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Pharma Tech Outlook | Wednesday, September 29, 2021
Owkin has decided to sign an agreement to make innovative mechanical improvements to assist AI-guided robot arms.
FREMONT, CA: Owkin, a pioneer in federated learning and AI technologies for medical research and clinical development, collaborates with Actelion Pharmaceuticals Ltd., one of Johnson & Johnson's Janssen Pharmaceutical Companies, to augment clinical trials with advanced machine learning methodologies. The purpose of this initial collaboration with Janssen's R&D Data Science team is to investigate novel machine learning-based methods for estimating treatment effects in clinical trials that utilize real-world data sources.
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Owkin and Janssen's R&D Data Science team will focus on novel double/debiased machine learning methodologies that enable adjustment for high-dimensional confounders and thereby overcome significant issues associated with current methods, such as bias and confounding. Detecting efficacy in small studies with external control cohorts, as is frequently the case with rare diseases, is difficult. Double/debiased machine learning, a technique first established in the domain of econometrics with assistance from Nobel laureate Esther Duflo, may be a strategy to achieve sufficient statistical power in this context.
Owkin's experience in machine learning and multimodal real-world data enables breakthroughs that can be used to aid decision-making in areas such as drug research and development, biomarker discovery, and clinical development processes.
This first collaboration with Janssen will focus on Pulmonary Arterial Hypertension (PAH), a rare, progressive illness in which the pressure in the lungs' blood arteries is increased, putting the heart under strain. Despite recent breakthroughs, PAH remain incurable and a disabling disease resulting in heart disease and premature mortality. Although PAH is difficult to diagnose, early detection and treatment are crucial for extending 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 outcomes of this project may be used to support regulatory submissions to health authorities, therefore putting much-needed methodological advances into practice. The approaches utilized in this project are not specific to any particular disease area. They have the potential to be applied in a variety of other applications throughout the discovery and development pipeline.
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