- 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 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
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 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. 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 has negative impact on 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
Goals of process characterization studies?
- identify porcess parameter that impact onto 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 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. To perform a scale down model qualification, commercial scale data (at set point conditions) is necessary. More information can be found in our article what is a bioproces scale down model?
When to start a process characterization study?
According to our consulting experience, process characterization studies take at least 12 month for completion. Hence, to be ready before licence application (and have a time buffer), it is best practice to start a minimum of 16 month before the planned licencure. Biotech companies typcally start process characterization activities once it becomes very likely that the product will go to market, which is after successfull completion of phase two clinical trials.
Statistical data analysis for process characterization studies
Statistical data analysis plays a critical role in process characterization studies.
- How to I use available data to support risk assessments (e.g. how to support failure mode and effect analysis)?
- How do I statistically demonstrate that my scale down models is appropriate?
- Which response variables should be studied in a experimental design?
- How many experiments do I need to conduct to proof a potential critical parameter not critical?
- How do I state statistically that a parmaeter is key, non-key, critical or non-critical?
- How do I statistically define normal operating and proofen accetable 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 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
- 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.
How to realize data management is a key questions 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 Exputec support leading biopharmaceutical companies in process characterization studies?
Exputec consulting and statistical services streamlines bioprocess characterization studies and interactions with regulatory authorities such as FDA and EMA by adopting statistical best practices.
inCyght data management, data visualization and data analytics software manages the constant stream of process and quality data in one intuitive software environment.