A systematic approach for estimating colloidal particle adsorption model parameters

The estimation of ion-exchange chromatography model parameters is crucial to enable efficient model-assisted biopharmaceutical downstream process development. Model calibration methods can be hindered by model limitations combined with parameter correlations, leading to time-consuming repeated parameter estimations. While Steric Mass Action isotherm estimation methods exist, there is a need for a systematic approach to estimate model parameters for an emerging Colloidal Particle Adsorption (CPA) model proposed by Briskot et al. This study presents a novel strategy that addresses this challenge, offering significant improvements.

Through a parameter sensitivity analysis, we identified key levers for improved CPA parameter estimation, enabling the prediction of elution behavior for low and high load densities in gradient and step elution mode. This analysis also revealed the correlation structure of parameters, allowing the establishment of a minimalized experimental data set for parameter estimation, by using one breakthrough, a high load and three low load density gradient elution experiments. Our workflow leverages a surrogate-assisted global-optimization tool, minimizing computationally expensive function evaluations during parameter fitting. Furthermore, we employed a customized objective function, specifically adapted to the model structure and sensitivity results, to enhance the solver's performance.

Our strategy was tested on three model proteins with molecular weights of approximately 50, 150 and 200 kDa using a strong cation exchange Poros 50 HS resin. Our final approach enabled high throughput CPA model calibration for single components. The resulting CPA models were able to describe non-binding protein-pulses, low and high-loaded gradient elution, break through, as well as isocratic elution experiments.