News GNS Unveils Platform to Predict New Therapy’s Likely Success in Real World at ISPOR GNS Unveils Platform to Predict New Therapy’s Likely Success in Real World at ISPOR by Patricia Silva, PhD | October 31, 2016 Share this article: Share article via email Copy article link GNS HealthcareĀ isĀ presenting a data-driven causal machine learning solution, called Efficacy to Effectiveness,Ā designed toĀ predict how potential therapies will actually perform in distinct populations. The data, being releasedĀ today at ISPOR 2016, used pre-launch data from a study comparingĀ Gilenya (fingolimod)Ā and other multiple sclerosis (MS) therapies to build and validate causal models thatĀ estimate Gilenya’s likely performanceĀ onĀ the market. The platform, demonstratedĀ in collaboration with Novartis (the developer of Gilenya) and Harvard University,Ā is designed to help inform pharmaceutical makersā pre-launch market strategies, and to create evidence supporting the benefits of aĀ new therapy to specific groups of patients. āClinical trials are the gold standard for assessing efficacy, but the real world ā where a drug is in competition with other therapies and is no longer constrained to a well-defined trial population ā is ultimately where new therapeutics have to perform,ā Iya Khalil, GNSā chief commercial officer, executive vice president and co-founder, said in a press release. GNS applies causal machine learning technology ā Ā data integration architecture, Ā causal inference and a simulation engine ā to predict which treatments will work best for given groups of patients before the treatments are actually released. According to the company, this āEfficacy to Effectivenessā approach helps to improve bothĀ patient outcomes and reduce costs. The poster presentation, āUsing Clinical Trial and Real World Data to Bridge Efficacy to Effectiveness of Fingolimod in Multiple Sclerosis Patients,ā describes how Gilenya’s real-world outcomes might be predictedĀ using pre-launch data fromĀ a retrospective study of Gilenya clinical trial data and observational administrative claims data. āThis 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,ā Khalil said. ISPOR 2016, theĀ International Society for Pharmacoeconimcs and Outcomes Research 19th Annual European Congress, opened in Vienna, Austria, on Oct. 29 and runs through Nov. 2. Print This Page About the Author Patricia Silva, PhD PatrĆcia holds a PhD in medical microbiology and infectious diseases from the Leiden University Medical Center, Netherlands, and completed a postdoctoral research fellowship at the Instituto de Medicina Molecular, Lisbon, Portugal. Her work in academia was mainly focused on molecular biology and the genetic traits of infectious agents such as viruses and parasites. PatrĆcia earned several travel awards to present her work at international scientific meetings. She is a published author of several peer-reviewed science articles. Tags Fingolimod, Gilenya, real world data
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