The Food and Drug Administration (FDA) guidance on continued process verification and the EU GMP Annex 15 requirements for ongoing process verification direct pharmaceutical and biopharmaceutical manufacturers to ensure that their processes remain in a continual state of control (a validated state) during the lifecycle of the product so that the strength, quality, and purity of the final drug product is maintained. Both regulatory agencies instruct manufacturers to develop sustained programs which collect and analyze product and process data to evaluate the state of control and to identify product or process problems as opportunities to implement improvements.
Process validation of a therapeutic product consists of 3 stages:
Process validation’s first two stages have a distinct endpoint while CPV is sustained throughout the commercial life of a drug molecule and requires a comprehensive strategy.
CPV necessitates continued and routine monitoring of the commercial manufacturing process to detect any variation from historical data or adverse trends, and to understand the sources of variation. If variation is detected, the biomanufacturer must determine the impact of the variation on the process and the product attributes and control the variation as needed to maintain product quality.
The CPV process flow consists of procedures, tools, and processes summarized in the eleven steps outlined below. Specifically, CPV consists of collecting process parameter data, trending it against statistical control limits, and calculating process capability and process performance (Cpk and Ppk) at defined intervals or after every few batches. CPV programs typically include the following components:
To learn more on CPV, read our eBook “Implementing Continued Process Verification”.
Automated by Bio4C ProcessPad™ Software:
For more information on our software platforms and how they can help simplify CPV, visit our data analytics software web page.
The initial step in designing a robust CPV program is the classification of parameters and the statistical treatment of data.
Parameter classifications are defined by process characterization, process validation, or in-process control description documents. These documents also set initial action or specification limits. Performance or input parameter types include:
Once the parameters are classified, limits for each are defined and applied. Specification limits and action limits are defined during process design and qualification stages. The first 15 to 30 batches (statistically significant) can be trended against these limits. Alert or statistical control limits are then defined based on these initial batches.
Statistical treatments are performed on the data to determine the respective statistical control limits for the performance parameters. Most process parameters will follow the distribution of a normal/Gaussian bell shaped curve while some will follow a skewed distribution.
How the data are distributed determines the procedure for setting up in-process control limits for the continued process verification.
Statistical process control (SPC) is an important element of CPV and a process control chart (Figure 1.) plays the most important role in SPC and any process monitoring program. A successful CPV system should not only create SPC charts from validated data, but it should also store, display, and evaluate control chart statistics based on historical limit changes.
Figure 1. Example of a process control chart.
A manufacturing process is a unique combination of its manufacturing environment comprised of machines, methods, and people engaged in the production process.
Process capability indices have been used in manufacturing to provide quantitative measures on process potential and performance. The output of a process can be a product characteristic or a process output parameter. Process capability indices (Ppk, Cpk) provide a common metric to evaluate and predict the performance of processes and summarize process performance relative to a set of specifications (i.e., quality boundary).
Process capability (Ppk, Cpk) for a normally distributed monitoring process parameter is calculated using the following equation:
Figure 2.Ppk and Cpk equations.
USL = Upper Specification Limit (for CPP) or Upper Action Limit (for KPP)
LSL = Lower Specification Limit (for CPP) or Upper Action Limit (for KPP)
Avg = Average or mean of the population under analysis
σ = Standard deviation of the population under analysis
σMR = Moving Range Standard Deviation
Cpk cannot be estimated for non-normal data as the average and standard deviations will not represent the non-normally distributed data correctly. Instead, Ppk is evaluated based on all of the data points in terms of percentile ranges. The following equation is used to estimate Ppk for a non-normally distributed monitoring process parameter.
Figure 3. Ppk equation for non-normally distributed monitoring process parameter.
USL = Upper Specification Limit (for CPP) or Upper Action Limit (for KPP)
LSL = Lower Specification Limit (for CPP) or Upper Action Limit (for KPP)
X0.50 = Median of the population under analysis
X0.99865 = 99.865th Percentile of the population under analysis
X0.00135 = 0.135th Percentile of the population under analysis
During process monitoring, a performance parameter will be subject to different monitoring modes depending upon the number of data points and accumulated history of the parameter.
Monitoring phases including:
Nelson or Western Electric rules should be established and used for out-of-trend detection and to determine if any process parameters are out of control. A batch violating any of the trending rules should be properly investigated and closed with appropriate corrective and preventative actions (CAPA).
Several types of reports are generated in support of CPV including:
Successfully executing CPV requires end-to-end data management throughout the process validation life cycle and can be extremely challenging if the appropriate tools and software are not used to facilitate data integration, analysis, and sharing across the manufacturing network.
Manually tracking critical batch data in spreadsheets and transferring it between systems is time-consuming, error prone, and can lead to data integrity issues. In addition, siloed data prevents teams from efficiently identifying causes of a deviation or an adverse trend, as well as tracking important trends that could lead to process improvement.
An integrated data software environment automates and simplifies CPV including:
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