Successful process characterization in biotech: A how-to-guide in 7 steps

Process validation (PV) aims at reassuring a manufacturer of constant product quality. It is also a regulatory requirement to achieve licensure of a pharmaceutical product. Failing to efficiently plan and execute activities on that stage leads to increased time-to-market. 

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 underscores the importance of showing statistical confidence about the chosen control strategy for critical process parameters (CPPs), input material and measured critical quality attributes (CQAs) during the manufacturing process (ICH Q8 (R2) 2009, 8). 

Every process characterization (PC) strategy aims to identify process parameters that impact 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.

In the following we show you how to successfully conduct a process characterization study (PCS) in 7 steps:

This is done by smooth integration of the following tasks, as shown in the graphic above:

1. Write a process validation master plan to achieve project transparency
2. Conduct a risk assessment to avoid unpleasant side effects
3. Start investigating impurity clearance
4. Perform scale down model comparison to detect offsets between scales
5. Plan and conduct efficient experiments to identify impact of PPs on CQAs
6. Set the right control strategy for individual unit operations
7. Establish a holistic control strategy

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Let us guide you through the process step by step:

1. Achieve project transparency with a Process Validation Master Plan 

You should start with a plan comprising all intended steps and goals. Data collection and evaluation need to be aligned as usually many internal stakeholders (process development, manufacturing, and manufacturing science departments) and external like CRO & CMO are involved. Transparent information about timelines, data flows, interfaces to departments/ stakeholders and deliverables guarantee timely delivery of the final PCS report and allows to include it into BLA filing.

With PAS-X CMC Consulting, Körber supports in the development of requirements for each task and herein delivers a detailed timeline that ensures timely delivery of regulatory documents and finish process characterization studies.

2. Avoid unpleasant side effects and conduct a risk assessment

The aim of a Failure Mode and Effects Analysis (FMEA) is to pre-select potential impacting factors for further experimental investigation and rate other factors as being not critical based on process expertise. It is very important to incorporate data-based prior information of occurrences and severities. By that, you reduce the impact of individual opinions on the final FMEA outcome. 

Our PAS-X CMC Consulting experts assist in regulatory compliant qualitative and quantitative risk analysis according to ICH Q9. 

3. Start investigating impurity clearance

Focus on the important unit operations that ensure reaching the quality target product profile for each critical quality attribute. Thereby, you can minimize the experimental and analytical effort as you do not have to investigate all your CQAs at each intermediate step in your manufacturing process. 

A biopharmaceutical process can be compared to a car: If we exchange the engine of a trashy vehicle, it will not run faster when it still has no tires. Therefore, it is important to understand which part (here unit operations) needs to be improved to consistently deliver product quality. This can only be answered when looking and modeling the entire process. 

Already at this point we can use the integrated process model to decide if we should invest into a run of a spiking study showing increased downstream capability or rather running an additional Design of experiments (DoE) run in upstream. Without this knowledge the wrong or too many runs are planned which is a cost driver in PC projects. 

4. Detect offsets between scales and perform SDM Qualification

Adapting a control strategy for large sale, scale down models (SDM) need to be established (ICH Q8). To achieve this, you should apply good industry standards to keep scale independent factors constant between the scales. 

Additionally, data must be provided to show that the performance of the scales is comparable, and offsets can be considered. Usually this is done using equivalence testing with a two-one-sided t-test (TOST). A cost-efficient alternative is to analyze scale runs together with DoE experiments and estimate the scale offsets from this analysis. This will also provide a good overview about possible differences in variance between the scales. 

5. Experiment and evaluate to gain knowledge fast

Generously invest time and energy into planning experiments as this is the key to efficiently generating knowledge. 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.

DoE are more statistically powerful than one factor at a time (OFAT) experiments with a smaller number of runs. Statistical power of an experiment shows you the likelihood to increase your process knowledge by conducting the planned trials. 

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:

You might argue that this will not work when you want to investigate five factors or more and if you need to plan a lot of runs. But consider this: An example using the scenario of five 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:

6. Set the Right Control Strategy for Individual Unit Operations

Setting the control strategy is the 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.

7. Establish a Holistic Control Strategy

If you want to set the control strategy based on the patients’ needs, you must meet overall drug substance specifications. This requires the understanding of how single PPs impact on changing CQAs in drug substance.

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. 

As a result of that holistic approach, the control strategy stays rigid where this is required to reach drug substance specifications. But it is as wide as possible to obtain manufacturing flexibility.

Executive Summary

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 the experienced consultants from Körber:

1. A process validation master plan ensures that scope, deliverables, and timelines are aligned between all stakeholders of a PCS.
2. Statistical occurrence analysis and new risk rating scales reduce subjectivity during risk assessments.
3. Innovative visualization techniques ensure fast and precise identification of critical unit operations to reach the required Quality Target Product Profile. Understanding how impurities are created and cleared in your process helps to plan the right type of experiments, i.e., spiking studies vs. DoE studies.
4. Statistical equivalence testing ensures not to overlook practically relevant differences between scales. These offsets need to be considered for predictions at manufacturing scale.
5. Cutting edge optimal experimental designs enable to identify main and interaction effects of PPs onto CQAs with a minimum number of experiments.
6. Establishment of statistically and practically relevant models enables the establishment of integrated process models. 
7. Only holistic definition of the control strategy using integrated process modeling 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 modeling can facilitate this work and ensure the right focus and reduction of experimental effort, setting of feasible control strategies for manufacturers and finally timely market entry. 

With PAS-X CMC Consulting, Körber assists with in-depth knowledge from its combined experience in data science and pharmaceutical production processes. Our consultants have 8+ years of experience in consulting and statistical services for process characterization and validation and have successfully contributed to bringing 20+ products to the market. 

Get into contact with our experts on any related questions or for detailed information on what we can provide.

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