Juvena Therapeutics and Eli Lilly forge a global licensing and research collaboration leveraging Juvena’s AI-driven JuvNET platform to discover secreted stem-cell proteins that enhance muscle mass and function. Juvena secures upfront funding, equity, and milestone-based payments, while Lilly obtains exclusive rights to develop and commercialize lead candidates targeting frailty and metabolic disorders.
Key points
Juvena’s JuvNET platform integrates proteomics, multi-omics, imaging, and AI to identify secreted stem-cell proteins.
The $650 million agreement grants Lilly exclusive development rights and milestone-based payments to Juvena.
Clinical candidates include JUV-161 for muscle regeneration and JUV-112 for fat breakdown and energy expenditure.
Why it matters:
This collaboration harnesses AI-driven proteomics to create novel muscle-regenerative therapies, promising to enhance healthspan by tackling frailty and obesity with precision biologics.
Q&A
What is the JuvNET platform?
How do secreted proteins promote muscle health?
What conditions are targeted by this collaboration?
What are milestone payments in pharma deals?
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Academy
AI-Powered Proteomics for Longevity
Proteomics is the large-scale study of proteins, the fundamental molecules that drive virtually all biological processes. In longevity science, analyzing protein composition and interactions can uncover mechanisms of aging such as cellular damage, inflammation, and metabolic decline. AI-powered proteomics merges machine learning algorithms with mass spectrometry and other high-throughput data to identify and quantify thousands of proteins simultaneously. By training AI models on these datasets, researchers detect subtle patterns and protein interactions that signal early signs of aging or disease, informing targeted interventions to promote healthy aging.
- Data integration: AI combines proteomics with genomics and metabolomics to build comprehensive aging profiles.
- Pattern detection: Machine learning reveals protein signatures associated with frailty and muscle loss.
- Target prioritization: Algorithms rank candidate proteins for therapeutic development, streamlining discovery.
This approach accelerates drug discovery by focusing laboratory resources on the most promising molecular targets, reducing time and cost compared to traditional trial-and-error methods.
Muscle Health and Aging
Muscle tissue naturally deteriorates with age, a process known as sarcopenia, leading to weakness, reduced mobility, and heightened risk of chronic conditions. Preserving muscle integrity is essential for maintaining independence and overall healthspan. Secreted proteins produced by human stem cells serve as signaling molecules that regulate muscle repair, inflammation, and energy metabolism. Therapeutic strategies harness these proteins to stimulate muscle regeneration, improve metabolic function, and support body composition maintenance.
- Sarcopenia causes: Hormonal shifts, decreased physical activity, and cumulative cellular damage impair muscle maintenance.
- Protein-based therapies: Biologic drugs deliver regenerative signals to muscle tissue, activating satellite cells and enhancing protein synthesis.
- AI-driven discovery: Computational platforms predict which secreted proteins most effectively boost muscle function and metabolic resilience.
By combining AI-enabled target discovery with biologic drug development, scientists aim to create novel treatments that preserve muscle mass during weight loss and aging, enabling healthier, more active lives in later years.