Mechanistic Modeling of Chromatography is a technology that has been around in academia for decades. Yet it has not been widely adapted in the pharmaceutical industry, despite the fact, that mechanistic models are explicitly mentioned as desirable in the ICH Q8 and Q11 guidelines. At Roche, we believe that this technology has the potential not only to speed up the downstream process development for innovative clinical products this will also enable better process understanding but nevertheless reduce costs in product development. This presentation will demonstrate on three case studies how we are currently using mechanistic modeling of chromatography from Tox/Phase I process development all the way towards the later stages of the product life cycle. One case study will demonstrate how mechanistic modeling of chromatography has enabled us to improve a process for a new complex molecule such that it incorporated a global facility fit from the start. A second case study will be used to illustrate the benefit of a mechanistic model at the process characterization and validation stage. It will be highlighted how a mechanistic model can be used to support the identification of critical process parameters, assess the impact on CQAs and help reduce the experimental burden. The third case study will show the potential of a mechanistic model for commercial stage downstream processes, and how it can be used for deeper process understanding even at large scale. To summarize all three case studies will be used to present an integrated mechanistic modeling framework for the development of a downstream process from early stage to the commercial production phase. It will be highlighted how modeling can increase speed to clinic while at the same time reduce cost. Lastly this presentation will explore on a conceptual level a rationally designed chromatographic process, that is based on molecular understanding of chromatography.

Mechanistic modeling of chromatography: An industry perspective and vision

253rd National Meeting (2017)

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