Using a Systematic Approach to Select Critical Process Parameters - Pharmaceutical Technology

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Using a Systematic Approach to Select Critical Process Parameters
Harmonized regulations call for a risk-based and systematic approach to evaluating and selecting CPPs for accurate process control. Critical process parameters (CPPs) and their associated process controls are crucial to drug development and process validation and to the evaluation of every manufacturing unit operation.

Pharmaceutical Technology
pp. s47-s46

Product and process specifications. Specification limits for the product and its process must be defined to protect the CQAs of the drug substance or drug product. These limits may be set based on a transfer function (e.g., how does X influence the Y response) from a characterization study or may be set statistically (based on some multiplier of sigma and/or risk) for those parameters that show no harm (i.e., clinical) and where variation is known. Specification limits will form a key basis for CPP determination. Specification limits are primarily defined for product control rather than for process control.

Validation of analytical methods. Limit of detection, limit of quantification, precision, and accuracy must be characterized for all analytical methods, and method validation must be completed. Once these steps are done, one can trust the numbers and know the error associated with any statistic of interest. Method validation should be done prior to product and process characterization studies and the design and implementation of process controls.

Figure 1: Factor response matrix and risk assessment.
Quality risk management for materials and operations. A formal QRM process should be in place to systematically examine all materials and unit operations for their potential influence to drug CQAs. Risk-ranking and other QRM tools are used to identify factors and unit operations that hold the greatest risk. Scientific understanding and historical data are typically the basis upon which potential risks are identified and prioritized. Candidate CPPs may be identified in this process that will later need to be ruled in or out based on data and identified risk.

Figure 2: Design space characterization. The white area is within the specification limits while the shaded area is not.
Design-space characterization. Many of the previous steps provide inputs to effective design-space characterization and optimization. DOE and multifactor studies are used to understand the sensitivity of key product and process parameters relative to drug-product and drug-substance specification limits. Factor selection prior to DOE generation is the most important step in design-space characterization. The matrix shown in Figure 1 is used in the identification of the factors and responses that should be characterized and completed as part of risk assessment prior to DOE design. One should take care to open up the range of the X factors sufficiently to understand their influence on Y response and to be representative of the normal operational range of the process. Figure 2 shows a clear picture of the design space generated from a characterized process.

Table I: Scaled estimates for a purification step.
Factor effect size and CPP selection. DOE and multifactor experiments can help to isolate the influence of every factor and interaction on the critical responses associated with the substance or product. Analysis of the DOE will generate the scaled estimates (one half the change in Y relative to the change in X) also known as half effects, as shown in Table I.

One can convert the scaled estimate into the full effect (total change in Y relative to change in X) and compare the full effect to the product specification tolerance. The formulas for conversion are as follows:

  • Full Effect = Scaled Estimates * 2
  • % of Tolerance = Abs (Scaled Estimates * 2) / (USL-* LSL) for two-sided limits
  • % of Design Margin= Abs (Scaled Estimates * 2) / (Average-LSL) for one-sided LSL only
  • % of Design Margin= Abs (Scaled Estimates * 2) / (USL-Average) for one-sided USL only

where, USL is the Upper Specified Limit, LSL is the Lower Specified Limit, and the average is the baseline process or product average from the DOE or other lots.


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