(84f) Creating a QbD Design Space Using Mechanistic Chromatography Modeling | AIChE

(84f) Creating a QbD Design Space Using Mechanistic Chromatography Modeling

Authors 

Watler, P. K. - Presenter, Hyde Engineering + Consulting


Recent ICH and FDA guidance has emphasized the importance of science based process design for the manufacture of pharmaceuticals. As the biopharmaceutical industry approaches the mature age of 30, and with increasing economic and health care pressures, it is increasingly important to better understand and optimize our manufacturing processes. The FDA's Moheb Nasr recently commented, Quality by Design (QbD) is ?A good scientific approach? which involves ?using good science? which ?will result in cost benefits for the industry?. The concept of ?Design Space? involves knowing how process inputs impact quality attributes across a broad range, and is a key element of QbD.

We will review the application of the ?Yamamoto Model? which was first developed in Japan in the 1980's and subsequently refined and applied to industrial settings in the following decades. In the US, this mechanistic chromatography model has been used to simulate, optimize and elucidate the design space of commercial scale biopharmaceutical separations. The ion exchange chromatography model is based upon the principles of ionic capacity and band spreading. In this case study, the separation was first developed in isocratic mode by varying column geometry, protein loading and elution velocity. The separation was then simulated at laboratory scale using a series of gradient experiments to explore the design space for ionic strength and superficial velocity parameters. The model was used to scale the separation to a large manufacturing column and resolution, purity and peak characteristics were comparable to the small-scale system. Using the model, gradient slope and bed height were further adjusted to improve productivity and reduce buffer usage. The model was additionally used to troubleshoot the separation and assess peak stability as a function of buffer composition and media ionic capacity. Recent mechanistic methods for predicting pressure drop in columns packed with compressible preparative media are presented in order to estimate the maximum operating flow rate and bed height.

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