Introduction
This guidance discusses the term critical process parameter (CPP) and considerations are described for identifying the CPPs that need validation. It is applicable to the manufacture of commercial intermediates, Active Pharmaceutical Ingredients (APIs). Drug Products (DPs) and DP packaging.
This document provides guidance for gmp sites on selecting which parameters are critical for validation of a process. A key component is the recommended use of a risk-based assessment.
Recommendations and Rationale
- Running the process outside the proven acceptable range (PAR) for the parameter results in a significant risk of producing material of unacceptable quality;
- The difference in quality is carried through the process to the finished product (intermediate for sale, API or DP) where it results in the product not meeting one or more pre-determined Critical Quality Attributes (CQAs).
Knowing the CQAs of the finished product is essential for identifying the CPPs for a process.
The CQAs are a subset of the product specifications that are of particular importance to ensure that the product meets its patient safety and efficacy expectations. Appendix II provides some examples of analytical measures that may be CQAs for APIs and finished DPs. Correlations of CQAs to process parameters that impact the CQAs is guided by process knowledge acquired from prior experience with similar processes, development, process scaling studies and from observations made during preparation of pilot and/or full-scale batches.
2. Steps for Identifying CPPs
For new processes, CPPs are identified during CoDevelopment. The CPPs are typically identified by Technical Support site personnel, if not previously identified during process development. For many processes, a recommended approach to identifying the CPPs for validation is to begin with identifying the product’s CQAs and the process parameters that directly and indirectly impact these CQAs.
A process parameter may affect a CQA in either a univariate (single variable effect) or multivariate manner (multiple variables each having an impact). Some CQAs may have no specifically related process controls or parameters. Assessing the criticality of a process parameter should include consideration of all of these potential situations.
A process parameter that impacts a CQA should be assumed to be a CPP unless it is demonstrated that control of the parameter is adequate to minimize the risk of operating outside the proven acceptable operating range for the parameter. Previous experience with similar processes can be used to assess if a particular process control is likely or unlikely to influence product quality.
Steps for selecting CPPs for validation may be summarized as:
- Identify CQAs and process parameters and controls that impact the CQAs.
- Establish strength of correlations between parameters/controls and the CQAs.
- Assess capability of process controls and risk of CQA failure.
- If the regulatory filing identifies parameters as CPPs, the validation should include those CPPs.
3. Interactions of parameters
A CQA can sometimes be affected by more than one process parameter. In this event, one parameter may often have a greater impact on the CQA than another parameter, or may provide a greater degree of control than another parameter. Deviation from one such parameter may influence the ability of the other parameter to adequately control the associated CQA.
In an API process, it is important to have good knowledge of how process impurities form and the fate of impurities before attempting to determine if process parameters impact product quality. The extent to which subsequent process controls can diminish or remove an impurity is an important component of this knowledge. Formation of a manageable amount of an impurity, for example, may not be a quality problem if a subsequent purification can reduce or remove that amount of the impurity to an acceptable level. Conversely, sometimes subsequent processing has little or no ability to remove a specific impurity, so minimizing or preventing its formation may become critical for the process.
Example 1:
In a given API process, control of a process-related impurity is primarily determined by controlling reaction temperature within the identified PAR and by preventing an extended reaction time. Additionally, the conditions under which the API is crystallized influence the ability to diminish the presence of this impurity. Reaction conditions (e.g., temperature and duration) and crystallizations conditions (e.g., solvent composition and temperature) should all be evaluated when determining which parameter(s) should be identified as critical.
A deviation that affects product quality should be carefully investigated to understand any and all process parameters that played a role in determining the quality outcome. For instance, a deviation associated with one process parameter may influence the ability of other parameters to adequately control product quality. With Example 1, a temperature deviation during the reaction can lead to a greater-than-usual amount of a process impurity and can influence when the reaction should be terminated, and these in turn may impact the ability of the product crystallization to provide adequate control. It may be appropriate to designate temperature and/or reaction time (or endpoint test) critical for this example, and based on the risk assessment some may also choose to identify the crystallization conditions critical as well.
Example 2:
For a DP tablet manufacturing process, compression parameters, the interaction of excipients and perhaps other factors may interact or independently impact the dissolution and hardness characteristics of the product. A design of experiments (DOE) study may help in understanding which of these factors should be identified as CPPs.
4. Using Risk Assessment
Risk assessment of parameters should include evaluation of any process parameters impacting product quality, either directly or indirectly. The risk assessment provides the justification to explain why lower risk is associated with the quality-related parameters that are not identified as CPPs. Parameters that have a reduced risk of affecting product quality are sometimes described as Key Process Parameters, in manufacturing of small molecule APIs.
While validation brings focus to the CPPs, all process parameters identified in the manufacturing instructions are typically monitored during preparation of every batch, so it is also expected that parameters not identified as CPPs will be kept within prescribed ranges.
Using risk assessment of process parameters for this purpose is consistent with strategies for selection of CPPs established by industry trade associations. Additional guidance on the use of risk assessments for validation is available.
For new products, a focus of the risk assessment will be on whether there is a direct relationship between the parameter and a CQA. It is recommended that the risk assessment evaluation follow the Co-Development work process, which provides support and training for conducting appropriate risk assessments for selecting CPPs. A decision tree for determining criticality, adapted from the RFT work process, is shown in Appendix II.
Examples of risk assessments are provided in Appendix IV.
The relationship between parameters and CQAs is generally well-established from a variety of sources. These may include technical reports and memos prepared during and after process development, documented change management information and incident investigations, annual/periodic quality reviews, production manufacturing records and process capability studies.
5. Interrelationship of CPPs with process support systems
The risk analysis used to help select the CPPs for validation may be influenced by the ability of the equipment and supporting systems to control process variables (e.g. temperature, pressure and agitation). The equipment’s capability to control process parameters within defined limits is typically demonstrated by commissioning and verification / qualification of the process equipment . Process Validation depends on the supporting systems – facilities, utilities, equipment and automated controls, measurement/analysis, and process – performing as expected to consistently manufacture a product that meets its CQAs. System verification (qualification) therefore should consider process parameters and controls that impact product quality.
6. Additional considerations
Other aspects of process control that are not operational parameters should be evaluated as part of the quality risk assessment. These may influence equipment qualification, method validation and/or additional studies that may be needed because of their importance to product quality. Examples include:
A performance parameter such as an In-process Control (IPC) that impacts a product CQA, for instance:
- For an API process, an in-process test performed to insure that a process operation meets a critical quality endpoint; and
- For a DP process, an in-process test used to prevent diluting of material beyond defined mix characteristics.
A filtration to remove insoluble particulate matter;
Environmental condition (e.g., temperature or humidity) that must be controlled because of an impact on a CQA;
Equipment set points and configurations that are not operational parameters but that may impact on a CQA;
Processing time limits, if the probable adverse consequence of exceeding a time limit risks unacceptable final product quality, such as:
- Permitting an excessive reaction time in a synthetic API process when this allows formation of an unacceptable amount of a process impurity not adequately controlled by other means;
- Delay in the processing of a mixture;
- Other hold time limits that should be identified to understand process capabilities.
Knowledge of the Proven Acceptable Range (PAR) for a process parameter may be established from:
- Experimentation during laboratory and/or pilot scale development of the process, typically done during development of the process or evaluation of potential process improvements.
- Experience with demonstration batches, historical batches, and/or commercial-scale production batches. Statistical analysis of data may sometimes be used to help establish PAR limits.
- Knowledge acquired from deviations and incidents;
- Experience with PARs in similar processes to make analogous products; or
- Theoretical considerations. For some parameters it may be preferable to document a theoretical rationale why they are expected not to be critical to product quality. The theoretical argument should support a conclusion that the parameter is not critical because the PAR is significantly wider than the normal operating range (NOR) defined for that parameter
Comparing the Normal Operating Range (NOR) to the PAR is one part of performing a risk assessment of potentially critical process parameters. The comparison will typically reveal one of three general situations:
The NOR is a significantly smaller range than the PAR (as depicted in Figure 1, where the value of Δ is relatively large). It is typical to conclude such parameters are not critical to product quality if the magnitude of Δ minimizes the risk of exceeding the PAR.
The NOR is close to one or both limits established by PAR (consider Figure 1 where the value of Δ is relatively small). In these cases, the parameter may be a CPP, unless modification of the ranges can be made to increase the magnitude of Δ by decreasing the NOR and/or increasing the PAR.
No PAR has been identified or historical information does not provide substantiation of acceptable ranges broader than the NOR. In this event, it may be possible to establish the PAR from historical experience with the process (using knowledge of both routine processing and from deviation investigations). It may be necessary to identify the parameter as a CPP if the NOR approximates the PAR established from historical experience. To conclude that it is not a CPP, further study may be necessary to establish a broader PAR, or constrict the NOR to increase Δ and minimize the risk of deviation outside of the established historical limits.
Figures in Appendix IV provide further illustration of the relationship between NOR and PAR.
The Edge of Failure (EOF) for a process parameter may coincide with a PAR limit or be beyond this limit. It is not unusual for an EOF limit to be unknown. While it can be helpful to know the EOF to enhance process understanding, experimentally determining an EOF can often be impractical or difficult in terms of development time and resources and is not necessary.
7. Documenting Knowledge of Process Parameters
Information describing the risk assessment, identified CQAs and associated process parameters that impact product quality should be documented to provide the rationales for selection of CPPs for validation. This information should include description of the probable adverse consequence(s) (the most likely undesirable outcome(s) of exceeding a specified control parameter range) expected in the event of a deviation from the PAR of a CPP. It is recommended to also describe the basis for determining why those parameters that impact quality but are not selected as CPPs were categorized as non-critical parameters.
Documenting the reason(s) for deciding that parameters that impact quality are identified as non-critical is recommended because:
Good documentation of process understanding will help with future analysis of proposed process changes and aid in assessing incident investigations;
Documenting this information helps with communicating process knowledge to those who in the future may be asked to provide technical support for the process. Process knowledge may be documented in any of several formats, such as study reports, technical reports or memos, or even directly in validation protocols. Regardless of format, the information should be readily retrievable and subject to document control and records retention.
8. Evaluation of changes
When evaluating the potential impact of a change (e.g., to the manufacturing instructions, equipment, manufacturing site, product specifications, or addition of a new product to existing equipment), the associated risk analysis of the parameters that impact product quality should be re-examined. While overall it may appear that no quality impact is likely, some aspects of a change can alter the potential risk of deviation, perhaps leading to a different conclusion of whether a parameter should be identified as a CPP. Changes involving scaling of the process or the use of different equipment need to be evaluated because they may influence the ability of processing to provide the desired degree of control of the process. Repeating the risk assessment should include all parameters that may impact product quality, not just those identified earlier as CPPs.