggest that the addition of daratumumab to standard regimens improves PFS for RRMM patients with high-risk cytogenetic factors, although its benefit for NDMM patients is not statistically significant. Pooling synthetic data enhances the robustness of conventional meta-analysis outcomes. The identification of HRs and corresponding confidence intervals through SynIPD improves the credibility of our proposed method, offering a reliable approach to evaluating the efficacy of daratumumab in high-risk patients. Since SynIPD includes individual covariate information, it provides deep clinical insights beyond merely enhancing meta-analysis. In other words, any statistical results derived from the original IPD can be effectively approximated using SynIPD.

7547 Background: Minimal residual disease (MRD) has been recently accepted by the Food and Drug Administration (FDA) as an endpoint for accelerated approval in Multiple Myeloma. However, emerging data from recent trials were not included in previous analyses. While literature-based meta-analyses on the correlation between MRD and approved clinical outcomes typically require extensive manual review, leveraging AI with expert-in-the-loop validation can efficiently generate reliable evidence from comprehensive clinical studies with up-to-date outcomes. Methods: An AI-assisted framework was developed to identify relevant studies and extract critical information via two independent objectives. The first objective examined trial-level associations, modeling treatment effects on MRD and clinical endpoints across patient populations using weighted least squares, with association strength measured by coefficients of determination (R²) and 95% confidence intervals (CIs). The second objective analyzed individual-level associations using synthetic individual patient data (SynthIPD) generated from the published Kaplan-Meier plots and summary statistics of patient subgroups. Results: AI-assisted screening identified eligible studies (>50 patients per treatment arm) reporting progression-free survival (PFS), overall survival (OS), and MRD-negative complete response rates (MRD-CR rate) using multi-parameter next-generation flow cytometry or sequencing methods (sensitivity threshold ≥10⁻⁵), expanding previous analyses from 15 to 20 two-arm studies. Trial-level analysis demonstrated an R² of 0.69 (95% CI 0.50–0.89) for PFS log hazard ratio versus MRD-CR rate log odds ratio. Analysis of synthetic individual data from Kaplan-Meier curves using a novel digitization method yielded a global odds ratio of 7.28 (95% CI 5.60–8.95) for individual-level correlation between MRD-CR rates and PFS outcomes. Conclusions: This study validates MRD-CR rate as an endpoint for accelerated approval in MM through rapid AI-assisted literature review and synthetic individual patient data. The findings demonstrate moderate correlation between MRD-CR rate and median PFS at both trial and individual levels, consistent with previous literature but incorporating additional eligible studies. The novel SynthIPD approach presents an efficient alternative to traditional data-sharing methods while maintaining analytical robustness. These results align with current Oncologic Drugs Advisory Committee (ODAC) surrogacy analysis methods and support the utility of MRD assessment in MM clinical trials.

Introduction: Advances in AI have enabled the generation of synthetic individual patient data (SynthIPD) that mimics the statistical properties of the real IPD, which can help generate subgroup-specific insights in the absence of subgroup-specific IPD. We apply this technology in evaluating Daratumumab treatment benefit in high-risk cytogenetic multiple myeloma subgroup. The addition of daratumumab to standard multiple myeloma (MM) regimens has been associated with improved response rates and clinical outcomes. A systematic review (meta-analysis) enables further evaluation of the effectiveness of daratumumab for MM patients with high-risk cytogenetic factors. However, concerns have been raised about meta-analyses due to the statistical model assumptions they impose. This study aims to address these limitations by employing SynthIPD to provide a more robust analysis. Methods: This study focuses on newly diagnosed multiple myeloma (NDMM) and relapsed or refractory multiple myeloma (RRMM) patients with high-risk cytogenetic profiles. We included six published studies: ALCYONE, CASSIOPEIA, and MAIA for NDMM, and CANDOR, CASTOR, and POLLUX for RRMM. Large language models were used to extract up-to-date clinical evidence from the literature. SynthIPD including cytogenetic risk profile were generated via digitization and an innovative optimization algorithm. Meta-analyses based on random effects models were then conducted, and the results were compared with those from pooled analyses using synthetic data. Results: SynthIPD for cytogenetic high-risk patients were generated for three studies for NDMM: ALCYONE (treatment vs control: n=53 vs n=45), CASSIOPEIA (n=82 vs n=86), MAIA (n=48 vs n=44); and three studies for RRMM: CANDOR(n=48 vs n=26), CASTOR (n=40 vs n=35), POLLUX (n=35 vs n=35). The generated hazard ratios (HRs) for each study closely aligned with the reported HRs for high-risk patients, with a maximum relative error of less than 2%. By pooling these datasets, we obtained SynthIPD datasets for NDMM and RRMM patients. Stratified Cox regression analyses yielded HRs of 0.764 (95% CI 0.529-1.104) for NDMM and 0.431 (95% CI 0.294-0.613) for RRMM. Random-effects meta-analyses showed HRs of 0.764 (95% CI 0.529-1.104) for NDMM and 0.425 (95% CI 0.287-0.621) for RRMM. Compared with traditional meta-analysis techniques, the synthetic data approach provides additional clinical insights that were not available in the original publications. For instance, the clinical results for the six studies within high-risk group (e.g., KM curves in high-risk subgroup) can be reproduced, which were not reported in the original studies. Conclusion: AI-generated synthetic data enhances the robustness of conventional meta-analysis outcomes. The identification of HRs and corresponding confidence intervals through SynthIPD enhances the credibility of our proposed method, offering a reliable approach to evaluating the efficacy of daratumumab in high-risk patients. Since SynthIPD includes individual covariate information, any statistical results derived from the original IPD can be effectively approximated using SynthIPD. Using the SynthIPD, we show that the addition of daratumumab to standard regimens improves PFS for RRMM patients with high-risk cytogenetic factors, although its benefit for NDMM patients is not statistically significant. This analysis can be extended to other multiple myeloma high risk subgroups.