Model-based process development for hydrophobic interaction chromatography by considering prediction uncertainty analysis

Abstract

Mechanistic models offer powerful tools for process development and optimization of hydrophobic interaction chromatography (HIC). Suitable parameter estimation approaches can efficiently calibrate the models, but some unavoidable biases between model prediction and actual experiment would reduce the credibility of the model’s applications. In this study, a well-calibrated HIC model was found some significant discrepancies between the predicted yield (97.3 %) and experimental yield (86.0 %) during the process optimization. Therefore, Bayesian inference with Markov Chain Monte Carlo method was employed to calculate the uncertainty of model parameters, which was then transformed into the uncertainty of model predictions. The results indicated that the model-predicted yield uncertainty interval was as large as 76.9∼96.5 %, which was consistent with the experiment. Moreover, the model prediction uncertainty analysis was integrated into process optimization to obtain a more reliable and low-risk separation condition. The re-optimized process significantly narrowed the uncertainty of the predicted yield (94.2∼98.9 %), and high experimental yield (95.8 %) was obtained. The results demonstrated that process optimization based on the uncertainty quantification could reasonably reflect model prediction deviations, assist process development and contribute to product quality improvement. Finally, a framework was proposed for process optimization based on the uncertainty analysis to improve the accuracy of model predictions and reducing the risk of model-based process development.