- What is a bioprocess scale down model;
- Regulatory guidance about scale down models;
- Pitfalls in developing scale-down models;
- Best practices to qualify scale down models.
What is a bioprocess 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
- 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.
Why is a “predictive” bioprocess scale down model so important?
- 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.
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.
Software to support scale down model development
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.