Downstream bioprocessing: recent advances and future promise

Effect of Monovalent Cations on the Structure and Dynamics of Multimodal Chromatographic Surfaces

While multimodal (MM) chromatography is a promising approach for purifying proteins, the lack of a fundamental understanding of how ion-ligand interactions govern selectivity limits its use in the biopharmaceutical industry. This study uses molecular dynamics simulations to study the interactions between simple monovalent cations and two commonly used structurally similar multimodal chromatography ligands, the Capto ligand and Nuvia cPrime, immobilized on the surface. On the Capto ligand surface, ion presence and type play a key role in modulating the formation of phenyl rings and carboxylate clusters. The flexible linkage attaching the Capto ligand to the self-assembled monolayer (SAM) surface allowed multiple ligands to form interactions with the small cations, while large cations interacted less strongly, following the order Li+ > Na+ > K+ > Cs+. Thus, smaller cations resulted in greater ordering on the surface and lower ion diffusivities, while larger cations resulted in less ordering and higher ion diffusivities, following the order Li+ < Na+ < K+ < Cs+. In contrast, due to the rigid attachment of Nuvia cPrime to the SAM surfaces, the cations bound less strongly and had a much smaller effect on ligand clustering or ordering. Additionally, ions in the presence of the Nuvia cPrime surface had generally greater diffusivities than those in the presence of the Capto ligand. Overall, the interaction of cations with the multimodal ligands can lead to unique configurations on the SAM that likely contribute to differential behavior in biological separations.

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.

Exploring preferred binding domains of IgG1 mAbs to multimodal adsorbents using a combined biophysics and simulation approach

In this work, we employ a recently developed biophysical technique that uses diethylpyrocarbonate (DEPC) covalent labeling and mass spectrometry for the identification of mAb binding patches to two multimodal cation exchange resins at different pH. This approach compares the labeling results obtained in the bound and unbound states to identify residues that are sterically shielded and thus located in the mAb binding domains. The results at pH 6 for one mAb (mAb B) indicated that while the complementarity determining region (CDR) had minimal interactions with both resins, the FC domain was actively involved in binding. In contrast, DEPC/MS data with another mAb (mAb C) indicated that both the CDR and FC domains were actively involved in binding. These results corroborated chromatographic retention data with these two mAbs and their fragments and helped to explain the significantly stronger retention of both the intact mAb C and its Fab fragment. In contrast, labeling results with mAb C at pH 7, indicated that only the CDR played a significant role in resin binding, again corroborating chromatographic data. The binding domains identified from the DEPC/MS experiments were also examined using protein surface hydrophobicity maps obtained using a recently developed sparse sampling molecular dynamics (MD) approach in concert with electrostatic potential maps. These results demonstrate that the DEPC covalent labeling/mass spectrometry technique can provide important information about the domain contributions of multidomain proteins such as monoclonal antibodies when interacting with multimodal resins over a range of pH conditions.

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.

Novel Surfactant Compatibility with Downstream Protein Bioprocesses

Downstream processing of antibodies consists of a series of steps aimed at purifying the product and ensuring it is delivered to formulators structurally and functionally intact. The process can be complex and time-consuming, involving multiple filtrations, chromatography, and buffer exchange steps that can interfere with product integrity. This study explores the possibility and benefits of adding N-myristoyl phenylalanine polyether amine diamide (FM1000) as a process aid. FM1000 is a nonionic surfactant that is highly effective at stabilizing proteins against aggregation and particle formation and has been extensively explored as a novel excipient for antibody formulations. In this work, FM1000 is shown to stabilize proteins against pumping-induced aggregation which can occur while transporting them between process units and within certain processes. It is also shown to prevent antibody fouling of multiple polymeric surfaces. Furthermore, FM1000 can be removed after some steps and during buffer exchange in ultrafiltration/diafiltration, if needed. Additionally, FM1000 was compared to polysorbates in studies focusing on surfactant retention on filters and columns. While the different molecular entities of polysorbates elute at different rates, FM1000 flows through purification units as a single molecule and at a faster rate. Overall, this work defines new areas of application for FM1000 within downstream processing and presents it as a versatile process aid, where its addition and removal are tunable depending on the needs of each product.

Evaluation of preferred binding regions on ubiquitin and IgG1 FC for interacting with multimodal cation exchange resins using DEPC labeling/mass spectrometry

There is significant interest in identifying the preferred binding domains of biological products to various chromatographic materials. In this work, we develop a biophysical technique that uses diethyl pyrocarbonate (DEPC) based covalent labeling in concert with enzymatic digestion and mass spectrometry to identify the binding patches for proteins bound to commercially available multimodal (MM) cation exchange chromatography resins. The technique compares the changes in covalent labeling of the protein in solution and in the bound state and uses the differences in this labeling to identify residues that are sterically shielded upon resin binding and, therefore, potentially involved in the resin binding process. Importantly, this approach enables the labeling of many amino acids and can be carried out over a pH range of 5.5-7.5, thus enabling the protein surface mapping at conditions of interest in MM cation exchange systems. The protocol is first developed using the model protein ubiquitin and the results indicate that lysine residues located on the front face of the protein show dramatic changes in DEPC labeling while residues present on other regions have minimal or no reductions. This indicates that the front face of ubiquitin is likely involved in resin binding. In addition, surface property maps indicate that the hypothesized front face binding region consists of overlapping positively charged and hydrophobic patches. The technique is then employed with an IgG1 F_C and the results indicate that residues on the C_H 2-C_H 3 interface and the hinge are significantly sterically shielded upon binding to the resin. Further, these regions are again associated with significant overlap of positively charged and hydrophobic patches. On the other hand, while, residues on the C_H 2 and the front face of the IgG1 F_C also exhibited some changes in DEPC labeling upon binding, these regions have less distinct charged and hydrophobic patches. Importantly, the hypothesized binding patches identified for both ubiquitin and F_C using this approach are shown to be consistent with previously reported NMR studies. In contrast to NMR, this new approach enables the identification of preferred binding regions without the need for isotopically labeled proteins or chemical shift assignments. The technique developed in this work sets the stage for the evaluation of the binding domains of a wide range of biological products to chromatographic surfaces, with important implications for designing biomolecules with improved biomanufacturability properties.

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.

Single-step rapid chromatographic purification and characterization of clinical stage oncolytic VSV-GP

Purification of viruses, especially for therapeutic purposes, is a tedious and challenging task. The challenges arise due to the size and surface complexity of the virus particles. VSV-GP is a promising oncolytic virus, which has been approved for phase I clinical trials by the Food and Drug Administration (FDA) of United States and Paul Ehrlich Institute (PEI) of Germany. The virus particles of VSV-GP are larger in size than vectors commonly used for gene therapy (e.g., adenovirus, adeno-associated virus, etc.). The current established proprietary clinical-grade manufacturing process for the purification of VSV-GP encompasses several chromatographic and non-chromatographic steps. In this study, we describe a new single-step purification process for the purification of VSV-GP virus, using cation exchange convective flow column with relatively higher yields. The purified virus was characterized for its quality attributes using TCID50 assay (for viral infectivity), host cell protein contaminant ELISA, SDS-PAGE, size exclusion chromatography (SEC), and cryo-electron microscopy. Furthermore, the purified viral therapeutic material was tested in vivo for its efficacy and safety. All these characterization methods demonstrated a therapeutic virus preparation of high purity and yield, which can be readily used for various studies.

Continuous manufacturing of monoclonal antibodies: Automated downstream control strategy for dynamic handling of titer variations

Handling long-term dynamic variability in harvest titer is a critical challenge in continuous downstream manufacturing. This challenge is becoming increasingly important with the advent of high-titer clones and modern upstream perfusion processes where the titer can vary significantly across the course of a campaign. In this paper, we present a strategy for real-time, dynamic adjustment of the entire downstream train, including capture chromatography, viral inactivation, depth filtration, polishing chromatography, and single-pass formulation, to accommodate variations in titer from 1-7 g/L. The strategy was tested in real time in a continuous downstream purification process of 36 h duration with induced titer variations. The dynamic control strategy leverages real-time NIR-based concentration sensors in the harvest material to continuously track the titer, integrated with an in-house Python-based control system that operates a BioSMB for carrying out capture and polishing chromatography, as well as a series of pumps and solenoid valves for carrying out viral inactivation and formulation. A set of 9 different methods, corresponding to the different harvest titers have been coded onto the Python controller. The methods have a varying number of chromatography columns (3-6 for Protein A and 2-10 for CEX), designed to ensure proper scheduling and optimize productivity across the entire titer variation space. The approach allows for a wide range of titers to be processed on a single integrated setup without having to change equipment or to re-design each time. The strategy also overcomes a key unexplored challenge in continuous processing, namely hand-shaking the downstream train to upstream conditions with long-term titer variability while maintaining automated operation with high productivity and robustness.

Electrospun Hydrophobic Interaction Chromatography (HIC) Membranes for Protein Purification

Responsive membranes for hydrophobic interaction chromatography have been fabricated by functionalizing poly(N-vinylcaprolactam) (PVCL) ligands on the substrate of electrospun regenerated cellulose nanofibers. Both static and dynamic binding capacities and product recovery were investigated using bovine serum albumin (BSA) and Immunoglobulin G (IgG) as model proteins. The effects of ligand chain length and chain density on static binding capacity were also studied. A static binding capacity of ~25 mg/mL of membrane volume (MV) can be achieved in optimal ligand grafting conditions. For dynamic binding studies, protein binding capacity increased with protein concentration from 0.1 to 1.0 g/L. Dynamic binding capacity increased from ~8 mg/mL MV at 0.1 g/L BSA to over 30 mg/mL at 1.0 g/L BSA. However, BSA recovery decreased as protein concentration increased from ~98% at 0.1 g/L BSA to 51% at 1 g/L BSA loading concentration. There is a clear trade-off between binding capacity and recovery rate. The electrospun substrate with thicker fibers and more open pore structures is superior to thinner fibrous membrane substrates.

Sustainable Design Approach for Modeling Bioprocesses from Laboratory toward Commercialization: Optimizing Chitosan Production

Enhancing the biochemical supply chain towards sustainable development requires more efforts to boost technology innovation at early design phases and avoid delays in industrial biotechnology growth. Such a transformation requires a comprehensive step-wise procedure to guide bioprocess development from laboratory protocols to commercialization. This study introduces a process design framework to guide research and development (R&D) through this journey, bearing in mind the particular challenges of bioprocess modeling. The method combines sustainability assessment and process optimization based on process efficiency indicators, technical indicators, Life Cycle Assessment (LCA), and process optimization via Water Regeneration Networks (WRN). Since many bioprocesses remain at low Technology Readiness Levels (TRLs), the process simulation module was examined in detail to account for uncertainties, providing strategies for successful guidance. The sustainability assessment was performed using the geometric mean-based sustainability footprint metric. A case study based on Chitosan production from shrimp exoskeletons was evaluated to demonstrate the method’s applicability and its advantages in product optimization. An optimized scenario was generated through a WRN to improve water management, then compared with the case study. The results confirm the existence of a possible configuration with better sustainability performance for the optimized case with a sustainability footprint of 0.33, compared with the performance of the base case (1.00).

Probing IgG1 FC-Multimodal Nanoparticle Interactions: A Combined Nuclear Magnetic Resonance and Molecular Dynamics Simulations Approach

In this study, NMR and molecular dynamics simulations were employed to study IgG1 F_C binding to multimodal surfaces. Gold nanoparticles functionalized with two multimodal cation-exchange ligands (Capto and Nuvia) were synthesized and employed to carry out solution-phase NMR experiments with the F_C. Experiments with perdeuterated ¹⁵N-labeled F_C and the multimodal surfaces revealed micromolar residue-level binding affinities as compared to millimolar binding affinities with these ligands in free solution, likely due to cooperativity and avidity effects. The binding of F_C with the Capto ligand nanoparticles was concentrated near an aliphatic cluster in the C_H2/C_H3 interface, which corresponded to a focused hydrophobic region. In contrast, binding with the Nuvia ligand nanoparticles was more diffuse and corresponded to a large contiguous positive electrostatic potential region on the side face of the F_C. Results with lower-ligand-density nanoparticles indicated a decrease in binding affinity for both systems. For the Capto ligand system, several aliphatic residues on the F_C that were important for binding to the higher-density surface did not interact with the lower-density nanoparticles. In contrast, no significant difference was observed in the interacting residues on the F_C to the high- and low-ligand density Nuvia surfaces. The binding affinities of F_C to both multimodal-functionalized nanoparticles decreased in the presence of salt due to the screening of multiple weak interactions of polar and positively charged residues. For the Capto ligand nanoparticle system, this resulted in an even more focused hydrophobic binding region in the interface of the C_H2 and C_H3 domains. Interestingly, for the Nuvia ligand nanoparticles, the presence of salt resulted in a large transition from a diffuse binding region to the same focused binding region determined for Capto nanoparticles at 150 mM salt. Molecular dynamics simulations corroborated the NMR results and provided important insights into the molecular basis of F_C binding to these different multimodal systems containing clustered (observed at high-ligand densities) and nonclustered ligand surfaces. This combined biophysical and simulation approach provided significant insights into the interactions of F_C with multimodal surfaces and sets the stage for future analyses with even more complex biotherapeutics.

Evaluation of guanidine-based multimodal anion exchangers for protein selectivity and orthogonality

In this paper, we examined the chromatographic behavior of a new class of guanidine-based multimodal anion exchange resins. The selectivities and protein recoveries on these resins were first evaluated using linear gradient chromatography with a model acidic protein library at pH 5, 6 and 7. While a single-guanidine based resin exhibited significant recovery issues at high ligand density, a bis-guanidine based resin showed high recoveries of all but two of the proteins evaluated in the study. In addition, the bis-guanidine resin showed a more pH dependent selectivity pattern as compared to the low density single-guanidine resin. The salt elution range for the low density single-guanidine and bis-guanidine resins was also observed to vary from 0.250 to 0.621 M and 0.162 to 0.828 M NaCl, respectively. A QSAR model was then developed to predict the elution behavior of these proteins on the guanidine prototypes at multiple pH with overall training and test scores of 0.88 and 0.85, respectively. In addition, molecular dynamics simulations were performed with these ligands immobilized on a self-assembled monolayer (SAM) to characterize their conformational preferences and to gain insight into the molecular basis of their chromatographic behavior. Finally, a recently developed framework was employed to evaluate the separability of the bis-guanidine resin as well as its orthogonality to the multimodal cation exchanger, Nuvia cPrime. This evaluation was carried out using a second model protein library which included both acidic and basic proteins. The results of this analysis indicated that the bis-guanidine prototype exhibited both higher pair separability (0.73) and pair enhancement (0.42) as compared to the less hydrophobic commercial Nuvia aPrime 4A with pair separability and enhancement factors of 0.57 and 0.22, respectively. The enhanced selectivity and orthogonality of this new multimodal anion exchange ligand may offer potential opportunities for bioprocessing applications.

Behavior of Water Near Multimodal Chromatography Ligands and Its Consequences for Modulating Protein-Ligand Interactions

Multimodal chromatography is a powerful approach for purifying proteins that uses ligands containing multiple modes of interaction. Recent studies have shown that selectivity in multimodal chromatographic separations is a function of the ligand structure and geometry. Here, we performed molecular dynamics simulations to explore how the ligand structure and geometry affect ligand-water interactions and how these differences in solution affect the nature of protein-ligand interactions. Our investigation focused on three chromatography ligands: Capto MMC, Nuvia cPrime, and Prototype 4, a structural variant of Nuvia cPrime. First, the solvation characteristics of each ligand were quantified via three metrics: average water density, fluctuations, and residence time. We then explored how solvation was perturbed when the ligand was bound to the protein surface and found that the probability of the phenyl ring dewetting followed the order: Capto MMC > Prototype 4 > Nuvia cPrime. To explore how these differences in dewetting affect protein-ligand interactions, we calculated the probability of each ligand binding to different types of residues on the protein surface and found that the probability of binding to a hydrophobic residue followed the same order as the dewetting behavior. This study illustrates the role that wetting and dewetting play in modulating protein-ligand interactions.

Benzotriazole-5-carboxylic as a mixed-mode ligand for chromatographic separation of antibody with enhanced adsorption capacity

Mixed-mode chromatography provides a promising strategy for industrial protein purification for its potential merit of balancing efficiency and cost-effectiveness. However, mixed-mode media with satisfactory selectivity and binding capacity towards antibody are still urgently needed. A new type of mixed-mode chromatography resin was prepared using benzotriazole-5-carboxylicas as ligand (BTA MM), and its application in antibody separation was explored. A typical pH-dependent protein binding was observed, and the neutral condition was favorable for antibody adsorption. Dynamic binding capacity of human immunoglobulin G (hIgG) was 57.7 mg/mL at pH 7.4 (10 mM phosphate buffer, containing 150 mM NaCl), while elution with acidic solutions (pH 3-4) could achieve a recovery of more than 85%. Protein adsorption on the resin showed a salt-independent manner, thus it could work under physiological solution conditions, with satisfied antibody selectivity. One-step purification of antibody components from human serum samples could obtain a product with the purity more than 84%. Satisfied performance was also observed when the adsorbent was used for purifying a IgG1-type monoclonal antibody (mAb) from cell culture supernatant. In addition, the benzotriazole adsorbent has been found stable enough to withstand autoclave sterilization and other harsh conditions, including 1 M NaOH, 1 M HCl, and 75% ethanol. The results proved the potential of this type of mixed-mode chromatography medium for industrial antibody purification.

Streamlining process characterization efforts using the high throughput ambr® crossflow system for ultrafiltration and diafiltration processing of monoclonal antibodies

Commercial process development for biopharmaceuticals often involves process characterization (PC) studies to gain process knowledge and understanding in preparation for process validation. One common approach to conduct PC activities is by using design-of-experiment, which can help determine the impact process parameter deviations may have on product quality attributes. Qualified scale-down systems are typically used to conduct these studies. For an ultrafiltration/diafiltration (UF/DF) application, however, a traditional scale-down still requires hundreds of milliliters of material per run and can only conduct one experiment at a time. This poses a challenge in resources as there could be 20+ experiments required for a typical UF/DF PC study. One solution to circumvent this is the use of high-throughput systems, which enable parallel experimentation by only using a fraction of the resources. Sartorius Stedim Biotech has recently commercialized the ambr® crossflow high-throughput system to meet this need. In this study, the performance of this system during a monoclonal antibody UF/DF step was first compared with a pilot- and a manufacturing-scale tangential flow filtration (TFF) system at a single operating condition. Due to material limitations, it was then compared to only the pilot-scale TFF system across wider ranges of transmembrane pressure; crossflow rate; and diafiltration concentration in a PC study. Permeate flux, aggregate content, process yield, pH/conductivity traces, retentate concentration, axial pressure drop, and turbidity values were measured at both scales. A good agreement was attained across scales, further supporting its potential use as a scale-down system.

Identification of preferred multimodal ligand-binding regions on IgG1 FC using nuclear magnetic resonance and molecular dynamics simulations

In this study, the binding of multimodal chromatographic ligands to the IgG1 F_C domain were studied using nuclear magnetic resonance and molecular dynamics simulations. Nuclear magnetic resonance experiments carried out with chromatographic ligands and a perdeuterated ¹⁵ N-labeled F_C domain indicated that while single-mode ion exchange ligands interacted very weakly throughout the F_C surface, multimodal ligands containing negatively charged and aromatic moieties interacted with specific clusters of residues with relatively high affinity, forming distinct binding regions on the F_C . The multimodal ligand-binding sites on the F_C were concentrated in the hinge region and near the interface of the C_H 2 and C_H 3 domains. Furthermore, the multimodal binding sites were primarily composed of positively charged, polar, and aliphatic residues in these regions, with histidine residues exhibiting some of the strongest binding affinities with the multimodal ligand. Interestingly, comparison of protein surface property data with ligand interaction sites indicated that the patch analysis on F_C corroborated molecular-level binding information obtained from the nuclear magnetic resonance experiments. Finally, molecular dynamics simulation results were shown to be qualitatively consistent with the nuclear magnetic resonance results and to provide further insights into the binding mechanisms. An important contribution to multimodal ligand-F_C binding in these preferred regions was shown to be electrostatic interactions and π-π stacking of surface-exposed histidines with the ligands. This combined biophysical and simulation approach has provided a deeper molecular-level understanding of multimodal ligand-F_C interactions and sets the stage for future analyses of even more complex biotherapeutics.

The Role of Ligand-Ligand Interactions in Multimodal Ligand Conformational Equilibria and Surface Pattern Formation

Multimodal chromatography uses multiple modes of interaction such as charge, hydrophobic, or hydrogen bonding to separate proteins. Recently, we used molecular dynamics (MD) simulations to show that ligands immobilized on surfaces can interact and associate with neighboring ligands to form hydrophobic and charge patches, which may have important implications for the nature of protein-surface interactions. Here, we study interfacial systems of increasing complexity-from a single immobilized multimodal ligand to high density surfaces-to better understand how ligand behavior is affected by the presence of a surface and the presence of other ligands in the vicinity, and how this behavior scales to larger systems. We find that tethering a ligand to a surface restricts its conformations to a subset of those observed in free solution, yet the ligand maintains flexibility in the plane of the surface and can form contacts with neighboring ligands. We find that although the formation of a contact between two neighboring ligands is slightly unfavorable, three neighboring ligands exhibit a preference for the formation of a fully connected cluster. To explore how these trends in ligand association extend to a larger surface with high density of ligands, we performed coarse-grained Monte Carlo (MC) simulations of a 132-ligand surface using ligand interactions parametrized based on free energies obtained from the three-ligand MD simulations. Despite their simplicity, the coarse-grained simulations qualitatively capture the cluster size distribution of ligands observed in detailed MD simulations. Quantitative differences between the two suggest opportunities for improvements in the coarse-grained energy function for efficient predictions of cluster and pattern formations. Our approach presents a promising route to the engineering of multimodal patterns for future chromatographic resin design.

Enhancement of lysozyme crystallization under ultrasound field

With the increasing demand for biopharmaceuticals, a method to crystallize biomolecule products with high quality, high yield and uniform size distribution as well as regular crystal habit is needed. In this work, ultrasound was used as a nucleation accelerator to decrease the energy barrier for lysozyme crystal formation. Crystallization experiments on egg-white lysozyme were carried out with and without ultrasound. The effect of ultrasound on induction time, metastable zone width, crystal size and morphology and process yield was investigated in detail. The nucleation-promoting effect produced by ultrasound is illustrated by the reduction of metastable zone width and induction time. By inducing faster nucleation, ultrasound leads to protein crystals grow at lower supersaturation levels with shorter induction time. It was found that ultrasound could result in uniform size distribution of the product due to the preventing of aggregation. However, long time continuous application of ultrasound could result in smaller particle size. Hence, ultrasonic-stop method was found to be a more appropriate strategy to enhance the crystallization process of proteins such as lysozyme.

Biotechnology and Bioprocesses: Their Contribution to Sustainability

Significant advancements in biotechnology have resulted in the development of numerous fundamental bioprocesses, which have consolidated research and development and industrial progress in the field. These bioprocesses are used in medical therapies, diagnostic and immunization procedures, agriculture, food production, biofuel production, and environmental solutions (to address water-, soil-, and air-related problems), among other areas. The present study is a first approach toward the identification of scientific and technological bioprocess trajectories within the framework of sustainability. The method included a literature search (Scopus), a patent search (Patentscope), and a network analysis for the period from 2010 to 2019. Our results highlight the main technological sectors, countries, institutions, and academic publications that carry out work or publish literature related to sustainability and bioprocesses. The network analysis allowed for the identification of thematic clusters associated with sustainability and bioprocesses, revealing different related scientific topics. Our conclusions confirm that biotechnology is firmly positioned as an emerging knowledge area. Its dynamics, development, and outcomes during the study period reflect a substantial number of studies and technologies focused on the creation of knowledge aimed at improving economic development, environmental protection, and social welfare.

Formation of Ligand Clusters on Multimodal Chromatographic Surfaces

Multimodal chromatography is a powerful tool which uses multiple modes of interaction, such as charge and hydrophobicity, to purify protein-based therapeutics. In this work, we performed molecular dynamics simulations of a series of multimodal cation-exchange ligands immobilized on a hydrophilic self-assembled monolayer surface at the commercially relevant surface density (1 ligand/nm²). We found that ligands that were flexible and terminated in a hydrophobic group had a propensity to aggregate on the surface, while less flexible ligands containing a hydrophobic group closer to the surface did not aggregate. For aggregating ligands, this resulted in the formation of a surface pattern that contained relatively large patches of hydrophobicity and charge whose sizes exceeded the length scale of the individual ligands. On the other hand, lowering the surface density to 1 ligand/3 nm² reduced or eliminated this aggregation behavior. In addition, the introduction of a flexible linker (corresponding to the commercially available ligand) enhanced cluster formation and allowed aggregation to occur at lower surface densities. Further, the use of flexible linkers enabled hydrophobic groups to collapse to the surface, reducing their accessibility. Finally, we developed an approach for quantifying differences in the observed surface patterns by calculating distributions of the patch size and patch length. This clustering phenomenon is likely to play a key role in governing protein-surface interactions in multimodal chromatography. This new understanding of multimodal surfaces has important implications for developing improved predictive models and designing new classes of multimodal separation materials.

High throughput screening of ultrafiltration and diafiltration processing of monoclonal antibodies via the ambr® crossflow system

As the biopharmaceutical industry moves toward high concentration of monoclonal antibody drug substance, additional development is required early on when material is still limited. A key constraint is the availability of predictive high-throughput low-volume filtration screening systems for bioprocess development. This particularly impacts final stages such as ultrafiltration/diafiltration steps where traditional scale-down systems need hundreds of milliliters of material per run. Recently, the ambr® crossflow system has been commercialized by Sartorius Stedim Biotech (SSB) to meet this need. It enables parallel high throughput experimentation by only using a fraction of typical material requirements. Critical parameters for predictive filtration systems include loading, mean transmembrane pressure (Δ P ¯ _TMP ), and crossflow rate (Q_F ). While axial pressure drop (ΔP_axial ) across the cartridge is a function of these parameters, it plays a key role and similar values should result across scales. The ambr® crossflow system is first presented describing typical screening experiments. Its performance is then compared to a traditional pilot-scale tangential flow filtration (TFF) at defined conditions. The original ambr® crossflow (CF) cartridge underperformed resulting in ~20x lower ΔP_axial than the pilot-scale TFF flat-sheet cassette. With an objective to improve the scalability of the system, efforts were made to understand this scale difference. The ambr® CF cartridge was successfully modified by restricting the flow of the feed channel, and thus increasing its ΔP_axial . Additional studies across a range of loading (100-823 gm^-2 ); Δ P ¯ _TMP (12-18 psi); and Q_F (4-8 L/min/m² ) were conducted in both scales. Comparable flux and aggregate levels were achieved.

Modular design: Implementing proven engineering principles in biotechnology

Modular design is at the foundation of contemporary engineering, enabling rapid, efficient, and reproducible construction and maintenance of complex systems across applications. Remarkably, modularity has recently been discovered as a governing principle in natural biological systems from genes to proteins to complex networks within a cell and organism communities. The convergent knowledge of natural and engineered modular systems provides a key to drive modern biotechnology to address emergent challenges associated with health, food, energy, and the environment. Here, we first present the theory and application of modular design in traditional engineering fields. We then discuss the significance and impact of modular architectures on systems biology and biotechnology. Next, we focus on the very recent theoretical and experimental advances in modular cell engineering that seeks to enable rapid and systematic development of microbial catalysts capable of efficiently synthesizing a large space of useful chemicals. We conclude with an outlook towards theoretical and practical opportunities for a more systematic and effective application of modular cell engineering in biotechnology.

Water on hydrophobic surfaces: mechanistic modeling of polyethylene glycol-induced protein precipitation

For the purification of biopharmaceutical proteins, liquid chromatography is still the gold standard. Especially with increasing product titers, drawbacks like slow volumetric throughput and high resin costs lead to an intensifying need for alternative technologies. Selective preparative protein precipitation is one promising alternative technique. Although the capability has been proven, there has been no precipitation process realized for large-scale monoclonal antibody (mAb) production yet. One reason might be that the mechanism behind protein phase behavior is not completely understood and the precipitation process development is still empirical. Mechanistic modeling can be a means for faster, material-saving process development and a better process understanding at the same time. In preparative chromatography, mechanistic modeling was successfully shown for a variety of applications. Lately, a new isotherm for hydrophobic interaction chromatography (HIC) under consideration of water molecules as participants was proposed, enabling an accurate description of HIC. In this work, based on similarities between protein precipitation and HIC, a new precipitation model was derived. In the proposed model, the formation of protein-protein interfaces is thought to be driven by hydrophobic effects, involving a reorganization of the well-ordered water structure on the hydrophobic surfaces of the protein-protein complex. To demonstrate model capability, high-throughput precipitation experiments with pure or prior to the experiments purified proteins lysozyme, myoglobin, bovine serum albumin, and one mAb were conducted at various pH values. Polyethylene glycol (PEG) 6000 was used as precipitant. The precipitant concentration as well as the initial protein concentration was varied systematically. For all investigated proteins, the initial protein concentrations were varied between 1.5 mg/mL and 12 mg/mL. The calibrated models were successfully validated with experimental data. This mechanistic description of protein precipitation process offers mathematical explanation of the precipitation behavior of proteins at PEG concentration, protein concentration, protein size, and pH.

Mathematical modelling of expanded bed adsorption - a perspective on in silico process design

Expanded bed adsorption (EBA) emerged in the early 1990s in an attempt to integrate the clarification, capture and initial product concentration/purification process. Several mathematical models have been put forward to describe its operation. However, none of the models developed specifically for EBA allows simultaneous prediction of bed hydrodynamics, mass transfer/adsorption and (unwanted) interactions and fouling. This currently limits the development and early optimization of EBA-based separation processes. In multiphase reactor engineering, the use of multiphase computational fluid dynamics has been shown to improve fundamental understanding of fluidized beds. To advance EBA technology, a combination of particle, equipment and process scale models should be used. By employing a cascade of multiscale simulations, the various challenges EBA currently faces can be addressed. This allows for optimal design and selection of equipment, materials and process conditions, and reduces risks and development times of downstream processes involving EBA. © 2018 The Authors. Journal of Chemical Technology & Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Sophisticated Cloning, Fermentation, and Purification Technologies for an Enhanced Therapeutic Protein Production: A Review

The protein productions strategies are crucial towards the development of application based research and elucidating the novel purification strategies for industrial production. Currently, there are few innovative avenues are studies for cloning, upstream, and purification through efficient bioprocess development. Such strategies are beneficial for industries as well as proven to be vital for effectual therapeutic protein development. Though, these techniques are well documented, but, there is scope of addition to current knowledge with novel and new approaches and it will pave new avenues in production of recombinant microbial and non-microbial proteins including secondary metabolites. In this review, we have focussed on the recent development in clone selection, various modern fermentation and purification technologies and future directions in these emerging areas. Moreover, we have also highlighted notable perspectives and challenges involved in the bioengineering of such proteins, including quality by design, gene editing and pioneering ideas. The biopharmaceutical industries continue to shift towards more flexible, automated platforms and economical product development, which in turn can help in developing the cost effective processes and affordable drug development for a large community.