PhaseV, a leader in software and machine learning (ML) for advanced clinical development optimization, today announced it has partnered with University Health Network (UHN), Canada’s No. 1 research hospital, to develop an ML-driven solution to tackle key challenges in the field of systemic lupus erythematosus (SLE).
SLE is a complex autoimmune disease affecting around 200,000 people in the U.S. alone. Currently, there is no cure, with treatment limited to end-organ damage treatment and induction remission. Despite significant efforts over the last decade, clinical trials in lupus have suffered a high failure rate, as the disease’s heterogeneity often leads to inconsistent patient responses to treatment. Additionally, varying trial endpoints have prevented new lupus drugs from achieving regulatory approval.
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“Like most systemic diseases, lupus appears in different ways. Our work with PhaseV could improve our understanding of this heterogeneity, as well as its mechanisms and treatment responses,” said Zahi Touma, MD, Clinician-Scientist, University Health Network, Rheumatology Arthritis Program and Associate Professor of Medicine, University of Toronto. “This allows for optimization in clinical trial design, leading to improved possibilities in the development of more effective SLE therapies.”
PhaseV’s proprietary Causal-ML platform leverages advanced algorithms that model disease progression and variability in treatment responses. By modeling disease progression using real-world data, it identifies subtle signals to enhance understanding of the prognostic factors that influence disease heterogeneity. This provides actionable insights to power more precise and effective clinical trial designs based on this information.
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“By unlocking a deeper understanding of the factors that impact treatment response, we can facilitate the design and execution of more targeted and effective clinical trials for SLE,” said Raviv Pryluk, PhD, CEO and Co-founder of PhaseV. “As a global leader in SLE research, UHN’s Lupus Clinic is the ideal partner for this initiative. The insights we reveal can inform future trial designs, improve patient selection, and increase the likelihood of successful drug development for the patients waiting for it.”
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Source – prnewswire
