High-throughput in silico workflow for optimization and characterization of multimodal chromatographic processes.

While high-throughput (HT) experimentation and mechanistic modeling have long been employed in chromatographic process development, it remains unclear how these techniques should be used in concert within development workflows. In this work, a process development workflow based on HT experiments and mechanistic modeling was constructed. The integration of HT and modeling approaches offers improved workflow efficiency and speed. This high-throughput in silico (HT-IS) workflow was employed to develop a Capto MMC polishing step for mAb aggregate removal. High-throughput batch isotherm data was first generated over a range of mobile phase conditions and a suite of analytics were employed. Parameters for the extended steric mass action (SMA) isotherm were regressed for the multicomponent system. Model validation was performed using the extended SMA isotherm in concert with the general rate model of chromatography using the CADET modeling software. Here, step elution profiles were predicted for eight RoboColumn runs across a range of ionic strength, pH, and load density. Optimized processes were generated through minimization of a complex objective function based on key process metrics. Processes were evaluated at lab-scale using two feedstocks, differing in composition. The results confirmed that both processes obtained high monomer yield (>85%) and removed ∼ 50 % $$ \sim 50\% $$ of aggregate species. Column simulations were then carried out to determine sensitivity to a wide range of process inputs. Elution buffer pH was found to be the most critical process parameter, followed by resin ionic capacity. Overall, this study demonstrated the utility of the HT-IS workflow for rapid process development and characterization.

Mixed-mode size-exclusion silica resin for polishing human antibodies in flow-through mode.

The polishing step in the downstream processing of therapeutic antibodies removes residual impurities from Protein A eluates. Among the various classes of impurities, antibody fragments are especially challenging to remove due to the broad biomolecular diversity generated by a multitude of fragmentation patterns. The current approach to fragment removal relies on ion exchange or mixed-mode adsorbents operated in bind-and-gradient-elution mode. However, fragments that bear strong similarity to the intact product or whose biophysical features deviate from the ensemble average can elude these adsorbents, and the lack of a chromatographic technology enabling robust antibody polishing is recognized as a major gap in downstream bioprocessing. Responding to this challenge, this study introduces size-exclusion mixed-mode (SEMM) silica resins as a novel chromatographic adsorbent for the capture of antibody fragments irrespective of their biomolecular features. The pore diameter of the silica beads features a narrow distribution and is selected to exclude monomeric antibodies, while allowing their fragments to access the pores where they are captured by the mixed-mode ligands. The static and dynamic binding capacity of the adsorbent ranged respectively between 30-45 and 25-33 gs of antibody fragments per liter of resin. Selected SEMM-silica resins also demonstrated the ability to capture antibody aggregates, which adsorb on the outer layer of the beads. Optimization of the SEMM-silica design and operation conditions - namely, pore size (10 nm) and ligand composition (quaternary amine and alkyl chain) as well as the linear velocity (100 cm/h), ionic strength (5.7 mS/cm), and pH (7) of the mobile phase - afforded a significant reduction of both fragments and aggregates, resulting into a final antibody yield up to 80% and monomeric purity above 97%.

Mechanistic model-based characterization of size-exclusion-mixed-mode resins for removal of monoclonal antibody fragments.

Although antibody fragments are a critical impurity to remove from process streams, few platformable purification techniques have been developed to this end. In this work, a novel size-exclusion-mixed-mode (SEMM) resin was characterized with respect to its efficacy in mAb fragment removal. Inverse size-exclusion chromatography showed that the silica-based resin had a narrow pore size distribution and a median pore radius of roughly 6.2 nm. Model-based characterization was carried out with Chromatography Analysis and Design Toolkit (CADET), using the general rate model and the multicomponent Langmuir isotherm. Model parameters were obtained from fitting breakthrough curves, performed at multiple residence times, for a mixture of mAb, aggregates, and an array of fragments (varying in size). Accurate fits were obtained to the frontal chromatographic data across a range of residence times. Model validation was then performed with a scaled-up column, altering residence time and feed composition from the calibration run. Accurate predictions were obtained, thereby illustrating the model's interpolative and extrapolative capabilities. Additionally, the SEMM resin achieved 90% mAb yield, 37% aggregate removal, 29% [Formula: see text] removal, 54% Fab/Fc removal, 100% Fc fragments removal, and a productivity of 72.3 g mAbL×h. Model predictions for these statistics were all within 5%. Simulated batch uptake experiments showed that resin penetration depth was directly related to protein size, with the exception of the aggregate species, and that separation was governed by differential pore diffusion rates. Additional simulations were performed to characterize the dependence of fragment removal on column dimension, load density, and feed composition. Fragment removal was found to be highly dependent on column load density, where optimal purification was achieved below 100 mg protein/mL column. Furthermore, fragment removal was dependent on column volume (constant load mass), but agnostic to whether column length or diameter was changed. Lastly, the dependence on feed composition was shown to be complex. While fragment removal was inversely related to fragment mass fraction in the feed, the extent depended on fragment size. Overall, the results from this study illustrated the efficacy of the SEMM resin in fragment and aggregate removal and elucidated relationships with key operational parameters through model-based characterization.

Isotherm model discrimination for multimodal chromatography using mechanistic models derived from high-throughput batch isotherm data.

In this work, we have examined an array of isotherm formalisms and characterized them based on their relative complexities and predictive abilities with multimodal chromatography. The set of isotherm models studied were all based on the stoichiometric displacement framework, with considerations for electrostatic interactions, hydrophobic interactions, and thermodynamic activities. Isotherm parameters for each model were first determined through twenty repeated fits to a set of mAb - Capto MMC batch isotherm data spanning a range of loading, ionic strength, and pH as well as a set of mAb - Capto Adhere batch data at constant pH. The batch isotherm data were used in two ways-spanning the full range of loading or consisting of only the high concentration data points. Predictive ability was defined through the model's capacity to capture prominent changes in salt gradient elution behavior with respect to pH for Capto MMC or unique elution patterns and yield losses with respect to gradient slope for Capto Adhere. In both cases, model performance was quantified using a scoring metric based on agreement in peak characteristics for column predictions and accuracy of fit for the batch data. These scores were evaluated for all twenty isotherm fits and their corresponding column predictions, thereby producing a statistical distribution of model performances. Model complexity (number of isotherm parameters) was then considered through use of the Akaike information criterion (AIC) calculated from the score distributions. While model performance for Capto MMC benefitted substantially from removal of low protein concentration data, this was not the case for Capto Adhere; this difference was likely due to the qualitatively different shapes of the isotherms between the two resins. Surprisingly, the top-performing (high accuracy with minimal number of parameters) isotherm model was the same for both resins. The extended steric mass action (SMA) isotherm (containing both protein-salt and protein-protein activity terms) accurately captured both the pH-dependent elution behavior for Capto MMC as well as loss in protein recovery with increasing gradient slope for Capto Adhere. In addition, this isotherm model achieved the highest median score in both resin systems, despite it lacking any explicit hydrophobic stoichiometric terms. The more complex isotherm models, which explicitly accounted for both electrostatic and hydrophobic interaction stoichiometries, were ill-suited for Capto MMC and had lower AIC model likelihoods for Capto Adhere due to their increased complexity. Interestingly, the ability of the extended SMA isotherm to predict the Capto Adhere results was largely due to the protein-salt activity coefficient, as determined via isotherm parameter sensitivity analyses. Further, parametric studies on this parameter demonstrated that it had a major impact on both binding affinity and elution behavior, therein fully capturing the impact of hydrophobic interactions. In summary, we were able to determine the isotherm formalisms most capable of consistently predicting a wide range of column behavior for both a multimodal cation-exchange and multimodal anion-exchange resin with high accuracy, while containing a minimized set of model parameters.

Evaluation of guanidine-based multimodal anion exchangers for protein selectivity and orthogonality.

In this paper, we examined the chromatographic behavior of a new class of guanidine-based multimodal anion exchange resins. The selectivities and protein recoveries on these resins were first evaluated using linear gradient chromatography with a model acidic protein library at pH 5, 6 and 7. While a single-guanidine based resin exhibited significant recovery issues at high ligand density, a bis-guanidine based resin showed high recoveries of all but two of the proteins evaluated in the study. In addition, the bis-guanidine resin showed a more pH dependent selectivity pattern as compared to the low density single-guanidine resin. The salt elution range for the low density single-guanidine and bis-guanidine resins was also observed to vary from 0.250 to 0.621 M and 0.162 to 0.828 M NaCl, respectively. A QSAR model was then developed to predict the elution behavior of these proteins on the guanidine prototypes at multiple pH with overall training and test scores of 0.88 and 0.85, respectively. In addition, molecular dynamics simulations were performed with these ligands immobilized on a self-assembled monolayer (SAM) to characterize their conformational preferences and to gain insight into the molecular basis of their chromatographic behavior. Finally, a recently developed framework was employed to evaluate the separability of the bis-guanidine resin as well as its orthogonality to the multimodal cation exchanger, Nuvia cPrime. This evaluation was carried out using a second model protein library which included both acidic and basic proteins. The results of this analysis indicated that the bis-guanidine prototype exhibited both higher pair separability (0.73) and pair enhancement (0.42) as compared to the less hydrophobic commercial Nuvia aPrime 4A with pair separability and enhancement factors of 0.57 and 0.22, respectively. The enhanced selectivity and orthogonality of this new multimodal anion exchange ligand may offer potential opportunities for bioprocessing applications.