A Framework for Technology Transfer to Satisfy the Requirements of the New Process Validation Guidance: Part 1

A risk-based model allows the manufacturer to fully consider process and product design at the outset of tech transfer.

By Bikash Chatterjee and Mark Mitchell, Pharmatech Associates

The technology transfer of a process, whether it is from R&D to commercial manufacturing or to another site or contract manufacturing organization (CMO) is a critical step in the lifecycle of any drug product, involving many steps. As major blockbuster drugs come off patent and large pharmaceutical companies look to bolster their pipeline through acquisition, the control and consistency of development data can vary dramatically. To make matters more complicated, the new Process Validation (PV) Guidance issued by FDA in January 2011 now defines three major stages of process validation that must be satisfied to consider the process validated.

With the present article, we will lay out a practical approach that addresses this complexity and propose to discuss and summarize the diverse factors required to describe the process, establish the control strategy and specify the acceptance criteria to successfully transfer a legacy or newly acquired process to another process train and satisfy the new guidance.

To illustrate, we will take a closer look at the methodologies employed and the challenges encountered as part of a recent technology transfer process validation exercise executed for a legacy product for a client organization, with references to the business unit and technology transfer team assembled for the project.

Through this real life example, Part I will discuss the approach taken to establish the design and control space for the final process. Part II will describe the Process Performance Qualification (PPQ) study design and acceptance criteria for Stage 2 and the approach taken to satisfy Stage 3 of the new PV guidance. 

The New PV Model
Under the 1987 guidance PV could be characterized as “quality by sampling and testing” while the new guidance would more appropriately describe validation as “quality by design and control.” Let’s look closer at the three distinct stages that make up the new definition of process validation:

  • Stage 1 Process Design: The commercial manufacturing process is based on knowledge gained through development and scale-up activities
  • Stage 2 Process Qualification: The process design is evaluated to determine if the process is capable of reproducible commercial manufacturing
  • Stage 3 Continued Process Verification: Ongoing assurance is gained during routine production that the process remains in a state of control

The PV roadmap uses a milestone-driven framework creating a phase gate process for each stage of the new process validation lifecycle as shown in Figure 1.



Focus on the Control of Parameters Instead of the Testing of Attributes

As the new PV guidance states:

  • Quality, safety, and efficacy are designed or built into the product.
  • Quality cannot be adequately assured merely by in-process and final product inspection and testing.
  • Each step of a manufacturing process is controlled to assure the finished product meets all quality attributes including specifications.1

Defining a knowledge space relating process parameters and material attributes to quality attributes allows us to establish a control strategy around the most critical process parameters. Stage 1, Process Design encompasses identification and control of critical process parameters to provide a high level of assurance that the critical quality attributes for the entire lot will meet the defined limits. In-process and finished product inspection and testing on a relatively small sample of the lot become merely a confirmation of that control. Stage 2, Process Qualification is a demonstration of that control of critical process parameters and their prediction of critical quality attributes, both within lot and lot-to-lot. Stage 3, Process Monitoring is the ongoing verification that critical process parameters remain in control and continue to predict the outcome of the testing of critical quality attributes. Process Monitoring also provides the continuing opportunity to evaluate any emergent critical process parameters, which may occur as a process, or as materials, equipment and facilities mature and potentially drift over time.

The key to control of a critical process parameter is to characterize the range for which operation within this range, keeping other parameters constant, will result in producing product that meets certain critical quality attributes, or the Proven Acceptable Range (PAR) as defined in ICH Q8. The PAR is established with data; these data are usually gathered during Process Design. Commercial production lots produced outside a PAR for a critical process parameter represent unknown quality and would be technically unsuitable for release despite acceptable in-process and final product inspection and testing.

Many companies establish a tighter range for production control called a Normal Operating Range (NOR), frequently seen on batch records. In these cases, excursions of a critical process parameter outside the NOR require a quality investigation to confirm that the PAR has not been exceeded. The NOR frequently represents the qualified limits of the control system used for the critical process parameter.

One possible relationship between the PAR and NOR is shown in Figure 2. The PAR limits are set by the minimum and maximum set point runs for the critical process parameter where the product meets its quality attributes. The actual data for the parameter will vary around the chosen set point, shown in the diagram by the shaded areas around the set point. Here, the NOR is shown as a narrower limit than the PAR. The NOR was determined by the qualified control limits of the parameter when operating at its set point; the NOR is used for the batch record limits of normal production data. The extremes of individual excursions around the set point limits of the PAR may be used to justify limited duration deviations, which may occur in production.

tech transfer
Legacy Products vs. New Molecular Entities (NME)

Legacy products represent a unique challenge for technology transfer and PV because of the inconsistency in terms of the development information available. NMEs have the advantage of gaining process understanding at small scale, with a focus on scale-up and/or TT. The ability to identify critical process parameters at small scale has economic advantages and also provides greater flexibility in terms of experimental design. Using the ICH Q8 definition it is possible to move from the knowledge space to the design space quickly and efficiently.  The new PV guidance recognizes this and states:


“Manufacturers of legacy products can take advantage of the knowledge gained from the original process development and qualification work as well as manufacturing experience to continually improve their processes. Implementation of the recommendations in this guidance for legacy products and processes would likely begin with the activities described in Stage 3.1”


The big difference with legacy products vs. NMEs as they relate to PV is that the baseline data gathering activity begins in Stage 3 of the PV lifecycle rather than Stage 1.

The Technology Transfer Framework

Gone are the days of simply comparing product performance against its release specification. The objective of technology transfer is to acquire the necessary process and product knowledge to establish a PAR and NOR for each unit operation that is consistent with the predicate process being transferred. Thus the new PV guidance requires the demonstration of process reproducibility in the PPQ phase of Stage 2.  Reproducibility effectively requires establishing acceptance criteria that are consistent with the process stability demonstrated in the predicate process. Reproducibility must be defined for within lot and between lot variability as part of the PPQ exercise.  The technology transfer framework used for this project  is based upon Pharmatech Associates’ PV model shown below in Figure 3 and will be discussed as follows:

tech transfer

Product Requirements Specification (PRS)
To illustrate, here is a case in point: the business unit of a pharmaceutical company acquired the rights to a controlled release anti-hypertensive tablet. The tablet had been manufactured for 15 years outside the U.S. and was to be transferred to the acquiring company’s main manufacturing site. A PRS was given to the development team defining the critical-to-quality attributes for the final tablet, including:

  • Greater than 50 percent Active Pharmaceutical Ingredient (API)
  • Round 200 mg tablet
  • Coated to mask taste
  • 12-hour drug release with the following specifications:
      - 4 hour dissolution 20-40 percent
      - 8 hour dissolution 65-85 percent

Technology Transfer Model: Process Understanding

Product Design
The technology transfer package included the formulation, raw material, API and finished product specifications and master batch records. No development report was ever written for the product.  The team looked at the Chemistry, Manufacturing and Control (CMC) section of the non-disclosure agreement to understand the composition and functionality of each component of the formulation.  The formulation is shown below in Table 1.

Table 1

The final product design revealed two key considerations for the downstream process characterization studies. First, the product has a fairly large loaded dose. This translates to a potentially lower risk of content uniformity issues which could translate to a more forgiving PAR and NOR for the final blend step.  Second, the primary controlled release component is limited to the coating step, which means if the upstream process steps can be shown not to impact the final drug release profile this will simplify the final process validation argument. The raw material specifications were either compendial or cut-sheet specifications from the supplier. Limited API characterization studies had been performed.  A comparison of the original process train and the new process train is shown below in Table 2. 

table 2

Critical Process Parameters/Risk Assessment
In the absence of a development report, the team turned to a tiered risk assessment approach for insight into the process design and sources of variability. The risk assessment was divided into two parts. The first evaluation compared each process step against the defined Critical Quality Attributes (CQA) in order to identify which process steps would require close characterization. Process steps with a High rating were then further evaluated. The second tier of the risk assessment evaluated the potential impact of the process parameters. Parameters were divided into scale independent and scale dependent variables.  Those parameters that were identified as having a High potential impact on CQAs were targeted for further study.  Scale dependent parameters required further experimental characterization. Scale independent parameters focused on an analysis of historical performance.  An example of the risk assessment at the process level is shown in Table 3.

table 3

The team also defined a process parameter as critical when it had an impact on the CQAs across the final PAR and NOR. This was a significant definition, which could have a profound impact on the number of parameters tracked in the Stage 3, Continuous monitoring portion of the PV process. Since the objective of every process development exercise is to identify a process design and control space which does not have an impact on the final product CQAs, parameters that did not move the product CQAs based upon their final PAR and NOR were not considered Critical Process Parameters (CPP) and would not become part of the final Stage 3 monitoring program.

Historical Data Analysis
The absence of development data establishing the PAR and NOR for the CPP can be ascertained to some extent by evaluating the historical behavior of each parameter along with the corresponding behavior of the CQAs for the unit operation. Data should be extracted from multiple batch records to determine whether the process is stable within lot and between lots.  In some cases, only mean data or composite data may be available. To do this the team went back into the batch records of approximately 30 lots across a period of one year to extract the necessary data. This exercise also gave some indication as to whether the parameter was truly a CPP, based upon whether it had an impact on the corresponding CQA for the unit operation.  The data for each unit operation were plotted as control charts and the process capability was determined. Excursions outside the 3 sigma limit of the control charts were investigated to determine if there were deviations associated with the events. An example of the control chart and capability histogram for fluid bed product bed temperature is shown below in Figures 4 and 5. Capability limits are based on a previously established PAR for the product bed temperature.

In addition, the corresponding CQA for the process—particle size—was evaluated to determine if there was any impact from the excursion.  Figure 6 shows the control chart for the particle size, the CQA for this process. A linear regression between the process parameter and the critical quality attribute is shown in Figure 7. This indicates no statistically significant relationship between the product bed temperature and the particle size through the range of data examined. It is likely that product bed temperature would not meet our definition of “critical process parameter” from this data. However, since historical analysis is not a controlled experiment where all other parameters are necessarily held constant, there may be other parameters or material attributes influencing the particle size data and disrupting the correlation.


 

This approach was repeated based upon the parameters that had a medium or high rating in the risk table. For these scale independent parameters the existing PAR ranges were used for the next phase of scale-up studies.

Characterization Studies
For those parameters that were scale dependent additional characterization studies were required to establish PAR and NOR that were consistent with the predicate process. For simply scalable processes like blending, single time-based blend uniformity studies may be adequate to identify the PAR and NOR for the new scale. For more complex unit operations, such as the coating operation, a Design of Experiments (DOE) approach may be more appropriate. The team developed a series of balanced orthogonal experiments to establish the PAR for these parameters.  This raises another good point to consider when confirming CPPs. By conducting the historical analysis first it is possible to reduce the number of variables in the experimental design which reduces the number of runs required.

Conclusion

The new guidance is moving the industry toward a quality-by-design philosophy for process validation. This translates to a more parametric approach rather than an attribute-based approach to process design. The application of a risk based model, considering the process and product design at the outset of the technology transfer project allows the application of scientific understanding to filter the potential list of parameters that may affect the process and product CQAs to a limited few.  The analysis of historical performance reduces the number of factors that may need to be characterized at the next scale. It also provides a foundation for establishing a baseline PAR and NOR for scale independent parameters when moving to the next scale, factoring in the larger scale equipment design and configuration.  Finally, applying a DOE approach to the few remaining scale dependent parameters will establish the corresponding PAR and NOR for the transferred process before moving to the process Control Stage of the roadmap.

In Part II of this case study we will discuss the considerations in developing an effective sampling plan and acceptance criteria for the Stage 2 PPQ along with how to transition to the Continuous Monitoring stage of the new PV guidance.


References:
1.    Guidance for Industry: Process Validation General Principles and Practices - FDA, January 2011.
2.    Wheeler, Donald, Understanding Variation: The Key to Managing Chaos, ISBN 0-945320-35-3, SPC Press, Knoxville, TN.
3.    Chatterjee, Wong and Rafa, Using Operational Excellence to Meet the New Process Validation Guidance, Pharmaceutical Engineering, Sept 2011.

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