The development of therapies for rare diseases and cancer faces a critical bottleneck: the lack of high-resolution, comprehensive data that links genetic variation to clinical presentation and treatment outcomes. This gap hinders progress at every stage—from identifying eligible patients and designing trials to refining diagnostic criteria and securing regulatory approval. While traditional sources of real-world data (RWD)—including electronic health records, insurance claims, and patient registries—offer some insight, they are often incomplete, inconsistently structured, or insufficient for the genotype-driven complexity of these conditions.
However, a vastly underutilized source of real-world evidence (RWE) resides in the scientific literature. Peer-reviewed publications have, for decades, captured invaluable data on patient phenotypes, variant pathogenicity, disease progression, biomarker behavior, and therapeutic outcomes across both rare diseases and oncology. Yet, transforming this unstructured textual data into structured, actionable insights is a complex, time-intensive, and error-prone endeavor—unless equipped with the right tools and expertise.
This is precisely where Genomenon’s approach to RWE for Precision Medicine redefines what’s possible—merging AI innovation with scientific rigor to unlock clinically meaningful insights from the literature at scale.
Genomenon’s Approach to RWE for Precision Medicine: Where AI Meets Clinical Insight
Genomenon launched RWE for Precision Medicine—a groundbreaking solution that fuses advanced artificial intelligence with expert genetic curation to extract clinically relevant insights from the published literature at scale.
At the core of this platform is Genomenon’s Genomic Graph (G3)—an AI-powered knowledge system that does far more than index information. G3 intelligently maps the intricate relationships between genetic variants, clinical phenotypes, therapeutic responses, and patient outcomes. By harnessing millions of full-text scientific publications and supplemental datasets—and enhanced through state-of-the-art large language models and retrieval-augmented generation (LLM-RAG) capabilities—G3 delivers comprehensive, high-resolution real-world evidence.
This approach closes critical knowledge gaps at both the variant and patient level, enabling therapeutic developers to design better-informed trials, refine diagnostic strategies, and accelerate drug development with confidence.
Precision-Driven Insight Across Indications
Genomenon’s RWE solution delivers high-fidelity insight:
- Curated Variant RWE: All published SNVs and indels are interpreted using ACMG/AMP guidelines by trained variant scientists. This evidence can then be submitted to publicly available databases like ClinVar and even be used to assess for prevalence of a disorder's birth prevalence.
- Curated Patient RWE: Analysis of genotype-phenotype correlations, case studies, treatment data, and disease progression insights, ensuring a detailed and accurate view of patient populations.
- Customizable RWE: From therapies to phenotypes for any disease group, our AI is able to extract nearly any entity from the literature, and find connections and relationships within that data. Our team of scientists then delivers that RWD into actionable RWE.
Unlike conventional RWD pipelines that are limited by data sparsity and variability, Genomenon’s platform draws from millions of full-text scientific articles and supplemental datasets. Powered by advanced large language models (LLMs) and retrieval-augmented generation (RAG) technologies, this AI-human hybrid framework ensures precision, contextual depth, and clinical relevance.
Our curation infrastructure comprises over 100 trained genetic scientists supported by a robust quality assurance team and standardized clinical-grade operating procedures. This ensures that every insight is not only evidence-backed but also actionable for therapeutic development.
Demonstrated Impact in Oncology and Rare Disease
The impact of Genomenon’s RWE for Precision Medicine is already evident across diverse indications:
- Expanded Trial Eligibility
In medullary thyroid cancer, our analysis of 3,800 RET variants increased trial-eligible variants from 33 to 138—substantially broadening patient inclusion and improving study feasibility. - Refined Diagnostic Criteria
In familial hypercholesterolemia, analysis of over 42,000 LDLR cases revealed that individuals with VUS had LDL-C levels comparable to those with pathogenic variants—prompting updates to diagnostic guidelines. - Improved Patient Detection
In Fabry disease, reanalysis of 1,700 GLA variants supported adjusting enzyme activity thresholds, improving the identification of affected individuals and expanding access to therapy.
These proof points underscore the broader value of literature-derived RWE—informing biomarker validation, supporting regulatory filings, and refining treatment guidelines across both monogenic and complex diseases.
Scaling Precision Medicine, From Rare Disease to Oncology
RWE for Precision Medicine is designed for scalability across thousands of rare diseases and a wide range of cancers. It equips pharmaceutical companies and clinical researchers with the curated evidence needed to:
- Accelerate clinical trial enrollment and minimize screen failure
- Strengthen regulatory submissions with genetically justified evidence
- Refine diagnostic and therapeutic strategies based on robust variant and patient-level insights
- Inform commercial decisions and market access planning with high-confidence data
In domains where every data point can shift a therapeutic outcome, Genomenon’s RWE for Precision Medicine provides the clarity, accuracy, and depth needed to move therapies forward faster—with greater confidence.
Contact our science team to explore how we can support your research or development efforts.
