ABSTRACT
The modeling of chromatographic separations can speed up downstream process development, reducing the time to market and corresponding development costs for new products such as pharmaceuticals. However, calibrating such models by identifying suitable parameter values for mass transport and sorption is a major, time-consuming challenge that can hinder model development and improvement. We therefore designed a new approach based on Bayesian optimization (BayesOpt) and Gaussian processes that reduced the time required to compute relevant chromatography parameters by up to two orders of magnitude compared to a multistart gradient descent and a genetic algorithm. We compared the three approaches side by side to process several internal and external datasets for ion exchange chromatography (based on a steric mass action isotherm) and hydrophobic interaction chromatography (a modified version of a recently published five-parameter isotherm) as well as different input data types (gradient elution data alone vs gradient elution and breakthrough data). We found that BayesOpt computation was consistently faster than the other approaches when using either single-core or 12-cores computer processing units. The error of the BayesOpt parameter estimates was higher than that of the competing algorithms, but still two orders of magnitude less than the variability of our experimental data, indicating BayesOpts applicability for chromatography modeling. The low computational demand of BayesOpt will facilitate rapid model development and improvement even for large datasets (e.g., > 100 proteins) and increase its suitability for research laboratories or small and medium enterprises lacking access to dedicated mainframe computers.
