Quality by Design: Industrial Case Studies on Defining and Implementing Design Space for Pharmaceutical Processes—Part 1 - How to use multivariate experiments to define acceptable ranges. - BioP


Quality by Design: Industrial Case Studies on Defining and Implementing Design Space for Pharmaceutical Processes—Part 1
How to use multivariate experiments to define acceptable ranges.

BioPharm International
Volume 21, Issue 12

Figure 3. Pareto chart showing RPN* scores for the operating parameters of a fermentation pre-induction step in a biotech process. Adapted from reference 6.
Failure mode and effects analysis (FMEA) is a commonly used tool to assess the potential degree of risk for operating parameters in a systematic manner and to prioritize activities (such as experiments) needed to understand the effect of these parameters on overall process performance.9 A team consisting of representatives from process development, manufacturing, and other relevant disciplines performs an assessment to determine severity, occurrence, and detection. The severity score measures the seriousness of a particular failure and is based on an estimate of the severity of a potential failure effect at the local (process) level or at the end product (patient impact) level. Occurrence and detection scores are based on an excursion outside the operating range that results in the identified failure. The occurrence score measures how frequently the failure might occur, and the detection score indicates the probability of timely detection and correction of the excursion before end-product use. All three scores are multiplied to obtain a risk priority number (RPN) and the RPN scores are then ranked to identify the parameters with sufficient risk to merit process characterization.

Figure 4a. Outcome of a process characterization study of a microbial fermentation step showing parameter estimates for impact on titer. All conditions were normalized against the average of the two center point runs.
Figure 3 illustrates the FMEA outcome for a microbial fermentation step in a biotech process. RPN scores were calculated following the procedure described above. Operating parameters that had an RPN score that exceeded a certain threshold were characterized using a qualified scaled-down model. Screening was first performed to identify the process parameters that had the greatest effect on percent solids, optical density (OD) profiles, and product titer. Twelve parameters were examined in the screening study, and based on the results, three parameters were examined further for their interactions. Those parameters were temperature, pH, and dissolved oxygen (DO). A design of experiments (DOE) study was designed to examine the main effect of these parameters on percent solids, optical density (OD) profiles, and product titer, along with their interactions.

Figure 4b. Illustration of design space for the fermentation process under consideration. The outer surface represents the design space and the inner one the operating space. Adapted from reference 6.
The outcome of the DOE study is illustrated in Figure 4a for the effect on product titer. It was found that none of the parameters had a significant effect on product quality (i.e., none was a critical process parameter). However, temperature, pH, and DO were found to affect cell growth and titer and thus were classified as key process parameters. According to the principles in the ICH Q8 guideline, a unit operation design space was established using the acceptable ranges for temperature, pH, and DO, as illustrated in Figure 4b. It also can be seen that the operating space, as defined by the operating ranges, is well nested inside the unit operation design space, indicating robustness of the process step per Figure 2.

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