Modeling scalability of impurity precipitation in downstream biomanufacturing.

Precipitation during the viral inactivation, neutralization and depth filtration step of a monoclonal antibody (mAb) purification process can provide quantifiable and potentially significant impurity reduction. However, robust commercial implementation of this unit operation is limited due to the lack of a representative scale-down model to characterize the removal of impurities. The objective of this work is to compare isoelectric impurity precipitation behavior for a monoclonal antibody product across scales, from benchtop to pilot manufacturing. Scaling parameters such as agitation and vessel geometry were investigated, with the precipitate amount and particle size distribution (PSD) characterized via turbidity and flow imaging microscopy. Qualitative analysis of the data shows that maintaining a consistent energy dissipation rate (EDR) could be used for approximate scaling of vessel geometry and agitator speeds in the absence of more detailed simulation. For a more rigorous approach, however, agitation was simulated via computational fluid dynamics (CFD) and these results were applied alongside a population balance model to simulate the trajectory of the size distribution of precipitate. CFD results were analyzed within a framework of a two-compartment mixing model comprising regions of high- and low-energy agitation, with material exchange between the two. Rate terms accounting for particle formation, growth and breakage within each region were defined, accounting for dependence on turbulence. This bifurcated model was successful in capturing the variability in particle sizes over time across scales. Such an approach enhances the mechanistic understanding of impurity precipitation and provides additional tools for model-assisted prediction for process scaling.

Factors affecting product association as a mechanism of host-cell protein persistence in bioprocessing.

Product association of host-cell proteins (HCPs) to monoclonal antibodies (mAbs) is widely regarded as a mechanism that can enable HCP persistence through multiple purification steps and even into the final drug substance. Discussion of this mechanism often implies that the existence or extent of persistence is directly related to the strength of binding but actual measurements of the binding affinity of such interactions remain sparse. Two separate avenues of investigation of HCP-mAb binding are reported here. One is the measurement of the affinity of binding of individual, commonly persistent Chinese hamster ovary (CHO) HCPs to each of a set of mAbs, and the other uses quantitative proteomic measurements to assess binding of HCPs in a null CHO harvested cell culture fluid (HCCF) to mAbs produced in the same cell line. The individual HCP measurements show that the binding affinities of individual HCPs to different mAbs can vary appreciably but are rarely very high, with only weak pH dependence. The measurements on the null HCCF allow estimation of individual HCP-mAb affinities; these are typically weaker than those seen in affinity measurements on isolated HCPs. Instead, the extent of binding appears correlated with the initial abundance of individual HCPs in the HCCF and the forms of the HCPs in the solution, i.e., whether HCPs are present as free molecules or as parts of large aggregates. Separate protein A chromatography experiments performed by feeding different fractions of a mAb-containing HCCF obtained by size-exclusion chromatography (SEC) showed clear differences in the number and identity of HCPs found in the protein A eluate. These results indicate a significant role for HCP-mAb association in determining HCP persistence through protein A chromatography, presumably through binding of HCP-mAb complexes to the resin. Overall, the results illustrate the importance of considering more fully the biophysical context of HCP-product association in assessing the factors that may affect the phenomenon and determine its implications. Knowledge of the abundances and the forms of individual or aggregated HCPs in HCCF are particularly significant, emphasizing the integration of upstream and downstream bioprocessing and the importance of understanding the collective properties of HCPs in addition to just the biophysical properties of individual HCPs.

Application of mechanistic modelling in membrane and fiber chromatography for purification of biotherapeutics - A review.

Mechanistic modelling is a simulation tool which has been effectively applied in downstream bioprocessing to model resin chromatography. Membrane and fiber chromatography are newer approaches that offer higher rates of mass transfer and consequently higher flow rates and reduced processing times. This review describes the key considerations in the development of mechanistic models for these unit operations. Mass transfer is less complex than in resin columns, but internal housing volumes can make modelling difficult, particularly for laboratory-scale devices. Flow paths are often non-linear and the dead volume is often a larger fraction of the overall volume, which may require more complex hydrodynamic models to capture residence time distributions accurately. In this respect, the combination of computational fluid dynamics with appropriate protein binding models is emerging as an ideal approach.

Identification and characterization of CHO host-cell proteins in monoclonal antibody bioprocessing.

Host-cell proteins (HCPs) are the foremost class of process-related impurities to be controlled and removed in downstream processing steps in monoclonal antibody (mAb) manufacturing. However, some HCPs may evade clearance in multiple purification steps and reach the final drug product, potentially threatening drug stability and patient safety. This study extends prior work on HCP characterization and persistence in mAb process streams by using mass spectrometry (MS)-based methods to track HCPs through downstream processing steps for seven mAbs that were generated by five different cell lines. The results show considerable variability in HCP identities in the processing steps but extensive commonality in the identities and quantities of the most abundant HCPs in the harvests for different processes. Analysis of HCP abundance in the harvests shows a likely relationship between abundance and the reproducibility of quantification measurements and suggests that some groups of HCPs may hinder the characterization. Quantitative monitoring of HCPs persisting through purification steps coupled with the findings from the harvest analysis suggest that multiple factors, including HCP abundance and mAb-HCP interactions, can contribute to the persistence of individual HCPs and the identification of groups of common, persistent HCPs in mAb manufacturing.

Predictive mechanistic modeling of loading and elution in protein A chromatography.

Protein A chromatography is an enabling technology in current manufacturing processes of monoclonal antibodies (mAbs) and mAb derivatives, largely due to its ability to reduce the levels of process-related impurities by several orders of magnitude. Despite its widespread application, the use of mathematical modeling capable of accurately predicting the full protein A chromatographic process, including loading, post-loading wash and elution stages, has been limited. This work describes a mechanistic modeling approach utilizing the general rate model (GRM), the capabilities of which are explored and optimized using two isotherm models. Isotherm parameters were estimated by inverse-fitting simulated breakthrough curves to experimental data at various pH values. The parameter values so obtained were interpolated across the relevant pH range using a best-fit curve, thus enabling their use in predictive modeling, including of elution over a range of pH. The model provides accurate predictions (< 3% mean error in 10% dynamic binding capacity predictions and ∼ 5% mean error in elution mass and pool volume predictions, both on scale-up) for various residence times, buffer conditions and elution schemes and its effectiveness for use in scale-up and process development is shown by applying the same parameters to larger columns and a wider range of residence times.

Combined Effects of Pressure and Ionic Strength on Protein-Protein Interactions: An Empirical Approach.

Proteins are exposed to hydrostatic pressure (HP) in a variety of ecosystems as well as in processing steps such as freeze-thaw, cell disruption, sterilization, and homogenization, yet pressure effects on protein-protein interactions (PPIs) remain underexplored. With the goal of contributing toward the expanded use of HP as a fundamental control parameter in protein research, processing, and engineering, small-angle X-ray scattering was used to examine the effects of HP and ionic strength on ovalbumin, a model protein. Based on an extensive data set, we develop an empirical method for scaling PPIs to a master curve by combining HP and osmotic effects. We define an effective pressure parameter that has been shown to successfully apply to other model protein data available in the literature, with deviations evident for proteins that do not follow the apparent Hofmeister series. The limitations of the empirical scaling are discussed in the context of the hypothesized underlying mechanisms.

Cell therapy biomanufacturing: integrating biomaterial and flow-based membrane technologies for production of engineered T-cells.

Adoptive T-cell therapies (ATCTs) are increasingly important for the treatment of cancer, where patient immune cells are engineered to target and eradicate diseased cells. The biomanufacturing of ATCTs involves a series of time-intensive, lab-scale steps, including isolation, activation, genetic modification, and expansion of a patient's T-cells prior to achieving a final product. Innovative modular technologies are needed to produce cell therapies at improved scale and enhanced efficacy. In this work, well-defined, bioinspired soft materials were integrated within flow-based membrane devices for improving the activation and transduction of T cells. Hydrogel coated membranes (HCM) functionalized with cell-activating antibodies were produced as a tunable biomaterial for the activation of primary human T-cells. T-cell activation utilizing HCMs led to highly proliferative T-cells that expressed a memory phenotype. Further, transduction efficiency was improved by several fold over static conditions by using a tangential flow filtration (TFF) flow-cell, commonly used in the production of protein therapeutics, to transduce T-cells under flow. The combination of HCMs and TFF technology led to increased cell activation, proliferation, and transduction compared to current industrial biomanufacturing processes. The combined power of biomaterials with scalable flow-through transduction techniques provides future opportunities for improving the biomanufacturing of ATCTs.

Polarizability Plays a Decisive Role in Modulating Association between Molecular Cations and Anions.

Electrostatic interactions involving proteins depend on not only the ionic charges involved but also their chemical identities. Here we examine the origins of incompletely understood differences in the strength of association of different pairs of monovalent molecular ions that are relevant to protein-protein and protein-ligand interactions. Cationic analogues of the basic amino acid side chains are simulated, along with oxyanionic analogues of cation-exchange ligands and acidic amino acids. Experimentally observed association trends with respect to the cations, but not anions, are captured by a nonpolarizable model. An effective continuum correction to account for electronic polarizability can capture both trends better but at the expense of fidelity to the underlying free energy landscape for ion-pair association. A polarizable model proves decisive in capturing experimentally suggested trends with respect to both cations and anions; critically, the free energy landscape for ion-pair association is itself altered, thus altering configurational sampling.

Closed-loop modeling of central and intrinsic cardiac nervous system circuits underlying cardiovascular control.

The baroreflex is a multi-input, multi-output control physiological system that regulates blood pressure by modulating nerve activity between the brainstem and the heart. Existing computational models of the baroreflex do not explictly incorporate the intrinsic cardiac nervous system (ICN), which mediates central control of the heart function. We developed a computational model of closed-loop cardiovascular control by integrating a network representation of the ICN within central control reflex circuits. We examined central and local contributions to the control of heart rate, ventricular functions, and respiratory sinus arrhythmia (RSA). Our simulations match the experimentally observed relationship between RSA and lung tidal volume. Our simulations predicted the relative contributions of the sensory and the motor neuron pathways to the experimentally observed changes in the heart rate. Our closed-loop cardiovascular control model is primed for evaluating bioelectronic interventions to treat heart failure and renormalize cardiovascular physiology.

Behavior of host-cell-protein-rich aggregates in antibody capture and polishing chromatography.

Recent work has shown that aggregates in monoclonal antibody (mAb) solutions may be made up not just of mAb oligomers but can also harbor hundreds of host-cell proteins (HCPs), suggesting that aggregate persistence through downstream purification operations may be related to HCP clearance. We have examined this in a primary analysis of aggregate persistence through processing steps that are typically implemented for HCP reduction, demonstrating that the phenomenon is relevant to depth filtration, protein A chromatography and flow-through anion-exchange (AEX) polishing. Confocal laser scanning microscopy observations show that aggregates compete with the mAb to adsorb specifically in protein A chromatography and that this competitive interaction is integral to the efficacy of protein A washes. Column chromatography reveals that the protein A elution tail can have a relatively high concentration of aggregates, which corroborates analogous observations from recent HCP studies. Similar measurements in flow-through AEX chromatography show that relatively large aggregates that harbor HCPs and that persist into the protein A eluate can be retained to an extent that appears to depend primarily on the resin surface chemistry. The total aggregate mass fraction of both protein A eluate pools (∼ 2.4 - 3.6%) and AEX flow-through fractions (∼ 1.5 - 3.2%) correlates generally with HCP concentrations measured using enzyme-linked immunosorbent assay (ELISA) as well as the number of HCPs that may be identified in proteomic analysis. This suggests that quantification of the aggregate mass fraction may serve as a convenient albeit imperfect surrogate for informing early process development decisions regarding HCP clearance strategies.

Engineering protein A ligands to mitigate antibody loss during high-pH washes in protein A chromatography.

Protein A chromatography is a workhorse in monoclonal antibody (mAb) manufacture since it provides effective separation of mAbs from impurities such as host-cell proteins (HCPs) in a single capture step. HCP clearance can be aided by the inclusion of a wash step prior to low-pH elution. Although high-pH washes can be effective in removing additional HCPs from the loaded column, they may also contribute to a reduced mAb yield. In this work we show that this yield loss is reflected in a pH-dependent variation of the equilibrium binding capacity of the protein A resin, which is also observed for the capacity of the Fc fragments alone and therefore not a result of steric interactions involving the Fab fragments in the intact mAbs. We therefore hypothesized that the high-pH wash loss was due to protonation or deprotonation of ionizable residues on the protein A ligand. To evaluate this, we applied a rational protein engineering approach to the Z domain (the Fc-binding component of most commercial protein A ligands) and expressed engineered mutants in E. coli. Biolayer interferometry and affinity chromatography experiments showed that some of the Z domain mutants were able to mitigate wash loss at high pH while maintaining similar binding characteristics at neutral pH. These experiments enabled elucidation of the roles of specific interactions in the Z domain - Fc complex, but more importantly offer a route to ameliorating the disadvantages of high-pH washes in protein A chromatography.

Analytical characterization of host-cell-protein-rich aggregates in monoclonal antibody solutions.

Host-cell proteins (HCPs) and high molecular weight (HMW) species have historically been treated as independent classes of impurities in the downstream processing of monoclonal antibodies (mAbs), but recent indications suggest that they may be partially linked. We have explored this connection with a shotgun proteomic analysis of HMW impurities that were isolated from harvest cell culture fluid (HCCF) and protein A eluate using size-exclusion chromatography (SEC). As part of the proteomic analysis, a cross-digest study was performed in which samples were analyzed using both the standard and native digest techniques to enable a fair comparison between bioprocess pools. This comparison reveals that the HCP profiles of HCCF and protein A eluate overlap substantially more than previous work has suggested, because hundreds of HCPs are conserved in aggregates that may be up to ~50 nm in hydrodynamic radius and that persist through the protein A capture step. Quantitative SWATH proteomics suggests that the majority of the protein A eluate's HCP mass is found in such aggregates, and this is corroborated by ELISA measurements on SEC fractions. The SWATH data also show that intra-aggregate concentrations of individual HCPs are positively correlated between aggregates that were isolated from HCCF and protein A eluate, and species that have generally been considered difficult to remove tend to be more concentrated than their counterparts. These observations support prior hypotheses regarding aggregate-mediated HCP persistence through protein A chromatography and highlight the importance of this persistence mechanism.

Unpacking the multimodal, multi-scale data of the fast and slow lanes of the cardiac vagus through computational modelling.

NEW FINDINGS: What is the topic of this review? The vagus nerve is a crucial regulator of cardiovascular homeostasis, and its activity is linked to heart health. Vagal activity originates from two brainstem nuclei: the nucleus ambiguus (fast lane) and the dorsal motor nucleus of the vagus (slow lane), nicknamed for the time scales that they require to transmit signals. What advances does it highlight? Computational models are powerful tools for organizing multi-scale, multimodal data on the fast and slow lanes in a physiologically meaningful way. A strategy is laid out for how these models can guide experiments aimed at harnessing the cardiovascular health benefits of differential activation of the fast and slow lanes. ABSTRACT: The vagus nerve is a key mediator of brain-heart signaling, and its activity is necessary for cardiovascular health. Vagal outflow stems from the nucleus ambiguus, responsible primarily for fast, beat-to-beat regulation of heart rate and rhythm, and the dorsal motor nucleus of the vagus, responsible primarily for slow regulation of ventricular contractility. Due to the high-dimensional and multimodal nature of the anatomical, molecular and physiological data on neural regulation of cardiac function, data-derived mechanistic insights have proven elusive. Elucidating insights has been complicated further by the broad distribution of the data across heart, brain and peripheral nervous system circuits. Here we lay out an integrative framework based on computational modelling for combining these disparate and multi-scale data on the two vagal control lanes of the cardiovascular system. Newly available molecular-scale data, particularly single-cell transcriptomic analyses, have augmented our understanding of the heterogeneous neuronal states underlying vagally mediated fast and slow regulation of cardiac physiology. Cellular-scale computational models built from these data sets represent building blocks that can be combined using anatomical and neural circuit connectivity, neuronal electrophysiology, and organ/organismal-scale physiology data to create multi-system, multi-scale models that enable in silico exploration of the fast versus slow lane vagal stimulation. The insights from the computational modelling and analyses will guide new experimental questions on the mechanisms regulating the fast and slow lanes of the cardiac vagus toward exploiting targeted vagal neuromodulatory activity to promote cardiovascular health.

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.

Characterization and implications of host-cell protein aggregates in biopharmaceutical processing.

In the production of biopharmaceuticals such as monoclonal antibodies (mAbs) and vaccines, the residual amounts of host-cell proteins (HCPs) are among the critical quality attributes. In addition to overall HCP levels, individual HCPs may elude purification, potentially causing issues in product stability or patient safety. Such HCP persistence has been attributed mainly to biophysical interactions between individual HCPs and the product, resin media, or residual chromatin particles. Based on measurements on process streams from seven mAb processes, we have found that HCPs in aggregates, not necessarily chromatin-derived, may play a significant role in the persistence of many HCPs. Such aggregates may also hinder accurate detection of HCPs using existing proteomics methods. The findings also highlight that certain HCPs may be difficult to remove because of their functional complementarity to the product; specifically, chaperones and other proteins involved in the unfolded protein response (UPR) are disproportionately present in the aggregates. The methods and findings described here expand our understanding of the origins and potential behavior of HCPs in cell-based biopharmaceutical processes and may be instrumental in improving existing techniques for HCP detection and clearance.

Towards continuous mAb purification: Clearance of host cell proteins from CHO cell culture harvests via "flow-through affinity chromatography" using peptide-based adsorbents.

The growth of advanced analytics in manufacturing monoclonal antibodies (mAbs) has highlighted the challenges associated with the clearance of host cell proteins (HCPs). Of special concern is the removal of "persistent" HCPs, including immunogenic and mAb-degrading proteins, that co-elute from the Protein A resin and can escape the polishing steps. Responding to this challenge, we introduced an ensemble of peptide ligands that target the HCPs in Chinese hamster ovary (CHO) cell culture fluids and enable mAb purification via flow-through affinity chromatography. This study describes their integration into LigaGuard™, an affinity adsorbent featuring an equilibrium binding capacity of ~30 mg of HCPs per mL of resin as well as dynamic capacities up to 16 and 22 mg/ml at 1- and 2-min residence times, respectively. When evaluated against cell culture harvests with different mAb and HCP titers and properties, LigaGuard™ afforded high HCP clearance, with logarithmic removal values (LRVs) up to 1.5, and mAb yield above 90%. Proteomic analysis of the effluents confirmed the removal of high-risk HCPs, including cathepsins, histones, glutathione-S transferase, and lipoprotein lipases. Finally, combining LigaGuard™ for HCP removal with affinity adsorbents for product capture afforded a global mAb yield of 85%, and HCP and DNA LRVs > 4.

Behavior of weakly adsorbing protein impurities in flow-through ion-exchange chromatography.

Flow-through ion-exchange chromatography is frequently used in polishing biotherapeutics, but the factors that contribute to impurity persistence are incompletely understood. A large number of dilute impurities may be encountered that exhibit physicochemical diversity, making the flow-through separation performance highly sensitive to process conditions. The analysis presented in this work develops two novel correlations that offer transferable insights into the chromatographic behavior of weakly adsorbing impurities. The first, based on column simulations and validated experimentally, delineates the relative contributions of thermodynamic, transport, and geometric properties in dictating the initial breakthrough volumes of dilute species. The Graetz number for mass transfer was found to generalize the transport contributions, enabling estimation of a threshold in the equilibrium constant below which impurity persistence is expected. Impurity adsorption equilibria are needed to use this correlation, but such data are not typically available. The second relationship presented in this work may be used to reduce the experimental burden of estimating adsorption equilibria as a function of ionic strength. A correlation between stoichiometric displacement model parameters was found by consolidating isocratic retention data for over 200 protein-pH-resin combinations from the extant literature. Coupled with Yamamoto's analysis of linear gradient elution data, this correlation may be used to estimate retentivity approximately from a single experimental measurement, which could prove useful in predicting host-cell protein chromatographic behavior.

Design of PLGA-Based Drug Delivery Systems Using a Physically-Based Sustained Release Model.

An extensive data set has been developed and used to further the progress of a model-informed design of controlled drug release. An improved drug-release model with mechanistic modeling of hydrolytic polymer degradation is used and validated by comparing model predictions to in vitro experiments. Combining parameter estimates from the literature with model fits to the data set, this study can aid in achieving a priori design of controlled drug release from a model PLGA release system. A systematic series of model release systems were formulated with FITC-labeled dextran, as a surrogate for biopharmaceuticals, in PLGA rods over a broad range of compositions. While general comparisons between the model and experiments were favorable, important discrepancies were identified for several formulations with significant first-phase drug release. Supported by cross-sectional fluorescence microscopy images of the FITC-dextran distribution within the rods, this first-phase release was attributed to a combination of two main factors: (1) percolation of the drug particles and (2) swelling of and pore formation in the rods due to water uptake. These observations indicate the importance of careful selection of the PLGA polymer grade when designing drug release systems but also reflect a need for better understanding of phenomena such as pore formation. Adapting model parameters, without modifying the physical processes included in the model, enabled accurate fitting of the experimental data for all formulations, highlighting the applicability of the model.

Robust mechanistic modeling of protein ion-exchange chromatography.

Mechanistic models for ion-exchange chromatography of proteins are well-established and a broad consensus exists on most aspects of the detailed mathematical and physical description. A variety of specializations of these models can typically capture the general locations of elution peaks, but discrepancies are often observed in peak position and shape, especially if the column load level is in the non-linear range. These discrepancies may prevent the use of models for high-fidelity predictive applications such as process characterization and development of high-purity and -productivity process steps. Our objective is to develop a sufficiently robust mechanistic framework to make both conventional and anomalous phenomena more readily predictable using model parameters that can be evaluated based on independent measurements or well-accepted correlations. This work demonstrates the implementation of this approach for industry-relevant case studies using both a model protein, lysozyme, and biopharmaceutical product monoclonal antibodies, using cation-exchange resins with a variety of architectures (SP Sepharose FF, Fractogel EMD SO3-, Capto S and Toyopearl SP650M). The modeling employs the general rate model with the extension of the surface diffusivity to be variable, as a function of ionic strength or binding affinity. A colloidal isotherm that accounts for protein-surface and protein-protein interactions independently was used, with each characterized by a parameter determined as a function of ionic strength and pH. Both of these isotherm parameters, along with the variable surface diffusivity, were successfully estimated using breakthrough data at different ionic strengths and pH. The model developed was used to predict overloads and elution curves with high accuracy for a wide variety of gradients and different flow rates and protein loads. The in-silico methodology used in this work for parameter estimation, along with a minimal amount of experimental data, can help the industry adopt model-based optimization and control of preparative ion-exchange chromatography with high accuracy.

Nanocrystalline protein domains via salting-out.

Protein salting-out is a well established phenomenon that in many cases leads to amorphous structures and protein gels, which are usually not considered to be useful for protein structure determination. Here, microstructural measurements of several different salted-out protein dense phases are reported, including of lysozyme, ribonuclease A and an IgG1, showing that salted-out protein gels unexpectedly contain highly ordered protein nanostructures that assemble hierarchically to create the gel. The nanocrystalline domains are approximately 10-100 nm in size, are shown to have structures commensurate with those of bulk crystals and grow on time scales in the order of an hour to a day. Beyond revealing the rich, hierarchical nanoscale to mesoscale structure of protein gels, the nanocrystals that these phases contain are candidates for structural biology on next-generation X-ray free-electron lasers, which may enable the study of biological macromolecules that are difficult or impossible to crystallize in bulk.