Using life, for life.
We measure what matters to patients, so better medicines reach them sooner.
From devices to decisions.
Usin’Life began in 2012 building medical devices that measured disease directly in patients, producing first-in-class, real-time endpoints in Parkinson’s disease, muscular and myotonic dystrophy, ALS and myasthenia gravis. In 2015, Biogen acquired the device portfolio, its data and know-how, and our founder joined Biogen to carry the work forward.
Since then, the technology has caught up with the idea. Wearables, speech analysis, EEG and AI can now measure how a disease changes daily life, and regulators have begun accepting these measures as trial endpoints.
Today Usin’Life returns to its original mission with Prepoint™, a platform that turns early clinical studies into evidence for the decisions that follow.
Our news archive records the journey since 2012. Read the news archive →
Three convictions.
Measure what patients feel.
A drug that works should show up in how people speak, move and sleep.
Decide early.
The answer to Phase 2 should be found in Phase 1.
Earn trust with evidence.
Every recommendation is traceable, and every prediction is tested.
Founder
Anil Tarachandani, PhD
Anil Tarachandani has spent three decades in drug development, from discovery to clinical proof of concept, with one thread running through it: measuring disease in patients.
Most recently, as Vice President of Clinical Development and Head of Translational Medicine at Verge Genomics, he built the clinical function and led an ALS Phase 1b that combined speech, mobility, sleep and activity endpoints with a new fluid biomarker across seven international sites. At Pfizer, he led teams developing digital endpoints in oncology and rare disease and secured FDA and EMA alignment on novel measures. As a consultant to Fulcrum Therapeutics, he led the digital biomarker strategy for an international FSHD trial using wearable devices.
Earlier, he directed experimental medicine and external innovation at Biogen, and at Merck contributed to cardiovascular programs including ZETIA® and VYTORIN® while leading early machine-learning analysis of large-scale screening data. His work in AI began as faculty at Carnegie Mellon University, developing image-analysis methods in collaboration with Merck and Intel.
He holds a PhD in Biology from the University of Mumbai and completed a postdoctoral fellowship at the University of Massachusetts.
Start with one study.
Add Prepoint Measure to the Phase 1 you’re already planning, as an exploratory endpoint — low risk, clear criteria. Your data stays yours.