CAMBRIDGE, Mass.--(BUSINESS WIRE)--Leading precision medicine company GNS Healthcare (GNS) will reveal a causal machine learning and data-driven solution, “Efficacy to Effectiveness,” at the ISPOR 19th Annual European Congress in Vienna, Austria. The solution, demonstrated in collaboration with global healthcare leader Novartis (NYSE: NVS and SIX: NOVN) and Harvard University scientists, predicts how new therapeutics will perform in the real world across a variety of specific populations, informing pharmaceutical makers’ pre-launch market access strategies and generating evidence to support value-based contracting upon launch of a new therapeutic.
The presentation will describe use of solely pre-launch data in a retrospective study to predict real-world outcomes of a multiple sclerosis therapeutic. GNS leveraged its causal machine learning platform REFS™ (Reverse Engineering and Forward Simulation) in combination with the Novartis multiple sclerosis treatment fingolimod (Gilenya®) clinical trial data and observational administrative claims data to predict relapse probability in real-world patients. Leveraging the clinical trial data, GNS constructed a head-to-head comparison between fingolimod and other disease-modifying therapies and used the REFS platform to build and validate causal models to estimate the effectiveness of fingolimod in the real-world market.
“Clinical trials are the gold standard for assessing efficacy, but the real world – where a drug is in competition with other therapies and is no longer constrained to a well-defined trial population – is ultimately where new therapeutics have to perform,” said Iya Khalil, Chief Commercial Officer, Executive Vice President and Co-Founder of GNS. “This work shows that, by leveraging a combination of causal machine learning and pre-launch data, launching a new therapeutic without visibility into its real-world performance can be a thing of the past.”
The poster presentation, “Using Clinical Trial and Real World Data to Bridge Efficacy to Effectiveness of Fingolimod in Multiple Sclerosis Patients,” identified by code PND8 in the ISPOR Poster Presentations Session I – Neurological Disorders, will be displayed Monday, October 31, in the Austria Centre, Vienna, Austria, from 8:45 a.m. to 2:15 p.m. CET, with an author-led discussion beginning at 1:15 p.m. CET.
About GNS Healthcare
GNS Healthcare applies causal machine learning technology to predict which treatments will work for which patients, improving individual patient outcomes and the health of populations, while reducing costs. The GNS technology is based on its MeasureBase™ data integration architecture and patented REFS™ (Reverse Engineering and Forward Simulation) causal inference and simulation engine. Health plans, bio-pharmaceutical companies, healthcare providers, foundations, academic medical centers, and self-insured employers use these cloud-based solutions to solve pressing and costly problems including metabolic syndrome, medication adherence, end-of-life care, preterm birth, personalized care pathways in specialty care, oncology, and diabetes, new drug target discovery, patient stratification in clinical trials, and more. GNS solutions focus on reducing adverse events, slowing disease progression, and improving therapeutic effectiveness through precision matching that maximizes impact on individual patient health outcomes while reducing wasteful spending and downstream medical costs.
Discovering what works, and for whom.
