Empirical correlations for axial dispersion coefficient and Peclet number in fixed-bed columns

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.

Sequential purification of cannabidiol by two-dimensional liquid chromatography combined with modeling and simulation of elution profiles

Cannabidiol (CBD) has garnered significant attention for its neuroprotective properties, and research on its therapeutic effects has increased dramatically in recent years. However, the systematic purification of CBD through scalable processes has remained bottleneck due to the structural similarities of the cannabinoids. Although preparative chromatography is considered as a potential solution, it is usually time-consuming and expensive. Therefore, the development of scalable strategy via fast and accurate optimization approach is crucial. The present study aimed to develop a sequential process for the scalable purification of CBD through an eco-friendly ethanolic extraction using ultrasonic assisted extraction, decarboxylation of cannabidiolic acid optimized by response surface methodology, followed by the development of off-line two-dimensional semi-preparative chromatography, boosted with stacked injection overloading. In the first dimension, a column packed with macroporous resin allows to enrich the target substance and then, the behavior of resin column for scale-up procedure were predicted and optimized by developed mathematical model. A C18 column was used in the second dimension. The CBD purity and recovery obtained were 94.3 and 82.1 %, respectively. A robust and reliable method was employed for CBD enrichment/purification, which can be generalized to other bioactive compounds in complex matrices.

Mechanistic modeling of empty-full separation in recombinant adeno-associated virus production using anion-exchange membrane chromatography

Recombinant adeno-associated viral vectors (rAAVs) have become an industry-standard technology in the field of gene therapy, but there are still challenges to be addressed in their biomanufacturing. One of the biggest challenges is the removal of capsid species other than that which contains the gene of interest. In this work, we develop a mechanistic model for the removal of empty capsids-those that contain no genetic material-and enrichment of full rAAV using anion-exchange membrane chromatography. The mechanistic model was calibrated using linear gradient experiments, resulting in good agreement with the experimental data. The model was then applied to optimize the purification process through maximization of yield studying the impact of mobile phase salt concentration and pH, isocratic wash and elution length, flow rate, percent full (purity) requirement, loading density (challenge), and the use of single-step or two-step elution modes. A solution from the optimization with purity of 90% and recovery yield of 84% was selected and successfully validated, as the model could predict the recovery yield with remarkable fidelity and was able to find process conditions that led to significant enrichment. This is, to the best of our knowledge, the first case study of the application of de novo mechanistic modeling for the enrichment of full capsids in rAAV manufacturing, and it serves as demonstration of the potential of mechanistic modeling in rAAV process development.

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.

Adsorptive removal of Pb(II) using nanostructured γ-alumina in a packed bed adsorber: Simulation using gPROMS

In this work, convective-dispersive and pore volume and surface diffusion models have been used to analyze Pb(II) adsorption from an aqueous solution over a nanostructured γ-alumina adsorbent in a packed bed adsorber. The models encompassing partial differential equation and a linear algebraic equation coupled with isotherm have been simulated in gPROMS using the backward finite difference approach. The predicted breakthrough curves of Pb(II) adsorption concerning flow rate, initial metal concentration, and bed height were matched with the experimental data. The accuracy of model predictions was analyzed through statistical measures such as coefficient of determination (R²), root mean square error, and chi-squared value. The simulation results also predicted the axial dispersion, distribution coefficient, mass transfer coefficient, pore volume, and surface diffusion coefficient, which are, otherwise, difficult to measure experimentally and, in turn, have been used to assess the mass transfer characteristics of continuous Pb(II) adsorption. Additionally, the values of breakthrough time, exhaustion time, adsorption column capacity, and mass transfer zone were determined as a function of flow rate, bed height, and initial metal concentration. Surface and pore volume diffusions (10^-11-10^-10 m²/s) apparently controlled the continuous adsorption process, with surface diffusion being dominant. The transport parameters evaluated in the current study could be beneficial for the large-scale Pb(II)/nanostructured γ-alumina adsorption system. As evident from the successful simulation, the developed gPROMS program can also be applied to other adsorbate/adsorbent systems with a slight modification concerning the operating parameters.

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.

Prediction of attachment efficiency using machine learning on a comprehensive database and its validation

Colloidal particles can attach to surfaces during transport, but the attachment depends on particle size, hydrodynamics, solid and water chemistry, and particulate matter. The attachment is quantified in filtration theory by measuring attachment or sticking efficiency (Alpha). A comprehensive Alpha database (2538 records) was built from experiments in the literature and used to develop a machine learning (ML) model to predict Alpha. The training (r-squared: 0.86) was performed using two random forests capable of handling missing data. A holdout dataset was used to validate the training (r-squared: 0.98), and the variable importance was explored for training and validation. Finally, an additional validation dataset was built from quartz crystal microbalance experiments using surface-modified polystyrene, poly (methyl methacrylate), and polyethylene. The experiments were performed in the absence or presence of humic acid. Full database regression (r-squared: 0.90) predicted Alpha for the additional validation with an r-squared of 0.23. Nevertheless, when the original database and the additional validation dataset were combined into a new database, both the training (r-squared: 0.95) and validation (r-squared: 0.70) increased. The developed ML model provides a data-driven prediction of Alpha over a big database and evaluates the significance of 22 input variables.

Industrial ion-exchange chromatography development using discontinuous Galerkin methods coupled with forward sensitivity analysis

In this work, a discontinuous Galerkin method coupled with forward sensitivity analysis (DG-FSA) is presented. The DG-FSA method is used to reduce computational cost required for model-based ion-exchange chromatography development using industrial load samples. As an example, the design of an anion-exchange chromatography step is considered. This step is used to purify an experimental peptide product called Protein G from Novo Nordisk A/S (Bagsværd, Denmark). The results demonstrate, that a fourth order DG-FSA method can reduce computational cost of inverse problems by a factor ×16 compared to a second (low) order DG-FSA method. Furthermore, the fourth-order DG-FSA method enable the computation of probability distributions of optimized processing conditions given uncertainty in model parameters or inputs. This analysis is not possible within a reasonable timeframe when applying the second (low) order DG-FSA method. The design procedure facilitates the optimization of the Protein G purification step. In an experimental validation run, the productivity is increased by 70% while sacrificing 4% yield at a similar purity constraint compared to an experiment with baseline performance.

Single event kinetic modeling for paraffin hydrocracking over an industrial Ni–W silica–aluminum catalyst

Computer-aided fundamental kinetic modeling for paraffin hydrocracking based on graphical solution and the single event concept.

Optimal loading flow rate trajectory in monoclonal antibody capture chromatography

In this paper, we determined the optimal flow rate trajectory during the loading phase of a mAb capture column. For this purpose, a multi-objective function was used, consisting of productivity and resin utilization. Several general types of trajectories were considered, and the optimal Pareto points were obtained for all of them. In particular, the presented trajectories include a constant-flow loading process as a nominal approach, a stepwise trajectory, and a linear trajectory. Selected trajectories were then applied in experiments with the state-of-the-art protein A resin mAb Select PrismA^TM, running in batch mode on a standard single-column chromatography setup, and using both a purified mAb solution as well as a clarified supernatant. The results show that this simple approach, programming the volumetric flow rate according to either of the explored strategies, can improve the process economics by increasing productivity by up to 12% and resin utilization by up to 9% compared to a constant-flow process, while obtaining a yield higher than 99%. The productivity values were similar to the ones obtained in a multi-column continuous process, and ranged from 0.23 to 0.35 mg/min/mL resin. Additionally, it is shown that a model calibration carried out at constant flow can be applied in the simulation and optimization of flow trajectories. The selected processes were scaled up to pilot scale and simulated to prove that even higher productivity and resin utilization can be achieved at larger scales, and therefore confirm that the trajectories are generalizable across process scales for this resin.

Microfluidic bioreactors for enzymatic synthesis in packed-bed reactors-Multi-step reactions and upscaling

Enzymatic synthesis of biochemical commodities is of upmost importance as it represents a greener alternative to traditional chemical synthesis and provides easier downstream processing strategies compared to fermentation-based processes. A microfluidic system used to optimize the enzymatic production of both levodopa (L-DOPA) and dopamine in both single-step and multistep-reaction sequences with yield of approximately 30 % for L-DOPA production and 70 % for dopamine production is presented. The system for L-DOPA production was then up-scaled (780-fold increase) to a milliliter scale system by maintaining similar mass transport properties resulting in the same yield, space-time yield and biocatalyst yield as its microscale counterpart. The results obtained for yield and biocatalyst yield (351.7 mg_L-DOPA mg^-1_Tyr h^-1) were similar to what is reported in the literature for similar systems, however the space-time yield (0.806 mg_L-DOPA L^-1 h^-1) was smaller. This work demonstrates a microfluidic bioreactor that can be used for complex optimizations that can be performed rapidly while reducing the consumption of reagents by immobilizing the catalyst on a carrier which can then be used in a packed-bed reactor, thus extending the enzyme life span.