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

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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.

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