You develop, optimize and characterize bioprocesses in laboratory scale. Yet, you expect that the insights from laboratory scale do not change during scale-up. Hence, your laboratory results are representative and predictive for the final manufacturing scale. To ensure that, you need a robust and predictive bioprocess scale down model. Developing the bioprocess scale down model is the core of your development and validation activities.
What is a bioprocess scale down model?
ICH and FDA process validation guidelines provide definitions for bioprocess scale down models. ICH Q11 states, that a scale-down model is a representation of the commercial process. You have to justify the predictive power of your scale down model:
„A scale-down model is a representation of the proposed commercial process“ ICH Q11, Step 4
“Small-scale models can be developed and used to support process development studies. The development of a model should account for scale effects and be representative of the proposed commercial process. A scientifically justified model can enable a prediction of quality, and can be used to support the extrapolation of operating conditions across multiple scales and equipment.” ICH Q11 Step 4
“It is important to understand the degree to which models represent the commercial process, including any differences that might exist, as this may have an impact on the relevance of information derived from the models.” FDA 2011 Process Validation Guideline
Available guideance points out:
- You need to understand differences between your scale-down model and the commercial process.
- You have to make proper use of the knowledge gained form scale-down models.
Authorities point out their importance of scale-down models. However, authorities provide no specific information on how to develop “compliant” scale-down models.
Why is a “predictive” bioprocess scale down model so important?
A non-predictive bioprocess scale down model has severe concequences:
- The management cost of goods sold (COGs) estimations are incorrect.
- Your bioprocess in manufacturing scale runs will run at suboptimal conditions.
- Your drug product quality attributes might change during scale-up. Hence, for a biosimilar processes, you might loose your product bioequivalence during scale-up.
- Regulatory bodies review the scale down model for process validation stage 1. Your validation campaign is at risk.
What can go wrong in designing a bioprocess scale down model?
But how to justify that your scale-down model is predictive? To do that, you have to understand what can go wrong. In process optimization and process characterization studies, you investigate the influence of potential critical process parameters (pCPPs) on your product quality (critical quality attributes, CQAs). You expect the same effects in small and large scale. However, this might not be the case due to scale-up effects. Figure 1 shows what can go wrong.
Figure 1, plot A: A perfect predictability of your scale-down model means that the same effect that you observe in small scale (blue trend) exists in large scale (rend trend).
Figure 1, plot B: At target process conditions, you observe an offset between your scale-down model and your commercial process. However, your effects still point at the same direction.
Figure 1, plot C: At target process conditions you observe no offset. However, your effects are different in large and small scale. Hence, your scale-down model is non-predictive.
Figure 1, plot D: At target process conditions, you observe an offset and your effects are different in large and small scale. Hence, your scale-down model is non-predictive.
As a bioprocess scientist dealing with scale-down models, you have to find out for each critical quality attribute (CQA) whether your scale-down model is case A (predictive), B (semi-predictive) or C and D (not predictive).
Case A is desired as your scale down model behaves similar to the manufacturing scale process. Therefore, your knowledge gained in small scale is directly transferable to the manufacturing scale. For case B, an offset in a certain quality attribute can still be accepted if the functional behavior of this quality attribute stays similar across scales. Case C and case D are undesirable.
As manufacturing scale experiments are expensive a commonly agreed on methodology for scale-down model qualification is to qualify the model on set-point conditions. With this approach, you rule out case B and case D. However, if you prove similarity on target you still not know whether your effects are similar (case A) or different (case C) across scales. So, case C represents the worst-case scenario, as it cannot be ruled out by common statistical methods. Looked at it theoretically, it is less improbable that different effects (straight lines with different slopes) intersect at target (case C) than they intersect anywhere else (case D). Therefore, different effects (case B) are less likely than similar effects (case A) at similar target behavior. However, to gain absolute assurance you need to perform large-scale experiments on other than target conditions.
Though, as discussed above case B might still be acceptable, but regarding the patients risk it is better to reject similarity of your scale-down model rather too much than too little.
Best practices to establish bioprocess scale down models
Best practice for the qualification of scale-down models are based on the combination of statistical tools (equivalence testing), multivariate data analysis, risk assessments. See also following post about one recent Exputec case study with Boehringer Ingelheim. Here the main messages:
- Explore multivariate differences between small and commercial scale by multivariate data analysis.
- Use equivalence testing & time series equivalence testing to demonstrate the quality of your scale-down model.
- Use a risk-based approach and simulations to deal with the risk of offsets.
- Implement the results to assess the overall criticality of the process parameters.
Figure 2: Illustration of the best-practice workflow for scale-down model qualification.
Software to support scale down model development
inCyght is a bioprocess data management and data analytics software. Leading biotech companies use inCyght for bioprocess development, process characterization and for scale down model development and qualification.
inCyght bioprocess data management and analysis platform
Exputec’s complete portfolio of consulting and software solutions with its profound statistical experience in bioprocess development, process validation and direct interactions with regulatory authorities.