Comparison of multi-column chromatography configurations through model-based optimization.

Integrated continuous bioprocessing has been identified as the next important phase of evolution in biopharmaceutical manufacturing. Multiple platform technologies to enable continuous processing are being developed. Multi-column counter-current chromatography is a step in this direction to provide increased productivity and capacity utilization to capture biomolecules like monoclonal antibodies (mAbs) present in the reactor harvest and remove impurities. Model-based optimization of two prevalent multi-column designs, 3-column and 4-column periodic counter-current chromatography (PCC) was carried out for different concentrations of mAbs in the feed, durations of cleaning-in-place and equilibration protocols. The multi-objective optimization problem comprising three performance measures, namely, product yield, productivity, and capacity utilization was solved using the Radial basis function optimization technique. The superficial velocities during load, wash, and elute operations, along with durations of distinct stages present in the multi-column operations were considered as decision variables. Optimization results without the constraint on number of wash volumes showed that 3-Column PCC performs better than 4-Column PCC. For example, at a feed concentration of 1.2 mg/mL, productivity, yield and capacity utilization, respectively, were 0.024 mg/mL.s, 0.94, and 0.94 for 3-Column PCC and 0.017 mg/mL.s, 0.87, and 0.83 for 4-column PCC. Similar trends were observed at higher feed concentrations also. However, when the constraint on number of wash volumes is included, 4-Column PCC was found to result in consistent productivity and product yield under different operating conditions but at the expense of reduced capacity utilization.

Digital twin of a continuous chromatography process for mAb purification: Design and model-based control.

Model-based design of integrated continuous train coupled with online process analytical technology (PAT) tool can be a potent facilitator for monitoring and control of Critical Quality Attributes (CQAs) in real time. Charge variants are product related variants and are often regarded as CQAs as they may impact potency and efficacy of drug. Robust pooling decision is required for achieving uniform charge variant composition for mAbs as baseline separation between closely related variants is rarely achieved in process scale chromatography. In this study, we propose a digital twin of a continuous chromatography process, integrated with an online HPLC-PAT tool for delivering real time pooling decisions to achieve uniform charge variant composition. The integrated downstream process comprised continuous multicolumn capture protein A chromatography, viral inactivation in coiled flow inverter reactor (CFIR), and multicolumn CEX polishing step. An online HPLC was connected to the harvest tank before protein A chromatography. Both empirical and mechanistic modeling have been considered. The model states were updated in real time using online HPLC charge variant data for prediction of the initial and final cut point for CEX eluate, according to which the process chromatography was directed to switch from collection to waste to achieve the desired charge variant composition in the CEX pool. Two case studies were carried out to demonstrate this control strategy. In the first case study, the continuous train was run for initially 14 h for harvest of fixed charge variant composition as feed. In the second case study, charge variant composition was dynamically changed by introducing forced perturbation to mimic the deviations that may be encountered during perfusion cell culture. The control strategy was successfully implemented for more than ±5% variability in the acidic variants of the feed with its composition in the range of acidic (13%-17%), main (18%-23%), and basic (59%-68%) variants. Both the case studies yielded CEX pool of uniform distribution of acidic, main and basic profiles in the range of 15 ± 0.8, 31 ± 0.3, and 53 ± 0.5%, respectively, in the case of empirical modeling and 15 ± 0.5, 31 ± 0.3, and 53 ± 0.3%, respectively, in the case of mechanistic modeling. In both cases, process yield for main species was >85% and the use of online HPLC early in the purification train helped in making quicker decision for pooling of CEX eluate. The results thus successfully demonstrate the technical feasibility of creating digital twins of bioprocess operations and their utility for process control.

Process Analytical Technology (PAT) Implementation for Membrane Operations in Continuous Manufacturing of mAbs: Model-Based Control of Single-Pass Tangential Flow Ultrafiltration.

Control of single pass tangential flow ultrafiltration (SPTFF) is crucial for continuous manufacturing of monoclonal antibodies (mAbs). Integrating SPTFF technology into continuous manufacturing trains requires successful resolution of several challenges that arise due to the complexity of mass transfer interactions across multi-membrane configurations, the significant effect of feed material attributes and process variability on flux, and the need for advanced scheduling. In this paper, we propose a real-time, automated monitoring and control strategy for SPTFF in continuous processing of mAbs. The approach leverages a previously developed model for predicting the VCF across an SPTFF module based on the gel polarization model of protein ultrafiltration. A distributed control system (DCS) architecture was created for integrating the monitoring sensors and control elements, including NIRS sensors for concentration monitoring, as well as weighing balances, pressure sensors, pumps, and valves. Two different SPTFF control strategies were developed, firstly for final formulation of the drug product into the drug substance (ultrafiltration and diafiltration), and secondly for in-line concentration between two chromatography steps. Case studies were designed with 15 runs to test the strategy with a range of deviations induced in the feed and process conditions. The retentate concentration was controlled to within 10% of the target value in all runs. The combination of real-time sensor data and model-based control effectively enabled automated and tightly controlled operation of the SPTFF step and is a key enabler of quality by design in continuous mAb manufacturing.