How to Perform Successful Process Characterization

How to Perform Successful Process Characterization

In a nutshell:

This blog post provides insights on how statistical tools can be applied to show the required confidence that the design of the manufacturing process is well understood, controlled and ready for routine production. Our blog post What is Process Characterization? offers further details on the regulatory background.


Process validation (PV) aims at reassuring a manufacturer of constant product quality. Moreover, it is a regulatory requirement to achieve licensure of a pharmaceutical product (FDA 2011; EMEA; EMA/213746/2017). Process Characterization is the term used for stage 1 of PV. Failing to efficiently plan and execute activities on that stage leads to increased time-to-market. Statistics play a pivotal role here, as is shown by the fact that in the 22 pages of the latest FDA guidance document on process validation, statistical approaches and the need of statisticians in a multi-disciplinary team is mentioned no less but 15 times. This underscores the importance of showing statistical confidence about the chosen control strategy for critical process parameters (CPPs), input material and measured CQAs during the manufacturing process (ICH Q8 (R2) 2009, 8).

Typical goals of a Process Characterization Strategy (Stage 1 PV):

Identify process parameters that impact on product quality and yield:

  • Identify interactions between process parameters and critical quality attributes
  • Justify and if necessary adjust manufacturing operating ranges and acceptance criteria
  • Ensure that the process delivers a product with reproducible yields and purity

Successful PC is achieved when:

  • Scope, deliverables and timelines are well aligned between all stakeholders
  • Evidence based decision making is increased and influence of the loudest voice during risk assessments is reduced
  • Equivalence testing is employed to identify potential offsets between scales
  • Impact of PPs onto CQAs is identified at minimal experimental effort
  • A model-based control strategy (e.g. PAR) is established for each unit operation
  • A holistic control strategy, taking the mutual interplay of all unit operations into account, is achieved using integrated process modelling. This shows that the manufacturing process is well understood and consistently delivers highest product quality in drug substance (DS) in the future.

Figure 2: Tasks and typical timeline of a process characterization study

This is done by smooth integration of the following tasks, as shown in Figure 2:

      1. Write a process validation master plan (PVM)
      2. Conduct a risk assessment (FMEA) and use data science methods to incorporate prior knowledge
      3. In parallel to FMEA it is possible to start investigating impurity clearance and start scale down model qualification to be ready for experiments
      4. Perfrom scale down model (SDM) qualification to detect offsets between scales.
      5. Plan and conduct efficient experiments to identify impact of process parameters (PPs) on critical quality attributes (CQAs)
      6. Model Based definition of a control strategy (e.g. PAR) for individual unit operations
      7. Results of purposefully planned experiments can be used to construct overall, integrated process models and set up optimal control strategies (PARs and design space, if applicable) with least possible restriction for manufacturing

1. Process Validation Master Plan (PVM)

Figure 3: Excerpt from a process validation master plan for a PCS study. Tasks = green boxes, requirements = purple boxes, intermediate results = blue boxes, deliverables = yellow boxes

Like every project, process validation needs a plan comprising all intended steps and goals. Especially if the project lasts for a longer period of one year or more, involves many stakeholders (process development, manufacturing and manufacturing science departments) and experimental planning, data collection and evaluation need to be aligned. Therefore we at Exputec usually set up a process validation master plan, including timelines, data flows, interfaces to departments/stakeholders and deliverables. This guarantees timely delivery of the final PCS report and allows to include it into BLA filing.

What does a process validation master plan contain?

  • Defines detailed steps and their input/output structure within a PCS.
  • Which tasks can be performed in parallel, which tasks are blocked by others?
  • Which tasks will be performed by which partner (CRO/CMO/Outsourcing)?
  • Which deliverables of the partners are required at which stage (yellow boxes in 3)?
  • Exputec supports in the development of requirements for each tasks and herein delivers a detailed timeline that ensures timely delivery of regulatory documents and finish process characterization studies.

Success factor:

    • Definition of lean responsibilities and timelines for all stakeholders.

2. Risk Assessment (FMEA)

Risk assessments (RAs) are probably the most subjective part during a PCS. This part is driven by individuals who rate potential failure modes and impacting factors onto the process. The aim is to pre-select potential impacting factors for further experimental investigation and rate other factors as being not critical based on process expertise. RAs are conducted in extensive meetings during which outspoken individuals – often from their position in the hierarchy – overrule opinions of others. Moreover, rating of occurrence, severity and detectability are rather assessed on gut feelings than on data based evidence of historical projects. All those factors lead to a very strong impact of “operators” onto the “experimental outcome”, i.e. the outcome of an FMEA depends heavily on which individuals are rating the risk.

While we acknowledge that RAs will always mainly contain process experts’ subjective look into the future, Exputec tries to incorporate data based prior information of occurrences and severities. By that, we reduce the impact of individual opinions on the final FMEA outcome.

Exputec assists in regulatory compliant qualitative and quantitative RA according to ICH Q9. Specifically, the following challenges are supported by Exputec during RA:

  • Get rid of the flaws and pitfalls via conducting RAs, e.g. using simple risk priority numbers (RPN). Such numbers are mathematically questionable and are not necessarily proportional to the risk:
    • S*O*D = RPN ≠ RISK
    • In respect to S/O/D, is having the same risk as ?
  • Leverage historical manufacturing to identify occurrence probabilities:

Figure 4: Statistical occurrence analysis investigates out of investigate range probability using historical manufacturing data.

Success factor:

    • Reduction of subjectivity throughout risk assessments and enabling evidence based decision making on potential risks.

3. Impurity Clearance Analysis

In many situations, focussing on essential tasks makes our daily work more efficient. The same is true when it comes to PCS.

The obvious question: Do I need to investigate all of my CQAs at each intermediate step in my manufacturing process? Answer: No, it is much more important to focus on the important unit operations that ensure reaching the quality target product profile (QTPP) for each CQA. Thereby, you can minimize the experimental and analytical effort.

  • Which CQA should serve as a response variable at which unit operation?
  • Where is a good spot to perform spiking studies?
  • Calculate your intermediate acceptance criteria.

Figure 5: Parallel coordinate plots, like shown above, help to identify which unit operation is capable of reducing impurities or increasing product quality.

Moreover, they provide a useful method to identify suspicious impurity clearance results and potential outlying analytical measurements.

Success factor:

    • Focus on critical unit operations that ensure meeting QTPP.

4. SDM Qualification

Scale down models (SDM) representative to the manufacturing scale (ICH Q8) need to be established. In order to achieve this, good industry standards need to be applied to keep scale independent factors constant between the scales. Additionally, data needs to be provided to show that the performance of the scales is comparable and offsets can be taken into account. Usually this is done using equivalence testing with a two-one-sided t-test (TOST). This has been discussed in detail here.

Figure 6: Results of the TOST test show if the 95% confidence interval of difference in means (black error bar) is within the pre-set equivalence acceptance criteria (EAC). If that is the case, as in this example, the equivalence test is passed.

Success factor:

    • Identify potential offsets between manufacturing scale and small scale which is used for further experimental assessment of impact of PPs on CQAs.

5. Experimental Design and Evaluation

Benjamin Franklin already knew that “by failing to prepare you are preparing to fail”. Generously invest time and energy into planning experiments as this is the key to efficiently generating knowledge.

In PCS studies, experimental criticality assessment is a central point to understand the impact of PPs on CQAs and thereby are of utmost importance to setting the right control strategy.

Statistical design of experiments (DoE) has a long list of successful applications in many industry fields ranging from agricultural science, automotive industry and also pharmaceutical process development. However, there are still concerns around the application of DoEs within the biopharmaceutical world like:

  • Why should I prefer DoEs over one factor at a time (OFAT) experiments?
  • How can I gain a maximum of information with a minimum number of experiments?

Addressing the first question, DoEs are more statistically powerful than OFAT experiments with less number of runs.  Statistical power of an experiment shows you the likelihood to increase your process knowledge by conducting the planned experiments. This can be visualized by looking at the screening space you cover when conducting an OFAT vs. a DoE:  With the same number of experiments, DoEs can cover a much large space in your screening space:

Figure 7: Organization of 5 runs within a OFAT series (left) and a DoE (right)

You might argue that this will not work when you want to investigate 5 factors or more and if you need to plan a lot of runs. But consider this: An example using the scenario of 5 parameters clearly shows that the number of runs required is even lower for a DoE and at the same time will lead to higher power values for the main effects and quadratic effects:

Figure 8: This plots shows that performing a DoE for 5 factors (n=13) gives you a chance of more than 80% to detect the main effect of process parameter 1. Whereas performing an OFAT for 5 factors requires at least 15 runs and yields only a power of 28% to detect the main effect. The power equals the chance of detecting an effect size of two times the residual model error. Run tables for DoE and OFAT are shown in Table 1 and Table 2 of the appendix, respectively.

Success factor:

    • Investigate the impact of a maximum number of PPs onto CQAs with minimal experimental effort

6. Set the Right Control Strategy for Individual Unit Operations

Setting the control strategy is the ultimate goal of a PCS study. This can be done either for each unit operation separately or in a holistic fashion. For a better understanding, think about a chain of workers in a manufacturing process. If one worker makes a mistake, others might be able to compensate. Herein, setting a control strategy for each worker individually does not accurately control for the overall failure rate. The same applies if we think about a chain of unit operations that might be able to contribute to the clearance of impurities. State of the art procedures use established models for individual unit operations to set a control strategy that might be much too conservative.

Figure 9: The intersection of intermediate acceptance limits and prediction/tolerance intervals of model predictions define the control range, e.g. the proven acceptable range (PAR). However, intermediate acceptance at any intermediate unit operation is difficult to establish.

Success factor:

    • Scientific sound definition of a control strategy of individual unit operations ensures performance and quality achieved at intermediate steps.

7. Establish a Holistic Control Strategy

In the field of biopharmaceuticals, if we want to set the control strategy based on the patients’ needs we aim to meet overall drug substance specifications. This requires the understanding of how single PPs impact on changing CQAs in drug substance. This knowledge can be established using integrated process models (IPMs). Read more on that here:

As a result of a parameter sensitivity analysis, out of specification (OOS) chances can be estimated as a function of PPs of any unit operation. A risk based decision criteria (e.g. a change to 5% OOS) can be used to determine the control limits for any PP. An example of such an analysis is shown below:

As a result of that holistic approach (where mutual beneficial interaction of multiple unit operations is taken into account), the control strategy stays rigid where this is required to reach drug substance specifications, but is as wide as possible to obtain manufacturing flexibility.

Figure 10: Parameter sensitivity analysis of one intermediate process parameter onto final drug substance out of specification probability.

Success factor:

  • Integrated process modelling captures the mutual interplay of multiple unit operations and predicts impact of Pp at any intermediate unit operation on DS product quality. Thereby, holistic criticality of PPs can be assessed and increases operational flexibility achieved.


A successful PCS study is key to reduce time-to-market of any biopharmaceutical product. This can be achieved by the unique workflow established by Exputec:

  • A process validation master plan ensures that scope, deliverables and timelines are aligned between all stakeholders of a PCS.
  • Statistical occurrence analysis and new risk rating scales reduce subjectivity during risk assessments.
  • Innovative visualization techniques ensure fast and precise identification of critical unit operations to reach QTPP.
  • Statistical equivalence testing ensures not to overlook practically relevant differences between scales. These offsets need to be taken into account for predictions at manufacturing scale.
  • Cutting edge optimal experimental designs enable to identify main and interaction effects of PPs onto CQAs with a minimum number of experiments.
  • Model based definition of control strategy for each individual unit operation.
  • Only holistic definition of a the control strategy using integrated process modelling ensures meeting drug substance specifications and thereby ensures patient’s safety.

Planning, analysis and execution of necessary experiments is at the center of the PCS work. Statistical tools such as data assisted risk assessment, DoE planning and integrated process modelling can facilitate this work and ensure the right focus & reduction of experimental effort, setting of feasible control strategies for manufacturers and finally timely market entry. Exputec assists with in-depth knowledge from its combined experience in data science and pharmaceutical production processes. Get into contact with our experts on any related questions or for detailed information on what we can provide.


CQA Critical quality attribute
DoE Design of experiments
EAC Equivalence acceptance criteria
FDA Food and drug administration
OFAT One factor at a time
OOS Out of specification
PAR Proven acceptable range
PC Process characterization
PCS Process characterization study
PP Process parameter
PVM Process validation master plan
QTPP Quality target product profile
RA Risk assessment
TOST Two one sided test


EMA/213746/2017 “EMA-FDA Questions and Answers:  Improving the Understanding of NORs, PARs, DSp and Normal Variability of Process Parameters.” Questions and Answers, 4.

EMEA “Guideline on Process Validation for Finished Products – Information and Data to Be Provided in Regulatory Submissions.”

FDA. 2011. “Guidance for Industry.” 2011.

ICH Q8 (R2). 2009. “Pharmaceutical Development Q8 (R2).”