This intensive workshop will delve into the cutting-edge integration of genetic and

proteomic profiling to overcome the “one-size-fits-all” limitation of current obesity therapeutics. By leveraging high-resolution molecular data, participants will learn to transition from broad-spectrum weight loss approaches to precision-led development strategies that identify high-responders and mitigate non-response early in the pipeline. The session will focus on utilizing multi-omic insights to enhance trial predictability, minimize late-stage attrition, and deliver personalized cardiometabolic solutions with proven clinical superiority. 

This session will cover:

• Implementing multi-omic patient stratification to move beyond BMI-based enrollment, using polygenic risk scores (PRS) and proteomic signatures to identify sub-populations with the highest probability of therapeutic response
• Decoding genetic drivers of non-response by analyzing the molecular heterogeneity of obesity (such as MC4R or INHBE variants) to predict why certain cohorts remain refractory to standard GLP-1/GIP therapies
• Utilizing proteomic biomarkers for early efficacy signals to identify real-time changes in metabolic health and adipose tissue distribution, providing objective proof-of-concept data long before significant weight loss is visible on the scale
• Integrating precision medicine into trial design through the use of biomarkerenriched protocols and adaptive “basket” trials that dynamically group patients based on their specific genetic metabolic drivers
• Establishing AI-driven predictive models that link large-scale genomic data to clinical outcomes, allowing development teams to optimize drug-target pairing and justify investment in personalized “next-generation” obesity assets
• Case study with Canary Cure: Integrating large-scale human genomic data and AI to identify the specific cross-talk between the CB1R and ZFP423 genes to deliver a dual gene-silencing therapy that precisely triggers the “browning” of white fat cells within 72 hours, ultimately providing an early proteomic proof-of-concept that identifies high-responders and secures a 25% increase in lean muscle mass for superior body composition results