Rhythm Pharmaceuticals and Genomenon will discuss their efforts to assemble a database of mutations associated with rare genetic disorders of obesity, and how this was optimized to facilitate a deep understanding of the variant landscape of melanocortin-4 receptor (MC4R)-pathway genes. This database may help identify MC4R-pathway deficient individuals who might benefit from future precision therapies.

WHY ATTEND? Developing an evidence-based view of the genetic contributors to human disease can help improve the diagnosis of rare disorders and drive important advances in precision drug development. Learn how Rhythm Pharmaceuticals partnered with Genomenon to inform their understanding of rare genetic disorders of obesity and help identify patients who might be appropriate for participation in clinical trials.

DETAILS: By indexing over 6 million full-text genomic articles using the Mastermind Genomic Search Engine, 120 genes and over 10,000 variants were identified as being associated with obesity in the medical literature. Each individual variant was interpreted using the evidence assembled through an automated technical process. This novel semi-automated approach to variant identification and annotation was accomplished via the Mastermind genomic database and vetted using American College of Medical Genetics and Genomics (ACMG) guidelines.

Join Alastair Garfield, PhD, Vice President, Translational Research & Development (TRAD) at Rhythm Pharmaceuticals and Dr. Mark Kiel, Founder and Chief Science Officer at Genomenon, as they share how a database of genes and variants associated with obesity was developed in less than 60 days, including scientific evidence complete with literature citations and ACMG interpretations for each mutation. The machine-learning driven process replaced several years of manual research of the scientific literature to find obesity-related mutations.

You will learn:

• The importance of published genetic evidence in ensuring the success of a drug candidate
• How to rapidly assemble a comprehensive biomarker database of this genetic evidence using data available in Mastermind
• Why automated approaches are required for such disease-variant projects