By Ali Afnan, Principal, Stepchange Pharma

As many of you know, I’ve spent years advocating the use of real-time process monitoring and control in the pharmaceutical industry. And, as any of you who have read my articles may have noticed, I’ve been disappointed by the fact that process analytical technology (PAT) has, so far, failed to become routine for most drug manufacturers.

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What are the must-read articles for would-be practitioners of QbD? I recently posed this question on LinkedIn, and have received several “nominations.” (And we welcome your suggestions.) One seminal article mentioned is “Quality by Design for Biopharmaceuticals,” published last year in Nature Biotechnology and coauthored by FDA’s Helen Winkle and consultant Anurag Rathore, then of Amgen. (Rathore has also coedited, with Rohin Mhatre, a book by the same title, published by Wiley in 2009.)

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This week on ISA’s InTech web site, automation consultant Joseph Alford provides an in-depth look at using automation to reduce process variability. As Alford spent 35 years at Eli Lilly and is a distinguished member of the Process Automation Hall of Fame, the article presents an authoritative, high-level perspective on the automation/variability dynamic.

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By Robert Tinder, Principal Scientist, Roche Pharmaceuticals

Editor’s Note: The author joined Roche in July 2010; the work discussed in this article took place previously at Wyeth Pharmaceuticals.

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Editor’s Note: As part of our site’s mission to further PAT and QbD within the industry, we will call attention to “success stories” of those companies that are already realizing the benefits of a more science- and risk-based approach. The following is an example, a summary of a talk that Amgen’s Duncan Low delivered last fall, at ISPE 2009. Please send your success stories, long or short, to pthomas@putman.net.

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In Leuven, Belgium, last month, researchers from the Technical University of Berlin shared results of their work investigating the use of Gaussian Mixture Density (GMD) approximation to analyze and reduce deviations in bioprocesses. At the 11th International Symposium on Computer Applications in Biotechnology, the presenters (Rossner, Heine, and King) described their use of a robust fermentation process model to predict and monitor reactions more robustly than with more heuristic classic approaches.

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