In the life of any drug product, the technology transfer of a process is a complex matter, made more complicated by the new definition of the Process Validation (PV) guidance issued by FDA in January 2011. In Part 1 of this series we have attempted to lay out a practical approach to successful transfer citing a real life example. We discussed the activities required to identify and establish an effective Proven Acceptable Range (PAR) and Normal Operating Range (NOR) for a legacy product as defined by the technology transfer framework used for this project. The framework is based upon Pharmatech Associates’ Process Validation (PV) model shown below in Figure 1.
Part 2 will pick up where we left off in the framework and discuss the considerations in developing an effective sampling plan and acceptance criteria for the Stage 2 Process Performance Qualification (PPQ) and how to transition to the Stage 3 Continuous Monitoring phase of the new PV guidance.
With the new PV guidance, the prerequisites for moving to PPQ are the same as the requirements defined in the original 1987 guidance. The expectation before moving forward to demonstrating process reproducibility includes completion of the following elements:
- Facility and Utility qualification
- Equipment qualification (IQ,OQ and PQ or equivalent)
- Analytical Method Validation is complete and Measurement System Analysis (MSA) has concluded that the resolution of the method is appropriate
- Cleaning Validation protocol; Cleaning method development and validation
- Upstream processing validation such as Gamma irradiation of components if applicable, etc, are complete for the new batch size
- Environmental Monitoring program is in place for the new facility
- Master Batch Record
- In-process testing equipment is qualified, MSA complete and acceptable, method validated and SOP in place
In a technology transfer exercise these elements must be applied to the new equipment and include the larger commercial batch size consideration. If all the elements are not complete prior to beginning the PPQ runs then a strategy may be developed, with the participation of QA, to allow concurrent processing of the PPQ lot and process prerequisites. For example, if cleaning validation has not been completed prior to the PPQ runs, and the PPQ lots are intended for commercial release, then a risk-based approach to the cleaning validation may be adopted with studies conducted concurrently with the manufacture of the lots with the caveat that the lots are not releasable until the cleaning validation program is complete. If such an approach is adopted then consideration must be given to both the major clean procedure, typically performed on equipment when changing products, and the minor clean procedure, typically performed during a product campaign.
In our case study process, all prerequisites were complete with the exception of cleaning validation which was conducted concurrently. The new process site used a matrix approach to cleaning validation, bracketing its products based upon an assessment of the API/Formulation solubility, potency, LD50 and difficulty-to-clean profiles. For the purposes of the PPQ runs, only the major clean procedure was used between lots since the minor clean procedure had not been qualified.
To establish a PPQ plan that is efficient in demonstrating process reproducibility the considerations for sampling testing and establishing acceptance criteria must be thoughtfully considered, especially for products with limited development- or performance data.
To cite the PV guidance, the objective of the Process Performance Qualification is to “confirm the process design and demonstrate that the commercial manufacturing process performs as expected.” The PPQ must “establish scientific evidence that the process is reproducible and will deliver quality products consistently.” We take key points from this objective in turn to establish acceptance criteria as in the following examples:
- Process performs as expected: commercial performance is inferred from process knowledge gained during the Process Design stage;
- Process is reproducible; process is under statistical control and is, therefore, predictable;
- Process delivers quality products consistently: process is statistically capable of producing product that meets specifications (and in-process limits) and will continue to do so.
The relationship between critical process parameters and quality attributes is defined through process knowledge. Therefore, when critical process parameters are controlled within their normal operating range (NOR), we should expect quality attributes to be statistically predictable and reproducible. It is clear that producing three commercial lots in a row to meet its specification limits is no longer sufficient to meet process qualification objectives. We must develop a statistical prediction for the acceptance criteria of quality attributes which is typically much more rigorous than simply meeting the specification limit.
Considerations on Sampling
Since the new PV guidance focuses on quality by design and control, there is greater interest in the identification and control of critical parameters to ensure that critical quality attributes throughout the lot are predictable. We cannot test the entire lot for the quality attributes, but we can control the parameters, and they should predict those quality attributes. Sampling and testing now become a verification of what we should already expect to occur.