Rare disease drug programs are built on a foundational assumption: that enough patients exist to justify the investment. When the published prevalence for your disease is 1 in 200,000, that number flows into every downstream decision: enrollment targets, commercial forecasts, orphan designation filings, investor models, and payer negotiations. If that number is wrong, the risk compounds across every stage of the program.
Proving population viability is the highest-stakes evidence problem in rare disease development. The evidence to solve it is already in the literature. The challenge is making it visible.
This case study documents how Genomenon worked with Inozyme Pharma to build a comprehensive evidence base for ENPP1 deficiency using published biomedical literature. The Genomenon team applied AI-powered search and expert curation to identify 154 patients (including 63 found only in published literature, not registries), evaluate 85 pathogenic and likely pathogenic variants, and build a Bayesian genetic prevalence estimate that was subsequently published in peer-reviewed literature. Clinical heterogeneity across presentations was documented at the patient level, including cardiovascular complications in later-onset cases.
The result: a 3.1x increase in research-backed prevalence (from 1 in 200,000 to 1 in 64,000 pregnancies), reshaping enrollment targets, commercial models, and the scientific foundation for the ENPP1 program.
