P39: Optimization of a Saccharomyces cerevisiae fermentation process for production of a therapeutic recombinant protein using a multivariate Bayesian approach

Various approaches have been applied to optimize the biologic product fermentation process and define the design space. In this presentation, we presented a stepwise approach to optimize a S. cerevisiae fermentation process through the risk assessment analysis, statistical design of experiments (DoE) and multivariate Bayesian predictive approach. The critical process parameters (CPP) were identified through the risk assessment. The surface response to each product critical quality attribute (CQA) was modeled. The interaction of the CPPs was also modeled for each CQA. A multivariate Bayesian predictive approach was used for multiple response surface optimization to indentify the region of process operating conditions where four or all five CQAs of the product met their specifications simultaneously. The calculated probability by this approach provided the figure to define the process design space. The approach presented here can be extended to other fermentation process optimization and the process design space quantitation.

Keywords: Fermentation process; central composite design; critical process parameter (CPP); critical quality attribute (CQA); multivariate Bayesian predictive approach; design space; quality by design (QbD)