Impact
Helping drug developers identify and prioritize rare disease patient populations to support more efficient clinical development
Objective
Estimate the incidence and prevalence of a rare, inherited neurological disorder to help a private sector biopharmaceutical company identify potential patient populations and inform clinical development decisions.
Approach
RTI applied its proprietary RUDI platform to combine population genomic data, a disease-specific variant classifier, and probabilistic modeling to estimate disease incidence and prevalence across relevant countries and ancestry groups.
Impact
The analysis gave the company a stronger evidence base for prioritizing potential patient populations, assessing clinical trial locations, and making more informed clinical development and market decisions.
The Challenge of Finding Patients with Ultra-Rare Diseases
Rare diseases collectively affect a substantial number of patients, yet each individual condition affects a small and often geographically dispersed population. Because rare genetic diseases are difficult to diagnose, they are often undercounted. This creates major barriers to bringing new therapies to market because sponsors must identify, diagnose, and enroll enough eligible patients to conduct rigorous clinical trials capable of demonstrating safety and efficacy. For ultra-rare diseases, recruiting a sufficient patient population is often one of the most significant obstacles to generating the evidence needed for regulatory approval and broad patient access.
Traditional epidemiologic approaches rely on sparse clinical data and fail to capture the true distribution of disease. Without knowing where affected patients are located globally, companies risk opening clinical trial sites in regions with few actual patients, stalling research and wasting critical resources.
A New Approach: Resolving Unclassified Variants
A private sector biopharmaceutical company developing a therapy for one such disorder engaged RTI to strengthen the evidence about where potentially eligible patients might be found. To assist them, RTI leveraged its proprietary RUDI platform, which translates large amounts of genomic data into actionable epidemiological insights.
The traditional challenge in genetic epidemiology is that most genetic mutations are "Variants of Uncertain Significance" (VUS)—meaning it is unknown whether they directly contribute to disease initiation or progression.
RUDI overcomes this barrier by expanding the evidence base. For each indication, the RTI team trains a predictive model on well-characterized, known variants of the gene of interest. The model is then applied to VUS to accurately classify these variants as benign (not contributing to the disease) or pathogenic (causing disease or progression). By analyzing major population-level databases that contain exomes from hundreds of thousands of healthy individuals from that population, the RTI model identified more than 100 previously unclassified variants as highly likely to be pathogenic.
Meet RUDI
Combining genetics and epidemiologic modeling to turn genomic uncertainty into rare disease insight.
Translating Genomics into Rare Disease Prevalence
Finding pathogenic variants was only the first step. The team then applied probabilistic modeling to calculate how frequently these mutations occur within the populations represented in the genomic databases and to estimate birth incidence. To translate incidence into estimates of the number of people living with the disease, RTI combined the genomic findings with 20 years of United Nations live-birth statistics and disease-specific mortality information from the available evidence base. The resulting model generated estimates of cases among people under age 20 across 38 countries, giving the client a clearer basis for comparing potential patient populations.
Strategic Insights for Clinical Development
The RUDI analysis revealed meaningful and sometimes unexpected differences among the populations evaluated, showing that the number of annual births alone was not a reliable indicator of where patients might be found. Some populations with fewer births had a comparatively higher estimated frequency of pathogenic variants, while others had a lower estimated frequency. These findings gave the client a stronger evidence base for prioritizing countries and populations for additional feasibility assessment and clinical trial planning.
Rather than treating all regions as equally promising recruitment locations, the client could focus its resources on areas more likely to support an efficient trial while preserving the broader goal of developing a therapy that could benefit patients worldwide.
This work demonstrates how integrating genomic science with advanced analytics can strengthen rare disease research and clinical development. By clarifying the patient landscape within the populations represented in available data and uncovering previously hidden differences among them, RTI's RUDI platform helps biopharmaceutical companies build more realistic market estimates, prioritize further research, and improve clinical trial planning and enrollment strategies. These data-driven decisions can support faster, more efficient development and help bring potentially life-changing therapies closer to the patients who need them most.
