Modeling magnetic separation for hIgG purification: A theoretical and experimental study

The rapid growth of pharmaceutical therapeutics and nutritionals over recent decades has intensified the demands on downstream processing, emphasizing the need for faster, scalable, and more agile separation and purification methods. Modern manufacturing must be flexible, digitally integrated, and capable of fast market delivery. High-gradient magnetic separation (HGMS) emerges as a promising solution for high-throughput downstream processes in life sciences, facilitating rapid capture of target biomolecules directly from complex fermentation media. Despite its potential, HGMS faces challenges in scalable plant design and optimal parameter determination. This study addresses these limitations by introducing a detailed mechanistic model aimed at optimizing HGMS for bioprocessing applications through in-silico simulations. Our approach integrates multiscale models predicting magnetic particle (MP) trajectories within complex fields and macroscopic mass balance models that account for key separation phenomena, including adsorption and desorption on MP surfaces. This is the first detailed simulation model for HGMS in bioprocessing, offering a flexible framework to incorporate phenomena specific to bio-separation, enhancing both its applicability and accuracy. The model’s validity was confirmed through a novel case study of human Immunoglobulin G capture from mammalian cell culture supernatant with HGMS. The results showed strong alignment between simulated and experimental data, achieving a maximum simulation error of 1.7% for hIgG recovery per elution cycle, a pH prediction accuracy with a maximum error of 0.04, and robust performance in handling varying MP concentrations and flow conditions for MP dynamics. These findings underscore the model’s effectiveness as a robust tool for optimizing the innovative HGMS technology and its broader application in bio-manufacturing.