Quality by Design: Industrial Case Studies on Defining and Implementing Design Space for Pharmaceutical Processes (Part 2) - Understanding the relationship between the process and CQAs. - BioPharm


Quality by Design: Industrial Case Studies on Defining and Implementing Design Space for Pharmaceutical Processes (Part 2)
Understanding the relationship between the process and CQAs.

BioPharm International
Volume 22, Issue 1

Monitoring a Design Space and Making Post-Approval Changes to Design Space

During commercial-scale manufacturing, the proactive trending of product performance on a continuous basis affords significant benefits through early detection of emerging issues and further optimization of the control strategy to ensure operation within the design space. By using tools such as design of experimentation and systematic problem solving approaches such as Six Sigma, manufacturing operations can resolve problems and continue to optimize processes. These tools allow for and encourage a systematic approach to resolving unknown sources of variability and improving manufacturing robustness.

Case Study 1: Tablet Dissolution

Table 1
The case study presented in Figure 1A describes the impact of subtle changes in raw material variability on product performance and the importance of continuous monitoring throughout the product lifecycle to ensure product quality. During routine monitoring of product performance for an extended-release tablet, an incidence of high variability in dissolution results was observed. Although all of the lots produced during this period met specifications, the trend in variability raised concerns about the potential for product quality problems to arise in the future. Data analysis to evaluate process capability with respect to dissolution at 12 h was carried out and the results suggested that supplemental tier 2 or tier 3 testing would be required to ensure product quality. Since the root cause of the upward trend in dissolution was not understood, a project was initiated using Six Sigma methodology to identify the root cause, design an improvement plan, and verify the impact of the corrective action. Six Sigma is a well known, structured approach to solving technical problems that have no known solution, have a measurable defect or problem, and identifiable causes. The steps used in a Six Sigma approach are 1) define the problem, 2) evaluate the ability to measure the problem, 3) analyze the problem using the appropriate method, 4) improve the process, and 5) implement the derived controls. In this instance, the project team used production data and analytical methods to identify the root cause and develop and implement the corrective action in a few months. Further evaluation of various parameters through multivariate analysis showed the root cause of variability to be directly related to raw material properties. The raw material properties affecting the dissolution rate were identified and a new tighter specification was defined and implemented to control the quality attributes of the incoming raw material. The data and trend shown to the left of the vertical red line in Figure 1A were before the implementation of controls, while the data to the right of the vertical red line are from post-implementation. Following completion of the Six Sigma "Improve" phase, the process was found to be significantly more robust, as seen in Figure 1B. A statistical analysis showed that the process capability was 0.86 before the Six Sigma project, and 1.93 afterward. The histogram in Figure 1B of mean 12-h dissolution for before and after the Six Sigma project illustrates the improved robustness and a shifting of the dissolution mean towards the center of the allowable specification range. Further, a predictive model was developed using a JMP software-based analysis of historical production data. A multivariate dissolution model was created to predict the 12-h dissolution on an ongoing proactive basis. A Pareto analysis of the data versus the CQA ranked the variables by correlation. Five factors, all quality attributes of the formulation excipent and drug substance, were found to be statistically significant. Figure 1C shows the prediction profiler from the model. The steeper the slope, whether positive or negative, the more that factor contributes to variability in dissolution. The statistical model was validated using four different excipient lots, each converted to 10–13 lots of tablets. The data are presented in Table 1. The predicted dissolution from the model versus the average 12-h dissolution from the multiple tablets lots gave a prediction error of about 1.0%.

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