Novel Multi-Omics Aging Clock

A multi-omics clock analyzes multiple layers of omics data with machine learning to predict remaining lifespan of mammals, particularly for animal model studies.  Large omics data sets may be used from combinations of genomics, proteomics, lipids, etc. available from blood or other simple sample collection.   This tool can robustly capture changes in this metric following short-term treatment with an anti-aging intervention. This timing reflects the likely point in life where a mammal might expect to receive such an intervention in the clinic. These drugs can then be prioritized for detailed examination in more costly preclinical longevity studies.


Over the past four decades hundreds of interventions have been identified that extend lifespan in invertebrate model systems of aging. One major bottleneck in translating these interventions into clinical therapies to increase healthy longevity and treat age-associated disease in humans is the high cost in both time and resources associated with conducting longevity studies in preclinical mammalian models. In principle, one solution to this problem is to identify early- to mid-life biomarkers that can predict lifespan and other late-life markers of healthy aging. However, aging is driven by a complex interplay between many molecular and cellular processes, and biomarkers that provide consistent predictive efficacy across interventions, tissues, and disease types have been historically elusive. Recent advances use omics data to develop biomarker panels—tens to thousands of individual molecular measurements (e.g., epigenetic markers, single-gene expression levels, metabolite levels)—to develop predictive “clocks” that can, to some degree, accurately predict biological age or related age-associated metrics. Among many potential uses for these clocks, one application with wide-ranging immediate benefits is in drug discovery and repositioning. 


  • Accurate prediction for remaining lifespan
  • Robustly capture changes in remaining lifespan following short-term treatment with anti-aging intervention
  • Improve animal study insights and streamline preclinical studies


  • Drug development
  • Intervention evaluation
  • Life science research


Contact Information

TTO Home Page:

Name: Laura Silva

Title: Sr. Licensing Manager, COS

Department: TLA