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

Contact us

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.