Improve Quality and Minimize Variability in Biotech Manufacturing with Stewart Fossceco

This week, we are pleased to have Stewart Fossceco, Head of Non-Clinical and Diagnostics Statistics at Zoetis and an expert in pharmaceutical manufacturing, join us on the Data in Biotech podcast. We sat down with Stewart to discuss implementing and improving Quality Assurance (QA) processes at every stage of biotech manufacturing, from optimizing assay design and minimizing variability in early drug development to scaling this up when moving to full production. Stewart talks from his experiences on the importance of experimental design, understanding variability data to inform business decisions, and the pitfalls of over measuring.  Along with host Ross Katz, Stewart discusses the value of statistical simulations in mapping out processes, identifying sources of variability, and what this looks like in practice. They also explore the importance of drug stability modeling and how to approach it to ensure product quality beyond the manufacturing process. Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences. --- Chapter Markers [1:39] Stewart starts by giving an overview of his career in biotech manufacturing. [3:54] Stewart talks about optimizing processes to control product quality in the early stages of the drug development process. [7:27] Ross asks Stewart to speak more about how to optimize and minimize the variability of assays to increase confidence in clinical results. [12:11] Stewart explains the importance of understanding how assay variability influences results and how to handle this when making business decisions. [14:13] Ross and Stewart discuss the issue of assay variability in relation to regulatory scrutiny. [17:07] Stewart walks through the benefits of using statistical simulation tools to better understand how an assay performs. [19:49] Stewart highlights the importance of understanding at which stage sampling has the greatest impact on decreasing variability [22:09] Stewart answers the question of how monitoring processes change when moving to full production scale. [26:39] Stewart outlines stability modeling and the importance of stability programs in biotech manufacturing. [30:38] Stewart shares his views on the biggest challenges that biotech manufacturers face around data. --- Download our latest white paper on “Using Machine Learning to Implement Mid-Manufacture Quality Control in the Biotech Sector.” Visit this link: https://connect.corrdyn.com/biotech-ml

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