Defining process design space for biotech products: case study of Pichia pastoris fermentation.

The concept of "design space" has been proposed in the ICH Q8 guideline and is gaining momentum in its application in the biotech industry. It has been defined as "the multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality." This paper presents a stepwise approach for defining process design space for a biologic product. A case study, involving P. pastoris fermentation, is presented to facilitate this. First, risk analysis via Failure Modes and Effects Analysis (FMEA) is performed to identify parameters for process characterization. Second, small-scale models are created and qualified prior to their use in these experimental studies. Third, studies are designed using Design of Experiments (DOE) in order for the data to be amenable for use in defining the process design space. Fourth, the studies are executed and the results analyzed for decisions on the criticality of the parameters as well as on establishing process design space. For the application under consideration, it is shown that the fermentation unit operation is very robust with a wide design space and no critical operating parameters. The approach presented here is not specific to the illustrated case study. It can be extended to other biotech unit operations and processes that can be scaled down and characterized at small scale.

Using statistical analysis for setting process validation acceptance criteria for biotech products.

This paper discusses the challenges of setting process validation acceptance criteria for biotech products for cases where using statistical tools is appropriate. Data are analyzed under three different scenarios that are frequently encountered in biotech applications. Scenario A represents the case when a small data set around center point conditions is available for setting acceptance criteria. Scenario B represents the case when a larger data set within normal operation conditions is available for setting acceptance criteria. Scenario C represents the case when a large characterization data set is available for setting acceptance criteria and it is possible to accurately model the impact of operation conditions on performance of the step. Statistical approaches including mean +/- 3SD, tolerance interval analysis, prediction profiler, and Monte Carlo simulation are applied to the different scenarios. Strengths and shortcomings of the different statistical tools are discussed, and the best approach for each scenario is recommended. It is shown that selection of the right statistical approach is a critical first step toward setting appropriate acceptance criteria.

A systematic approach for scale-down model development and characterization of commercial cell culture processes.

The objective of process characterization is to demonstrate robustness of manufacturing processes by understanding the relationship between key operating parameters and final performance. Technical information from the characterization study is important for subsequent process validation, and this has become a regulatory expectation in recent years. Since performing the study at the manufacturing scale is not practically feasible, development of scale-down models that represent the performance of the commercial process is essential to achieve reliable process characterization. In this study, we describe a systematic approach to develop a bioreactor scale-down model and to characterize a cell culture process for recombinant protein production in CHO cells. First, a scale-down model using 2-L bioreactors was developed on the basis of the 2000-L commercial scale process. Profiles of cell growth, productivity, product quality, culture environments (pH, DO, pCO2), and level of metabolites (glucose, glutamine, lactate, ammonia) were compared between the two scales to qualify the scale-down model. The key operating parameters were then characterized in single-parameter ranging studies and an interaction study using this scale-down model. Appropriate operation ranges and acceptance criteria for certain key parameters were determined to ensure the success of process validation and the process performance consistency. The process worst-case condition was also identified through the interaction study.