Model-based prediction of monoclonal antibody retention in ion-exchange chromatography

Model based process optimization of an industrial chromatographic process for separation of lactoferrin from bovine milk

Model based process development using predictive mechanistic models is a powerful tool for in-silico downstream process development. It allows to obtain a thorough understanding of the process reducing experimental effort. While in pharma industry, mechanistic modeling becomes more common in the last years, it is rarely applied in food industry. This case study investigates risk ranking and possible optimization of the industrial process of purifying lactoferrin from bovine milk using SP Sepharose Big Beads with a resin particle diameter of 200 µm, based on a minimal number of lab-scale experiments combining traditional scale-down experiments with mechanistic modeling. Depending on the location and season, process water pH and the composition of raw milk can vary, posing a challenge for highly efficient process development. A predictive model based on the general rate model with steric mass action binding, extended for pH dependence, was calibrated to describe the elution behavior of lactoferrin and main impurities. The gained model was evaluated against changes in flow rate, step elution conditions, and higher loading and showed excellent agreement with the observed experimental data. The model was then used to investigate the critical process parameters, such as water pH, conductivity of elution steps, and flow rate, on process performance and purity. It was found that the elution behavior of lactoferrin is relatively consistent over the pH range of 5.5 to 7.6, while the elution behavior of the main impurities varies greatly with elution pH. As a result, a significant loss in lactoferrin is unavoidable to achieve desired purities at pH levels below pH 6.0. Optimal process parameters were identified to reduce water and salt consumption and increase purity, depending on water pH and raw milk composition. The optimal conductivity for impurity removal in a low conductivity elution step was found to be 43 mS/cm, while a conductivity of 95 mS/cm leads to the lowest overall salt usage during lactoferrin elution. Further increasing the conductivity during lactoferrin elution can only slightly lower the elution volume thus can also lead to higher total salt usage. Low flow rates during elution of 0.2 column volume per minute are beneficial compared to higher flow rates of 1 column volume per minute. The, on lab-scale, calibrated model allows predicting elution volume and impurity removal for large-scale experiments in a commercial plant processing over 106 liters of milk per day. The successful model extrapolation was possible without recalibration or detailed knowledge of the manufacturing plant. This study therefore provides a possible pathway for rapid process development of chromatographic purification in the food industries combining traditional scale-down experiments with mechanistic modeling.

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.

Ion Exchange Chromatographic Methods for Purification of Therapeutic Antibodies

Ion Exchange Chromatography has been a critical unit operation for manufacturing of therapeutic antibodies. Cation and anion exchange chromatography are used extensively to remove process-related as well as product-related impurities to obtain the final product. In this chapter, we describe the methods for separating and purifying charge variants and aggregates for manufacturing of monoclonal antibodies. The methods related to removal of host cell impurities such as host cell DNA and host cell proteins are also described. With minimal modifications, the protocols described here can be employed to purify any monoclonal antibody.

Protein adsorption on ion exchange adsorbers: A comparison of a stoichiometric and non-stoichiometric modeling approach

For mechanistic modeling of ion exchange (IEX) processes, a profound understanding of the adsorption mechanism is important. While the description of protein adsorption in IEX processes has been dominated by stoichiometric models like the steric mass action (SMA) model, discrepancies between experimental data and model results suggest that the conceptually simple stoichiometric description of protein adsorption provides not always an accurate representation of nonlinear adsorption behavior. In this work an alternative colloidal particle adsorption (CPA) model is introduced. Based on the colloidal nature of proteins, the CPA model provides a non-stoichiometric description of electrostatic interactions within IEX columns. Steric hindrance at the adsorber surface is considered by hard-body interactions between proteins using the scaled-particle theory. The model's capability of describing nonlinear protein adsorption is demonstrated by simulating adsorption isotherms of a monoclonal antibody (mAb) over a wide range of ionic strength and pH. A comparison of the CPA model with the SMA model shows comparable model results in the linear adsorption range, but significant differences in the nonlinear adsorption range due to the different mechanistic interpretation of steric hindrance in both models. The results suggest that nonlinear adsorption effects can be overestimated by the stoichiometric formalism of the SMA model and are generally better reproduced by the CPA model.

Association equilibrium model. I. Influence of pH and salt concentration on ion-exchanger

An association equilibrium model is presented in this work to illustrate the charged state of an ion-exchange adsorbent in electrolytic solution. This semi-empirical model considers the adsorption equilibrium of hydrogen ions and small-molecular salt ions with adsorbents, and it can be used to describe the effects of pH and salt concentration on the zeta potential, associated hydrogen ions and ionic capacity of adsorbents. The association equilibrium parameters of four commercial adsorbents were obtained by experimental data fitting. The model fitted the experimental data well, and their coefficients of determination (R²) of four adsorbents ranged from 0.924 to 0.994. The ratio coefficients of the association reaction with hydrogen ions ranged from 0.15 to 0.44 and those with salt counter-ions were all one. These data demonstrated that association reactions followed stoichiometric law, but that ionizable groups on ion-exchangers could not freely ionize as small molecule ions in solution. In this way, the performance of ion-exchange adsorbents can be characterized based on the zeta potential and dissociated hydrogen ions, and the results from this model were consistent with that from the manufacturer. Furthermore, this model could easily be expanded for multi-component systems.

Accounting for host cell protein behavior in anion-exchange chromatography

Host cell proteins (HCP) are a problematic set of impurities in downstream processing (DSP) as they behave most similarly to the target protein during separation. Approaching DSP with the knowledge of HCP separation behavior would be beneficial for the production of high purity recombinant biologics. Therefore, this work was aimed at characterizing the separation behavior of complex mixtures of HCP during a commonly used method: anion-exchange chromatography (AEX). An additional goal was to evaluate the performance of a statistical methodology, based on the characterization data, as a tool for predicting protein separation behavior. Aqueous two-phase partitioning followed by two-dimensional electrophoresis provided data on the three physicochemical properties most commonly exploited during DSP for each HCP: pI (isoelectric point), molecular weight, and surface hydrophobicity. The protein separation behaviors of two alternative expression host extracts (corn germ and E. coli) were characterized. A multivariate random forest (MVRF) statistical methodology was then applied to the database of characterized proteins creating a tool for predicting the AEX behavior of a mixture of proteins. The accuracy of the MVRF method was determined by calculating a root mean squared error value for each database. This measure never exceeded a value of 0.045 (fraction of protein populating each of the multiple separation fractions) for AEX. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1453-1463, 2016.

Systematic interpolation method predicts protein chromatographic elution from batch isotherm data without a detailed mechanistic isotherm model

Predicting protein elution for overloaded ion exchange columns requires models capable of describing protein binding over broad ranges of protein and salt concentrations. Although approximate mechanistic models are available, they do not always have the accuracy needed for precise predictions. The aim of this work is to develop a method to predict protein chromatographic behavior from batch isotherm data without relying on a mechanistic model. The method uses a systematic empirical interpolation (EI) scheme coupled with a lumped kinetic model with rate parameters determined from HETP measurements for non-binding conditions, to numerically predict the column behavior. For two experimental systems considered in this work, predictions based on the EI scheme are in excellent agreement with experimental elution profiles under highly overloaded conditions without using any adjustable parameters. A qualitative study of the sensitivity of predicting protein elution profiles to the precision, granularity, and extent of the batch adsorption data shows that the EI scheme is relatively insensitive to the properties of the dataset used, requiring only that the experimental ranges of protein and salt concentrations overlap those under which the protein actually elutes from the column and possess a ± 10% measurement precision.