Criticality Management of a Drug Product and its Manufacturing Process - Pharmaceutical Technology

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Criticality Management of a Drug Product and its Manufacturing Process
Criticality management combines pharmaceutical product, process, and material knowledge and risk management in one approach, which is reflected in a single document.


Pharmaceutical Technology
Volume 9, Issue 32, pp. 6680


Table III: Compression-process parameters and active pharmaceutical ingredient (API) attributes that affect critical-quality attributes or manufacturability.
Based on the overview, multivariate experiments are designed in a reasonably broad space. Experiments are preferably designed by DOE, where it helps and is supported by PAT (where it helps and is available), and stability studies, where relevant.


Table IV: Target, normal operating ranges, design space, and risk analysis for parameters of a compression process.
In the second step, the outcome of these experiments is used to define the process parameters, material attributes, and the attributes of the pharmaceutical intermediate or in-process product that influence the end-product CQAs. This evaluation is based either on statistical significance in the experiments or on strong prior knowledge. In the absence of statistical significance, the parameter or attribute is not influential and therefore considered not critical. Reasons and references to the experiments or reports are captured in a tabular format (see Table III). In this way, the criticality-management document represents the product knowledge and process understanding in a clear and concise way and can be retrieved quickly, with references to underlying results and details.


Figure 3 (ALL FIGURES ARE COURTESY OF THE AUTHORS.)
In the third step, for every process parameter or material attribute that proved to be influential, one must define the normal operating range and its part in the design space (see Table IV). The normal operating range and design space must take into account the multivariate combinations and interactions and be valid at full scale. If the manufacturing equipment has no advanced controls such as feed-forward- and feedback-control mechanisms, one must set a clear operating range for each influential parameter or material attribute. For example, one can reduce the operating space to a regular square or cube to allow the operators to understand the area in which the process must be controlled (see Figure 3).

When the design space is irregular, a mathematical equation can be provided together with the boundaries in which this equation is valid. If only two process parameters play a role, they can be represented by an overlaid contour plot. If more parameters play a role, then more or more complex graphs are needed to represent the design space. When the design space is large, one can simplify the representation of the design space by presenting it as a cube or square within the contours of the design space. Thus the design space is reduced, but it can be represented as a set of multivariate proven acceptable ranges: one for each influential parameter or material attribute, taking into account all combinations and interactions with other parameters or material attributes. Multivariate proven acceptable ranges defined within the design space are a substitute for the design space, but univariately defined proven acceptable ranges do not constitute a design space.

Steps 1–3 in Figure 2 represent the knowledge part of criticality management. Steps 4 and 5 represent the risk management based on this process knowledge and the translation into a suitable control strategy.


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