Analysis and optimal design of batch and two‐column continuous chromatographic frontal processes for monoclonal antibody purification. Issue 9 (13th April 2021) - Shi, Ce; Vogg, Sebastian; Lin, Dong‐Qiang; Sponchioni, Mattia; Morbidelli, Massimo
Abstract: The increasing demand for efficient and robust processes in the purification of monoclonal antibodies (mAbs) has recently brought frontal chromatography to the forefront. Applied during the polishing step, it enables the removal of high molecular weight aggregates from the target product, achieving high purities. Typically, this process is operated in batch using a single column, which makes it intrinsically subjected to a purity‐yield tradeoff. This means that high purities can only be achieved at the cost of lowering the product yield and vice versa. Recently, a two‐column continuous implementation of frontal chromatography, referred to as Flow2, was developed. Despite being able of alleviating the purity‐yield tradeoff typical of batch operations, the increase in the number of process parameters complicates its optimal design, with the risk of not exploiting its full potential. In this study, we developed an ad hoc design procedure (DP) suitable for the optimization of both batch frontal chromatography and Flow2 in terms of purity, yield, and productivity. This procedure provided similar results as a multiobjective optimization based on genetic algorithm but with lower computational effort. Then, batch and Flow2 operated at their optimal conditions were compared. Besides showing a more favorable Pareto front of yield and productivity at a specified purity, the Flow2 process demonstrated improved robustness compared to the batch process with respect to modifications in the loading linear velocity, washing buffer ionic strength and loading time, thus providing an appealing operation for integrated processes. Abstract : Flow2 is a two‐column continuous implementation of frontal chromatography. An ad hoc design procedure (DP) is developed to optimize batch and Flow2, allowing to save computational effort compared to genetic algorithm. Then, this DP is used to evaluate the influence of the operating parameters and robustness of both batch and Flow2.