Simulation model for overloaded monoclonal antibody variants separations in ion-exchange chromatography

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.

Metal-Chelating Peptides Separation Using Immobilized Metal Ion Affinity Chromatography: Experimental Methodology and Simulation

Metal-Chelating Peptides (MCPs), obtained from protein hydrolysates, present various applications in the field of nutrition, pharmacy, cosmetic etc. The separation of MCPs from hydrolysates mixture is challenging, yet, techniques based on peptide-metal ion interactions such as Immobilized Metal Ion Affinity Chromatography (IMAC) seem to be efficient. However, separation processes are time consuming and expensive, therefore separation prediction using chromatography modelling and simulation should be necessary. Meanwhile, the obtention of sorption isotherm for chromatography modelling is a crucial step. Thus, Surface Plasmon Resonance (SPR), a biosensor method efficient to screen MCPs in hydrolysates and with similarities to IMAC might be a good option to acquire sorption isotherm. This review highlights IMAC experimental methodology to separate MCPs and how, IMAC chromatography can be modelled using transport dispersive model and input data obtained from SPR for peptides separation simulation.

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.

A First Approach towards Adsorption-Oriented Physics-Informed Neural Networks: Monoclonal Antibody Adsorption Performance on an Ion-Exchange Column as a Case Study

Adsorption systems are characterized by challenging behavior to simulate any numerical method. A novel field of study emerged within the numerical method in the last two years: the physics-informed neural network (PINNs), the application of artificial intelligence to solve partial differential equations. This is a complete new standpoint for solving engineering first-principle models, which up to that date was not explored in the field of adsorption systems. Therefore, this work proposed the evaluation of PINN to address the numerical solutions of a fixed-bed column where a monoclonal antibody is purified. The PINNs solution is compared with a traditional numerical method. The results show the accuracy of the proposed PINNs when compared with the numerical method. This points towards the potential of this technique to address complex numerical problems found in chemical engineering.

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.

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.

Analysis of complex protein elution behavior in preparative ion exchange processes using a colloidal particle adsorption model

A fundamental understanding of the protein retention mechanism in preparative ion exchange (IEX) chromatography columns is essential for a model-based process development approach. For the past three decades, the mechanistic description of protein retention has been based predominantly on the steric mass action (SMA) model. In recent years, however, retention profiles of proteins have been reported more frequently for preparative processes that are not consistent with the mechanistic understanding relying on the SMA model. In this work, complex elution behavior of proteins in preparative IEX processes is analyzed using a colloidal particle adsorption (CPA) model. The CPA model is found to be capable of reproducing elution profiles that cannot be described by the traditional SMA model. According to the CPA model, the reported complex behavior can be ascribed to a strong compression and concentration of the elution front in the lower unsaturated part of the chromatography column. As the unsaturated part of the column decreases with increasing protein load density, exceeding a critical load density can lead to the formation of a shoulder in the peak front. The general applicability of the model in describing preparative IEX processes is demonstrated using several industrial case studies including multiple monoclonal antibodies on different IEX adsorber systems. In this context, the work covers both salt controlled and pH-controlled protein elution.

Mechanistic Modeling of Preparative Column Chromatography for Biotherapeutics

Chromatography has long been, and remains, the workhorse of downstream processing in the production of biopharmaceuticals. As bioprocessing has matured, there has been a growing trend toward seeking a detailed fundamental understanding of the relevant unit operations, which for some operations include the use of mechanistic modeling in a way similar to its use in the conventional chemical process industries. Mechanistic models of chromatography have been developed for almost a century, but although the essential features are generally understood, the specialization of such models to biopharmaceutical processing includes several areas that require further elucidation. This review outlines the overall approaches used in such modeling and emphasizes current needs, specifically in the context of typical uses of such models; these include selection and improvement of isotherm models and methods to estimate isotherm and transport parameters independently. Further insights are likely to be aided by molecular-level modeling, as well as by the copious amounts of empirical data available for existing processes.

Highland games: A benchmarking exercise in predicting biophysical and drug properties of monoclonal antibodies from amino acid sequences

Biopharmaceutical product and process development do not yet take advantage of predictive computational modeling to nearly the degree seen in industries based on smaller molecules. To assess and advance progress in this area, spirited coopetition (mutually beneficial collaboration between competitors) was successfully used to motivate industrial scientists to develop, share, and compare data and methods which would normally have remained confidential. The first "Highland Games" competition was held in conjunction with the October 2018 Recovery of Biological Products Conference in Ashville, NC, with the goal of benchmarking and assessment of the ability to predict development-related properties of six antibodies from their amino acid sequences alone. Predictions included purification-influencing properties such as isoelectric point and protein A elution pH, and biophysical properties such as stability and viscosity at very high concentrations. Essential contributions were made by a large variety of individuals, including companies which consented to provide antibody amino acid sequences and test materials, volunteers who undertook the preparation and experimental characterization of these materials, and prediction teams who attempted to predict antibody properties from sequence alone. Best practices were identified and shared, and areas in which the community excels at making predictions were identified, as well as areas presenting opportunities for considerable improvement. Predictions of isoelectric point and protein A elution pH were especially good with all-prediction average errors of 0.2 and 1.6 pH unit, respectively, while predictions of some other properties were notably less good. This manuscript presents the events, methods, and results of the competition, and can serve as a tutorial and as a reference for in-house benchmarking by others. Organizations vary in their policies concerning disclosure of methods, but most managements were very cooperative with the Highland Games exercise, and considerable insight into common and best practices is available from the contributed methods. The accumulated data set will serve as a benchmarking tool for further development of in silico prediction tools.

Downstream Processing Technologies/Capturing and Final Purification : Opportunities for Innovation, Change, and Improvement. A Review of Downstream Processing Developments in Protein Purification

Increased pressure on upstream processes to maximize productivity has been crowned with great success, although at the cost of shifting the bottleneck to purification. As drivers were economical, focus is on now on debottlenecking downstream processes as the main drivers of high manufacturing cost. Devising a holistically efficient and economical process remains a key challenge. Traditional and emerging protein purification strategies with particular emphasis on methodologies implemented for the production of recombinant proteins of biopharmaceutical importance are reviewed. The breadth of innovation is addressed, as well as the challenges the industry faces today, with an eye to remaining impartial, fair, and balanced. In addition, the scope encompasses both chromatographic and non-chromatographic separations directed at the purification of proteins, with a strong emphasis on antibodies. Complete solutions such as integrated USP/DSP strategies (i.e., continuous processing) are discussed as well as gains in data quantity and quality arising from automation and high-throughput screening (HTS). Best practices and advantages through design of experiments (DOE) to access a complex design space such as multi-modal chromatography are reviewed with an outlook on potential future trends. A discussion of single-use technology, its impact and opportunities for further growth, and the exciting developments in modeling and simulation of DSP rounds out the overview. Lastly, emerging trends such as 3D printing and nanotechnology are covered. Graphical Abstract Workflow of high-throughput screening, design of experiments, and high-throughput analytics to understand design space and design space boundaries quickly. (Reproduced with permission from Gregory Barker, Process Development, Bristol-Myers Squibb).

Model-based monitoring of industrial reversed phase chromatography to predict insulin variants

Downstream processing in the manufacturing biopharmaceutical industry is a multistep process separating the desired product from process- and product-related impurities. However, removing product-related impurities, such as product variants, without compromising the product yield or prolonging the process time due to extensive quality control analytics, remains a major challenge. Here, we show how mechanistic model-based monitoring, based on analytical quality control data, can predict product variants by modeling their chromatographic separation during product polishing with reversed phase chromatography. The system was described by a kinetic dispersive model with a modified Langmuir isotherm. Solely quality control analytical data on product and product variant concentrations were used to calibrate the model. This model-based monitoring approach was developed for an insulin purification process. Industrial materials were used in the separation of insulin and two insulin variants, one eluting at the product peak front and one eluting at the product peak tail. The model, fitted to analytical data, used one component to simulate each protein, or two components when a peak displayed a shoulder. This monitoring approach allowed the prediction of the elution patterns of insulin and both insulin variants. The results indicate the potential of using model-based monitoring in downstream polishing at industrial scale to take pooling decisions.

Protein adsorption in polyelectrolyte brush type cation-exchangers

Ion exchange chromatography materials functionalized with polyelectrolyte brushes (PEB) are becoming an integral part of many protein purification steps. Adsorption onto these materials is different than that onto traditional materials, due to the 3D partitioning of proteins into the polyelectrolyte brushes. Despite this mechanistic difference, many works have described the chromatographic behavior of proteins on polyelectrolyte brush type ion exchangers with much of the same methods as used for traditional materials. In this work, unconventional chromatographic behavior on polyelectrolyte brush type materials is observed for several proteins: the peaks shapes reveal first anti-Langmuirian and then Langmuirian types of interactions, with increasing injection volumes. An experimental and model based description of these materials is carried out in order to explain this behavior. The reason for this behavior is shown to be the 3D partitioning of proteins into the polyelectrolyte brushes: proteins that fully and readily utilize the 3D structure of the PEB phase during adsorption show this behavior, whereas those that do not show traditional ion exchange behavior.

Charge Variants of an Avastin Biosimilar Isolation, Characterization, In Vitro Properties and Pharmacokinetics in Rat

The similarity between a proposed biosimilar product and the reference product can be affected by many factors. This study is designed to examine whether any subtle difference in the distribution of the charge variants of an Avastin biosimilar can affect its in vitro potency and in vivo PK. Here, the acidic, basic and main peak fractions of a biosimilar product were isolated using high-performance cation-exchange chromatography and were subjected to various studies to compare their in vitro properties and in vivo PK profile. A serial of analytical methods, including size exclusion chromatography (SEC), imaged capillary isoelectric focusing (icIEF) capillary zone electrophoresis (CZE) and cation-exchange chromatography (CEX-HPLC) were also used to characterize the isolated charge variants. The kinetics constant was measured using a Biacore X100 system. The study indicates the biosimilar product has a high similarity with avastin in physicochemical properties. The potency in vitro and PK profile in rat of charge variants and biosimilar product are consistent with avastin.

Model-based description of peptide retention on doped reversed-phase media

Reversed-phase (RP) chromatography is one of the main tools for the preparative purification of therapeutic peptides. In previous works [1,2], a new type of RP chromatography, doped reversed-phase chromatography (DRP) was presented. By adding small amounts (up to 15% of the surface ligands) of repulsive ion exchange ligands to a traditional RP material, significant improvements in peptide purification performance were observed, at the same or in similar operating conditions. These improvements included increases in selectivity in diluted conditions (up to twice as high), increases in yield in preparative conditions (up to 20% higher) and in productivity in preparative conditions (up to twice as high), when compared to RP materials [2]. A proper physical model is developed in this work to quantitatively explain and rationalize this behavior. The developed model is then used to correctly fit the retention data of several peptides in different buffering conditions. The increase in selectivity is related to a controlled decrease in free surface area available for adsorption due to the ionic ligands creating a repulsive sphere the analytes cannot enter. This decrease in adsorption surface is calculated using Debye-Hückel theory, and in combination with linear solvent strength theory, allows for the quantitative description of peptide retention on DRP media.

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.

Doping reversed-phase media for improved peptide purification

The purification of therapeutic peptides is most often performed using one or more reversed phase chromatography steps. This ensures high purities while keeping the costs of purification under control. In this paper, a doped reversed phase chromatographic material is tested and compared to traditional reversed phase materials. The doping consists of adding limited amounts of ion exchange ligands to the surface of the material to achieve orthogonal separation and increase the non-hydrophobic interactions with the surface. These ionic groups can either be attractive (opposite charge), or repulsive (same charge) to the peptide. The benefit of this new doped reversed phase material is shown through increases in selectivity in diluted conditions and yield and productivity in overloaded (i.e. industrial) conditions. It is the conjectured that all performance characteristics should increase using repulsive doping groups, whereas these characteristics should decrease when using attractive doping groups. This conjecture is shown to be true through several examples, including purifications of industrially relevant peptide crudes, in industrially relevant conditions. Moreover, the effect of ionic strength and organic modifier concentration was explored and shown to be in line with the expected behavior.