Purifying biopharmaceuticals: knowledge-based chromatographic process development

Clear insight into complex multimodal resins and impurities to overcome recombinant protein purification challenges: A review

Increasing attention has been paid to the purity of therapeutic proteins imposing extensive costs and challenges to the downstream processing of biopharmaceuticals. One of the efforts, that has been exerted to overcome such limitations, was developing multimodal or mixed-mode chromatography (MMC) resins for launching selective, orthogonal, non-affinity purification platforms. Despite relatively extensive usage of MMC resins, their real potential and fulfillment have not been extensively reviewed yet. In this work, the explanation of practical and key aspects of downstream processing of recombinant proteins with or without MMC resins was debated, as being useful for further purification process development. This review has been written as a step-by-step guide to deconvolute both inherent protein purification and MMC complexities. Here, after complete elucidation of the potential of MMC resins, the effects of frequently used additives (mobile phase modifiers) and their possible interactions during the purification process, the critical characteristics of common product-related impurities (e.g., aggregates, charge variants, fragments), host-related impurities (e.g., host cell protein and DNA) and process related impurities (e.g., endotoxin, and viruses) with solved or unsolved challenges of traditional and MMC resins have been discussed. Such collective experiences which are reported in this study could be considered as an applied guide for developing successful downstream processing in challenging conditions by providing a clear insight into complex MMC resins and impurities.

Experimental characterization and prediction of Escherichia coli host cell proteome retention during preparative chromatography

Purification of recombinantly produced biopharmaceuticals involves removal of host cell material, such as host cell proteins (HCPs). For lysates of the common expression host Escherichia coli (E. coli) over 1500 unique proteins can be identified. Currently, understanding the behavior of individual HCPs for purification operations, such as preparative chromatography, is limited. Therefore, we aim to elucidate the elution behavior of individual HCPs from E. coli strain BLR(DE3) during chromatography. Understanding this complex mixture and knowing the chromatographic behavior of each individual HCP improves the ability for rational purification process design. Specifically, linear gradient experiments were performed using ion exchange (IEX) and hydrophobic interaction chromatography, coupled with mass spectrometry-based proteomics to map the retention of individual HCPs. We combined knowledge of protein location, function, and interaction available in literature to identify trends in elution behavior. Additionally, quantitative structure-property relationship models were trained relating the protein 3D structure to elution behavior during IEX. For the complete data set a model with a cross-validated R2 of 0.55 was constructed, that could be improved to a R2 of 0.70 by considering only monomeric proteins. Ultimately this study is a significant step toward greater process understanding.

Proteomics-based method to comprehensively model the removal of host cell protein impurities

Mechanistic models mostly focus on the target protein and some selected process- or product-related impurities. For a better process understanding, however, it is advantageous to describe also reoccurring host cell protein impurities. Within the purification of biopharmaceuticals, the binding of host cell proteins to a chromatographic resin is far from being described comprehensively. For a broader coverage of the binding characteristics, large-scale proteomic data and systems level knowledge on protein interactions are key. However, a method for determining binding parameters of the entire host cell proteome to selected chromatography resins is still lacking. In this work, we have developed a method to determine binding parameters of all detected individual host cell proteins in an Escherichia coli harvest sample from large-scale proteomics experiments. The developed method was demonstrated to model abundant and problematic proteins, which are crucial impurities to be removed. For these 15 proteins covering varying concentration ranges, the model predicts the independently measured retention time during the validation gradient well. Finally, we optimized the anion exchange chromatography capture step in silico using the determined isotherm parameters of the persistent host cell protein contaminants. From these results, strategies can be developed to separate abundant and problematic impurities from the target antigen.

In silico mediated workflow for rapid development of downstream processing: Orthogonal product-related impurity removal for a Fc-containing therapeutic

Therapeutic formats derived from the monoclonal antibody structure have been gaining significant traction in the biopharmaceutical market. Being structurally similar to mAbs, most Fc-containing therapeutics exhibit product-related impurities in the form of aggregates, charge variants, fragments, and glycoforms, which are inherently challenging to remove. In this work, we developed a workflow that employed rapid resin screening in conjunction with an in silico tool to identify and rank orthogonally selective processes for the removal of product-related impurities from a Fc-containing therapeutic product. Linear salt gradient screens were performed at various pH conditions on a set of ion-exchange, multimodal ion-exchange, and hydrophobic interaction resins. Select fractions from the screening experiments were analyzed by three different analytical techniques to characterize aggregates, charge variants, fragments, and glycoforms. The retention database generated by the resin screens and subsequent impurity characterization were then processed by an in silico tool that generated and ranked all possible two-step resin sequences for the removal of product-related impurities. A highly-ranked process was then evaluated and refined at the bench-scale to develop a completely flowthrough two-step polishing process which resulted in complete removal of the Man5 glycoform and aggregate impurities with a 73% overall yield. The successful implementation of the in silico mediated workflow suggests the possibility of a platformable workflow that could facilitate polishing process development for a wide variety of mAb-based therapeutics.

From protein structure to an optimized chromatographic capture step using multiscale modeling

Optimizing a biopharmaceutical chromatographic purification process is currently the greatest challenge during process development. A lack of process understanding calls for extensive experimental efforts in pursuit of an optimal process. In silico techniques, such as mechanistic or data driven modeling, enhance the understanding, allowing more cost-effective and time efficient process optimization. This work presents a modeling strategy integrating quantitative structure property relationship (QSPR) models and chromatographic mechanistic models (MM) to optimize a cation exchange (CEX) capture step, limiting experiments. In QSPR, structural characteristics obtained from the protein structure are used to describe physicochemical behavior. This QSPR information can be applied in MM to predict the chromatogram and optimize the entire process. To validate this approach, retention profiles of six proteins were determined experimentally from mixtures, at different pH (3.5, 4.3, 5.0, and 7.0). Four proteins at different pH's were used to train QSPR models predicting the retention volumes and characteristic charge, subsequently the equilibrium constant was determined. For an unseen protein knowing only the protein structure, the retention peak difference between the modeled and experimental peaks was 0.2% relative to the gradient length (60 column volume). Next, the CEX capture step was optimized, demonstrating a consistent result in both the experimental and QSPR-based methods. The impact of model parameter confidence on the final optimization revealed two viable process conditions, one of which is similar to the optimization achieved using experimentally obtained parameters. The multiscale modeling approach reduces the required experimental effort by identification of initial process conditions, which can be optimized.

Large-scale purification of a deprotected macrocyclic peptide by supercritical fluid chromatography (SFC) integrated with liquid chromatography in discovery chemistry

A macrocyclic peptide A was successfully purified in large quantities (∼30 g) in >95 % purity by an integrated two-step orthogonal purification process combining supercritical fluid chromatography (SFC) with medium-pressure reverse-phase liquid chromatography (MP-RPLC). MP-RPLC was used to fractionate the crude peptide A, remove unwanted trifluoroacetic acid (TFA) originating from the peptide A cleavage off the resin, and convert the peptide A into ammonium acetate salt form, prior to the final purification by SFC. A co-solvent of methanol/acetonitrile containing ammonium acetate and water in CO2 was developed on a Waters BEH 2-Ethylpyridine column. The developed SFC method was readily scaled up onto a 5 cm diameter column to process multi-gram quantities of the MP-RPLC fraction to reach > 95 % purity with a throughput/productivity of 0.96 g/h. The incorporation of SFC with MP-RPLC has been demonstrated to have a broader application in other large-scale polypeptide purifications.

DOE-based process optimization for development of efficient methods for purification of recombinant hepatitis B surface antigen from Pichia pastoris feedstock using Capto adhere resin

Background

The multimodal chromatography resins, such as Capto adhere, are considered good candidates to be utilized in downstream processing due to their high capacity and selectivity; however, their multimodal interactions lead to an intricacy in the adsorption-desorption patterns and systematic characterization of conditions for process steps is necessary.

Methods

Capto adhere, a strong ion exchanger with multimodal functionality, was used in this study for the final aim of recombinant hepatitis B surface antigen (rHBsAg) purification from Pichia pastoris (P. pastoris) industrial feedstock. Optimization of various parameters was done using the design of experiments (DOE) approach to determine the best binding and non-binding conditions.

Results

Maximum rHBsAg binding on Capto adhere occurred in 20 mM sodium acetate, pH 4.5, and a binding capacity of about 0.75 mg/ml was achieved, which was much higher than rHBsAg binding capacity of other resins reported so far. In elution optimization investigations, it was revealed that 1 M arginine (buffered in 50 mM sodium phosphate, pH 6.5) was the most efficient eluting agent. The binding and elution optimal conditions were utilized for further purification of rHBsAg from P. pastoris industrial feedstock in bind-elute mode, and the recovery and purity of the obtained rHBsAg were about 60% and 100%, respectively. Following optimization in the flow-through purification mode, the target protein recovery was significantly increased (up to 97%) and the target protein purity of more than 95% was achievable. SEC-HPLC analysis showed that the obtained retention times for the purified rHBsAg were similar to those reported previously.

Conclusions

These results suggest that Capto adhere under such optimized conditions can be considered as a good candidate for efficient purification of rHBsAg from P. pastoris industrial feedstock in downstream processing.

Status and future developments for downstream processing of biological products: Perspectives from the Recovery XIX yield roundtable discussions

Governments and biopharmaceutical organizations aggressively leveraged expeditious communication capabilities, decision models, and global strategies to make a COVID-19 vaccine happen within a period of 12 months. This was an unusual effort and cannot be transferred to normal times. However, this focus on a single vaccine has also led to other treatments and drug developments being sidelined. Society expects the pharmaceutical industry to provide an uninterrupted supply of medicines. However, it is often overlooked how complex the manufacture of these compounds is and what logistics are required, not to mention the time needed to develop new drugs. The overarching theme, therefore, is patient access and how we can help ensure access and extend it to low- and middle-income countries. Despite unceasing efforts to make medications available to all patient populations, this must never be done at the expense of patient safety. A major fraction of the costs in biopharmaceutical manufacturing are for drug discovery, process development, and clinical studies. Infrastructure costs are very difficult to quantify because they often depend on whether a greenfield facility or an existing, depreciated facility is used or adapted for a new product. To accelerate process development concepts of platform process and prior knowledge are increasingly leveraged. While more traditional protein therapeutics continue to dominate the field, we are also experiencing the exciting emergence and evolution of other therapeutic formats (bispecifics, tetravalent mAbs, antibody-drug conjugates, enzymes, peptides, etc.) that offer unique treatment options for patients. Protein modalities are still dominant, but new modalities are being developed that can be learned from including advanced therapeutics-like cell and gene therapies. The industry must develop a model-based strategy for process development and technologies such as continuous integrated biomanufacturing must be adopted. The overall conclusion is that the pandemic pace was unsustainable, focused on vaccine delivery at the expense of other modalities/disease targets, and had implications for professional and personal life (work-life balance). Routinely reducing development time from 10 years to 1 year is nearly impossible to achieve. Environmental aspects of sustainable downstream processing are also described.

Use of Transient Transfection for cGMP Manufacturing of eOD-GT8 60mer, a Self-Assembling Nanoparticle Germline-Targeting HIV-1 Vaccine Candidate

We describe the current Good Manufacturing Practice (cGMP) production and subsequent characterization of eOD-GT8 60mer, a glycosylated self-assembling nanoparticle HIV-1 vaccine candidate and germline targeting priming immunogen. Production was carried out via transient expression in the human embryonic kidney 293 (HEK293) cell line followed by a combination of purification techniques. A large-scale cGMP (200 L) production run yielded 354 mg of the purified eOD-GT8 60mer drug product material, which was formulated at 1 mg/mL in 10% sucrose in phosphate-buffered saline (PBS) at pH 7.2. The clinical trial material was comprehensively characterized for purity, antigenicity, glycan composition, amino acid sequence, and aggregation and by several safety-related tests during cGMP lot release. A comparison of the purified products produced at the 1 L scale and 200 L cGMP scale demonstrated the consistency and robustness of the transient transfection upstream process and the downstream purification strategies. The cGMP clinical trial material was tested in a Phase 1 clinical trial (NCT03547245), is currently being stored at -80 °C, and is on a stability testing program as per regulatory guidelines. The methods described here illustrate the utility of transient transfection for cGMP production of complex products such as glycosylated self-assembling nanoparticles.

Therapeutic proteins: developments, progress, challenges, and future perspectives

Proteins are considered magic molecules due to their enormous applications in the health sector. Over the past few decades, therapeutic proteins have emerged as a promising treatment option for various diseases, particularly cancer, cardiovascular disease, diabetes, and others. The formulation of protein-based therapies is a major area of research, however, a few factors still hinder the large-scale production of these therapeutic products, such as stability, heterogenicity, immunogenicity, high cost of production, etc. This review provides comprehensive information on various sources and production of therapeutic proteins. The review also summarizes the challenges currently faced by scientists while developing protein-based therapeutics, along with possible solutions. It can be concluded that these proteins can be used in combination with small molecular drugs to give synergistic benefits in the future.

Design and characterization of an electrochemically-modulated membrane chromatography device

Membrane separations offer a compelling alternative to traditional chromatographic methods by overcoming mass transport limitations. We introduce an additional degree of freedom in modulating membrane chromatography by using metalized membranes in a potential-driven process. Investigating the impact of a gold coating on membrane characteristics, the sputtered gold layer enhances the surface conductivity with stable electrochemical behavior. However, this comes at the expense of reduced permeability, wettability, and static binding capacity (∼ 474 µg g-1 of maleic acid). The designed device displayed a homogenous flow distribution, and the membrane electrodes exhibit predominantly capacitive behavior during potential application. Modulating the electrical potential during the adsorption and desorption phase strongly influenced the binding and elution behavior of anion-exchange membranes. Switching potentials between ±1.0 V vs. Ag/AgCl induces desorption, confirming the process principle. Elution efficiency reaches up to 58 % at -1.0 V vs. Ag/AgCl in the desorption phase without any alteration of the mobile phase. Increasing the potential perturbation ranging from +1.0 V to -1.0 V vs. Ag/AgCl resulted in reduced peak width and improved elution behavior, demonstrating the feasibility of electrochemically-modulated membrane chromatography. The developed process has great potential as a gentle and sustainable separation step in the biotechnological and chemical industry.

Predicting multimodal chromatography of therapeutic antibodies using multiscale modeling

Multimodal chromatography has emerged as a powerful method for the purification of therapeutic antibodies. However, process development of this separation technique remains challenging because of an intricate and molecule-specific interaction towards multimodal ligands, leading to time-consuming and costly experimental optimization. This study presents a multiscale modeling approach to predict the multimodal chromatographic behavior of therapeutic antibodies based on their sequence information. Linear gradient elution (LGE) experiments were performed on an anionic multimodal resin for 59 full-length antibodies, including five different antibody formats at pH 5.0, 6.0, and 7.0 that were used for parameter determination of a linear adsorption model at low loading density conditions. Quantitative structure-property relationship (QSPR) modeling was utilized to correlate the adsorption parameters with up to 1374 global and local physicochemical descriptors calculated from antibody homology models. The final QSPR models employed less than eight descriptors per model and demonstrated high training accuracy (R² > 0.93) and reasonable test set prediction accuracy (Q² > 0.83) for the adsorption parameters. Model evaluation revealed the significance of electrostatic interaction and hydrophobicity in determining the chromatographic behavior of antibodies, as well as the importance of the HFR3 region in antibody binding to the multimodal resin. Chromatographic simulations using the predicted adsorption parameters showed good agreement with the experimental data for the vast majority of antibodies not employed during the model training. The results of this study demonstrate the potential of sequence-based prediction for determining chromatographic behavior in therapeutic antibody purification. This approach leads to more efficient and cost-effective process development, providing a valuable tool for the biopharmaceutical industry.

Simultaneous Purification of Human Interferon Alpha-2b and Serum Albumin Using Bioprivileged Fluorinated Ionic Liquid-Based Aqueous Biphasic Systems

Interferon alpha-2b (IFN-α2b) is an essential cytokine widely used in the treatment of chronic hepatitis C and hairy cell leukemia, and serum albumin is the most abundant plasma protein with numerous physiological functions. Effective single-step aqueous biphasic system (ABS) extraction for the simultaneous purification of IFN-α2b and BSA (serum albumin protein) was developed in this work. Effects of the ionic liquid (IL)-based ABS functionalization, fluorinated ILs (FILs; [C​2C​1Im][C​4F​9SO​3] and [N​1112(OH)][C​4F​9SO​3]) vs. mere fluoro-containing IL ([C​4C​1Im][CF​3SO​3]), in combination with sucrose or [N​1112(OH)][H​2PO​4] (well-known globular protein stabilizers), or high-charge-density salt K​3PO​4 were investigated. The effects of phase pH, phase water content (%wt), phase composition (%wt), and phase volume ratio were investigated. The phase pH was found to have a significant effect on IFN-α2b and BSA partition. Experimental results show that simultaneous single-step purification was achieved with a high yield (extraction efficiency up to 100%) for both proteins and a purification factor of IFN-α2b high in the enriched IFN-α2b phase (up to 23.22) and low in the BSA-enriched phase (down to 0.00). SDS-PAGE analysis confirmed the purity of both recovered proteins. The stability and structure of IFN-α2b and BSA were preserved or even improved (FIL-rich phase) during the purification step, as evaluated by CD spectroscopy and DSC. Binding studies of IFN-α2b and BSA with the ABS phase-forming components were assessed by MST, showing the strong interaction between FILs aggregates and both proteins. In view of their biocompatibility, customizable properties, and selectivity, FIL-based ABSs are suggested as an improved purification step that could facilitate the development of biologics.

Digital twin in high throughput chromatographic process development for monoclonal antibodies

The monoclonal antibody (mAb) industry is becoming increasingly digitalized. Digital twins are becoming increasingly important to test or validate processes before manufacturing. High-Throughput Process Development (HTPD) has been progressively used as a tool for process development and innovation. The combination of High-Throughput Screening with fast computational methods allows to study processes in-silico in a fast and efficient manner. This paper presents a hybrid approach for HTPD where equal importance is given to experimental, computational and decision-making stages. Equilibrium adsorption isotherms of 13 protein A and 16 Cation-Exchange resins were determined with pure mAb. The influence of other components in the clarified cell culture supernatant (harvest) has been under-investigated. This work contributes with a methodology for the study of equilibrium adsorption of mAb in harvest to different protein A resins and compares the adsorption behavior with the pure sample experiments. Column chromatography was modelled using a Lumped Kinetic Model, with an overall mass transfer coefficient parameter (kov). The screening results showed that the harvest solution had virtually no influence on the adsorption behavior of mAb to the different protein A resins tested. kov was found to have a linear correlation with the sample feed concentration, which is in line with mass transfer theory. The hybrid approach for HTPD presented highlights the roles of the computational, experimental, and decision-making stages in process development, and how it can be implemented to develop a chromatographic process. The proposed white-box digital twin helps to accelerate chromatographic process development.

Parameter-by-parameter estimation method for adsorption isotherm in hydrophobic interaction chromatography

Hydrophobic interaction chromatography (HIC) is used as a critical polishing step in the downstream processing of biopharmaceuticals. Normally the process development of HIC is a cumbersome and time-consuming task, and the mechanical models can provide a powerful tool to characterize the process, assist process design and accelerate process development. However, the current estimation of model parameters relies on the inverse method, which lacks an efficient and logical parameter estimation strategy. In this study, a parameter-by-parameter (PbP) method based on the theoretical derivation and simplifying assumptions was proposed to estimate the Mollerup isotherm parameters for HIC. The method involves three key steps: (1) linear regression (LR) to estimate the salt-protein interaction parameter and the equilibrium constant; (2) linear approximation (LA) to estimate the stoichiometric parameter and the maximum binding capacity; and (3) inverse method to estimate the protein-protein interaction parameter and the kinetic coefficient. The results indicated that the LR step should be used for dilution condition (loading factor below 5%), while the LA step should be conducted when the isotherm is in the transition or nonlinear regions. Six numerical experiments were conducted to implement the PbP method. The results demonstrated that the PbP method developed allows for the systematic estimation of HIC parameters one-by-one, effectively reducing the number of parameters required for inverse method estimation from six to two. This helps prevent non-identifiability of structural parameters. The feasibility of the PbP-HIC method was further validated by real-world experiments. Moreover, the PbP method enhances the mechanistic understanding of adsorption behavior of HIC and shows a promising application to other stoichiometric displacement model-derived isotherms.

Fully automated minicolumn chromatography

Miniaturized chromatography columns (minicolumns) operated by automated liquid handlers are an integral part of bioprocess purification development. However, these systems can be limited in both their efficiency and accessibility. Because the minicolumn chromatography operation itself is higher throughput, the lower throughput pre- and post-operation activities become the bottleneck of the workflow. Additionally, method writing and operation of the systems while varying multiple parameters, using a design of experiments approach for example, can be error-prone and resource intensive. Here, we have developed a fully automated minicolumn chromatography system to both address these bottlenecks and improve the accessibility of these systems by allowing users to enter chromatography-relevant information through a simplified user interface. Methods have been developed to automate buffer preparation and protein solution titration leveraging modeling and integrated pH probes with feedback control. Chromatogram generation and fraction pooling has additionally been automated to improve the efficiency of post-chromatography operations. We have also demonstrated the flexibility of the system through an example run where both bind-and-elute chromatography and flowthrough chromatography experiments were performed in parallel. Additionally, all methodology and parameters to operate the system have been shared. We hope this will help interested parties improve the efficiency and accessibility of their minicolumn chromatography systems.

Effect of plasmid DNA isoforms on preparative anion exchange chromatography

Increased need for plasmid DNA (pDNA) with sizes above 10 kbp (large pDNA) in gene therapy and vaccination brings the need for its large-scale production with high purity. Chromatographic purification of large pDNA is often challenging due to low process yields and column clogging, especially using anion-exchanging columns. The goal of our investigation was to evaluate the mass balance and pDNA isoform composition at column outlet for plasmids of different sizes in combination with weak anion exchange (AEX) monolith columns of varying channel size (2, 3 and 6 µm channel size). We have proven that open circular pDNA (OC pDNA) isoform is an important driver of reduced chromatographic performance in AEX chromatography. The main reason for the behaviour is the entrapment of OC pDNA in chromatographic supports with smaller channel sizes. Entrapment of individual isoforms was characterised for porous beads and convective monolithic columns. Convective entrapment of OC pDNA isoform was confirmed on both types of stationary phases. Porous beads in addition showed a reduced recovery of supercoiled pDNA (on an 11.6 kbp plasmid) caused by diffusional entrapment within the porous structure. Use of convective AEX monoliths or membranes with channel diameter >3.5 µm has been shown to increase yields and prevent irreversible pressure build-up and column clogging during purification of plasmids at least up to 16 kbp in size.

Antibody sequence-based prediction of pH gradient elution in multimodal chromatography

Multimodal chromatography has emerged as a promising technique for antibody purification, owing to its capacity to selectively capture and separate target molecules. However, the optimization of chromatography parameters remains a challenge due to the intricate nature of protein-ligand interactions. To tackle this issue, efficient predictive tools are essential for the development and optimization of multimodal chromatography processes. In this study, we introduce a methodology that predicts the elution behavior of antibodies in multimodal chromatography based on their amino acid sequences. We analyzed a total of 64 full-length antibodies, including IgG1, IgG4, and IgG-like multispecific formats, which were eluted using linear pH gradients from pH 9.0 to 4.0 on the anionic mixed-mode resin Capto adhere. Homology models were constructed, and 1312 antibody-specific physicochemical descriptors were calculated for each molecule. Our analysis identified six key structural features of the multimodal antibody interaction, which were correlated with the elution behavior, emphasizing the antibody variable region. The results show that our methodology can predict pH gradient elution for a diverse range of antibodies and antibody formats, with a test set R² of 0.898. The developed model can inform process development by predicting initial conditions for multimodal elution, thereby reducing trial and error during process optimization. Furthermore, the model holds the potential to enable an in silico manufacturability assessment by screening target antibodies that adhere to standardized purification conditions. In conclusion, this study highlights the feasibility of using structure-based prediction to enhance antibody purification in the biopharmaceutical industry. This approach can lead to more efficient and cost-effective process development while increasing process understanding.

Characterisation of the E. coli HMS174 and BLR host cell proteome to guide purification process development

Mass-spectrometry-based proteomics is increasingly employed to monitor purification processes or to detect critical host cell proteins in the final drug substance. This approach is inherently unbiased and can be used to identify individual host cell proteins without prior knowledge. In process development for the purification of new biopharmaceuticals, such as protein subunit vaccines, a broader knowledge of the host cell proteome could promote a more rational process design. Proteomics can establish qualitative and quantitative information on the complete host cell proteome before purification (i.e., protein abundances and physicochemical properties). Such information allows for a more rational design of the purification strategy and accelerates purification process development. In this study, we present an extensive proteomic characterisation of two E. coli host cell strains widely employed in academia and industry to produce therapeutic proteins, BLR and HMS174. The established database contains the observed abundance of each identified protein, information relating to their hydrophobicity, the isoelectric point, molecular weight, and toxicity. These physicochemical properties were plotted on proteome property maps to showcase the selection of suitable purification strategies. Furthermore, sequence alignment allowed integration of subunit information and occurrences of post-translational modifications from the well-studied E. coli K12 strain.

Determining buffer conditions for downstream processing of VLP-based recombinant hepatitis B surface antigen using multimodal resins in bind-elute and flow-through purification modes

The difficulties in purification of VLP-based recombinant hepatitis B surface antigen (rHBsAg) are mainly emerged from inefficient semi-purification step plus proteins physicochemical properties and these issues make the downstream processing (DSP) very lengthy and expensive. In this study, optimization of rHBsAg (recombinantly-expressed in Pichia pastoris) DSP was performed using selection of buffering conditions in the semi-purification step. In the semi-purification optimization step, up to 73% of the protein impurities were eliminated and the utmost increase in rHBsAg purity (ca. 3.6-fold) was achieved using 20 mM sodium acetate, pH 4.5. By using rHBsAg binding and nonbinding situations obtained from the response surface plot in design of experiments (DOE), additional bind-elute and flow-through purification mode experiments were conducted and rHBsAg with high purity (near 100%) and recovery (> 83%) was achieved. Following assessment of critical quality attributes (i.e., purity, particle size distribution, host cell DNA, host cell protein, secondary structures, specific activity and relative potency), it was indicated that the characteristics of rHBsAg purified by the new DSP were similar or superior to the ones obtained from conventional DSP. The purification performance of the resin was constantly retained (97-100%) and no significant resin damage took place after 10 adsorption-elution-cleaning cycles. The new DSP developed for production of rHBsAg in this study can substitute the conventional one with granting satisfactory target protein quality, long-lasting resin efficacy, shorter and less expensive process. This process may be also employable for purification of both non-VLP- and VLP- based target proteins expressed in the yeast.

Standardized method for mechanistic modeling of multimodal anion exchange chromatography in flow through operation

Multimodal chromatography offers an increased selectivity compared to unimodal chromatographic methods and is often employed for challenging separation tasks in industrial downstream processing (DSP). Unfortunately, the implementation of multimodal polishing into a generic downstream platform can be hampered by non-robust platform conditions leading to a time and cost intensive process development. Mechanistic modeling can assist experimental process development but readily applicable and easy to calibrate multimodal chromatography models are lacking. In this work, we present a mechanistic modeling aided approach that paves the way for an accelerated development of anionic mixed-mode chromatography (MMC) for biopharmaceutical purification. A modified multimodal isotherm model was calibrated using only three chromatographic experiments and was employed in the retention prediction of four antibody formats including a Fab, a bispecific, as well as an IgG1 and IgG4 antibody subtype at pH 5.0 and 6.0. The chromatographic experiments were conducted using the anionic mixed-mode resin Capto adhere at industrial relevant process conditions to enable flow through purification. An existing multimodal isotherm model was reduced to hydrophobic interactions in the linear range of the adsorption isotherm and successfully employed in the simulation of six chromatographic experiments per molecule in concert with the transport dispersive model (TDM). The model reduction to only three parameters did prevent structural parameter non-identifiability and enabled an analytical isotherm parameter determination that was further refined by incorporation of size exclusion effects of the selected multimodal resin. During the model calibration, three linear salt gradient elution experiments were performed for each molecule followed by an isotherm parameter uncertainty assessment. Lastly, each model was validated with a set of step and isocratic elution experiments. This standardized modeling approach facilitates the implementation of multimodal chromatography as a key unit operation for the biopharmaceutical downstream platform, while increasing the mechanistic insight to the multimodal adsorption behavior of complex biologics.

Ionic-liquid-based approaches to improve biopharmaceuticals downstream processing and formulation

The emergence of biopharmaceuticals, including proteins, nucleic acids, peptides, and vaccines, revolutionized the medical field, contributing to significant advances in the prophylaxis and treatment of chronic and life-threatening diseases. However, biopharmaceuticals manufacturing involves a set of complex upstream and downstream processes, which considerably impact their cost. In particular, despite the efforts made in the last decades to improve the existing technologies, downstream processing still accounts for more than 80% of the total biopharmaceutical production cost. On the other hand, the formulation of biological products must ensure they maintain their therapeutic performance and long-term stability, while preserving their physical and chemical structure. Ionic-liquid (IL)-based approaches arose as a promise alternative, showing the potential to be used in downstream processing to provide increased purity and recovery yield, as well as excipients for the development of stable biopharmaceutical formulations. This manuscript reviews the most important progress achieved in both fields. The work developed is critically discussed and complemented with a SWOT analysis.

Prediction of peptides retention behavior in reversed-phase liquid chromatography based on their hydrophobicity

Hydrophobicity is an important physicochemical property of peptides and proteins. It is responsible for their conformational changes, stability, as well as various chemical intramolecular and intermolecular interactions. Enormous efforts have been invested to study the extent of hydrophobicity and how it could influence various biological processes, in addition to its crucial role in the separation and purification endeavor as well. Here, we have reviewed various studies that were carried out to determine the hydrophobicity starting from (i) simple amino acids solubility behavior, (ii) experimental approach that was undertaken in the reversed-phase liquid chromatography mode, and ending with (iii) some examples of more advanced computational and machine learning models.

The use of predictive models to develop chromatography-based purification processes

Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory.

Recent Advances and Future Directions in Downstream Processing of Therapeutic Antibodies

Despite the advent of many new therapies, therapeutic monoclonal antibodies remain a prominent biologics product, with a market value of billions of dollars annually. A variety of downstream processing technological advances have led to a paradigm shift in how therapeutic antibodies are developed and manufactured. A key driver of change has been the increased adoption of single-use technologies for process development and manufacturing. An early-stage developability assessment of potential lead antibodies, using both in silico and high-throughput experimental approaches, is critical to de-risk development and identify molecules amenable to manufacturing. Both statistical and mechanistic modelling approaches are being increasingly applied to downstream process development, allowing for deeper process understanding of chromatographic unit operations. Given the greater adoption of perfusion processes for antibody production, continuous and semi-continuous downstream processes are being increasingly explored as alternatives to batch processes. As part of the Quality by Design (QbD) paradigm, ever more sophisticated process analytical technologies play a key role in understanding antibody product quality in real-time. We should expect that computational prediction and modelling approaches will continue to be advanced and exploited, given the increasing sophistication and robustness of predictive methods compared to the costs, time, and resources required for experimental studies.

Membrane Chromatography and Fractionation of Proteins from Whey—A Review

Membrane chromatography (MC) is an emerging bioseparation technology combining the principles of membrane filtration and chromatography. In this process, one type of molecule is adsorbed in the stationary phase, whereas the other type of molecule is passed through the membrane pores without affecting the adsorbed molecule. In subsequent the step, the adsorbed molecule is recovered by an elution buffer with a unique ionic strength and pH. Functionalized microfiltration membranes are usually used in radial flow, axial flow, and lateral flow membrane modules in MC systems. In the MC process, the transport of a solute to a stationary phase is mainly achieved through convection and minimum pore diffusion. Therefore, mass transfer resistance and pressure drop become insignificant. Other characteristics of MC systems are a minimum clogging tendency in the stationary phase, the capability of operating with a high mobile phase flow rate, and the disposable (short term) application of stationary phase. The development and application of MC systems for the fractionation of individual proteins from whey for investigation and industrial-scale production are promising. A significant income from individual whey proteins together with the marketing of dairy foods may provide a new commercial outlook in dairy industry. In this review, information about the development of a MC system and its applications for the fractionation of individual protein from whey are presented in comprehensive manner.

An Experimental and Modeling Combined Approach in Preparative Hydrophobic Interaction Chromatography

Chromatography is a technique widely used in the purification of biopharmaceuticals, and generally consists of several chromatographic steps. In this work, Hydrophobic Interaction Chromatography (HIC) is investigated as a polishing step for the purification of therapeutic proteins. Adsorption mechanisms in hydrophobic interaction chromatography are still not completely clear and a limited amount of published data is available. In addition to new data on adsorption isotherms for some proteins (obtained both by high-throughput and frontal analysis method), and a comparison of different models proposed in the literature, two different approaches are compared in this work to investigate HIC. The predictive approach exploits an in-house code that simulates the behavior of the component in the column using the model parameters found from the fitting of experimental data. The estimation approach, on the other hand, exploits commercial software in which the model parameters are found by the fitting of a few experimental chromatograms. The two approaches are validated on some bind-elute runs: the predictive approach is very informative, but the experimental effort needed is high; the estimation approach is more effective, but the knowledge gained is lower. The second approach is also applied to an in-development industrial purification process and successfully resulted in predicting the behavior of the system, allowing for optimization with a reduction in the time and amount of sample needed.

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.

Microchromatography integrated with impedance sensor for bioprocess optimization: Experimental and numerical study of column efficiency for evaluation of scalability

In the last decade, there has been a growing interest in developing microfluidic systems as new scale-down models for accelerated and cost-effective biopharmaceutical process development. Nonetheless, the research in this field is still in its infancy and requires further investigation to simplify and accelerate the microfabrication process. In addition, integration of different label-free sensors into the microcolumn systems has utmost importance to minimize result discrepancies during the scale-up process. In this study, we developed a simple, low-cost integrated microcolumn (26 µl). Micromilling technology was employed to define the geometry and shape of microfluidic structures using poly(methylmethacrylate) (PMMA). The design of PMMA microstructure was transferred to polydimethylsiloxane (PDMS), and interdigitated planar microelectrodes (IDE) were integrated into the system. To evaluate the scalability of the developed microcolumn column, column performance was assessed and compared with a conventional 1-ml prepacked column. Computational Fluid Dynamics (CFD) studies were performed for both columns to understand the differences between theoretical and experimental results regarding retention time and peak broadening. Despite obtaining an acceptable asymmetric factor for the microcolumn (1.03 ± 0.02), the reduced plate height value was still higher than the recommended range with the value of 4.14 ± 0.18. Nevertheless, the consistency and significant improvement of microcolumn efficiency compared to previous studies provide the possibility of developing robust simulation tools for transferring acquired experimental data for larger-scale units.

Purification of Classical Swine Fever Virus E2 Subunit Vaccines Based on High Affinity Peptide Ligand

BACKGROUND

The purification of expressed proteins is the most critical part of subunit-- vaccine production. Protein-purification methods such as affinity chromatography and ion exchange still have the shortcomings of being time consuming and complicated. With the rapid development of computational molecular-simulation technology, structure-based peptide-ligand design has become feasible. Objection: We aimed to apply molecular docking for a peptide ligand designed for classical swine fever virus (CSFV) E2 purification.

METHODS

Computational-derived peptides were synthesized, and the in vitro binding interaction with E2 was investigated. The effects of purification on E2 were also evaluated.

RESULTS

The best peptide recognizing E2 was P₆, which had a sequence of KKFYWRYWEH. Based on kinetic surface plasmon resonance (SPR) analysis, the apparent affinity constant of P6 was found to be 148 nM. Importantly, P₆ showed suitable binding affinity and specificity for E2 purification from transgenic rice seeds. Evaluation of immune antibodies in mice showed that the antibody- blocking rate on day 42 after inoculation reached 86.18% and 90.68%.

CONCLUSION

The computational-designed peptide in this study has high sensitivity and selectivity and is thus useful for the purification of CSFV E2. The novel method of design provided a broad platform and powerful tool for protein-peptide screening, as well as new insights into CSFV vaccine design.

Abiotic Stimuli-Responsive Protein Affinity Reagent for IgG

We describe an approach for the discovery of protein affinity reagents (PARs). Abiotic synthetic hydrogel copolymers can be "tuned" for selective protein capture by the type and ratios of functional monomers included in their polymerization and by the polymerization conditions (i.e., pH). By screening libraries of hydrogel nanoparticles (NPs) containing charged and hydrophobic groups against a protein target (IgG), a stimuli-responsive PAR is selected. The robust carbon backbone synthetic copolymer is rapidly synthesized in the chemistry laboratory from readily available monomers. The production of the PAR does not require living cells and is free from biological contamination. The capture and release of the protein by the copolymer NP is reversible. IgG is sequestered from human serum at pH 6.5 and following a wash step, the purified protein is released by elevating the pH to 7.3. The binding and release of the protein occur without denaturation. The abiotic material functions as a selective PAR for the F(ab')₂ domain of IgG for pull-down and immunoprecipitation experiments and for isolation and purification of proteins from complex biological mixtures.

Hybrid Models for the simulation and prediction of chromatographic processes for protein capture

The biopharmaceutical industries are continuously faced with the pressure to reduce the development costs and accelerate development time scales. The traditional approach of heuristic-based or platform process-based optimization is soon getting obsolete, and more generalized tools for process development and optimization are required to keep pace with the emerging trends. Thus, advanced model-based methods that can reduce the can ensure accelerated development of robust processes with minimal experiments are necessary. Though mechanistic models for chromatography are quite popular, their success is limited by the need to have accurate knowledge of adsorption isotherms and mass transfer kinetics. As an alternative, in this work, a hybrid modeling approach is proposed. Thereby, the chromatographic unit behavior is learned by a combination of neural network and mechanistic model while fitting suitable experimental breakthrough curves. Since this approach does not require identifying suitable mechanistic assumptions for all the phenomena, it can be developed with lower effort. Thus, allowing the scientists to concentrate their focus on process development. The performance of the hybrid model is compared with the mechanistic Lumped kinetic Model for in-silico data and experiments conducted on a system of industrial relevance. The flexibility of the hybrid modeling approach results in about three times higher accuracies compared to Lumped Kinetic Model. This is validated for five different isotherm models used to simulate data, with the hybrid model showing about two to three times lower prediction errors in all the cases. Not only in prediction, but we could also show that the hybrid model is more robust in extrapolating across process conditions with about three times lower error than the LKM. Additionally, it could be demonstrated that an appropriately tailored formulation of the hybrid model can be used to generate representations for the underlying principles such as adsorption equilibria and mass transfer kinetics.

A cuboid chromatography device having short bed-height gives better protein separation at a significantly lower pressure drop than a taller column having the same bed-volume

Simultaneously reducing the bed-height and increasing the area of cross-section, while keeping the bed-volume the same, would substantially reduce the pressure drop across a process chromatography column. This would minimize problems such as resin compaction and non-uniformity in column packing, which are commonly faced when using soft chromatographic media. However, the increase in macroscale convective dispersion due to the increase in column diameter, and the resultant loss in resolution would far outweigh any potential benefit. Cuboid-packed bed devices have lower macroscale convective dispersion compared to their equivalent cylindrical columns. In this paper, we discuss how and why a flat cuboid chromatography device having a short bed-height gives better protein separation, at a significantly lower pressure drop, than a taller column having the same bed-volume. First, we explored this option based on computational fluid dynamic (CFD) simulations. Depending on the flow rate, the pressure drop across the flat cuboid device was lower than that in the tall column by a factor of 6.35 to 6.4 (i.e. less than 1/6^th the pressure). The CFD results also confirmed that the macroscale convective dispersion within the flat cuboid device was significantly lower. Head-to-head separation experiments using a 1 mL flat cuboid device having a bed-height of 10 mm, and a 1 mL tall column having a bed-height of 25.8 mm, both packed with the same chromatographic media, were carried out. The number of theoretical plates per unit bed-height was on an average, around 2.5 time times greater with the flat cuboid device, while the total number of theoretical plates in the two devices were comparable. At any given superficial velocity, the height equivalent of a theoretical plate in the tall column was on an average, higher by a factor 2.5. Binary protein separation experiments showed that at any given flow rate, the resolution obtained using the flat cuboid device was significantly higher than that obtained with the tall column. This work opens up the possibility of designing and developing short bed-height chromatography devices for carrying out high-resolution biopharmaceutical purifications, at very low pressures.

Interferon-Based Biopharmaceuticals: Overview on the Production, Purification, and Formulation

The advent of biopharmaceuticals in modern medicine brought enormous benefits to the treatment of numerous human diseases and improved the well-being of many people worldwide. First introduced in the market in the early 1980s, the number of approved biopharmaceutical products has been steadily increasing, with therapeutic proteins, antibodies, and their derivatives accounting for most of the generated revenues. The success of pharmaceutical biotechnology is closely linked with remarkable developments in DNA recombinant technology, which has enabled the production of proteins with high specificity. Among promising biopharmaceuticals are interferons, first described by Isaacs and Lindenmann in 1957 and approved for clinical use in humans nearly thirty years later. Interferons are secreted autocrine and paracrine proteins, which by regulating several biochemical pathways have a spectrum of clinical effectiveness against viral infections, malignant diseases, and multiple sclerosis. Given their relevance and sustained market share, this review provides an overview on the evolution of interferon manufacture, comprising their production, purification, and formulation stages. Remarkable developments achieved in the last decades are herein discussed in three main sections: (i) an upstream stage, including genetically engineered genes, vectors, and hosts, and optimization of culture conditions (culture media, induction temperature, type and concentration of inducer, induction regimens, and scale); (ii) a downstream stage, focusing on single- and multiple-step chromatography, and emerging alternatives (e.g., aqueous two-phase systems); and (iii) formulation and delivery, providing an overview of improved bioactivities and extended half-lives and targeted delivery to the site of action. This review ends with an outlook and foreseeable prospects for underdeveloped aspects of biopharma research involving human interferons.

Towards in silico Process Modeling for Vaccines

Chemical, manufacturing, and control development timelines occupy a significant part of vaccine end-to-end development. In the on-going race for accelerating timelines, in silico process development constitutes a viable strategy that can be achieved through an artificial intelligence (AI)-driven or a mechanistically oriented approach. In this opinion, we focus on the mechanistic option and report on the modeling competencies required to achieve it. By inspecting the most frequent vaccine process units, we identify fluid mechanics, thermodynamics and transport phenomena, intracellular modeling, hybrid modeling and data science, and model-based design of experiments as the pillars for vaccine development. In addition, we craft a generic pathway for accommodating the modeling competencies into an in silico process development strategy.

Dilemma on plasmid DNA purification: binding capacity vs selectivity

Plasmid DNA chromatography is a powerful field in constant development and evolution. The use of this technique is considered mandatory in the production of an efficient and safe formulation to be applied for plasmid-mediated gene therapy. Concerning this, the search for an ideal chromatographic support/ligand combination motivated scientist to pursue a continuous improvement on the plasmid chromatography performance, looking for a progression on the ligands and supports used. The present review explores the different approaches used over time to purify plasmid DNA, ambitioning both high recovery and high purity levels. Overall, it is presented a critical discussion relying on the relevance of the binding capacity versus selectivity of the supports.

High-Throughput Process Development: II-Membrane Chromatography

Membrane chromatography is gradually emerging as an alternative to conventional column chromatography. It alleviates some of the major disadvantages associated with the latter, including high-pressure drop across the column bed and dependence on intraparticle diffusion for the transport of solute molecules to their binding sites within the pores of separation media. In the last decade, it has emerged as a method of choice for final polishing of biopharmaceuticals, in particular, monoclonal antibody products. The relevance of such a platform is high in view of the constraints with respect to time and resources that the biopharma industry faces today.This protocol describes the steps involved in performing HTPD of a membrane chromatography step. It describes the operation of a commercially available device (AcroPrep™ Advance filter plate with Mustang S membrane from Pall Corporation). This device is available in 96-well format with a 7 μL membrane in each well. We will discuss the challenges that one faces when performing such experiments as well as possible solutions to alleviate them. Besides describing the operation of the device, the protocol also presents an approach for statistical analysis of the data that are gathered from such a platform. A case study involving the use of the protocol for examining ion-exchange chromatography of the Granulocyte Colony Stimulating Factor (GCSF), a therapeutic product, is briefly discussed. This is intended to demonstrate the usefulness of this protocol in generating data that are representative of the data obtained at the traditional lab scale. The agreement in the data is indeed very significant (regression coefficient 0.9866). We think that this protocol will be of significant value to those involved in performing high-throughput process development of membrane chromatography.

Purification of protein therapeutics via high-affinity supramolecular host-guest interactions

Efficient purification is crucial to providing large quantities of recombinant therapeutic proteins, such as monoclonal antibodies and cytokines. However, affinity techniques for manufacturing protein therapeutics that use biomolecule-conjugated agarose beads that harness specific biomolecular interactions suffer from issues related to protein denaturation, contamination and the need to maintain biomolecule-specific conditions for efficient protein capture. Here, we report a versatile and scalable method for the purification of recombinant protein therapeutics. The method exploits the high-affinity and controllable host-guest interactions between cucurbit[7]uril (CB[7]) and selected guests such as adamantylammonium. We show that the Herceptin (the brand name of trastuzumab, a monoclonal antibody drug used to treat breast cancer) and the much smaller cytokine interferon α-2a can be purified by site-specifically tagging them with adamantylammonium using the enzyme sortase A, followed by high-affinity binding with CB[7]-conjugated agarose beads and the recovery of the protein using a guest with a stronger affinity for CB[7]. The thermal and chemical stability of CB[7] beads and their scalability, recyclability and low cost may also make them advantageous for the manufacturing of biosimilars.

Chromatography bioseparation technologies and in-silico modelings for continuous production of biotherapeutics

The potential of continuous bioprocessing is hindered by the bottlenecks of chromatography processing, which continues to be executed in batch mode. Highlighting the critical drawbacks of batch chromatography, this review underscores the transition that the industry has made by implementing continuous upstream process without devising a working model for downstream chromatography operations. Even though multitude of process development initiatives have commenced, the review emphasizes the first principle models of chromatography on which these initiatives are built. Various models of continuous chromatography, which are essential, but not limited to multi-column systems, employed to congeal a unified process are reviewed. Advancements made by several mechanistic models and simulations to maximize productivity and performance are described, in an attempt to provide the integral tools. The modeling tools can be used for development of a strong model based control strategy and can be embedded into the continuous chromatography framework. The review addresses the limitations and challenges of the current modeling methods for development of robust mechanistic modeling and efficient unit operation platform in continuous chromatography.

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.

Reduce, Reuse and Recycle in Protein Chromatography: Development of an Affinity Adsorbent from Waste Paper and Its Application for the Purification of Proteases from Fish By-Products

In the present study, we report the development of a cellulose-based affinity adsorbent and its application for the purification of proteases from fish by-products. The affinity adsorbent was synthesized using cellulose microfibers as the matrix, isolated from recycled newspapers using the acid precipitation method. As an affinity ligand, the triazine dye Cibacron Blue 3GA (CB3GA) was used and immobilized directly onto the cellulose microfibers. Absorption equilibrium studies and frontal affinity chromatography were employed to evaluate the chromatographic performance of the adsorbent using as model proteins bovine serum albumin (BSA) and lysozyme (LYS). Absorption equilibrium studies suggest that the adsorption of both proteins obeys the Langmuir isotherm model. The kinetics of adsorption obey the pseudo-second-order model. The affinity adsorbent was applied for the development of a purification procedure for proteases from Sparus aurata by-products (stomach and pancreas). A single-step purification protocol for trypsin and chymotrypsin was developed and optimized. The protocol afforded enzymes with high yields suitable for technical and industrial purposes.

Current technologies to endotoxin detection and removal for biopharmaceutical purification

Endotoxins are the major contributors to the pyrogenic response caused by contaminated pharmaceutical products, formulation ingredients, and medical devices. Recombinant biopharmaceutical products are manufactured using living organisms, including Gram-negative bacteria. Upon the death of a Gram-negative bacterium, endotoxins (also known as lipopolysaccharides) in the outer cell membrane are released into the lysate where they can interact with and form bonds with biomolecules, including target therapeutic compounds. Endotoxin contamination of biologic products may also occur through water, raw materials such as excipients, media, additives, sera, equipment, containers closure systems, and expression systems used in manufacturing. The manufacturing process is, therefore, in critical need of methods to reduce and remove endotoxins by monitoring raw materials and in-process intermediates at critical steps, in addition to final drug product release testing. This review paper highlights a discussion on three major topics about endotoxin detection techniques, upstream processes for the production of therapeutic molecules, and downstream processes to eliminate endotoxins during product purification. Finally, we have evaluated the effectiveness of endotoxin removal processes from a perspective of high purity and low cost.