Characterization of a Saccharomyces cerevisiae fermentation process for production of a therapeutic recombinant protein using a multivariate Bayesian approach

Abstract

The principle of quality by design (QbD) has been widely applied to biopharmaceutical manufacturing processes. Process characterization is an essential step to implement the QbD concept to establish the design space and to define the proven acceptable ranges (PAR) for critical process parameters (CPPs). In this study, we present characterization of a Saccharomyces cerevisiae fermentation process using risk assessment analysis, statistical design of experiments (DoE), and the multivariate Bayesian predictive approach. The critical quality attributes (CQAs) and CPPs were identified with a risk assessment. The statistical model for each attribute was established using the results from the DoE study with consideration given to interactions between CPPs. Both the conventional overlapping contour plot and the multivariate Bayesian predictive approaches were used to establish the region of process operating conditions where all attributes met their specifications simultaneously. The quantitative Bayesian predictive approach was chosen to define the PARs for the CPPs, which apply to the manufacturing control strategy. Experience from the 10,000 L manufacturing scale process validation, including 64 continued process verification batches, indicates that the CPPs remain under a state of control and within the established PARs. The end product quality attributes were within their drug substance specifications. The probability generated with the Bayesian approach was also used as a tool to assess CPP deviations. This approach can be extended to develop other production process characterization and quantify a reliable operating region. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:799-812, 2016.

Saccharomyces cerevisiae morphological changes and cytokinesis arrest elicited by hypoxia during scale-up for production of therapeutic recombinant proteins

“…In recent work, focus was placed on characterizing the yeast morphological changes and understanding the underlying metabolic mechanisms leading to those modifications triggered by hypoxia at the industrial production scale. The morphology of different cell subpopulations and their distribution in the different phases of the yeast mitotic life cycle at different levels of dissolved oxygen are described using a proven small scale model (SSM) of the production process [13, 14]. From a metabolic perspective, we interpret our results in terms of the reduced availability of precursors triggered by hypoxia resulted in impaired cell wall formation, lack of integrity and functionality.…”