Figure 1: Factor response matrix and risk assessment.
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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 2: Design space characterization. The white area is within the specification limits while the shaded area is not.
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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.
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.
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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.
Table II: Values considered key operating parameters to product, process, and design performance.
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One possible justification for determining CPPs is given in the following example. To normalize and standardize the effect
size, the percent of tolerance for two-sided specification limits and the percent of design margin for one-sided limits were
used to evaluate the effect size of a factor and or interaction. Values of <10% were not considered practically significant.
Values of 11–19% were considered to be key operating parameters and values >20% were considered to be CPPs critical to product,
process and design performance, as shown in Table II. Although thresholds for criticality are somewhat arbitrary, they have
been set relative to the design-space explored and as a percentage of the CQA attribute and therefore should have product
performance relevance.
Application of CPPs for control. CPP selection typically comes from several sources, including risk assessments, scientific knowledge, and characterization
and optimization studies. Once all CPPs have been identified, the next step is to determine practical application of them
for process control. Typical considerations include: ease of use and/or ease of adjustment; safety and other risk factors;
on-line or in-line measurement; and off-line or near-line measurement. Just knowing that a factor is critical and knowing
the relative effect size of the factor to the product specifications and CQAs is a great start but it is not sufficient. Care
needs to be exercised to make sure the CPP factors can be used in a safe and effective way to consistently adjust process
parameters to their intended targets. Linking statistical process control (SPC) and process analytical technology (PAT) methods
to the sensitivities identified during CPP selection is a big plus and ties the adjustment method together with process monitoring
and control methods (5).
Thomas A. Little is president of Thomas A. Little Consulting, 12401 N Wildflower Lane Highland, UT 84003, tel. 925.285.1847, drlittle@dr-tom.com
REFERENCES
1. ICH, Q8 (R2) Pharmaceutical Development (2009).
2. ICH, Q9 Quality Risk Management (2006).
3. ICH, Q10 Pharmaceutical Quality System (2009).
4. ICH, Q11 Development and Manufacture of Drug Substances, Step 4 document (2012).
5. FDA, Guidance for Industry: PAT—A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance (2004).
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