What is process characterization?

Highlights

• What is process characterization for biological drugs?;
• Which regulatory guidance is available?;
• Goals & timing within the product lifecycle?
• Role of data management & statistics in process characterization studies;

Process characterization is an essential step in the commercialization of a new (biological-) drug. For drug product commercialization, manufacturers must validate the drug’s manufacturing process. This ensures that the manufacturing process delivers a quality product consistently and that the patient is not at risk.

"Process Validation is defined as the collection and evaluation of data, from the process design stage throughout production, which establishes scientific evidence that a process is capable of consistently delivering quality product FDA process validation guideline"

If you want to learn how to perform a successful process characterization study and what steps need to be taken, visit this blog post.

Recently, US and European regulators have issued new process validation guidelines. The new guidelines now emphasize:

• the demonstration of process understanding;
• risk-based identification of critical process parameters;
• implementation of well-validated control strategies.

As a result of the new guidelines, it is now the state of the art that drug manufacturers thoroughly investigate and “characterize” the manufacturing processes. Interestingly, the term “process characterization” is not used by the regulators. Therefore, you will not find it in the EMAs and FDAs process validation guidelines. Yet, CMOs and (bio-) pharmaceutical companies use the term “process characterization” to describe their activities related to stage 1 process validation.

“Process characterization is how industry puts stage 1 process validation into practice.”

Why do manufacturers run process characterization studies? 

Reason 1: Achieve compliance. Ultimately, the product should reach the patient. To achieve this, manufacturers must validate the manufacturing process. Process characterization is an integral part of stage 1 process validation. 

Reason 2: Avoid registration delays. A delay in the commercialization of a product negatively impacts patients: They have no access to a drug they can benefit from. Furthermore, a delay in the commercialization costs money:

Costly registration delays resulting from poorly understood processes and failed validation batches can cost a company tens of millions of dollars James E. Seely, Ph.D., Robert J. Seely A Rational, Step-Wise Approach to Process Characterization

Reason 3: Avoid failed batches. Failed commercial batches in biologics manufacturing cost companies millions of dollars. A thoroughly characterized process with the right control strategy avoids deviations and failed batches in commercial manufacturing.

Goals of process characterization studies? 

• identify process parameters that impact product quality and yield;
• justify manufacturing operating ranges and acceptance criteria;
• identify interactions between process parameters and critical quality attributes;
• ensure that the process delivers a product with reproducible yields and purity.

Do I need to include data from a commercial scale in my process characterization study? 

Yes, because you need to qualify your scale-down model. Scale-down model qualification is an integral part of process characterization studies. Commercial-scale data (setpoint conditions) is necessary to perform a scale-down model qualification. More information can be found in our article what is a bioprocess scale down model? 

When to start a process characterization study? 

According to our consulting experience, process characterization studies take at least 12 months to complete. Hence, to be ready before the license application (and have a time buffer), it is best to start a minimum of 16 months before the planned licensure. Biotech companies typically start process characterization activities once it becomes very likely that the product will go to market after successfully completing phase two clinical trials. 

Statistical data analysis for process characterization studies

Statistical data analysis plays a critical role in process characterization studies.

Biotech companies address the following questions using statistical data analysis for successful process characterizations:

• How do I use available data to support risk assessments (e.g., failure mode and effect analysis)?
• How do I statistically demonstrate that my scale-down models are appropriate?
• Which response variables should be studied in an experimental design?
• How many experiments do I need to conduct to prove a potential critical parameter is not critical?
• How do I state statistically that a parameter is key, non-key, critical, or non-critical?
• How do I statistically define normal operating and proven acceptable ranges?
• How do I stack together multiple DoEs to identify optimization potential and predict out-of-specification (OOS) events?

Data management in process characterization studies? 

… process validation is defined as the collection and evaluation of data, from the process design stage through commercial production, which establishes scientific evidence that a process is capable of consistently delivering a quality product. FDA process validation guideline Authorities stress the importance of data collection in the process validation guidelines. Essential activity in process characterization is the collection of (non-GMP) data to support the regulatory filing. This includes data from

• upstream (fermentation)
• primary recovery
• downstream
• quality
• small scale (laboratory)
• manufacturing scale
• commercial-like runs
• DoE experiments, one factor at a time experiments
• … and many more

In process characterization studies, data structures and types are highly complex (time-series data, one-point quality data, SDS page scans…). Data originates from a broad selection of sources (laboratory equipment, LIMS systems, large-scale manufacturing process control systems, manufacturing execution systems). Data from laboratory (small scale experiments) and large scale manufacturing needs to be integrated for joint analysis.

Realizing data management is a key question that professionals involved with process characterization studies have to answer.

Read more about data management and how this supports analytics workflows in “best practices for fermentation data analytics”. 

How does Körber support leading biopharmaceutical companies in process characterization studies?

Körber consulting and statistical services streamlines bioprocess characterization studies and interactions with regulatory authorities such as FDA and EMA by adopting statistical best practices.

PAS-X Savvy data management, data visualization and data analytics software manages the constant stream of process and quality data in one intuitive software environment.