In this stage, data from the development phase are gathered and analyzed to define the commercial manufacturing process. By understanding the commercial process, a framework for quality specifications can be established and used as the foundation of a control strategy.
Process design is the first of three stages of process validation. Data from the development phase is gathered and analyzed to understand end-to-end system processes. These data are used to establish benchmarks for quality and
production control.
Design of experiment (DOE) Design of experiments is used to discover possible relationships and sources of variation as quickly as possible. A cost-benefit analysis should be conducted to determine if such an operation is necessary.
Quality by design (QBD) Quality by design is an approach to pharmaceutical manufacturing that stresses quality should be built into products rather than tested in products; that product quality should be considered at the earliest possible stage rather than at the end of the manufacturing process. Input variables are isolated in order to identify the root cause of potential quality issues and the manufacturing process is adapted accordingly.
Process analytical technology (PAT) Process analytical technology is used to measure critical process parameters (CPP) and critical quality attributes (CQA). PAT facilitates measurement of quantitative production variables in real time and allows access to relevant manufacturing feedback. PAT can also be used in the design process to generate a process qualification.
Critical process parameters (CPP) Critical process parameters are operating parameters that are considered essential to maintaining product output within specified quality target guidelines.
Critical quality attributes (CQA) Critical quality attributes (
CQA) are chemical, physical, biological, and microbiological attributes that can be defined, measured, and continually monitored to ensure final product outputs remain within acceptable quality limits. CQA are an essential aspect of a manufacturing control strategy and should be identified in stage 1 of process validation:
process design. During this stage, acceptable limits, baselines, and data collection and measurement protocols should be established. Data from the design process and data collected during production should be kept by the manufacturer and used to evaluate
product quality and
process control. Historical data can also help manufacturers better understand operational process and input variables as well as better identify true deviations from quality standards compared to false positives. Should a serious product quality issue arise, historical data would be essential in identifying the sources of errors and implementing corrective measures. ==Stage 2: Process Performance Qualification==