Barriers to clinical trials in the NCLs include requisite small clinical trial sample sizes, high trial costs, and complex trial operations. Master Protocol trials are innovative trial designs that may address these barriers by using shared infrastructure, enabling adaptive designs and accelerating timelines. Through the Network for Excellence in Neuroscience Clinical Trials (NeuroNEXT), a National Institutes of Health’s National Institute of Neurological Disorders and Stroke -funded academic trials network, we brought together leading clinical trialists, biostatisticians, and rare disease advocates for a one-day virtual conference to discuss how to implement Master Protocol trials in the NCLs.
The conference was attended by 185 registrants, including clinicians, industry representatives, researchers, and patient advocacy representatives. The conference was divided into three sessions: Master Protocol Design and Case Examples, Current Status of NCL Therapeutics, and Designing a Master Protocol Trial in the NCLs. Models and lessons from the Healy ALS platform trial and the Neurofibromatosis platform-basket trial were presented.
Breakout groups discussed potential paths forward for the NCLs: 1) umbrella trials of gene-targeted therapies and a 2) basket trials of a small molecule drugs. Select barriers to an umbrella trial approach include: difficulty with industry buy-in in a competitive landscape, ethical concerns with inclusion of a control group, and difficulty selecting common endpoints. Select challenges to a basket trial approach include: establishing the threshold of non-clinical data required for each NCL, determining inclusion criteria (stage of disease, NCL phenotype), selection of trial duration appropriate for multiple NCLs. Based on the current degree of NCL and pipeline knowledge, basket trials appear to be optimal for phase 1/2 trials. Across both approaches, benefits included the potential to increase efficiency of trials, opportunity for shared learning and pooled analyses for select development questions, and the ability to adapt to real-time data.