Autosomal Dominant Tubulointerstitial Kidney Disease Clinical Trial Simulator: Case Reports of Model-Informed Drug...

Autosomal Dominant Tubulointerstitial Kidney Disease (ADTKD) is the third most common inherited monogenic kidney disorder. Mutations in UMOD and MUC1 account for most cases, with the disease characterized by progressive eGFR decline leading to kidney failure. No disease-modifying therapies exist, and transplantation is the only current option. Designing trials for ADTKD is hampered by small patient numbers, variable progression rates, and uncertainty around optimal endpoints. Building on natural history data from Wake Forest School of Medicine, nonlinear mixed-effects models were developed to describe eGFR decline in UMOD and MUC1 variants. These models formed the foundation for a web-based clinical trial simulation (CTS) tool (https://app.cop.ufl.edu/adtkd/) built in R Shiny. The tool allows users to define trial populations, configure design parameters, and estimate statistical power by simulating placebo vs. assumed treatment arms. Drug effects were modeled as percentage changes in key parameters of the developed disease progression models describing individual-level eGFR trajectory over age: DPT₅₀ (age at which eGFR is half of its maximum decline), ${\gamma }_1$ and ${\gamma }_2$ (steepness before and after DPT₅₀). Both slope-based eGFR change and end-of-trial eGFR measures functioned as effective surrogate endpoints. Herein, we present the model-based CTS tool developed for ADTKD and demonstrate its use in designing and evaluating clinical trial scenarios, illustrated by two representative case studies. The CTS tool provides a pragmatic framework for optimizing ADTKD trial design. By enabling scenario testing and highlighting genotype-specific considerations, it supports efficient, cost-effective planning and represents an example of model-informed drug development in rare kidney diseases.