Clinical Trials for Wolfram Syndrome Neurodegeneration: Novel Design, Endpoints, and Analysis Models

Abstract

Objective: Wolfram syndrome, an ultra-rare condition, currently lacks effective treatment options. The rarity of this disease presents significant challenges in conducting clinical trials, particularly in achieving sufficient statistical power (e.g., 80%). The objective of this study is to propose a novel clinical trial design based on real-world data to reduce the sample size required for conducting clinical trials for Wolfram syndrome.

Methods: We propose a novel clinical trial design with three key features aimed at reducing sample size and improve efficiency: (i) Pooling historical/external controls from a longitudinal observational study conducted by the Washington University Wolfram Research Clinic. (ii) Utilizing run-in data to estimate model parameters. (iii) Simultaneously tracking treatment effects in two endpoints using a multivariate proportional linear mixed effects model.

Results: Comprehensive simulations were conducted based on real-world data obtained through the Wolfram syndrome longitudinal observational study. Our simulations demonstrate that this proposed design can substantially reduce sample size requirements. Specifically, with a bivariate endpoint and the inclusion of run-in data, a sample size of approximately 30 per group can achieve over 80% power, assuming the placebo progression rate remains consistent during both the run-in and randomized periods. In cases where the placebo progression rate varies, the sample size increases to approximately 50 per group.

Conclusions: For rare diseases like Wolfram syndrome, leveraging existing resources such as historical/external controls and run-in data, along with evaluating comprehensive treatment effects using bivariate/multivariate endpoints, can significantly expedite the development of new drugs.

Keywords: Wolfram syndrome; historical/external controls; multivariate endpoints; multivariate proportional model; run-in data.

Figure 1:

Demonstration of how clinical trials might use run-in data. Left: a trial design that uses the same disease progression rate for placebo during the run-in period and the randomized period; Right: a trial design that uses different disease progression rates for placebo during the run-in period and the randomized period.

Figure 2:

Power comparison between models with vs. without run-in data for the univariate endpoint of visual acuity. A and B: The disease progression rate for placebo participants is assumed to be the same throughout both the run-in and randomized periods (as in left panel in Figure 1). C and D: The disease progression rate for placebo participants is assumed to be slower in the randomized than the run-in period (as in right panel in Figure 1). 30%, 40% reduction: 30%, 40% reduction in the disease progression relative to the placebo group. No Run-in LME (purple lines): without run-in data analyzed using LME model 1. Run-in LME (blue lines): with run-in data analyzed using LME model 1. Run-in LME % Effect: (yellow lines) with run-in data analyzed using LME model 2

Figure 3:

Power comparison between models with bivariate vs. univariate endpoints and with vs. without run-in data. A and B: The disease progression rate for placebo participants is assumed to be the same throughout both run-in and randomized periods (as in left panel in Figure 1). C and D: The disease progression rate for placebo participants is assumed to be slower during the randomized than run-in period (as in right panel in Figure 1). 30%, 40% reduction: 30%, 40% reduction in the disease progression relative to the placebo group. Univariate Visual Acuity: visual acuity analyzed by LME model 1. Univariate Thalamus Volume: thalamus volume analyzed by LME model 1; No-Run-in Bivariate Endpoint: bivariate endpoint of visual acuity and thalamus volume analyzed by model 3 without run-in data; Run-in Bivariate Endpoint: bivariate endpoint of visual acuity and thalamus volume analyzed by model 3 with run-in data.