A modeling framework for uncertainty quantification in filter-plate-based high-throughput screening of the...

Area of Science:

• Biotechnology
• Chemical Engineering
• Biopharmaceutical Manufacturing

Background:

• High-throughput screening (HTS) of chromatography partition coefficients (Kp) is crucial for protein purification process development.
• Existing methods lack quantitative guidance on experimental uncertainty propagation in filter-plate-based Kp assays.
• This limits informed decision-making in purification process design and modeling.

Purpose of the Study:

• To develop and validate a Monte Carlo (MC) framework for uncertainty propagation in filter-plate-based Kp HTS.
• To identify dominant experimental error sources affecting Kp measurements.
• To provide a quantitative basis for optimizing HTS experimental design and improving Kp data reliability.

Main Methods:

• An experimentally calibrated MC framework was developed to model uncertainty propagation.
• Experimental errors were characterized using a monoclonal antibody (mAb) monomer on POROS XS cation-exchange media.
• Variance decomposition was employed to identify major contributors to Kp uncertainty.

Main Results:

• The MC framework accurately reproduced experimental Kp variability and error magnitudes.
• Uncertainty in resin slurry distribution and supernatant concentration were the dominant error sources (≈97% of total Kp variance).
• Analysis of the liquid-to-solid phase ratio (β) revealed a trade-off between measurement precision and protein material demand.

Conclusions:

• The developed framework provides a quantitative basis for uncertainty-aware HTS design.
• It enables more reliable Kp data generation for protein purification process development.
• Application-specific recommendations for phase ratios can be derived, optimizing experimental design.