Biopharma · Statistical Analysis

Scale Down Model (SDM) for Bioprocess: Equivalence Test,Outcome, Benefits and Conclusion

Qualifying a Scale Down Model (SDM) requires more than engineering precision : it demands a rigorous statistical framework.

Topic: Equivalence Testing Level: Advanced Application: CMC, Process Development, Pharma

Qualifying a Scale Down Model (SDM) requires more than engineering precision, it demands a rigorous statistical framework. This article covers engineering parameters that govern SDM development, equivalence testing approach using TOST, how to interpret outcomes, broader benefits of SDMs in biopharma, and key conclusions for scientists and statisticians.

Engineering Parameters for Upstream SDM Development

When developing an upstream scale-down model, process parameters must be carefully categorised to ensure correct scaling strategy is applied to each. Three parameter types govern the design approach.

Engineering parameters considerations for upstream scale-down model (SDM) development
Volume dependent Parameters
Volume Non-dependent Parameters
Non-linear Parameters
Parameter Type
  • Batch Medium
  • Inoculum volume
  • Feed Volume
  • Supplement
  • Filter area
  • Seed Density
  • pH
  • DO
  • Temperature
  • Feed Condition (%)
  • Impeller agitation
  • Oxygen/Air aeration
Operating Parameters
Volumetrically scale down
Similar set point at both scale
Comparable: Tip speed, Mixing time, KLa and Air flow (vvm)
Scale up strategy
Volumetrically scale down
Similar set point at both scale
Comparable: Tip speed, Mixing time, KLa and Air flow (vvm)

SDM Qualification Criteria by Equivalence Test

The equivalence test provides a statistically rigorous method to confirm that process and quality attributes at small scale are comparable to those at manufacturing scale. Two key requirements form the basis of this approach:

  • Process and quality attributes profile at small and manufacturing scale must be assessed.
  • Two one-sided test (TOST) shall be used to demonstrate equivalence of process and quality attributes between small scale and manufacturing scale.
Equivalence Decision Rule: The small scale shall be considered equivalent if confidence interval (90% CI) for difference in means falls within Equivalence Acceptance Criteria (EAC) by a two one-sided test (TOST). The manufacturing batches data from at-scale are used for deriving equivalence acceptable criteria (EAC) for process and quality attributes.
TOST Equivalence Test — SDM Titer (Average of Lot 1 to 12)
0.0 0.2 0.4 0.6 0.8 1.0 Lower Target Upper

Possible Outcomes of Two One-Sided Test (TOST)

The TOST produces four distinct outcomes depending on where the 90% confidence interval of the difference in means falls relative to the Lower EAC and Upper EAC boundaries.

TOST Outcome Visualisation
Lower EAC Mean EAC Upper EAC Equivalent Equivalent in sample mean only Failed to be Equivalent Inequivalent Sample observed mean
Note: Error bars represent 90% confidence interval of difference in means.

TOST Outcomes and SDM Acceptance

Each TOST outcome maps directly to an SDM acceptance decision. When full equivalence is not achieved, directionality analysis provides an additional pathway to confirm SDM qualification for specific attributes.

Possible outcomes of the two one-sided test (TOST)
SDM Acceptance
Equivalent
Yes
Equivalent in mean only
Yes, accept SDM qualify (with very low risk)
Failed to be equivalent
No, go for directionality analysis (consistent and magnified response). Based on output, SDM qualification can be confirmed for specific attribute.
Inequivalent
No, go for directionality analysis (consistent and magnified response). Based on output, SDM qualification can be confirmed for specific attribute.
Dealing with Offsets:
The statistical evaluation of at-target performance of individual attributes. When is an offset acceptable, when not, and what to do:
  • • Offset consistent across scales
  • • Magnified parameter effect in model
    • - Factor effect directionality and ranking, direct prediction
    • - Robust interpretation possible by comparison to scale-down
  • • Output not representative
    • - i.e., unrelated or opposite to effect at full-scale

Bioprocess Scale Down Model : Benefits

Scale-down models have become an indispensable tool across the biopharmaceutical development lifecycle, offering three core strategic advantages.

Scale-down models are increasingly adopted by the biopharmaceutical industry as an essential tool for process characterisation and lifecycle management.
They are utilized to investigate deviations and monitor process performance at commercial scales, directly supporting continuous improvement studies.
SDMs empower process specialists to quickly gain process knowledge and translate it into optimal operating conditions in a cost-effective and timely manner.

Conclusions

Effective SDM development and qualification requires close collaboration between statisticians and process scientists. The following key takeaways summarise the critical considerations for the field.

1
Statisticians and scientists need to communicate the entirety of the scaling problem and statistical considerations starting in SDM development — not as a retrospective validation step.
2
Statistical considerations interface with the SDM problem in many ways beyond SDM qualification analysis — from design, data compilation, and parameter selection through to regulatory submission.
3
There are opportunities to design and utilise limited off-target data, enabling broader process understanding even when on-target runs are constrained.
4
There are opportunities for new statistical methods to combine small- and large-scale data, strengthening predictive model performance and regulatory confidence.

References

  • › Process characterization and Design Space definition Christian Hakemeyer et al. — Roche Diagnostics GmbH / Pharma Technical Development / Genentech
  • › Systematic Approach for Scale-Down Model Development and Characterization of Commercial Cell Culture Processes Feng Li, Yasunori Hashimura, Robert Pendleton, Jean Harms, Erin Collins, Brian Lee — Process Engineering and Cellular Science and Technology, Amgen Inc., Thousand Oaks, CA
  • › Scale-down model qualification of ambr® 250 high-throughput mini-bioreactor system for two commercial-scale mAb processes Matthew Manahan, Michael Nelson, Jonathan J. Cacciatore, Jessica Weng, Sen Xu, Jennifer Pollard

Need expert DoE design evaluation for your bioprocess?

Graphtal evaluates power, aliasing, prediction variance, and model validity before you run a single experiment.

Go Back

Tell Us About Your Project

Our team of experienced professionals at Graphtal is ready to transform your project from idea to
reality, ensuring alignment with your organisation goals through advanced data analytics and
predictive modelling.


Let's simplify your work

Want to make Insightful Analytics for Smarter Decisions?

Let's connect.