(T-043) Model-informed Clinical Trial Design for Autosomal Dominant Tubulointerstitial Kidney Disease: Integrating Disease Progression and Trial Simulation

Shyam Ramesh – University of Florida; Mark Rogge – University of Florida; Jongjin Kim – University of Central Florida; Sanghoon Kang – University of Florida; Kendrah Kidd – Wake Forest School of Medicine; Adrienne Williams – Wake Forest School of Medicine; Julie Roignot – Sail Bio Inc.; Katherine Blakeslee – Sail Bio Inc.; Anthony Bleyer – Wake Forest School of Medicine; Sarah Kim – University of Florida

Objectives: Autosomal dominant tubulointerstitial kidney disease (ADTKD) is a rare genetic disorder commonly caused by mutations in uromodulin (UMOD) or mucin-1 (MUC1) [1]. Its rarity, variability in onset, and slow progression challenge clinical trial recruitment, study design, and distinguishing drug effects from natural history. This work aimed to develop a disease progression model and model-based Clinical Trial Simulation (CTS) tool to inform optimal trial designs for ADTKD variants.

Methods: Models were developed using Monolix2024R1. The graphical user interface of the CTS tool was developed using R (v4.2.3) and Simulx. Longitudinal individual-level data from a natural history study (WFUHS IRB0000352; n=371 UMOD, n=233 MUC1; age >18 years) were split into training and test datasets (4:1) while maintaining covariate proportions. eGFR trajectories were modeled using nonlinear mixed-effects modeling, incorporating baseline characteristics. A high-risk case study was defined using chronic kidney disease trial criteria (end-stage kidney disease ≤40 years, age 18–40, eGFR 10–45 mL/min/1.73m²). Only 8.9% of subjects met these criteria; a ctree-based synthesizing method was applied to enrich this subgroup while preserving covariate patterns. Change from baseline to last eGFR was evaluated as the surrogate endpoint in the trial simulations. Two-sided t-tests (α = 0.05) compared treatment vs. placebo in simulated trials.

Results: A sigmoid Imax model with distinct steepness parameters (γ) before and after inflection points best described eGFR decline. UMOD and MUC1 had similar initial γ₁ (~1), but MUC1 declined faster post-inflection (γ₂ = 10.23 vs. 6.34). Baseline eGFR and age significantly affected between-subject variability. The CTS tool included: i) selection criteria, ii) trial design, and iii) power estimation. Simulations indicated that detecting a drug effect required ≥45% (UMOD) and ≥10% (MUC1) increase in DPT50 (age at which eGFR is half of its maximum change). To reach 80% power, detecting change from baseline to last eGFR required only 75 vs. 150 subjects/group for UMOD, and 50 vs. 100 subjects/group for MUC1, over 3 years.

Conclusions: The model with variable steepness pre- and post-inflection accurately described eGFR decline using baseline characteristics, enabling subgroup analyses. The CTS tool will help inform ADTKD trial optimization by identifying ideal sample size and duration through simulation before actual execution.

Citations: [1] Autosomal dominant tubulointerstitial kidney disease (ADTKD) is a rare genetic disorder commonly caused by mutations in uromodulin (UMOD) or mucin-1 (MUC1).
Devuyst O, Olinger E, Weber S, Eckardt KU, Kmoch S, Rampoldi L, et al. Autosomal dominant tubulointerstitial kidney disease. Nat Rev Dis Primers. 2019 Sep 5;5(1):60.

Keywords: Rare Disease, Disease Progression, Trial Simulation