Researchers at Longevity Global integrate machine learning with biomarker analysis to build "aging clocks" and digital twin models that simulate treatment responses. Using virtual clinical trials, they accelerate identification of effective anti-aging interventions, shortening timelines from years to weeks and fostering venture and pharmaceutical investment in precision longevity therapies.

Key points

• AI-driven digital twins simulate individual aging and treatment responses in silico.
• Epigenetic aging clocks derived from multi-omics biomarkers predict biological age.
• In silico virtual clinical trials shorten evaluation timelines from years to weeks.
• Machine learning identifies candidate senolytics and personalized therapies efficiently.
• Integration of AI models attracts venture capital and pharmaceutical investment.

Why it matters: By harnessing AI to simulate patient-specific aging trajectories and accelerate biomarker identification, this approach promises to transform longevity research, shifting from time-consuming clinical trials to rapid in silico validation. The enhanced efficiency and precision could redefine therapeutic development for aging-related conditions and democratize access to personalized anti-aging therapies.