A Rational, Step-Wise Approach to Process Characterization

Aug 01, 2003

As we scale up manufacturing processes, increase production rates, and strive to minimize batch failures in an increasingly stringent regulatory environment, it is valuable to characterize processes at the levels appropriate for each product development stage (preclinical through phase 3 and commercial manufacturing). Amgen currently characterizes several aspects of the manufacturing process comparable to that performed by other biotech companies. The increased understanding of each process that results from these characterizations can vary - from minimal knowledge, during development and non-GMP runs, to detailed knowledge of each unit operation or group of unit operations in late clinical development. Predictably, in-depth process characterization requires a significant commitment of resources and time.

The overall goal of adequate process characterization for commercial manufacturing processes is to ensure efficient and successful validation and the assurance of consistent process performance. More specifically, the characterization should provide:

  • an understanding of the role of each process step, such as where impurities are cleared during a particular purification step
  • an awareness of the effect of process inputs (operating parameters) on process outputs (performance parameters) and identification of key operating and performance parameters
  • assurance that the process delivers consistent product yields and purity in all operating ranges
  • acceptance parameters for in-process performance parameters.

This article describes a characterization strategy that is cost-effective and consistent.

Compliance and Business Drivers Process characterization has often been viewed as a "check the box" activity or something that was completed but which required little scientific rigor. This was most likely a result of cost cutting. Thorough process characterization requires a significant resource from process development and analytical departments. Many companies now recognize the benefits of sound process characterization - and, unfortunately, the cost of incompletely understood processes (1).

For compliance, process characterization needs to identify key operating and performance parameters, justify operating ranges and acceptance criteria, recognize interactions between key variables, and ensure that the process delivers a product with reproducible yields and purity. This is the complete and rational approach to process validation. Costly registration delays resulting from poorly understood processes and failed validation batches can cost a company tens of millions of dollars.

From a business standpoint, process characterization can improve run success rates and lead to incremental, but significant, yield improvements. Better process understanding can also minimize - and aid in the investigation of - process deviations.

Timing of the Studies To conserve resources, waiting until after phase 2 is the best time to start "intensive" process characterization work. At that point, the commercial process should be developed, and you know whether or not the product is likely to make it to market. Unfortunately, the driver for process characterization timing is the start of conformance or validation lots. Therefore the work should be completed by that point so that information gained from it can be used to support operating ranges and acceptance criteria for validation protocols.


Figure 1. Timing of process characterization studies; it's preferable for work to start at the end of phase 2, but it needs to be completed before the start of conformance or validation lots.
At least 12 to 15 months should be allowed for process characterization studies; therefore, they should start no later than 15 months before the conformance runs (see Figure 1). Sometimes phase 2 studies are complete by this point; but if phase 2 data are still pending, process characterization resources may have to be spent "at risk" to ensure completion of the work before the process validation runs. Fortunately, characterization studies can sometimes be ramped up slowly during the first few months, which may provide the time to complete the phase 2 studies, conserving resources until a clearer picture of the product's viability is obtained. Figure 2 shows the "process flow" involved in characterizing the manufacturing steps leading up to the validation studies.

Precharacterization Work Three steps are involved in precharacterization:

  • mining data and assessing risk
  • qualifying a scale-down model
  • developing process characterization protocols.


Process Characterization Terminology
Step 1: Data mining and risk assessment. Data mining - a retrospective review of historical data - and risk assessment analyses determine what operating parameters need to be examined experimentally during process characterization. Data from lab notebooks, technical reports, process histories, run summaries, and manufacturing records are combined with a list of the operating parameters and provisional operating ranges for each unit operation. The data are then compiled and reviewed by the team that developed the process to be characterized. Frequently, information on operating ranges from tests done during process development can help identify the parameters most likely to affect a process (2).

Once data mining is complete, risk should be assessed for each unit operation on the effect and likelihood of an excursion from operating ranges. Hazard analysis and critical control points (HACCP) (3), failure mode and effects analysis (FMEA) (4-7), cause-and-effect diagrams (8), and other risk assessment tools can be used for risk analyses. FMEA assigns a numerical rating (typically 1 to 10) to the severity of an excursion from operating parameter ranges, the probability of an excursion, and the likelihood of detecting an excursion before it has an effect on the product (4-7). The combined risk factor - the risk priority number (RPN) - is a multiple of the three variables, rating each parameter from 1 to 1,000 (4-7).