Frequency of formal statistical analysis varies with the level of risk impacting product quality. High risks are reviewed more often to respond more quickly to out-of-control conditions that could risk product quality. Since key process parameters and performance attributes are indicative of performance, not quality, the frequency of review may be decided on a case-by-case basis.
Sources of information from other quality and manufacturing systems such as change control; scheduled preventive maintenance, calibration, or production interruptions; and customer complaints should be made available during reviews. These may help explain unexpected shifts or variation in the production process.
Intuition and visual assessment of tabular data is inefficient at separating inherent lot-to-lot “common cause” variation from the “special cause” variation. A “special cause” is an event or trend in the data set that is statistically unlikely to occur if the process is maintaining a level of control. There are several quality and statistical references (12) that summarize the various graphical statistical tools (e.g., control charts and capability charts) and how to interpret them (e.g., Western Electric or Nelson’s Rules). Different statistical techniques may be employed commensurate with the risk of the parameter or attribute being analyzed and the frequency of its analysis. More complicated tools such as charts of cumulative sum (CuSum) can provide an earlier warning of changes in the mean than more standard control charts.
All statistical methods have the risk of false warnings or can suffer from over interpretation. Trigger events such as single out-of-control points, oscillations, trends, and mean shifts do not necessarily suggest a risk to quality or require immediate corrective actions. They may also provide long-term opportunities for continuous improvements to reduce variation. CPV is also a useful means of assessing the effect of process change control.
The design space relationships between CPPs and CQAs should be refined as the CPV process collects data on more and more lots. It is not as straightforward as a designed experiment because production data is confounded with several CPPs moving within their control space and potentially interacting in their effects on a CQA. Despite this, the CPV is an additional body of data and process knowledge, which includes more real-life variations in equipment, personnel, and materials than any planned study. Consequently, periodic assessment of the continuum of criticality should be made.
The knowledge derived from Stage 3 (continued process verification) can be used to drive continuous improvement initiatives. High-risk CPPs represent the greatest impact on CQAs and represent the best opportunity to improve quality through reducing variation. Control strategies such as process analytical technology (PAT) may be allowed for better control, a reduced NOR and, therefore, reduced variation relative to its PAR. A lower likelihood (occurrence) of exceeding its PAR may be sufficient to reduce the risk level of the process parameter. The fewer number of high-risk CPPs and critical material attributes (CMAs), the more robust a process becomes at producing quality product.
Conclusion: Continuum of criticality throughout the process validation lifecycle
The continuum of criticality as applied to parameters and attributes is a framework for assessing risk at each stage of the process validation lifecycle:
• In Stage 1 (process design):
a. Risk levels of CQAs are assigned based on severity to patients
b. Process parameters are related to CQAs by unit operation
c. Prior knowledge and scientific knowledge is applied to assign initial risk levels to process parameters
d. Risk levels are used to apply staged DOEs to process characterization studies
e. Models developed from DOEs quantify process parameter criticality and form a design space to ensure quality of CQAs
f. Control strategy defines the NOR of CPPs.
• In Stage 2 (process qualification):
a. Control strategy provides acceptance criteria for equipment qualification
b. Risk-based and statistical methods use the continuum of criticality to determine the number of PPQ lots required
c. Risk levels for CQAs determine statistical acceptance criteria and sampling plans for PPQ.
• In Stage 3 (continued process verification):
a. Risk levels determine monitoring and review frequency of parameters and attributes
b. CPV statistical tools are commensurate with the risk level of the parameter/attribute being analyzed
c. Ongoing verification supports and/or refines the design space
d. Low-risk or non-CPPs may be shown to have higher impact
e. High-risk CPPs offer opportunities for continuous improvement and potential to reduce risk-level of the parameter.
By applying a continuum rather than a binary method to criticality throughout the lifecycle, we can set priorities to focus time and resources in areas of greatest impact to quality including experimental design, design space development, acceptance criteria, data monitoring, and continuous improvement.
References
1. FDA, Guidance for Industry, Process Validation: General Principles and Practices, Revision 1 (Rockville, MD, January 2011).
2. ICH, Q8(R2) Harmonized Tripartite Guideline, Pharmaceutical Development, Step 4 version (August 2009).
3. ICH, Q9 Harmonized Tripartite Guideline, Quality Risk Management (June 2006).
4. ICH, Q10, Harmonized Tripartite Guideline, Pharmaceutical Quality System (April 2009).
5. J. J. Peterson, J Biopharm. Stat. 18 (5) 959-975 (2008).
6. ASTM, E2500-07, Standard Guide for Specification, Design, and Verification of Pharmaceutical and Biopharmaceutical Manufacturing Systems and Equipment (West Conshohocken, PA, 2012).
7. PDA, Technical Report 60, Process Validation: A Lifecycle Approach (Bethesda, MD, 2013).
8. ISPE, Product Quality Lifecycle Initiative (PQLI) Good Practice Guide, Overview of Product Design, Development, and Realization: A Science- and Risk-Based Approach to Implementation (Tampa, FL, Oct 2010).
9. ISPE, Product Quality Lifecycle Initiative (PQLI) Good Practice Guide, Part 1 - Product Realization using QbD, Concepts and Principles (Tampa, FL, Nov 2010).
10. ISPE, Product Quality Lifecycle Initiative (PQLI®) Discussion Paper, Topic 1 - Stage 2 Process Validation: Determining and Justifying the Number of Process Performance Qualification Batches (www.ispe.org, accessed Aug. 20, 2012).
11. ISO, ISO 16269-6:2014, Statistical Interpretation of Data, Part 6 - Determination of Statistical Tolerance Intervals (Geneva, Switzerland, Jan. 23, 2014).
12. PDA, Technical Report 59: Utilization of Statistical Methods for Production Monitoring (Bethesda, MD, 2012).
About the Author
Mark Mitchell is principal engineer at Pharmatech Associates.