What You Should Know:
– In a decisive move towards faster and more efficient drug development, QuantHealth, a leading AI-powered clinical trial design company, has secured a strategic investment of $17M.
– The new capital, fueled by Accenture Ventures alongside a prominent CRO and additional investors, bolsters QuantHealth’s total Series A funding and propels its mission to revolutionize clinical trials through AI simulations.
Saving Millions with 86% Predictive Accuracy
QuantHealth’s proprietary AI technology boasts an impressive 86% accuracy rate in predicting clinical trial outcomes on the binary endpoint metric. This remarkable feat is powered by a massive dataset of 350 million patients, vast biomedical knowledge graphs, and comprehensive clinical trial data. This potent combination allows QuantHealth’s platform to simulate thousands of protocol variations, uncovering the optimal design for success.
Reducing Risk, Expediting Development
The pharmaceutical industry faces a daunting reality: clinical trials are expensive and fraught with uncertainty. With a staggering cost exceeding $1 billion for a single new drug, failure rates hover around 90%, draining $50 billion annually. QuantHealth’s platform tackles this challenge head-on by enabling R&D teams to:
– Lower risks: By simulating trials at scale, QuantHealth identifies potential pitfalls before they arise, minimizing costly failures.
– Expedite development: Identifying the optimal trial design upfront streamlines the entire process, bringing life-saving treatments to patients faster.
– Optimize drug development: QuantHealth’s platform ensures the right drug reaches the right population through precise matching, maximizing success rates.
Orr Inbar, co-founder and CEO of QuantHealth, sees the Accenture Ventures partnership as a catalyst for global expansion: “Accenture’s expertise in data and AI will be invaluable as we scale our platform and reach pharmaceutical and biotech companies worldwide.” This global reach will ultimately translate to faster development of effective treatments for patients in need.
