Sep 21, 2022
How can organizations use advanced analytics to complement, enhance, and accelerate current QA practices? That’s one the key questions explored during Linda Sullivan’s interview with Timothé Ménard, Head Quality Data Science & Bioethics Coach in Product Development Quality at F. Hoffmann-La Roche. In his role at Roche, Ménard has been leading the product development Quality Data Science Team since January 2018.
From simple analytics methods to machine learning, Ménard’s team is creating and implementing data-driven solutions that help understand, early detect, and predict clinical and PV quality issues.
Sullivan notes that, in the last six years, many organizations have been implementing risk-based quality management approaches, based on evolving revisions to guidance such as ICH E6R2 and E8R1, which require specific data sets. According to Ménard, we should leverage and focus not just on clinical trial data but also on operational data, which is sometimes more challenging more than clinical data.
In regard to identifying and tracking quality issues, Ménard points out that analytics now can be applied on program and study levels, which expands critical analysis far beyond the site level and enables biopharmaceutical companies to accelerate the drug approval process. Ménard notes that his organization has been able to glean key insights from data that might not have been available a decade ago.
Want to suggest a topic for CTO? Just email Linda Sullivan at lsullivan@wcgclinical.com.