Methodologies for model calibration to assist the design of a preparative ion-exchange step for antibody purification

Coupling of multivariate curve resolution-alternating least squares and mechanistic hard models to investigate antibody purification from human plasma using ion exchange chromatography

A two steps proposal for the purification of immunoglobulin G from human blood plasma is investigated. The first step is precipitation using cold ethanol based on the Cohn method with some modification and the second step is a chromatographic separation by DEAE-Sepharose FF resin as a weak anion exchanger. The presence of interferent in the region3 of chromatographic fractions, which is co-eluted with IgG, restricts the application of the mechanistic chromatography model. Therefore, multivariate cure resolution-alternating least squares (MCR-ALS) as a soft method is employed on measured absorbance data matrix from eluted fractions to recover pure concentration and spectral profiles. Besides, possible solutions for resolved concentration and spectral profiles are investigated. The reaction-dispersive model as a mechanistic hard model for the column is utilized for the evaluation of the ion exchange chromatography. Using a genetic algorithm as a global optimization method, mobile phase modulator (MPM) adsorption model parameters such as β, k_des,0, and K_eq,0, were fitted to the concentration profiles from MCR-ALS as 1.96, 2.87×10^-4 m³ mol^-1s^-1, and 1883, respectively. Furthermore, a new resampling incorporated non-parametric statistics is conducted to assess parameters' uncertainty. Values of 2.00, 1.10×10^-3 m³ mol^-1s^-1, and 549.80 are estimated median, and values of 0.05, 2.5×10^-3, and 691.00 are calculated interquartile range (IQR) for β, k_des,0, and K_eq,0, respectively. Finally, results show three and two outliers for β and k_des,0, respectively.

Automation of Modeling and Calibration of Integrated Preparative Protein Chromatography Systems

With the increasing global demand for precise and efficient pharmaceuticals and the biopharma industry moving towards Industry 4.0, the need for advanced process integration, automation, and modeling has increased as well. In this work, a method for automatic modeling and calibration of an integrated preparative chromatographic system for pharmaceutical development and production is presented. Based on a user-defined system description, a system model was automatically generated and then calibrated using a sequence of experiments. The system description and model was implemented in the Python-based preparative chromatography control software Orbit.

Advanced score system and automated search strategies for parameter estimation in mechanistic chromatography modeling

Least squares estimation of unknown parameters from measurement data is a well-established standard method in chromatography modeling but can suffer from critical disadvantages. The description of real-world systems is generally prone to unaccounted mechanisms, such as dispersion in external holdup volumes, and systematic measurement errors, such as caused by pump delays. In this scenario, matching the shape between simulated and measured chromatograms has been found to be more important than the exact peak positions. We have therefore developed a new score system that separately accounts for the shape, position and height of individual peaks. A genetic algorithm is used for optimizing these multiple objectives. Even for non-conflicting objectives, this approach shows superior convergence in comparison to single-objective gradient search, while conflicting objectives indicate incomplete models or inconsistent data. In the latter case, Pareto optima provide important information for understanding the system and improving experiments. The proposed method is demonstrated with synthetic and experimental case studies of increasing complexity. All software is freely available as open source code (https://github.com/modsim/CADET-Match).

Antibodies purification from human plasma using fractionation, chromatography and gel electrophoresis assisted by multivariate analysis of complimentary absorption and fluorescence spectra

Employing simple precipitation (fractionation) using Cohn method and weak anion exchange chromatography with DEAE resin, antibodies such as Immunoglobulin G are purified from human plasma. Fractions are eluted from column in four different regions depending on washing NaCl concentrations. Absorbance and excitation-emission fluorescence spectral data are measured for separated chromatographic fractions and analyzed using Multivariate Curve Resolution- Alternating Least Squares (MCR-ALS) and Parallel Factor Analysis (PARAFAC) techniques. Resolved concentration and spectral profiles provided information about existing components in each fraction. Protein and non-protein components are distinguished considering their resolved pure spectra and information from the two applied spectroscopic techniques is complementary. A number of components displayed both fluorescence and absorbance signals. When concentration of component (protein or non-protein) in sample is low and no significant absorbance signal is observed, sensitive fluorescence is useful to recognize the component and for non-fluorescent components absorbance spectra are utilized. Electrophoresis is utilized for separation of proteins in each fraction and showed that one distinguished protein from fluorescence and/or absorbance data can be a group of proteins with similar pure spectra and retention volume. Results showed presence of two protein in the first region (IgM and IgA), a group of proteins in second region (IgM, α-globulin, and IgG), a pure protein in third region (IgG), and a group of β-globulin proteins in fifth region. It is well and clearly shown that multivariate analysis of different data sets with complementary information is necessary for better interpretation of such technically simple and biochemically complicated systems.

Development of a multi-functional concurrent assay using weak cation-exchange solid-phase extraction (WCX-SPE) and reconstitution with a diluted sample aliquot for anti-doping analysis

RATIONALE

In addition to the development of adequate screening methods for multiple compounds, the World Anti-Doping Agency (WADA) requires anti-doping laboratories to analyze prohibited substances and their metabolites from various classes. This task presents a difficult challenge for all agencies and interests involved in the field of doping control.

METHODS

A screening method is reported in which hybrid sample preparation was performed using a combination of weak cation-exchange solid-phase extraction (WCX-SPE) and the 'Dilute and Shoot' strategy in order to take advantage of both the methodologies. Target substances were extracted using a WCX cartridge and reconstituted with a diluted sample aliquot that included 20% of an untreated urine sample. The target substances were further analyzed by high-performance liquid chromatography/triple quadrupole mass spectrometry (LC/MS).

RESULTS

The SPE procedure was optimized using a cartridge-washing step, elution conditions, and elution volume. The cartridge-washing step, which was performed using 10% methanol, improved the overall recovery of target substances. Since the recovery was observed to vary according to the pH of the eluting solution, we applied an elution step using both an acid and a basic organic solvent to achieve complementary recovery. Reconstitution of the diluted aliquot sample was performed to recover the polar substances.

CONCLUSIONS

The method was validated and applied to real samples in accordance with the external quality assessment scheme of WADA and to the previously reported samples that had provided positive test results. This novel method using hybrid sample preparation and LC/MS could be useful to screen multiple classes of the 264 targeted substances in anti-doping analysis.

Impact of ligand density on the optimization of ion-exchange membrane chromatography for viral vector purification

The effect of ligand density on anion-exchange membrane chromatography (AEXmc) for the purification of recombinant baculoviruses (rBVs), potential viral vectors in clinical applications, is studied by surface plasmon resonance on customized AEX surfaces and gradient elution experiments on Sartobind D membrane prototypes with different diethylamine ligand densities, complemented by dynamic light scattering analysis for estimation of the hydrodynamic particle size of the various biologics. A chromatographic-column model based on the steric mass action model of ion exchange is employed to analyze the gradient-elution AEXmc experiments, extrapolate the results to other operating conditions, and provide directions for process improvement. Although counterintuitively, the experimental evidence provided in this study shows that the lowering of ligand density is beneficial for rBV purification by AEXmc in bind-and-elute-mode, because it decreases the residual concentrations of host cell protein, dsDNA, and non-infective rBVs in the eluted product cut, and increases the overall yield by roughly 20% over current standard values. Overall, we present a case study on how rational design can streamline downstream process development.

2-D DIGE to expedite downstream process development for human monoclonal antibody purification

Two-dimensional fluorescence difference gel electrophoresis (2-D DIGE) is an established method for assessing protein expression strategies, understanding pathogenesis mechanisms, characterizing biomarkers, and controlling therapeutic processes. We applied 2-D DIGE to facilitate the development of a purification process for a recombinant IgG1 antibody against Rhesus D antigen expressed by Chinese hamster ovary cells. The variability of two expression clones as well as the influence of cell viability on the host-cell protein pattern was assessed quantitatively. Up to 800 different spots were identified. 2-D DIGE showed that differences in cell viability had more influence on the protein expression pattern than did the expression clone itself. After purification of the IgG from different culture supernatants, the protein patterns on 2-D DIGE were identical, indicating the validity of purification scheme.

Application of a chromatography model with linear gradient elution experimental data to the rapid scale-up in ion-exchange process chromatography of proteins

We applied the model described in our previous paper to the rapid scale-up in the ion exchange chromatography of proteins, in which linear flow velocity, column length and gradient slope were changed. We carried out linear gradient elution experiments, and obtained data for the peak salt concentration and peak width. From these data, the plate height (HETP) was calculated as a function of the mobile phase velocity and iso-resolution curve (the separation time and elution volume relationship for the same resolution) was calculated. The scale-up chromatography conditions were determined by the iso-resolution curve. The scale-up of the linear gradient elution from 5 to 100mL and 2.5L column sizes was performed both by the separation of beta-lactoglobulin A and beta-lactoglobulin B with anion-exchange chromatography and by the purification of a recombinant protein with cation-exchange chromatography. Resolution, recovery and purity were examined in order to verify the proposed method.

Model based robustness analysis of an ion-exchange chromatography step

Process development, optimization and robustness analysis for chromatographic separation are often entirely based on experimental work and generic knowledge. This paper describes a model-based approach that can be used to gain process knowledge and assist in the robustness analysis of an ion-exchange chromatography step using a model-based approach. A kinetic dispersive model, where the steric mass action model accounts for the adsorption is used to describe column performance. Model calibration is based solely on gradient elution experiments at different gradients, flow rates, pH and column loads. The position and shape of the peaks provide enough information to calibrate the model and thus single-component experiments can be avoided. The model is calibrated to the experiments and the confidence intervals for the estimated parameters are used to account for the model error throughout the analysis. The model is used to predict the result of a robustness analysis conducted as a factorial experiment and to design a robust pooling approach. The confidence intervals are used in a "worst case" approach where the parameters for the components are set at the edge of their confidence intervals to create a worst case for the removal of impurities at each point in the factorial experiment. The pooling limit was changed to ensure product quality at every point in the factorial analysis. The predicted purities and yields were compared to the experimental results to ensure that the prediction intervals cover the experimental results.

Manufacturing of recombinant therapeutic proteins in microbial systems

Recombinant therapeutic proteins have gained enormous importance for clinical applications. The first recombinant products have been produced in E. coli more than 20 years ago. Although with the advent of antibody-based therapeutics mammalian expression systems have experienced a major boost, microbial expression systems continue to be widely used in industry. Their intrinsic advantages, such as rapid growth, high yields and ease of manipulation, make them the premier choice for expression of non-glycosylated peptides and proteins. Innovative product classes such as antibody fragments or alternative binding molecules will further expand the use of microbial systems. Even more, novel, engineered production hosts and integrated technology platforms hold enormous potential for future applications. This review summarizes current applications and trends for development, production and analytical characterization of recombinant therapeutic proteins in microbial systems.

Optimisation and robustness analysis of a hydrophobic interaction chromatography step

Process development, optimisation and robustness analysis for chromatography separations are often entirely based on experimental work and generic knowledge. The present study proposes a method of gaining process knowledge and assisting in the robustness analysis and optimisation of a hydrophobic interaction chromatography step using a model-based approach. Factorial experimental design is common practice in industry today for robustness analysis. The method presented in this study can be used to find the critical parameter variations and serve as a basis for reducing the experimental work. In addition, the calibrated model obtained with this approach is used to find the optimal operating conditions for the chromatography column. The methodology consists of three consecutive steps. Firstly, screening experiments are performed using a factorial design. Secondly, a kinetic-dispersive model is calibrated using gradient elution and column load experiments. Finally, the model is used to find optimal operating conditions and a robustness analysis is conducted at the optimal point. The process studied in this work is the separation of polyclonal IgG from BSA using hydrophobic interaction chromatography.

Generic method for systematic phase selection and method development of biochromatographic processes

Even if the first protein therapeutics are now for more than 20 years on the market the selection of suitable adsorbents for the preparative downstream processing (DSP) of these biomolecules as well as the method development towards process conditions are still based mainly on 'trial and error'. Therefore, theses processes are not perfectly efficient, but indeed very time consuming and laborious. In this study a novel systematic method is introduced to find a suitable adsorbent (not necessarily the best one) with appropriate separation parameters for a specific separation with reduced effort. Following this strategy, the adsorbents must first be packed into columns under preparative conditions and then characterized completely with regard to, e.g. pressure drop, k'-values, plate heights (HETP curves), selectivity and capacity by using test substances, which are similar in their characteristics (molecular mass, size, charge distribution, hydrophobicity) to the target proteins. With the database once determined, a preselection of most suitable adsorbents including separation parameters is made regarding chromatographic and also economical properties. After this, preparative experiments must be conducted with a reduced number of adsorbents to figure out the individual influence of side components. This approach is demonstrated for the separation of an exemplary industrial protein mixture using cation-exchange chromatography (CEX). Characterization of different weak CEX-adsorbents is illustrated. After comparing these phases with each other, a first preselection and a prediction of suitable adsorbents is made. In the following preparative separation conditions (load, velocity, gradient) are determined for the preparative separations using the database and results of some additional experiments. The final comparison of separation performance in preparative scale confirms this selection and so the applicability of the new method.

Using computer simulation to assist in the robustness analysis of an ion-exchange chromatography step

This paper presents a methodology to gain process knowledge and assist in the robustness analysis of an ion-exchange step in a protein purification process using a model-based approach. Factorial experimental design is common practice in industry today to obtain robustness characterization of unit operations with respect to variations in process parameters. This work aims at providing a better insight into what process variations affect quality and to further reduce the experimental work to the regions of process variation that are of most interest. This methodology also greatly increases the ability to predict process performance and promotes process understanding. The model calibration part of the methodology involves three consecutive steps to calibrate a steric mass action (SMA) ion-exchange chromatography model. Firstly, a number of gradient elution experiments are performed. Secondly, experimental breakthrough curves have to be generated for the proteins if the adsorption capacity of the medium for each component is not known. Thirdly, a multi-component loading experiment is performed to calibrate the multi-component effects that cannot be determined from the single-component experiments. The separation process studied in this work is the separation of polyclonal IgG from a mixture containing IgG, myoglobin and BSA. The calibrated model is used to simulate six process variations in a full factorial experiment. The results of the simulations provide information about the importance of the different process variations and the simulations are also used to determine the crucial points for the process parameter variations. The methodology can be used to assist in the robustness analysis normally performed in the pharmaceutical industry today as it is able to predict the impact on process performance resulting from variations in salt concentration, column load, protein concentration and flow rate.

Model-based optimization of a preparative ion-exchange step for antibody purification

A method using a model-based approach to design and optimize an ion-exchange step in a protein purification process is proposed for the separation of IgG from a mixture containing IgG, BSA and myoglobin. The method consists of three steps. In the first step, the model is calibrated against carefully designed experiments. The chromatographic model describes the convective and dispersive flow in the column, the diffusion in the adsorbent particles, and the protein adsorption using Langmuir kinetics with mobile phase modulators (MPM). In the second step, the model is validated against a validation experiment and analyzed. In the third and final step, the operating conditions are optimized. In the optimization step, the loading volume and the elution gradient are optimized with regard to the most important costs: the fixed costs and the feed cost. The optimization is achieved by maximizing the objective functions productivity (i.e. the production rate for a given amount of stationary phase) and product yield (i.e. the fraction of IgG recovered in the product stream). All optimization is conducted under the constraint of 99% purity of the IgG. The model calibration and the analysis show that this purification step is determined mainly by the kinetics, although as large a protein as IgG is used in the study. The two different optima resulting from this study are a productivity of 2.7 g IgG/(s m3) stationary phase and a yield of 90%. This model-based approach also gives information of the robustness of the chosen operating conditions. It is shown that the bead diameter could only be increased from 15 microm to 35 microm with maximum productivity and a 99% purity constraint due to increased diffusion hindrance in larger beads.