Mechanistic modeling of multimodal chromatography: from first-principles to principal applications

Abstract

Biotherapeutics, since their inception, have played a key role in the worldwide treatment of complex health conditions (e.g., cancer, autoimmune disorders, and viral infections). To meet the ever-increasing demand for these medicines, strategies for accelerating the development of robust manufacturing processes, while maintaining product efficacy and patient safety, are paramount. With continual improvements in upstream productivity and the repeated introduction of biotherapeutics with complex impurity profiles, the fulfillment of this demand has led to an increasingly greater burden on downstream purification processes. This, in turn, has required great improvements on both the efficacy of bioseparations and the speed with which new processes are developed. Chromatography, often referred to as the workhorse of the downstream process, has been dominated, for the most part, by standard single-mode techniques (e.g., ion exchange, hydrophobic interaction, and size-exclusion). To address the need for more effective chromatographic processes, multimodal chromatography has been investigated as an alternative to provide improved selectivity over its single-mode counterparts. The unique selectivity of multimodal chromatography is owed to its combination of multiple synergistic modes of interaction. However, multimodal chromatography has seen comparatively limited adoption in industrial purification processes. The primary reason for this is the increased difficulty of purification process development for multimodal ligands, due to their complex, nonintuitive behavior. To address this quandary, high-throughput experimentation and mechanistic process modeling have been demonstrated to be instrumental in streamlining the development of multimodal chromatographic processes. High-throughput techniques can rapidly identify appropriate operational windows, while mechanistic modeling can be used for process optimization and, importantly, to improve process understanding. In the first part of this thesis, these two techniques are used in concert to develop efficient and highly effective in silico workflows for process characterization and development. First, high-throughput batch isotherm data were generated for each resin with a monoclonal antibody (mAb) product, across a wide range of mobile phase conditions. An array of isotherm formalisms was applied for the multimodal cation exchange (MMCEX) resin Capto MMC and the multimodal anion exchange (MMAEX) resin Capto Adhere. These models differed in their consideration of electrostatic interactions, hydrophobic interactions, and thermodynamic activities, and were all based on the stoichiometric displacement framework. For each model, twenty sets of isotherm parameters were regressed from the batch data through repeated fits. Column linear gradient elution experiments were performed across a range of pHs for Capto MMC and across a range of gradient slopes for Capto Adhere. Each set of isotherm parameters was used to predict the column elution behavior. From this, the efficacy and consistency of each model formulation to both perform column predictions and to fit the batch data was determined. Not only did this investigation develop a workflow for rapid model selection but also resulted in several key findings regarding the nature of these isotherm formalisms. These findings showed that, for the Capto MMC resin, batch data fit quality was a poor indicator of column prediction quality, and predictions could be improved by excluding low concentration data. For both resins, the extended steric mass action (SMA) isotherm obtained excellent predictions, where more complex isotherm formalisms, containing explicit hydrophobic contributions, were less effective. This work was then extended to predictions of step elution, using the extended SMA model, with the Capto MMC resin and two mAbs. In these experiments, the ionogenic weak cationic groups of Capto MMC produced induced pH gradients (pH transients) that dramatically impacted the step elution profiles. The modeling strategy was expanded to include consideration of pH transients, and a systematic investigation was carried out to characterize the influence of pH transients as a function of buffer composition. Subsequently, these modeling strategies were applied to a complex multicomponent system containing mAb, aggregates, charge variants, and residual host cell proteins (HCP). Isotherm parameters were determined for the multicomponent batch data, where multiple analytical techniques were applied to quantify aggregates, charge variants, and HCP. Model validation was performed through predictions of a set of RoboColumn step elution experiments at a range of ionic strength, pH, and protein load density. Next, the model was deployed to perform process optimization and a purification step was designed to remove aggregates while not significantly altering the charge variant content. Finally, this process was characterized in silico and key process parameters were identified. Here, pH of the elution step was found to be the most strongly contributing factor, which was consistent with the high pH sensitivity of Capto MMC seen earlier. In the second part of this thesis, the focus remained on the mechanistic modeling of multimodal chromatography, but shifted towards emergent biological products and novel chromatographic materials. First, a model was constructed for a bispecific antibody (bsAb) and a complex set of product-related impurities with the MMCEX resin Capto MMC ImpRes. Here, isotherm parameters were regressed from a set of linear gradient elution and step elution experiments over a wide range of pH conditions. The pH transients model was also extended to account for the divalent buffer system, bis-tris propane. pH was observed to greatly influence the selectivity between the bsAb and its impurities. The mechanistic model accurately captured these effects and was subsequently used to optimize a multistep purification scheme, which provided nearly complete clearance of all impurities, with the exception of the mispaired light-chain species. In silico process characterization was then performed, which again identified that the pH of the elution steps was the most impactful process parameter. Next, a novel size-exclusion multimodal (SEMM) resin was characterized using mechanistic modeling, with respect to its ability to remove mAb fragments. First, the pore size distribution of this resin was characterized using inverse size-exclusion chromatography, which identified that the distribution was monodisperse and had a mean size slightly larger than that of the mAb. A model mixture containing a distribution of fragments along with mAb and aggregates was generated. Column breakthrough experiments were performed with this mixture, and the resulting curves were fit to obtain parameters for the multicomponent Langmuir isotherm and the general rate model of chromatography. Model-based process scale-up was performed, which demonstrated the model’s extrapolative capabilities. Next, simulated batch uptake experiments illustrated that size-based partitioning was the mechanism of separation. Finally, hypotheses regarding the system’s dependence on certain input variables were investigated using simulations. These simulations identified that fragment removal efficacy was purely a function of column volume—agnostic to whether length or diameter was changed. Additionally, complex relationships were identified with respect to feed composition. Finally, avenues for future work were identified that address the limitations of this research as well as identify entirely new directions to expand the current understanding of mechanistic modeling for multimodal chromatography.