PROCESS LIFECYCLE VALIDATION - Risk Management Applications in Pharmaceutical and Biopharmaceutical Manufacturing - page 176

product quality. In addition, one major concern addressed with CV is the impact
to product quality because of contamination or cross-contamination from other
products and processes. Because of the similarities, this chapter discusses quality
risk management (QRM) application in PV, and also describes relevant aspects
of CV. The chapter is not meant to provide a detailed description on how to
perform PV and CV. Instead, it discusses utilization of a risk-based approach
to develop and implement a sound, efficient validation program. A risk-based
approach can utilize various formal and informal risk assessment (RA) tools.
8.2 REGULATORY GUIDANCE FOR QRM IN PROCESS VALIDATION
QRM has been described in various recent regulatory guidances for several
aspects of PV, such as the following:
Lifecycle approach
• QRM can be used at different stages during product and process develop-
ment and manufacturing (e.g., risk analyses and functional relationships
linking material attributes and process parameters to product critical qual-
ity attributes (CQAs)) [5].
Extent of validation
• An RA approach should be used to determine the scope and extent of
validation and revalidation [2,6], including the sampling, testing, and
amount of data required.
Critical quality attributes and CPPs
• QRM should be used to prioritize or rank the list of potential critical qual-
ity attributes (pCQAs and CPPs for subsequent evaluation and validation
studies. An iterative process of QRM and experimentation can identify
relevant CQAs and assess the extent their variation impacts product qual-
ity [5,7]. RAs and experimentation can be used to establish relationships
between CQAs and CPPs in the manufacturing process. On the basis of
these relationships, a control strategy can be designed to demonstrate that
a product of uniform quality is produced consistently. Material attributes
that may have an impact on product CQAs should also be evaluated [5].
Design of experiments
• RA tools should be used to screen potential variables for design of
experiments (DoE) studies in process development and characterization
to minimize the total number of experiments needed while maximizing
knowledge gained [4].
Sampling plans and statistical confidence levels
• Risk analysis should be used to determine the confidence intervals (e.g.,
80, 90, 95, 99, 99.5%) to be used in determining sampling and acceptance
criteria [4], particularly for final dosage forms.