In total, approximately 7,000 rare diseases have been identified—with many more being discovered every year. While a single orphan disease may affect just a handful of individuals, the global impact is significant: in the United States alone between 25 and 35 million people are estimated to be diagnosed with a rare disease, with 50-66 percent of known rare diseases affecting children. There is a clear, unmet clinical need within the rare disease community, which has led to an increased focus on developing novel therapies.
One of the key advances in rare disease drug development has been patient segmentation in clinical trials, based on increased understanding of disease biology, identification of mutations and the use of biomarkers. This segmentation allows for more targeted clinical trials, but poses challenges associated with small patient populations within each segment. By definition, a rare disease affects a small group of individuals (ranging anywhere from less than 10 patients to a few thousand), and segmentation further divides this population. Given this limitation, robust preclinical data packages are essential to increasing the chance that clinical endpoints will be met in trials. Identifying endpoints in research models, using translational methods such as imaging or cognitive testing, can be used repurposed as endpoints in clinical trials.