Delivered the most comprehensive variant and patient landscape ever compiled for rare genetic disorders of obesity, directly supporting inclusion and exclusion criteria, endpoint definition, and recruitment projections.
Provided robust literature-based evidence to explore broader MC4R-pathway–related indications beyond initial rare obesity targets.
Linked diseases associated with obesity with novel genes within the pathway, and identified a comprehensive list of the variants associated with these genes through rigorous mapping of variant nomenclature inconsistencies and unifying legacy data to reveal pathway-related genes not previously linked to obesity in public databases.
Rhythm Pharmaceuticals is advancing therapies for rare genetic disorders of obesity linked to the MC4R pathway. For its lead program, Setmelanotide, which was subsequently approved, Rhythm needed trial designs that reflected the full spectrum of rare obesity genetics - from canonical MC4R loss-of-function mutations to variants in adjacent pathway genes.
Prior to engaging Genomenon, Rhythm’s internal team had spent years manually curating variants from the literature. Despite their deep expertise, the process was slow and incomplete. “You can report a variant by its genomic location, cDNA position, or protein change. Unless you know every alias, it’s easy to leave something behind,” noted Dr. Alastair Garfield, VP of Translational Research & Development at Rhythm.
Public databases like ClinVar lacked many rare variants and offered little phenotypic context, making it difficult to plan inclusive trials or anticipate label expansion opportunities. Without a complete, validated dataset, Rhythm risked underestimating eligible patient numbers, missing high-prevalence subpopulations, and overlooking adjacent pathway genes with label expansion potential.
Variant Landscape: Over 10,000 mutations across 120 obesity-related genes were extracted, normalized, and classified using ACMG/AMP guidelines. Many were absent from public databases, expanding Rhythm’s view of potential trial-eligible mutations. Legacy nomenclature was reconciled to ensure no variant was excluded due to outdated or alternate naming.
Patient Landscape: Clinical data from published cases were curated to include demographics, phenotypes, natural history, and outcomes. This enabled genotype–phenotype correlations, identification of hallmark traits like early-onset hyperphagia, and prioritization of variants most likely to impact trial endpoints.
Evidence-Based Trial Feasibility: The dataset enabled the design of inclusion criteria that captured the widest eligible population without diluting trial power. Literature-cited prevalence and variant distribution data informed site selection and recruitment projections.
Pathway-Wide Variant Mapping: By aggregating variants across the MC4R pathway and related genes, Genomenon identified candidate targets for future indications. “Even within rare obesity, there are subsets,” said Garfield. “The more we understand the genes and variants, the better we can stratify the population for treatment.”
Phenotypic Diversity Evidence: Documentation of phenotype variations - including differences in onset age, hyperphagia severity, and comorbidities - provided Rhythm with evidence to justify expansion into related but distinct rare obesity subtypes.
Market-Ready Indication Prioritization: Integrated genetic and phenotypic data allowed Rhythm to prioritize new targets based on prevalence, documented treatment responses, and regulatory viability.
AI-Powered, Human-Validated Curation: Mastermind’s AI rapidly identified variant and patient data from millions of publications, with expert scientists reviewing every classification for accuracy.
Variant nomenclature reconciliation: Resolved inconsistencies across decades of literature, a key barrier in rare obesity variant curation.
Integrated pathway view: Mapped variants across canonical and peripheral pathway genes, creating a strategic blueprint for both initial trials and label expansion.
“We had spent years compiling this data manually. Genomenon delivered a complete, trial-ready dataset in weeks, and we use it daily for variant interpretation and pathway expansion planning.”
Dr. Alastair Garfield, VP of Translational Research & Development at Rhythm
Expanded Trial Eligibility: Captured all pathogenic and likely pathogenic variants across the MC4R pathway, ensuring no eligible patient group was overlooked due to incomplete variant data.
Improved Recruitment Accuracy: Identified high-prevalence variant clusters and hallmark phenotypes to target specific sites and geographies, increasing the probability of meeting enrollment targets.
Supported Data-Driven Label Expansion: Delivered refined, ethnicity-adjusted prevalence estimates, identifying Delivered pathway-wide variant mapping and literature-backed candidate gene identification, enabling Rhythm to prioritize new indications based on scientific and market opportunity.
Enhanced Regulatory Confidence: Provided transparent, reference-cited evidence packages aligned to ACMG/AMP guidelines, supporting both trial protocol submissions and long-term indication expansion plans.
We help provide insights into key genetic drivers of diseases and relevant biomarkers. By working together to understand this data, we enable scientists and researchers to make more informed decisions on programs of interest. To learn more about how we can partner together to find your genomic variant solutions, we invite you to click on the link below.
