Molecular basis of high viscosity in concentrated antibody solutions: Strategies for high concentration drug product development

A Novel MATLAB®-Algorithm-Based Video Analysis to Quantitatively Determine Solution Creeping in Intact Pharmaceutical Glass Vials

During the filling process of a biopharmaceutical drug product (DP), a liquid DP film might creep up the inner vial wall which is barely discernible, appears as milky-white haze after lyophilisation and is known as fogging. Creeping and fogging are mainly dependent on the primary packaging material surface and its hydration, vial preparation process as well as DP composition. The occurrence of both can impede visual inspection and might lead to DP rejection. Hence, our studies focused on the early detection of liquid solution and glass vial surface interaction directly after filling. For a fast and highly sensitive evaluation a novel video-based analysis was used. To our knowledge, this is the first time a MATLAB®-algorithm-based video analysis was applied to quantitatively determine creeping time-resolved. Furthermore, creeping in dependence of vial processing sites, surfactant type and concentration, filling temperature, and vial format were investigated. The results were verified using orthogonal conventional methods such as surface tension, wetting behaviour, and contact angle measurements, as well as ToF-SIMS, ICP-MS, and SEM. Additionally, the methods applied were assessed regarding their cross-validation capability. The observations indicate that the vial preparation process can have a pronounced impact on alteration of the glass vial surface and related creeping behaviour of the filled solution.

Effects of Monovalent Salt on Protein-Protein Interactions of Dilute and Concentrated Monoclonal Antibody Formulations

In this study, we used sodium chloride (NaCl) to extensively modulate non-specific protein-protein interactions (PPI) of a humanized anti-streptavidin monoclonal antibody class 2 molecule (ASA-IgG2). The changes in PPI with varying NaCl (C_NaCl) and monoclonal antibody (mAb) concentration (C_mAb) were assessed using the diffusion interaction parameter k_D and second virial coefficient B₂₂ measured from solutions with low to moderate C_mAb. The effective structure factor S(q)_eff measured from concentrated mAb solutions using small-angle X-ray and neutron scattering (SAXS/SANS) was also used to characterize the PPI. Our results found that the nature of net PPI changed not only with C_NaCl, but also with increasing C_mAb. As a result, parameters measured from dilute and concentrated mAb samples could lead to different predictions on the stability of mAb formulations. We also compared experimentally determined viscosity results with those predicted from interaction parameters, including k_D and S(q)_eff. The lack of a clear correlation between interaction parameters and measured viscosity values indicates that the relationship between viscosity and PPI is concentration-dependent. Collectively, the behavior of flexible mAb molecules in concentrated solutions may not be correctly predicted using models where proteins are considered to be uniform colloid particles defined by parameters derived from low C_mAb.

Computational models for studying physical instabilities in high concentration biotherapeutic formulations

Computational prediction of the behavior of concentrated protein solutions is particularly advantageous in early development stages of biotherapeutics when material availability is limited and a large set of formulation conditions needs to be explored. This review provides an overview of the different computational paradigms that have been successfully used in modeling undesirable physical behaviors of protein solutions with a particular emphasis on high-concentration drug formulations. This includes models ranging from all-atom simulations, coarse-grained representations to macro-scale mathematical descriptions used to study physical instability phenomena of protein solutions such as aggregation, elevated viscosity, and phase separation. These models are compared and summarized in the context of the physical processes and their underlying assumptions and limitations. A detailed analysis is also given for identifying protein interaction processes that are explicitly or implicitly considered in the different modeling approaches and particularly their relations to various formulation parameters. Lastly, many of the shortcomings of existing computational models are discussed, providing perspectives and possible directions toward an efficient computational framework for designing effective protein formulations.

Design of a Novel Fab-Like Antibody Fragment with Enhanced Stability and Affinity for Clinical use

With increasing interest in applying recombinant monoclonal antibodies (mAbs) in human medicine, engineered mAb fragments with reduced size and improved stability are in demand to overcome current limitations in clinical use. Herein, a novel Fab-like antibody fragment generated via an in silico-based engineering approach where the CH1 and CL domains of Fab are replaced by the IgG1 CH3 domains is described. This construct, designated as FabCH3, maintains the natural N-terminus and C-terminus of IgG antibody, can be expressed at a high level in bacterial cells and, importantly, exhibits much higher stability and affinity than the parental Fab when tested in a mesothelin-specific Fab m912, as well as a vascular endothelial growth factor A (VEGFA)-specific Fab Ranibizumab (in vivo). The high-resolution crystal structures of m912 FabCH3 and m912 Fab are determined, and the comparative analysis reveals more rigid structures in both constant domains and complementarity-determining regions of FabCH3, explaining its enhanced stability and affinity. Overall, the stabilized FabCH3 described in this report provides a versatile platform for engineering Fab-like antibody fragments with higher stability and antigen-binding affinity that can be used as a distinct class of antibody therapeutics.

Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics

Machine learning has been recently used to predict therapeutic antibody aggregation rates and viscosity at high concentrations (150 mg/ml). These works focused on commercially available antibodies, which may have been optimized for stability. In this study, we measured accelerated aggregation rates at 45°C and viscosity at 150 mg/ml for 20 preclinical and clinical-stage antibodies. Features obtained from molecular dynamics simulations of the full-length antibody and sequences were used for machine learning model construction. We found a k-nearest neighbors regression model with two features, spatial positive charge map on the CDRH2 and solvent-accessible surface area of hydrophobic residues on the variable fragment, gives the best performance for predicting antibody aggregation rates (r = 0.89). For the viscosity classification model, the model with the highest accuracy is a logistic regression model with two features, spatial negative charge map on the heavy chain variable region and spatial negative charge map on the light chain variable region. The accuracy and the area under precision recall curve of the classification model from validation tests are 0.86 and 0.70, respectively. In addition, we combined data from another 27 commercial mAbs to develop a viscosity predictive model. The best model is a logistic regression model with two features, number of hydrophobic residues on the light chain variable region and net charges on the light chain variable region. The accuracy and the area under precision recall curve of the classification model are 0.85 and 0.6, respectively. The aggregation rates and viscosity models can be used to predict antibody stability to facilitate pharmaceutical development.

Dipolar Interactions and Protein Hydration in Highly Concentrated Antibody Formulations

Molecular interaction mechanisms in high-concentrated protein systems are of fundamental importance for the rational development of biopharmaceuticals such as monoclonal antibody (mAb) formulations. In such high-concentrated protein systems, the intermolecular distances between mAb molecules are reduced to the size of the protein diameter (approx. 10 nm). Thus, protein-protein interactions are more pronounced at high concentrations; so a direct extrapolation of physicochemical properties obtained from measurements at a low protein concentration of the corresponding properties at a high protein concentration is highly questionable. Besides the charge-charge interaction, the effects of molecular crowding, dipolar interaction, changes in protein hydration, and self-assembling tendency become more relevant. Here, protein hydration, protein dipole moment, and protein-protein interactions were studied in protein concentrations up to 200 mg/mL (= 1.3 mM) in different formulations for selected mAbs using dielectric relaxation spectroscopy (DRS). These data are correlated with the second virial coefficient, A₂, the diffusion interaction parameter, k_D, the elastic shear modulus, G', and the dynamic viscosity, η. When large contributions of dipolar protein-protein interactions were observed, the tendency of self-assembling and an increase in solution viscosity were detected. These effects were examined using specific buffer conditions. Furthermore, different types of protein-water interactions were identified via DRS, whereby the effect of high protein concentration on protein hydration was investigated for different high-concentrated liquid formulations (HCLFs).

Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods

Although antibodies have become the fastest-growing class of therapeutics on the market, it is still challenging to develop them for therapeutic applications, which often require these molecules to withstand stresses that are not present in vivo. We define developability as the likelihood of an antibody candidate with suitable functionality to be developed into a manufacturable, stable, safe, and effective drug that can be formulated to high concentrations while retaining a long shelf life. The implementation of reliable developability assessments from the early stages of antibody discovery enables flagging and deselection of potentially problematic candidates, while focussing available resources on the development of the most promising ones. Currently, however, thorough developability assessment requires multiple in vitro assays, which makes it labor intensive and time consuming to implement at early stages. Furthermore, accurate in vitro analysis at the early stage is compromised by the high number of potential candidates that are often prepared at low quantities and purity. Recent improvements in the performance of computational predictors of developability potential are beginning to change this scenario. Many computational methods only require the knowledge of the amino acid sequences and can be used to identify possible developability issues or to rank available candidates according to a range of biophysical properties. Here, we describe how the implementation of in silico tools into antibody discovery pipelines is increasingly offering time- and cost-effective alternatives to in vitro experimental screening, thus streamlining the drug development process. We discuss in particular the biophysical and biochemical properties that underpin developability potential and their trade-offs, review various in vitro assays to measure such properties or parameters that are predictive of developability, and give an overview of the growing number of in silico tools available to predict properties important for antibody development, including the CamSol method developed in our laboratory.

Deciphering the high viscosity of a therapeutic monoclonal antibody in high concentration formulations by microdialysis-hydrogen/deuterium exchange mass spectrometry

High concentration formulations of therapeutic monoclonal antibodies (mAbs) are highly desired for subcutaneous injection. However, high concentration formulations can exhibit unusual molecular behaviors, such as high viscosity or aggregation, that present challenges for manufacturing and administration. To understand the molecular mechanism of the high viscosity exhibited by high concentration protein formulations, we analyzed a human IgG4 (mAb1) at high protein concentrations using sedimentation velocity analytical ultracentrifugation (SV-AUC), X-ray crystallography, hydrogen/deuterium exchange mass spectrometry (HDX-MS), and protein surface patches analysis. Particularly, we developed a microdialysis HDX-MS method to determine intermolecular interactions at different protein concentrations. SV-AUC revealed that mAb1 displayed a propensity for self-association of Fab-Fab, Fab-Fc, and Fc-Fc. mAb1 crystal structure and HDX-MS results demonstrated self-association between complementarity-determining regions (CDRs) and Fc through electrostatic interactions. HDX-MS also indicated Fab-Fab interactions through hydrophobic surface patches constructed by mAb1 CDRs. Our multi-method approach, including fast screening of SV-AUC as well as interface analysis by X-ray crystallography and HDX-MS, helped to elucidate the high viscosity of mAb1 at high concentrations as induced by self-associations of Fab-Fc and Fab-Fab.

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.

New insight into the importance of formulation variables on parenteral growth hormone preparations: potential effect on the injection-site pain

Reducing injection-site pain (ISP) in patients with chronic conditions such as growth hormone deficiency is a valuable strategy to improve patient compliance and therapeutic efficiency. Thus understanding different aspects of pain induction following subcutaneous injection of biotherapeutics and identifying the responsible factors are vital. Here we have discussed the effects of formulation's viscosity, concentration, osmolality, buffering agents, pH, and temperature as well as injection volume, dosing frequency, and different excipients on ISP following subcutaneous injection of commercially available recombinant human growth hormone products. Our literature review found limited available data on the effects of different components of parenteral rhGH products on ISP. This may be due to high cost associated with conducting various clinical trials to assess each excipient in the formulation or to determine the complex interactions of different components and its impact on ISP. Recently, conducting molecular dynamics simulation studies before formulation design has been recommended as an alternative and less-expensive approach. On the other hand, the observed inconsistencies in the available data is mainly due to different pain measurement approaches used in each study. Moreover, it is difficult to translate data obtained from animal studies to human subjects. Despite all these limitations, our investigation showed that components of parenteral rhGH products can significantly contribute to ISP. We suggest further investigation is required for development of long acting, buffer-free, preservative-free formulations. Besides, various excipients are currently being investigated for reducing ISP which can be used as alternatives for common buffers, surfactants or preservatives in designing future rhGH formulations.

Separating clinical antibodies from repertoire antibodies, a path to in silico developability assessment

Approaches for antibody discovery have seen substantial improvement and success in recent years. Yet, advancing antibodies into the clinic remains difficult because therapeutic developability concerns are challenging to predict. We developed a computational model to simplify antibody developability assessment and enable accelerated early-stage screening. To this end, we quantified the ability of hundreds of sequence- and structure-based descriptors to differentiate clinical antibodies that have undergone rigorous screening and characterization for drug-like properties from antibodies in the human repertoire that are not natively paired. This analysis identified 144 descriptors capable of distinguishing clinical from repertoire antibodies. Five descriptors were selected and combined based on performance and orthogonality into a single model referred to as the Therapeutic Antibody Developability Analysis (TA-DA). On a hold-out test set, this tool separated clinical antibodies from repertoire antibodies with an AUC = 0.8, demonstrating the ability to identify developability attributes unique to clinical antibodies. Based on our results, the TA-DA score may serve as an approach for selecting lead antibodies for further development.

Abbreviations: Affinity-Capture Self-Interaction Nanoparticle Spectroscopy (AC-SINS), Area Under the Curve (AUC), Complementary-Determining Region (CDR), Clinical-Stage Therapeutics (CST), Framework (FR), Monoclonal Antibodies (mAbs), Observed Antibody Space (OAS), Receiver Operating Characteristic (ROC), Size-Exclusion Chromatography (SEC), Structural Aggregation Propensity (SAP), Therapeutic Antibody Developability Analysis (TA-DA), Therapeutic Antibody Profiler (TAP), Therapeutic Structural Antibody Database (Thera-SAbDab), Variable Heavy (VH), Variable Light (VL).

Discovery of compounds with viscosity-reducing effects on biopharmaceutical formulations with monoclonal antibodies

•

Computational screening yielded 44 new viscosity-reducing agents on two model mAbs.

•

Dual excipients for viscosity reduction and solution buffering were discovered.

•

Compounds with three or more charges reduce the viscosity of model mAb formulations.

•

Filtering based on physicochemical properties can be applied to other mAb formulations.

For the development of concentrated monoclonal antibody formulations for subcutaneous administration, the main challenge is the high viscosity of the solutions. To compensate for this, viscosity reducing agents are commonly used as excipients. Here, we applied two computational chemistry approaches to discover new viscosity-reducing agents: fingerprint similarity searching, and physicochemical property filtering. In total, 94 compounds were selected and experimentally evaluated on two model monoclonal antibodies, which led to the discovery of 44 new viscosity-reducing agents. Analysis of the results showed that using a simple filter that selects only compounds with three or more charge groups is a good ‘rule of thumb’ for selecting potential viscosity-reducing agents for two model monoclonal antibody formulations.

US FDA-approved therapeutic antibodies with high-concentration formulation: summaries and perspectives

Thirty four (34) of the total US FDA approved 103 therapeutic antibody drugs, accounts for one third of the total approved mAbs, are formulated with high protein concentration (100 mg/mL or above) which are the focus of this article. The highest protein concentration of these approved mAbs is 200 mg/mL. The dominant administration route is subcutaneous (76%). Our analysis indicates that it may be rational to implement a platform formulation containing polysorbate, histidine and sucrose to accelerate high concentration formulation development for antibody drugs. Since 2015, the FDA approval numbers are significantly increased which account for 76% of the total approval numbers, i.e., 26 out of 34 highly concentrated antibodies. Thus, we believe that the high concentration formulations of antibody drugs will be the future trend of therapeutic antibody formulation development, regardless of the challenges of highly concentrated protein formulations.

Differences in human IgG1 and IgG4 S228P monoclonal antibodies viscosity and self-interactions: Experimental assessment and computational predictions of domain interactions

Human/humanized IgG4 antibodies have reduced effector function relative to IgG1 antibodies, which is desirable for certain therapeutic purposes. However, the developability and biophysical properties for IgG4 antibodies are not well understood. This work focuses on the head-to-head comparison of key biophysical properties, such as self-interaction and viscosity, for 14 human/humanized, and chimeric IgG1 and IgG4 S228P monoclonal antibody pairs that contain the identical variable regions. Experimental measurements showed that the IgG4 S228P antibodies have similar or higher self-interaction and viscosity than that of IgG1 antibodies in 20 mM sodium acetate, pH 5.5. We report sequence and structural drivers for the increased viscosity and self-interaction detected in IgG4 S228P antibodies through a combination of experimental data and computational models. Further, we applied and extended a previously established computational model for IgG1 antibodies to predict the self-interaction and viscosity behavior for each antibody pair, providing insight into the structural characteristics and differences of these two isotypes. Interestingly, we observed that the IgG4 S228P swapped variants, where the CH3 domain was swapped for that of an IgG1, showed reduced self-interaction behavior. These domain swapped IgG4 S228P molecules also showed reduced viscosity from experiment and coarse-grained simulations. We also observed that experimental diffusion interaction parameter (kD) values have a high correlation with computational diffusivity prediction for both IgG1 and IgG4 S228P isotypes.

Abbreviations: AHc, constant region Hamaker constant; AHv, variable region Hamaker constant; CDRs, Complementarity-determining regions; CG, Coarse-grained model; CH1, Constant heavy chain 1; CH2 Constant heavy chain 2; CH3 Constant heavy chain 3; chgCH3 Effective charge on the CH3 region; CL Constant light chain; cP, Centipoise; DLS, Dynamic light scattering; Fab, Fragment antigen-binding; Fc, Fragment crystallizable; Fv, Variable domaing; (r) Radial distribution function; H1 CDR1 of Heavy Chain; H2 CDR2 of Heavy Chain; H3 CDR3 of Heavy Chain; HVI, High viscosity index; IgG1 human immunoglobulin of IgG1 subclass; IgG4 human immunoglobulin of IgG4 subclass; kD, Diffusion interaction parameter; L1 CDR1 of Light Chain; L2 CDR2 of Light Chain; L3 CDR3 of Light Chain; mAb, Monoclonal antibody; MD, Molecular dynamics; PPI Protein–protein interactions; SCM, Spatial charge map; UP-SEC, Ultra-high-performance size-exclusion chromatography; VH, Variable domain of Heavy Chain; VL, Variable domain of Light Chain

Effects of Impurities from Sugar Excipient on Filtrate Flux during Ultrafiltration and Diafiltration Process

Sugar excipients such as sucrose and maltose are widely used for biopharmaceutical formulation to improve protein stability and to ensure isotonicity for administration. However, according to recent literature, pharmaceutical-grade sucrose contained nanoparticulate impurities (NPIs) that result in protein aggregation and degradation. The objective of this study was to evaluate the filtrate flux behavior of sugar solution during ultrafiltration (UF) and diafiltration (DF). Filtrate flux data were obtained using either a tangential flow filtration (TFF) system for DF experiments or a normal flow filtration system for UF experiments. In diafiltration experiments, which were performed using 7 g/L of human immunoglobulin G in a 20 mM histidine buffer with the 100 mM sucrose or maltose, the filtrate flux with sucrose solution decreased significantly. In contrast, the one with maltose solution was in good correspondence with the calculated filtrate flux accounting for the effects of solution viscosity. This large decline in the flux was also observed during UF experiments, in which the presence of NPIs was identified by dynamic light scattering analysis and by capturing an SEM image of the membrane surface after filtration. In addition, highly purified sucrose resulted in a much lower flux decline in TFF in the absence of NPIs. These results provide important insights into the factors governing the optimization of the UF/DF process using appropriate excipients for biopharmaceutical formulation.

Towards an improved prediction of concentrated antibody solution viscosity using the Huggins coefficient

The viscosity of a monoclonal antibody solution must be monitored and controlled as it can adversely affect product processing, packaging and administration. Engineering low viscosity mAb formulations is challenging as prohibitive amounts of material are required for concentrated solution analysis, and it is difficult to predict viscosity from parameters obtained through low-volume, high-throughput measurements such as the interaction parameter, k_D, and the second osmotic virial coefficient, B₂₂. As a measure encompassing the effect of intermolecular interactions on dilute solution viscosity, the Huggins coefficient, k_h, is a promising candidate as a parameter measureable at low concentrations, but indicative of concentrated solution viscosity. In this study, a differential viscometry technique is developed to measure the intrinsic viscosity, [η], and the Huggins coefficient, k_h, of protein solutions. To understand the effect of colloidal protein-protein interactions on the viscosity of concentrated protein formulations, the viscometric parameters are compared to k_D and B₂₂ of two mAbs, tuning the contributions of repulsive and attractive forces to the net protein-protein interaction by adjusting solution pH and ionic strength. We find a strong correlation between the concentrated protein solution viscosity and the k_h but this was not observed for the k_D or the b₂₂, which have been previously used as indicators of high concentration viscosity. Trends observed in [η] and k_h values as a function of pH and ionic strength are rationalised in terms of protein-protein interactions.

Toward a flow-dependent phase-stability criterion: Osmotic pressure in sticky flowing suspensions

Equilibrium phase instability of colloids is robustly predicted by the Vliegenthart-Lekkerkerker (VL) critical value of the second virial efficient, but no such general criterion has been established for suspensions undergoing flow. A transition from positive to negative osmotic pressure is one mechanical hallmark of a change in phase stability in suspensions and provides a natural extension of the equilibrium osmotic pressure encoded in the second virial coefficient. Here, we propose to study the non-Newtonian rheology of an attractive colloidal suspension using the active microrheology framework as a model for focusing on the pair trajectories that underlie flow stability. We formulate and solve a Smoluchowski relation to understand the interplay between attractions, hydrodynamics, Brownian motion, and flow on particle microstructure in a semi-dilute suspension and utilize the results to study the viscosity and particle-phase osmotic pressure. We find that an interplay between attractions and hydrodynamics leads to dramatic changes in the nonequilibrium microstructure, which produces a two-stage flow-thinning of viscosity and leads to pronounced flow-induced negative osmotic pressure. We summarize these findings with an osmotic pressure heat map that predicts where hydrodynamic enhancement of attractive bonds encourages flow-induced aggregation or phase separation. We identify a critical isobar-a flow-induced critical pressure consistent with phase instability and a nonequilibrium extension of the VL criterion.

Intrinsic physicochemical profile of marketed antibody-based biotherapeutics

Successful biologic drug discovery and development involves finding functional as well as developable candidates. Once a candidate has been demonstrated to be functional, the next step is to determine whether it can be translated into a drug product. This requires that the candidate can withstand stresses encountered during manufacturing, shipping, and storage. Additionally, it must be safe, efficacious, and possess good pharmacology. In silico analyses of the variable regions of 77 marketed antibody-based biotherapeutics have revealed five nonredundant physicochemical descriptors. Distributions of these descriptors, observed for marketed biotherapeutics, can help prioritize a drug candidate for experimental testing at early discovery stages, guide engineering efforts to further optimize it, and help increase the productivity of biologic drug discovery and development.

Feeding biopharma pipelines with biotherapeutic candidates that possess desirable developability profiles can help improve the productivity of biologic drug discovery and development. Here, we have derived an in silico profile by analyzing computed physicochemical descriptors for the variable regions (Fv) found in 77 marketed antibody-based biotherapeutics. Fv regions of these biotherapeutics demonstrate significant diversities in their germlines, complementarity determining region loop lengths, hydrophobicity, and charge distributions. Furthermore, an analysis of 24 physicochemical descriptors, calculated using homology-based molecular models, has yielded five nonredundant descriptors whose distributions represent stability, isoelectric point, and molecular surface characteristics of their Fv regions. Fv regions of candidates from our internal discovery campaigns, human next-generation sequencing repertoires, and those in clinical-stages (CST) were assessed for similarity with the physicochemical profile derived here. The Fv regions in 33% of CST antibodies show physicochemical properties that are dissimilar to currently marketed biotherapeutics. In comparison, physicochemical characteristics of ∼29% of the Fv regions in human antibodies and ∼27% of our internal hits deviated significantly from those of marketed biotherapeutics. The early availability of this information can help guide hit selection, lead identification, and optimization of biotherapeutic candidates. Insights from this work can also help support portfolio risk assessment, in-licensing, and biopharma collaborations.

Molecular computations of preferential interactions of proline, arginine.HCl, and NaCl with IgG1 antibodies and their impact on aggregation and viscosity

Preferential interactions of excipients with the antibody surface govern their effect on the stability of antibodies in solution. We probed the preferential interactions of proline, arginine.HCl (Arg.HCl), and NaCl with three therapeutically relevant IgG1 antibodies via experiment and simulation. With simulations, we examined how excipients interacted with different types of surface patches in the variable region (Fv). For example, proline interacted most strongly with aromatic surfaces, Arg.HCl was included near negative residues, and NaCl was excluded from negative residues and certain hydrophobic regions. The differences in interaction of different excipients with the same surface patch on an antibody may be responsible for variations in the antibody's aggregation, viscosity, and self-association behaviors in each excipient. Proline reduced self-association for all three antibodies and reduced aggregation for the antibody with an association-limited aggregation mechanism. The effects of Arg.HCl and NaCl on aggregation and viscosity were highly dependent on the surface charge distribution and the extent of exclusion from highly hydrophobic patches. At pH 5.5, both tended to increase the aggregation of an antibody with a strongly positive charge on the Fv, while only NaCl reduced the aggregation of the antibody with a large negative charge patch on the Fv. Arg.HCl reduced the viscosities of antibodies with either a hydrophobicity-driven mechanism or a charge-driven mechanism. Analysis of this data presents a framework for understanding how amino acid and ionic excipients interact with different protein surfaces, and how these interactions translate to the observed stability behavior.

Denaturing and Native Mass Spectrometric Analytics for Biotherapeutic Drug Discovery Research: Historical, Current, and Future Personal Perspectives

Mass spectrometry (MS) plays a key role throughout all stages of drug development and is now as ubiquitous as other analytical techniques such as surface plasmon resonance, nuclear magnetic resonance, and supercritical fluid chromatography, among others. Herein, we aim to discuss the history of MS, both electrospray and matrix-assisted laser desorption ionization, specifically for the analysis of antibodies, evolving through to denaturing and native-MS analysis of newer biologic moieties such as antibody-drug conjugates, multispecific antibodies, and interfering nucleic acid-based therapies. We discuss challenging therapeutic target characterization such as membrane protein receptors. Importantly, we compare and contrast the MS and hyphenated analytical chromatographic methods used to characterize these therapeutic modalities and targets within biopharmaceutical research and highlight the importance of appropriate MS deconvolution software and its essential contribution to project progression. Finally, we describe emerging applications and MS technologies that are still predominantly within either a development or academic stage of use but are poised to have significant impact on future drug development within the biopharmaceutic industry once matured. The views reflected herein are personal and are not meant to be an exhaustive list of all relevant MS performed within biopharmaceutical research but are what we feel have been historically, are currently, and will be in the future the most impactful for the drug development process.

Rational optimization of a monoclonal antibody improves the aggregation propensity and enhances the CMC properties along the entire pharmaceutical process chain

The discovery of therapeutic monoclonal antibodies (mAbs) primarily focuses on their biological activity favoring the selection of highly potent drug candidates. These candidates, however, may have physical or chemical attributes that lead to unfavorable chemistry, manufacturing, and control (CMC) properties, such as low product titers, conformational and colloidal instabilities, or poor solubility, which can hamper or even prevent development and manufacturing. Hence, there is an urgent need to consider the developability of mAb candidates during lead identification and optimization. This work provides a comprehensive proof of concept study for the significantly improved developability of a mAb variant that was optimized with the help of sophisticated in silico tools relative to its difficult-to-develop parental counterpart. Interestingly, a single amino acid substitution in the variable domain of the light chain resulted in a three-fold increased product titer after stable expression in Chinese hamster ovary cells. Microscopic investigations revealed that wild type mAb-producing cells displayed potential antibody inclusions, while the in silico optimized variant-producing cells showed a rescued phenotype. Notably, the drug substance of the in silico optimized variant contained substantially reduced levels of aggregates and fragments after downstream process purification. Finally, formulation studies unraveled a significantly enhanced colloidal stability of the in silico optimized variant while its folding stability and potency were maintained. This study emphasizes that implementation of bioinformatics early in lead generation and optimization of biotherapeutics reduces failures during subsequent development activities and supports the reduction of project timelines and resources.

Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning

The optimization of therapeutic antibodies is time-intensive and resource-demanding, largely because of the low-throughput screening of full-length antibodies (approximately 1 × 10³ variants) expressed in mammalian cells, which typically results in few optimized leads. Here we show that optimized antibody variants can be identified by predicting antigen specificity via deep learning from a massively diverse space of antibody sequences. To produce data for training deep neural networks, we deep-sequenced libraries of the therapeutic antibody trastuzumab (about 1 × 10⁴ variants), expressed in a mammalian cell line through site-directed mutagenesis via CRISPR-Cas9-mediated homology-directed repair, and screened the libraries for specificity to human epidermal growth factor receptor 2 (HER2). We then used the trained neural networks to screen a computational library of approximately 1 × 10⁸ trastuzumab variants and predict the HER2-specific subset (approximately 1 × 10⁶ variants), which can then be filtered for viscosity, clearance, solubility and immunogenicity to generate thousands of highly optimized lead candidates. Recombinant expression and experimental testing of 30 randomly selected variants from the unfiltered library showed that all 30 retained specificity for HER2. Deep learning may facilitate antibody engineering and optimization.

Characterization of Opalescence in low Volume Monoclonal Antibody Solutions Enabled by Microscale Nephelometry

Monoclonal antibody (mAb)-based drugs are often prone to unfavorable solution behaviors including high viscosity, opalescence, phase separation, and aggregation at the high concentrations needed to enable patient-centric subcutaneous dosage forms. Given that these can have a detrimental impact on manufacturability, stability, and delivery, approaches to identifying, monitoring, and controlling these behaviors during drug development are critical. Opalescence presents a significant challenge due to its relationship to liquid-liquid phase separation. Quantitative characterization of opalescence via turbidimetry is often restrictive due to large volume requirements (>2 mL) and alternative microscale approaches based on light transmittance (Eckhardt et al., J Pharm Sci Technol. 1994, 48: 64-70) may pose challenging with respect to accuracy. To address the need for accurate and quantitative microscale opalescence measurements, we have evaluated the use of a 'de-tuned' static light scattering detector which requires <10 μL sample per measurement. We show that tuning of the laser power to a range far below that of traditional light scattering measurements results in a stable detector response that can be accurately calibrated to the nephelometric turbidity unit (NTU) scale using appropriate standards. The calibrated detector signal yields NTU values for mAbs and other protein solutions that are comparable to a commercial turbidimeter. We used this microscale approach to characterize the opalescence of 48 commercial mAb drug products and found that the majority have opalescence below 15 NTU. However, in products with mAb concentrations greater than 75 mg/mL, a broad range of opalescence was observed, in a few cases greater than 20 NTU. These measurements as well as nephelometric characterization of several IgG1 and IgG4 mAbs across a broad pH range highlight subclass-specific tendencies toward opalescence in high concentration solutions.

Characterization and Modeling of Reversible Antibody Self-Association Provide Insights into Behavior, Prediction, and Correction

Reversible antibody self-association, while having major developability and therapeutic implications, is not fully understood or readily predictable and correctable. For a strongly self-associating humanized mAb variant, resulting in unacceptable viscosity, the monovalent affinity of self-interaction was measured in the low μM range, typical of many specific and biologically relevant protein-protein interactions. A face-to-face interaction model extending across both the heavy-chain (HC) and light-chain (LC) Complementary Determining Regions (CDRs) was apparent from biochemical and mutagenesis approaches as well as computational modeling. Light scattering experiments involving individual mAb, Fc, Fab, and Fab'2 domains revealed that Fabs self-interact to form dimers, while bivalent mAb/Fab'2 forms lead to significant oligomerization. Site-directed mutagenesis of aromatic residues identified by homology model patch analysis and self-docking dramatically affected self-association, demonstrating the utility of these predictive approaches, while revealing a highly specific and tunable nature of self-binding modulated by single point mutations. Mutagenesis at these same key HC/LC CDR positions that affect self-interaction also typically abolished target binding with notable exceptions, clearly demonstrating the difficulties yet possibility of correcting self-association through engineering. Clear correlations were also observed between different methods used to assess self-interaction, such as Dynamic Light Scattering (DLS) and Affinity-Capture Self-Interaction Nanoparticle Spectroscopy (AC-SINS). Our findings advance our understanding of therapeutic protein and antibody self-association and offer insights into its prediction, evaluation and corrective mitigation to aid therapeutic development.

Cross-Reactivity and Functionality of Approved Human Immune Checkpoint Blockers in Dogs

BACKGROUND

Rodent cancer models have limitations in predicting efficacy, tolerability and accompanying biomarkers of ICIs in humans. Companion dogs suffering from neoplastic diseases have gained attention as a highly relevant translational disease model. Despite successful reports of PD-1/PD-L1 blockade in dogs, no compounds are available for veterinary medicine.

METHODS

Here, we assessed suitability of seven FDA-approved human ICIs to target CTLA-4 or PD-1/PD-L1 in dogs. Cross-reactivity and blocking potential was assessed using ELISA and flow cytometry. Functional responses were assessed on peripheral blood mononuclear cells (PBMCs) derived from healthy donors (n = 12) and cancer patient dogs (n = 27) as cytokine production after stimulation. Immune composition and target expression of healthy donors and cancer patients was assessed via flow cytometry.

RESULTS

Four candidates showed cross-reactivity and two blocked the interaction of canine PD-1 and PD-L1. Of those, only atezolizumab significantly increased cytokine production of healthy and patient derived PBMCs in vitro. Especially lymphoma patient PBMCs responded with increased cytokine production. In other types of cancer, response to atezolizumab appeared to correlate with a lower frequency of CD8 T cells.

CONCLUSIONS

Cross-functionality of atezolizumab encourages reverse translational efforts using (combination) immunotherapies in companion dog tumor patients to benefit both veterinary and human medicine.

Predicting Antibody Developability Profiles Through Early Stage Discovery Screening

Monoclonal antibodies play an increasingly important role for the development of new drugs across multiple therapy areas. The term 'developability' encompasses the feasibility of molecules to successfully progress from discovery to development via evaluation of their physicochemical properties. These properties include the tendency for self-interaction and aggregation, thermal stability, colloidal stability, and optimization of their properties through sequence engineering. Selection of the best antibody molecule based on biological function, efficacy, safety, and developability allows for a streamlined and successful CMC phase. An efficient and practical high-throughput developability workflow (100 s-1,000 s of molecules) implemented during early antibody generation and screening is crucial to select the best lead candidates. This involves careful assessment of critical developability parameters, combined with binding affinity and biological properties evaluation using small amounts of purified material (<1 mg), as well as an efficient data management and database system. Herein, a panel of 152 various human or humanized monoclonal antibodies was analyzed in biophysical property assays. Correlations between assays for different sets of properties were established. We demonstrated in two case studies that physicochemical properties and key assay endpoints correlate with key downstream process parameters. The workflow allows the elimination of antibodies with suboptimal properties and a rank ordering of molecules for further evaluation early in the candidate selection process. This enables any further engineering for problematic sequence attributes without affecting program timelines.

Protein intrinsic viscosity determination with the Viscosizer TD instrument: reaching beyond the initially expected applications

Intrinsic viscosity is a key hydrodynamic parameter to understand molecular structure and hydration, as well as intramolecular interactions. Commercially available instruments measure intrinsic viscosity by recording the macromolecular mobility in a capillary. These instruments monitor Taylor dispersion using an absorbance or fluorescence detector. By design, these instruments behave like U-tube viscometers. To our knowledge, there are no studies to date showing that the Viscosizer TD instrument (Malvern-Panalytical) is able to measure the intrinsic viscosity of macromolecules. In this study, we then performed our assays on the Poly(ethylene oxide) polymer (PEO), used classically as a standard for viscometry measurements and on three model proteins: the bovine serum albumin (BSA), the bevacizumab monoclonal antibody, and the RTX Repeat Domain (RD) of the adenylate cyclase toxin of Bordetella pertussis (CyaA). The presence of P20 in the samples is critical to get reliable results. The data obtained with our in-house protocol show a strong correlation with intrinsic viscosity values obtained using conventional techniques. However, with respect to them, our measurements could be performed at relatively low concentrations, between 2 and 5 mg/ml, using only 7 µL per injection. Altogether, our results show that the Viscosizer TD instrument is able to measure intrinsic viscosities in a straightforward manner. This simple and innovative approach should give a new boost to intrinsic viscosity measurements and should reignite the interest of biophysicists, immunologists, structural biologists and other researchers for this key physicochemical parameter.

Machine Learning Applied to Determine the Molecular Descriptors Responsible for the Viscosity Behavior of Concentrated Therapeutic Antibodies

Predicting the solution viscosity of monoclonal antibody (mAb) drug products remains as one of the main challenges in antibody drug design, manufacturing, and delivery. In this work, the concentration-dependent solution viscosity of 27 FDA-approved mAbs was measured at pH 6.0 in 10 mM histidine-HCl. Six mAbs exhibited high viscosity (>30 cP) in solutions at 150 mg/mL mAb concentration. Combining molecular modeling and machine learning feature selection, we found that the net charge in the mAbs and the amino acid composition in the Fv region are key features which govern the viscosity behavior. For mAbs whose behavior was not dominated by charge effects, we observed that high viscosity is correlated with more hydrophilic and fewer hydrophobic residues in the Fv region. A predictive model based on the net charges of mAbs and a high viscosity index is presented as a fast screening tool for classifying low- and high-viscosity mAbs.

Dissecting the molecular basis of high viscosity of monospecific and bispecific IgG antibodies

Some antibodies exhibit elevated viscosity at high concentrations, making them poorly suited for therapeutic applications requiring administration by injection such as subcutaneous or ocular delivery. Here we studied an anti-IL-13/IL-17 bispecific IgG₄ antibody, which has anomalously high viscosity compared to its parent monospecific antibodies. The viscosity of the bispecific IgG₄ in solution was decreased by only ~30% in the presence of NaCl, suggesting electrostatic interactions are insufficient to fully explain the drivers of viscosity. Intriguingly, addition of arginine-HCl reduced the viscosity of the bispecific IgG₄ by ~50% to its parent IgG level. These data suggest that beyond electrostatics, additional types of interactions such as cation-π and/or π-π may contribute to high viscosity more significantly than previously understood. Molecular dynamics simulations of antibody fragments in the mixed solution of free arginine and explicit water were conducted to identify hotspots involved in self-interactions. Exposed surface aromatic amino acids displayed an increased number of contacts with arginine. Mutagenesis of the majority of aromatic residues pinpointed by molecular dynamics simulations effectively decreased the solution's viscosity when tested experimentally. This mutational method to reduce the viscosity of a bispecific antibody was extended to a monospecific anti-GCGR IgG₁ antibody with elevated viscosity. In all cases, point mutants were readily identified that both reduced viscosity and retained antigen-binding affinity. These studies demonstrate a new approach to mitigate high viscosity of some antibodies by mutagenesis of surface-exposed aromatic residues on complementarity-determining regions that may facilitate some clinical applications.

Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods

There is intense and widespread interest in developing monoclonal antibodies as therapeutic agents to treat diverse human disorders. During early-stage antibody discovery, hundreds to thousands of lead candidates are identified, and those that lack optimal physical and chemical properties must be deselected as early as possible to avoid problems later in drug development. It is particularly challenging to characterize such properties for large numbers of candidates with the low antibody quantities, concentrations, and purities that are available at the discovery stage, and to predict concentrated antibody properties (e.g., solubility, viscosity) required for efficient formulation, delivery, and efficacy. Here we review key recent advances in developing and implementing high-throughput methods for identifying antibodies with desirable in vitro and in vivo properties, including favorable antibody stability, specificity, solubility, pharmacokinetics, and immunogenicity profiles, that together encompass overall drug developability. In particular, we highlight impressive recent progress in developing computational methods for improving rational antibody design and prediction of drug-like behaviors that hold great promise for reducing the amount of required experimentation. We also discuss outstanding challenges that will need to be addressed in the future to fully realize the great potential of using such analysis for minimizing development times and improving the success rate of antibody candidates in the clinic.

Product-Specific Impact of Viscosity Modulating Formulation Excipients During Ultra-High Concentration Biotherapeutics Drug Product Development

Developing ultra-high concentration biotherapeutics drug products can be challenging due to increased viscosity, processing, and stability issues. Excipients used to alleviate these concerns are traditionally evaluated at lower protein concentrations. This study investigates whether classically known modulators of stability and viscosity at low (<50 mg/mL) to high (>50 - 150 mg/mL) protein concentrations are beneficial in ultra-high (>150 mg/mL) concentration protein formulations and drug products. This study evaluates the effect of arginine monohydrochloride, proline, and lysine monohydrochloride on viscosity and concentratability at different high and ultra-high protein concentrations using a monoclonal antibody, mAbN, formulation as a candidate protein system. The effect of excipients on the viscosity and concentratability (rate and extent) was different at high versus ultra-high protein concentrations. These results highlight that classical excipients in literature known to modulate protein interactions at low protein concentrations and reduce viscosity at high protein concentrations may need to be evaluated at target protein concentrations in a product-specific manner while developing ultra-high concentration biologics drug products.

Development challenges of high concentration monoclonal antibody formulations

High concentration monoclonal antibody drug products represent a special segment of biopharmaceuticals. In contrast to other monoclonal antibody products, high concentration monoclonal antibodies are injected subcutaneously helping increase patient compliance and reduce the number of hospital patient visits. It is important to note that a high protein concentration (≥50 mg/mL) poses a challenge from a product development perspective. Colloidal properties, physical and chemical protein stability should be considered during formulation, primary packaging and manufacturing process development as well as optimization of other dosage form-related parameters. The aim of such development work is to obtain a drug product capable of maintaining appropriate protein structure throughout its shelf-life and ensure proper and accurate dosage upon administration.

Use of computed tomography to assess subcutaneous drug dispersion with recombinant human hyaluronidase PH20 in a swine model

INTRODUCTION

Subcutaneous (SC) formulations of therapeutics with recombinant human hyaluronidase PH20 (rHuPH20) are currently approved across various disease indications. The rHuPH20-mediated enzymatic degradation of SC hyaluronan (HA) facilitates bulk fluid flow and dispersion of co-administered therapeutics. However, current methods of quantifying dispersion in the SC space are limited. Here, a novel method is outlined to quantify and follow rapid SC volumetric dispersion of a representative therapeutic fluid in the presence of rHuPH20 using computed tomography (CT).

METHODS

Ten Yucatan miniature swine were randomized to three groups. Animals received simultaneous infusions of contrast agent (CA) alone (left side of the animal) or in combination with rHuPH20 (right side) at infusion rates of 2.5, 5, or 10 mL/min. Spiral CT scans (1.5 mm thickness) were conducted before and after the infusion and at regular time intervals throughout. Scans were used to create three-dimensional (3D) reconstructions of the fluid pockets and analyze surface area, volume, and sphericity.

RESULTS

3D reconstruction showed increased dispersion of CA with rHuPH20 compared with CA alone, with fenestration and increased dispersion in the craniocaudal and lateromedial directions. The CA with rHuPH20 fluid pockets showed an average increase of 46% in surface area (p = 0.001), a 35% increase in volume (p = 0.001) and a 17% decrease in sphericity post-infusion compared with CA alone at 30 min post-infusion.

DISCUSSION

This exploratory study confirms the value of CT imaging as a non-invasive method of assessing real-time spatial and temporal behavior of SC-administered fluids. This technique could help to assess the dispersion pattern of novel rHuPH20 SC co-formulations.

Analysis of antibody self-interaction by bio-layer interferometry as tool to support lead candidate selection during preformulation and developability assessments

Developability assessment of therapeutic mAb candidates before entering CMC development mitigates the risk of later failure because of manufacturing and stability issues. For mAbs derived from library based screenings, such evaluation starts with the first panning and ends with the selection of a lead candidate. This candidate should show, amongst others, high affine target binding and beneficial conformational as well as chemical stability. In addition, colloidal stability, reflected by the self-interaction propensity, should be superior in order to reduce aggregate formation and unacceptably high viscosity at elevated protein concentrations. Here, we present a study demonstrating the application of self-interaction bio-layer interferometry (SI-BLI) in a developability assessment, including the evaluation of preformulations. We reveal that the formulation rankings based on SI-BLI, DLS and viscosity measurements correlate. SI-BLI provides a deeper understanding of influencing factors on mAb self-interaction such as ionic strength or cation species. The attractive mAb self-interaction propensity was significantly more suppressed by Mg²⁺ compared to Na⁺. SI-BLI can be performed in high throughput with minimal material and sample preparation needs. Therefore, it can be applied in early stages of developability assessment going beyond the use of a platform formulation and a small number of analysis, to screen more parameters before proceeding with candidate selection and further extensive development.

Computational Characterization of Antibody-Excipient Interactions for Rational Excipient Selection Using the Site Identification by Ligand Competitive Saturation-Biologics Approach

Protein therapeutics typically require a concentrated protein formulation, which can lead to self-association and/or high viscosity due to protein-protein interaction (PPI). Excipients are often added to improve stability, bioavailability, and manufacturability of the protein therapeutics, but the selection of excipients often relies on trial and error. Therefore, understanding the excipient-protein interaction and its effect on non-specific PPI is important for rational selection of formulation development. In this study, we validate a general workflow based on the site identification by ligand competitive saturation (SILCS) technology, termed SILCS-Biologics, that can be applied to protein therapeutics for rational excipient selection. The National Institute of Standards and Technology monoclonal antibody (NISTmAb) reference along with the CNTO607 mAb is used as model antibody proteins to examine PPIs, and NISTmAb was used to further examine excipient-protein interactions, in silico. Metrics from SILCS include the distribution and predicted affinity of excipients, buffer interactions with the NISTmAb Fab, and the relation of the interactions to predicted PPI. Comparison with a range of experimental data showed multiple SILCS metrics to be predictive. Specifically, the number of favorable sites to which an excipient binds and the number of sites to which an excipient binds that are involved in predicted PPIs correlate with the experimentally determined viscosity. In addition, a combination of the number of binding sites and the predicted binding affinity is indicated to be predictive of relative protein stability. Comparison of arginine, trehalose, and sucrose, all of which give the highest viscosity in combination with analysis of B₂₂ and k_D and the SILCS metrics, indicates that higher viscosities are associated with a low number of predicted binding sites, with lower binding affinity of arginine leading to its anomalously high impact on viscosity. The present study indicates the potential for the SILCS-Biologics approach to be of utility in the rational design of excipients during biologics formulation.

Williams G, Brocchini S, Awwad S. Injectables and Depots to Prolong Drug Action of Proteins and Peptides

Proteins and peptides have emerged in recent years to treat a wide range of multifaceted diseases such as cancer, diabetes and inflammation. The emergence of polypeptides has yielded advancements in the fields of biopharmaceutical production and formulation. Polypeptides often display poor pharmacokinetics, limited permeability across biological barriers, suboptimal biodistribution, and some proclivity for immunogenicity. Frequent administration of polypeptides is generally required to maintain adequate therapeutic levels, which can limit efficacy and compliance while increasing adverse reactions. Many strategies to increase the duration of action of therapeutic polypeptides have been described with many clinical products having been developed. This review describes approaches to optimise polypeptide delivery organised by the commonly used routes of administration. Future innovations in formulation may hold the key to the continued successful development of proteins and peptides with optimal clinical properties.

Computational approaches to therapeutic antibody design: established methods and emerging trends

Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.

Aggregation, liquid-liquid phase separation, and percolation behaviour of a model antibody fluid constrained by hard-sphere obstacles

This study is concerned with the behaviour of proteins within confinement created by hard-sphere obstacles. An individual antibody molecule is depicted as an assembly of seven hard spheres, organized to resemble a Y-shaped (on average) antibody (7-bead model) protein. For comparison with other studies we, in one case, model the protein as a hard sphere decorated by three short-range attractive sites. The antibody has two Fab and one Fc domains located in the corners of the letter Y. In this calculation, only the Fab-Fab and Fab-Fc attractive pair interactions are possible. The confinement is formed by the randomly distributed hard-sphere obstacles fixed in space. Aside from size exclusion, the obstacles do not interact with antibodies, but they affect the protein-protein correlation. We used a combination of the scaled-particle theory, Wertheim's thermodynamic perturbation theory and the Flory-Stockmayer theory to calculate: (i) the second virial coefficient of the protein fluid, (ii) the percolation threshold, (iii) cluster size distributions, and (iv) the liquid-liquid phase separation as a function of the strength of the various pair interactions of the protein and the model parameters, such as protein concentration and the packing fraction of obstacles. The conclusion is that hard-sphere obstacles strongly decrease the critical density and also, but to a much lesser extent, the critical temperature. Also, the confinement enhances clustering, making the percolating region broader. The effect depends on the model parameters, such as the packing fraction of obstacles η0, the inter-site interaction strength εIJ, and the ratio between the size of the obstacle σ0 and the size of one bead of the model antibody σhs; the value of this ratio is varied here from 2 to 5. Interestingly, at low to moderate packing fractions of obstacles, the second virial coefficient first slightly decreases (destabilization), and the slope depends on the observation temperature, but then at higher values of η0 it increases. The calculated values of the second virial coefficient also depend on the size of the obstacles.

Physicochemical Rules for Identifying Monoclonal Antibodies with Drug-like Specificity

The ability of antibodies to recognize their target antigens with high specificity is fundamental to their natural function. Nevertheless, therapeutic antibodies display variable and difficult-to-predict levels of nonspecific and self-interactions that can lead to various drug development challenges, including antibody aggregation, abnormally high viscosity, and rapid antibody clearance. Here we report a method for predicting the overall specificity of antibodies in terms of their relative risk for displaying high levels of nonspecific or self-interactions at physiological conditions. We find that individual and combined sets of chemical rules that limit the maximum and minimum numbers of certain solvent-exposed amino acids in antibody variable regions are strong predictors of specificity for large panels of preclinical and clinical-stage antibodies. We also demonstrate how the chemical rules can be used to identify sites that mediate nonspecific interactions in suboptimal antibodies and guide the design of targeted sublibraries that yield variants with high antibody specificity. These findings can be readily used to improve the selection and engineering of antibodies with drug-like specificity.

Modeling and mitigation of high-concentration antibody viscosity through structure-based computer-aided protein design

For an antibody to be a successful therapeutic many competing factors require optimization, including binding affinity, biophysical characteristics, and immunogenicity risk. Additional constraints may arise from the need to formulate antibodies at high concentrations (>150 mg/ml) to enable subcutaneous dosing with reasonable volume (ideally <1.0 mL). Unfortunately, antibodies at high concentrations may exhibit high viscosities that place impractical constraints (such as multiple injections or large needle diameters) on delivery and impede efficient manufacturing. Here we describe the optimization of an anti-PDGF-BB antibody to reduce viscosity, enabling an increase in the formulated concentration from 80 mg/ml to greater than 160 mg/ml, while maintaining the binding affinity. We performed two rounds of structure guided rational design to optimize the surface electrostatic properties. Analysis of this set demonstrated that a net-positive charge change, and disruption of negative charge patches were associated with decreased viscosity, but the effect was greatly dependent on the local surface environment. Our work here provides a comprehensive study exploring a wide sampling of charge-changes in the Fv and CDR regions along with targeting multiple negative charge patches. In total, we generated viscosity measurements for 40 unique antibody variants with full sequence information which provides a significantly larger and more complete dataset than has previously been reported.

Structure guided maturation of a novel humanized anti-HBV antibody and its preclinical development

We have reported that E6F6, a mouse monoclonal antibody, is a promising treatment option for patients with chronic hepatitis B (CHB). A humanized E6F6 antibody B11 with affinity loss was obtained by CDR-grafting approach. To address this issue, in silico affinity maturation through scanning mutagenesis using CHARMM force field methods was performed on an predicted immune complex model of the B11:HBsAg. We chose four variants with top increased interaction energy for further characterization. The antibody huE6F6-1 within two point mutations (Heavy Chain: Asp65Val; His66Leu) was identified to restore the parental antibody's high binding affinity, neutralization activity, and potent efficacy of viral suppression in vivo. Crystal structure (1.8 Å resolution) based molecular docking proved more stabilized and compact hydrogen bond interactions formed in huE6F6-1.The smaller and dispersed HBV immune complexes of huE6F6-1 by electron microscopy suggested it will have the same therapeutic efficacy as the parental E6F6 mAb. Preclinical study and pharmacokinetics of huE6F6-1 demonstrated that it is a stable and desirable lead candidate to improve the clinical management of CHB. Notably, our structure guided approach may facilitate the humanization and affinity maturation of other rodent antibody candidates during drug development.