NEW YORK, Sept. 15, 2021 (GLOBE NEWSWIRE) -- Owkin, a startup pioneering Federated Learning and AI technologies for medical research and clinical development, announces an agreement with Actelion Pharmaceuticals Ltd., one of the Janssen Pharmaceutical Companies of Johnson & Johnson, to augment clinical trials with advanced machine learning methodologies. The aim of this initial project with Janssen's R&D Data Science team is to investigate innovative machine learning-based methods for the estimation of treatment effect in clinical trials involving real-world data sources.
Owkin's expertise with machine learning and multimodal real-world data powers innovations that can be leveraged to support decision-making in Drug Research and Development, biomarker identification, and clinical development processes.
Owkin and Janssen's R&D Data Science team will focus on innovative double/debiased machine learning-based approaches that enable adjustment for high-dimensional confounders to overcome important challenges of standard methods, such as bias and confounding. Detecting efficacy with small trials and external control cohorts, which is often the case for rare diseases, is a challenge. Double/debiased machine learning, a method developed originally in the context of econometrics with contributions from Nobel Prize recipient Esther Duflo, may be a way of achieving sufficient statistical power in this particular setting.
This first project with Janssen focuses on Pulmonary Arterial Hypertension (PAH), a rare, progressive disease where the pressure in the blood vessels of the lungs is elevated, resulting in stress on the heart. Despite recent advances, PAH still has no cure and remains a severely debilitating condition that leads to heart disease and early death. PAH is difficult to diagnose, but early diagnosis and treatment are critical to helping improve life expectancy.
Gilles Wainrib, Owkin Co-Founder and Chief Science Officer states, "We're thrilled to embark on this project to demonstrate how imperative machine learning methodologies are to improve clinical trial design and evaluation. Ultimately this has potential to help bring safe and effective drugs to patients faster."
The results from this project could potentially support regulatory submissions to health authorities, bringing much needed methodological innovations into practice. The methodologies deployed with this project are disease area-agnostic and have the potential to be used in multiple other applications throughout the discovery and development pipeline.
About Owkin:
Owkin is a French-American startup that specializes in AI and Federated Learning for medical research. Owkin's mission is to connect the global healthcare industry through the safe and responsible use of data and application of artificial intelligence, for faster and more effective research. Owkin was founded in 2016 by Dr. Thomas Clozel, MD, a clinical research doctor and former assistant professor in clinical hematology, and Dr. Gilles Wainrib, Ph.D., a pioneer in the field of artificial intelligence in biology.
Owkin leverages life science and machine learning expertise to make drug development and clinical trial design more targeted and cost-effective. Owkin applies its cutting-edge machine learning algorithms across a broad network of academic medical centers, creating dynamic models that not only predicts disease evolution and treatment outcomes, but can also be used in clinical trials for enhanced analysis, high-value subgroup identification, development of novel biomarkers, and the creation of both synthetic control arms and surrogate endpoints. The end result? Better treatments for patients developed faster and at a lower cost.
Owkin has published several high-profile scientific achievements in top journals such as Nature Medicine, Nature Communications, Hepatology and presented results at conferences such as the American Society of Clinical Oncology.
For more information, please visit www.owkin.com, follow @OWKINscience on Twitter
Media contact: Talia Lliteras at Talia.Lliteras@owkin.com
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