Presentation (CTI)

OBJECTIVES: C3 Glomerulopathy (C3G) is a rare, progressive complement-mediated kidney disease frequently leading to end-stage renal disease (ESRD). Clinical trials rely on surrogate biomarkers, such as estimated glomerular filtration rate (eGFR), proteinuria or Urine Protein-Creatinine Ratio (UPCR). This study aimed to develop a predictive survival model to quantify how these surrogate markers translate into hard renal outcomes, bridging short-term trial efficacy to long-term ESRD-free survival under iptacopan treatment.
METHODS: Pseudo-individual patient data were reconstructed from Kaplan-Meier curves extracted via a systematic literature review that identified 12 relevant international C3G cohort studies. A parametric Bayesian hierarchical Weibull survival model was developed to synthesize evidence while accounting for study heterogeneity. Baseline Chronic Kidney Disease (CKD) stage, proteinuria, eGFR rate and UPCR at 6 months were integrated as primary independent covariates. The framework was then used to simulate long-term disease progression by mapping the clinical efficacy of iptacopan, observed in the Phase 3 APPEAR-C3G trial versus Standard of Care, across clinical trial and real-world population scenarios.
RESULTS: The model demonstrated excellent fit with historical data. Baseline CKD stages 4-5 and higher baseline proteinuria were confirmed as key independent predictors of ESRD. In the APPEAR-C3G trial, iptacopan achieved a significant 35.1% relative reduction in 24h-UPCR at 6 months (p=0.0014) and stabilized eGFR decline. Extrapolated through the model, iptacopan’s treatment effects led to an estimated 20% to 50% risk reduction for ESRD. The projected median delay in requiring renal replacement therapy was estimated up to 10 years, with maximum benefits achieved when initiating therapy at earlier CKD stages.
CONCLUSIONS: By significantly reducing proteinuria and mitigating eGFR decline, iptacopan is projected to meaningfully delay progression to ESRD. This framework successfully translates short-term surrogate biomarker improvements into long-term survival outcomes, providing vital quantitative inputs for future health technology assessments.