Prediction of Aggregation Prone Regions of Therapeutic Proteins

Stability of Protein Pharmaceuticals: Recent Advances

There have been significant advances in the formulation and stabilization of proteins in the liquid state over the past years since our previous review. Our mechanistic understanding of protein-excipient interactions has increased, allowing one to develop formulations in a more rational fashion. The field has moved towards more complex and challenging formulations, such as high concentration formulations to allow for subcutaneous administration and co-formulation. While much of the published work has focused on mAbs, the principles appear to apply to any therapeutic protein, although mAbs clearly have some distinctive features. In this review, we first discuss chemical degradation reactions. This is followed by a section on physical instability issues. Then, more specific topics are addressed: instability induced by interactions with interfaces, predictive methods for physical stability and interplay between chemical and physical instability. The final parts are devoted to discussions how all the above impacts (co-)formulation strategies, in particular for high protein concentration solutions.'

Epitope Identification of an mGlu5 Receptor Nanobody Using Physics-Based Molecular Modeling and Deep Learning Techniques

The world has witnessed a revolution in therapeutics with the development of biological medicines such as antibodies and antibody fragments, notably nanobodies. These nanobodies possess unique characteristics including high specificity and modulatory activity, making them promising candidates for therapeutic applications. Identifying their binding mode is essential for their development. Experimental structural techniques are effective to get such information, but they are expensive and time-consuming. Here, we propose a computational approach, aiming to identify the epitope of a nanobody that acts as an agonist and a positive allosteric modulator at the rat metabotropic glutamate receptor 5. We employed multiple structure modeling tools, including various artificial intelligence algorithms for epitope mapping. The computationally identified epitope was experimentally validated, confirming the success of our approach. Additional dynamics studies provided further insights on the modulatory activity of the nanobody. The employed methodologies and approaches initiate a discussion on the efficacy of diverse techniques for epitope mapping and later nanobody engineering.

Druggability properties of a L309K mutation in the antibody CH2 domain

In the early stages of antibody drug development, it is imperative to conduct a comprehensive assessment and enhancement of the druggability attributes of potential molecules by considering their fundamental physicochemical properties. This study specifically concentrates on the surface-exposed hydrophobic region of the candidate antibody aPDL1-WT and explores the effectiveness of the L309K mutation strategy. The resulting aPDL1-LK variant demonstrates a notable enhancement over the original antibody in addressing the issue of aggregation and formation of large molecular impurities under accelerated high-temperature conditions. The mutated molecule, aPDL1-LK, exhibits excellent physicochemical properties such as hydrophilicity, conformational stability, charge variant stability, post-translational modifications, and serum stability. In terms of biological function, aPDL1-LK maintains the same glycosylation pattern as the original antibody and shows no significant difference in affinity for antigen hPDL1 protein, CD16a-F158, CD64, CD32a-H131, and complement C1q, compared to aPDL1-WT. The L309K mutation results in an approximately twofold reduction in its affinity for CD16a-V158 and CD32a-R131. In vitro biological assays, including antibody-dependent cell-mediated cytotoxicity (ADCC), antibody-dependent cellular phagocytosis (ADCP), and complement-dependent cytotoxicity (CDC), reveal that the L309K mutation may decrease CD16a-V158-mediated ADCC activity due to the mutation-induced decrease in ligand affinity, while not affect CD32a-R131-mediated ADCP activity. In conclusion, the L309K mutation offers a promising strategy to enhance the druggability properties of candidate antibodies.

PNAbind: Structure-based prediction of protein-nucleic acid binding using graph neural networks

The recognition and binding of nucleic acids (NAs) by proteins depends upon complementary chemical, electrostatic and geometric properties of the protein-NA binding interface. Structural models of protein-NA complexes provide insights into these properties but are scarce relative to models of unbound proteins. We present a deep learning approach for predicting protein-NA binding given the apo structure of a protein (PNAbind). Our method utilizes graph neural networks to encode spatial distributions of physicochemical and geometric properties of the protein molecular surface that are predictive of NA binding. Using global physicochemical encodings, our models predict the overall binding function of a protein and can discriminate between specificity for DNA or RNA binding. We show that such predictions made on protein structures modeled with AlphaFold2 can be used to gain mechanistic understanding of chemical and structural features that determine NA recognition. Using local encodings, our models predict the location of NA binding sites at the level of individual binding residues. Binding site predictions were validated against benchmark datasets, achieving AUROC scores in the range of 0.92-0.95. We applied our models to the HIV-1 restriction factor APOBEC3G and show that our predictions are consistent with experimental RNA binding data.

Unveiling the affinity-stability relationship in anti-measles virus antibodies: a computational approach for hotspots prediction

Recent years have seen an uptick in the use of computational applications in antibody engineering. These tools have enhanced our ability to predict interactions with antigens and immunogenicity, facilitate humanization, and serve other critical functions. However, several studies highlight the concern of potential trade-offs between antibody affinity and stability in antibody engineering. In this study, we analyzed anti-measles virus antibodies as a case study, to examine the relationship between binding affinity and stability, upon identifying the binding hotspots. We leverage in silico tools like Rosetta and FoldX, along with molecular dynamics (MD) simulations, offering a cost-effective alternative to traditional in vitro mutagenesis. We introduced a pattern in identifying key residues in pairs, shedding light on hotspots identification. Experimental physicochemical analysis validated the predicted key residues by confirming significant decrease in binding affinity for the high-affinity antibodies to measles virus hemagglutinin. Through the nature of the identified pairs, which represented the relative hydropathy of amino acid side chain, a connection was proposed between affinity and stability. The findings of the study enhance our understanding of the interactions between antibody and measles virus hemagglutinin. Moreover, the implications of the observed correlation between binding affinity and stability extend beyond the field of anti-measles virus antibodies, thereby opening doors for advancements in antibody research.

Mechanistic Insights into the Adsorption of Monoclonal Antibodies at the Water/Vapor Interface

Monoclonal antibodies (mAbs) are active components of therapeutic formulations that interact with the water-vapor interface during manufacturing, storage, and administration. Surface adsorption has been demonstrated to mediate antibody aggregation, which leads to a loss of therapeutic efficacy. Controlling mAb adsorption at interfaces requires a deep understanding of the microscopic processes that lead to adsorption and identification of the protein regions that drive mAb surface activity. Here, we report all-atom molecular dynamics (MD) simulations of the adsorption behavior of a full IgG1-type antibody at the water/vapor interface. We demonstrate that small local changes in the protein structure play a crucial role in promoting adsorption. Also, interfacial adsorption triggers structural changes in the antibody, potentially contributing to the further enhancement of surface activity. Moreover, we identify key amino acid sequences that determine the adsorption of antibodies at the water-air interface and outline strategies to control the surface activity of these important therapeutic proteins.

Probing the Protein-Excipient Interaction in the Orally Delivered Protein by Solid-State Hydrogen-Deuterium Exchange Mass Spectrometry and Molecular Dynamics

The oral administration of protein therapeutics in solid dosage form is gaining popularity due to its benefits, such as improved medication adherence, convenience, and ease of use for patients compared to traditional parental delivery. However, formulating oral biologics presents challenges related to pH barriers, enzymatic breakdown, and poor bioavailability. Therefore, understanding the interaction between excipients and protein therapeutics in the solid state is crucial for formulation development. In this Letter, we present a case study focused on investigating the role of excipients in protein aggregation during the production of a solid dosage form of a single variable domain on a heavy chain (VHH) protein. We employed solid-state hydrogen-deuterium exchange coupled with mass spectrometry (ssHDX-MS) at both intact protein and peptide levels to assess differences in protein-excipient interactions between two formulations. ssHDX-MS analysis revealed that one formulation effectively prevents protein aggregation during compaction by blocking β-sheets across the VHH protein, thereby preventing β-sheet-β-sheet interactions. Spatial aggregation propensity (SAP) mapping and cosolvent simulation from molecular dynamics (MD) simulation further validated the protein-excipient interaction sites identified through ssHDX-MS. Additionally, the MD simulation demonstrated that the interaction between the VHH protein and excipients involves hydrophilic interactions and/or hydrogen bonding. This novel approach holds significant potential for understanding protein-excipient interactions in the solid state and can guide the formulation and process development of orally delivered protein dosage forms, ultimately enhancing their efficacy and stability.

Optimizing Excipient Properties to Prevent Aggregation in Biopharmaceutical Formulations

Excipients are included within protein biotherapeutic solution formulations to improve colloidal and conformational stability but are generally not designed for the specific purpose of preventing aggregation and improving cryoprotection in solution. In this work, we have explored the relationship between the structure and antiaggregation activity of excipients by utilizing coarse-grained molecular dynamics modeling of protein-excipient interaction. We have studied human serum albumin as a model protein, and we report the interaction of 41 excipients (polysorbates, fatty alcohol ethoxylates, fatty acid ethoxylates, phospholipids, glucosides, amino acids, and others) in terms of the reduction of solvent accessible surface area of aggregation-prone regions, proposed as a mechanism of aggregation prevention. Polyoxyethylene sorbitan had the greatest degree of interaction with aggregation-prone regions, decreasing the solvent accessible surface area of APRs by 20.7 nm2 (40.1%). Physicochemical descriptors generated by Mordred are employed to probe the structure-property relationship using partial least-squares regression. A leave-one-out cross-validated model had a root-mean-square error of prediction of 4.1 nm2 and a mean relative error of prediction of 0.077. Generally, longer molecules with a large number of alcohol-terminated PEG units tended to interact more, with qualitatively different protein interactions, wrapping around the protein. Shorter or less ethoxylated compounds tend to form hemimicellar clusters at the protein surface. We propose that an improved design would feature many short chains of 5 to 10 PEG units in many distinct branches and at least some hydrophobic content in the form of medium-length or greater aliphatic chains (i.e., six or more carbon atoms). The combination of molecular dynamics simulation and quantitative modeling is an important first step in an all-purpose protein-independent model for the computer-aided design of stabilizing excipients.

Antibody association in solution: cluster distributions and mechanisms

Understanding factors that affect the clustering and association of antibodies molecules in solution is critical to their development as therapeutics. For 19 different monoclonal antibody (mAb) solutions, we measured the viscosities, the second virial coefficients, the Kirkwood-Buff integrals, and the cluster distributions of the antibody molecules as functions of protein concentration. Solutions were modeled using the statistical-physics Wertheim liquid-solution theory, representing antibodies as Y-shaped molecular structures of seven beads each. We found that high-viscosity solutions result from more antibody molecules per cluster. Multi-body properties such as viscosity are well predicted experimentally by the 2-body Kirkwood-Buff quantity, G22, but not by the second virial coefficient, B22, and well-predicted theoretically from the Wertheim protein-protein sticking energy. Weakly interacting antibodies are rate-limited by nucleation; strongly interacting ones by propagation. This approach gives a way to relate micro to macro properties of solutions of associating proteins.

Comparative Analysis of Protein Surface Hydrophobicity Maps Determined by Sparse Sampling INDUS and Spatial Aggregation Propensity

Protein surface hydrophobicity plays a central role in various biological processes such as protein folding and aggregation, as well as in the design and manufacturing of biotherapeutics. While the hydrophobicity of protein surface patches has been linked to their constituent residue hydropathies, recent research has shown that protein surface hydrophobicity is more complex and characterized by the response of water to these surfaces. In this work, we employ water density perturbations to map the surface hydrophobicity of a set of model proteins using sparse indirect umbrella sampling simulations (SSI). This technique is used to identify hydrophobic surface patches for the set of model proteins, and the results are compared to those obtained from the widely adopted spatial aggregation propensity (SAP) technique. While SAP-based calculations show agreement with SSI in some cases, there are several examples of disagreement. We identify four general classes of difference in behavior and study factors that contribute to these differences. We find that the SAP method can sometimes mask the effect of weakly nonpolar or isolated nonpolar residues that can lead to strong hydrophobic patches on the protein surface. In addition, hydrophobic patches identified by SAP can exhibit shifts in both position and strength on the SSI map. Our results demonstrate that the combination of topography and chemical context controls the hydrophobicity of a given patch above and beyond the intrinsic polarity of the residues present on the patch surface. The availability of more accurate protein hydrophobicity maps in concert with new classes of hydrophobic molecular descriptors may create significant opportunities for in silico prediction of protein behavior for a range of applications, such as protein design, biomanufacturability, and downstream bioprocessing.

IgLM: Infilling language modeling for antibody sequence design

Discovery and optimization of monoclonal antibodies for therapeutic applications relies on large sequence libraries but is hindered by developability issues such as low solubility, high aggregation, and high immunogenicity. Generative language models, trained on millions of protein sequences, are a powerful tool for the on-demand generation of realistic, diverse sequences. We present the Immunoglobulin Language Model (IgLM), a deep generative language model for creating synthetic antibody libraries. Compared with prior methods that leverage unidirectional context for sequence generation, IgLM formulates antibody design based on text-infilling in natural language, allowing it to re-design variable-length spans within antibody sequences using bidirectional context. We trained IgLM on 558 million (M) antibody heavy- and light-chain variable sequences, conditioning on each sequence's chain type and species of origin. We demonstrate that IgLM can generate full-length antibody sequences from a variety of species and its infilling formulation allows it to generate infilled complementarity-determining region (CDR) loop libraries with improved in silico developability profiles. A record of this paper's transparent peer review process is included in the supplemental information.

ProGen2: Exploring the boundaries of protein language models

Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial-intelligence-driven protein design. However, we lack a sufficient understanding of how very large-scale models and data play a role in effective protein model development. We introduce a suite of protein language models, named ProGen2, that are scaled up to 6.4B parameters and trained on different sequence datasets drawn from over a billion proteins from genomic, metagenomic, and immune repertoire databases. ProGen2 models show state-of-the-art performance in capturing the distribution of observed evolutionary sequences, generating novel viable sequences, and predicting protein fitness without additional fine-tuning. As large model sizes and raw numbers of protein sequences continue to become more widely accessible, our results suggest that a growing emphasis needs to be placed on the data distribution provided to a protein sequence model. Our models and code are open sourced for widespread adoption in protein engineering. A record of this paper's Transparent Peer Review process is included in the supplemental information.

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.

Embedding Dynamics in Intrinsic Physicochemical Profiles of Market-Stage Antibody-Based Biotherapeutics

Adequate stability, manufacturability, and safety are crucial to bringing an antibody-based biotherapeutic to the market. Following the concept of holistic in silico developability, we introduce a physicochemical description of 91 market-stage antibody-based biotherapeutics based on orthogonal molecular properties of variable regions (Fvs) embedded in different simulation environments, mimicking conditions experienced by antibodies during manufacturing, formulation, and in vivo. In this work, the evaluation of molecular properties includes conformational flexibility of the Fvs using molecular dynamics (MD) simulations. The comparison between static homology models and simulations shows that MD significantly affects certain molecular descriptors like surface molecular patches. Moreover, the structural stability of a subset of Fv regions is linked to changes in their specific molecular interactions with ions in different experimental conditions. This is supported by the observation of differences in protein melting temperatures upon addition of NaCl. A DEvelopability Navigator In Silico (DENIS) is proposed to compare mAb candidates for their similarity with market-stage biotherapeutics in terms of physicochemical properties and conformational stability. Expanding on our previous developability guidelines (Ahmed et al. Proc. Natl. Acad. Sci. 2021, 118 (37), e2020577118), the hydrodynamic radius and the protein strand ratio are introduced as two additional descriptors that enable a more comprehensive in silico characterization of biotherapeutic drug candidates. Test cases show how this approach can facilitate identification and optimization of intrinsically developable lead candidates. DENIS represents an advanced computational tool to progress biotherapeutic drug candidates from discovery into early development by predicting drug properties in different aqueous environments.

Rational Design and Production of Bioactive Analogs of Recombinant Human Keratinocyte Growth Factor (rhKGF) with Reduced Aggregation Propensity

Recombinant human keratinocyte growth factor (rhKGF) is a highly aggregation-prone therapeutic protein. The present study aimed to reduce aggregation propensity of rhKGF by engineering the aggregation hotspots. Initially, 21 mutants were designed based on the previously-identified aggregation-prone regions (APRs) and then four of them including mutants No. 4 (L91K, I119K), 7 (V13S, L91K), 14 (L91D, I119D), and 21 (A51E) were selected based on molecular dynamics (MD) simulations for further experimental studies. The recombinantly produced rhKGF and mutants were analyzed regarding secondary structure, thermal stability, aggregation propensity, and biological activity. Far-UV CD spectroscopy showed that the mutants have similar secondary structure with rhKGF. A51E mutant showed enhanced stability and decreased monomer loss under heat stress suggesting its reduced aggregation propensity compared to rhKGF. Mutant No. 14 showed higher stability and less aggregation tendency than mutant No. 4 indicating that only mutations decreasing pI of rhKGF are effective in reducing its aggregation tendency. All of the mutants were at least as potent as rhKGF in stimulating proliferation of MCF-7 epithelial cells. Our results identified A51E as an equally potent, more stable, and less aggregation-prone analog of rhKGF which could be a promising alternative drug candidate for the commercially available rhKGF (Palifermin).

Identifying developability risks for clinical progression of antibodies using high-throughput in vitro and in silico approaches

With the growing significance of antibodies as a therapeutic class, identifying developability risks early during development is of paramount importance. Several high-throughput in vitro assays and in silico approaches have been proposed to de-risk antibodies during early stages of the discovery process. In this review, we have compiled and collectively analyzed published experimental assessments and computational metrics for clinical antibodies. We show that flags assigned based on in vitro measurements of polyspecificity and hydrophobicity are more predictive of clinical progression than their in silico counterparts. Additionally, we assessed the performance of published models for developability predictions on molecules not used during model training. We find that generalization to data outside of those used for training remains a challenge for models. Finally, we highlight the challenges of reproducibility in computed metrics arising from differences in homology modeling, in vitro assessments relying on complex reagents, as well as curation of experimental data often used to assess the utility of high-throughput approaches. We end with a recommendation to enable assay reproducibility by inclusion of controls with disclosed sequences, as well as sharing of structural models to enable the critical assessment and improvement of in silico predictions.

SUMO: In Silico Sequence Assessment Using Multiple Optimization Parameters

To select the most promising screening hits from antibody and VHH display campaigns for subsequent in-depth profiling and optimization, it is highly desirable to assess and select sequences on properties beyond only their binding signals from the sorting process. In addition, developability risk criteria, sequence diversity, and the anticipated complexity for sequence optimization are relevant attributes for hit selection and optimization. Here, we describe an approach for the in silico developability assessment of antibody and VHH sequences. This method not only allows for ranking and filtering multiple sequences with regard to their predicted developability properties and diversity, but also visualizes relevant sequence and structural features of potentially problematic regions and thereby provides rationales and starting points for multi-parameter sequence optimization.

Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies

Biologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computational methods to predict biophysical properties from protein sequence, potentially in combination with high throughput assays, could decrease timelines and increase the success rates for therapeutic developability engineering by eliminating lengthy and expensive cycles of recombinant protein production and testing. To support development of high-quality predictive models for antibody developability, we designed a sequence-diverse panel of 83 effector functionless IgG1 antibodies displaying a range of biophysical properties, produced and formulated each protein under standard platform conditions, and collected a comprehensive package of analytical data, including in vitro assays and in vivo mouse pharmacokinetics. We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. Our models predict with 87% accuracy whether viscosity at 150 mg/mL is above or below a threshold of 15 centipoise (cP) and with 75% accuracy whether the area under the plasma drug concentration-time curve (AUC0-672 h) in normal mouse is above or below a threshold of 3.9 × 106 h x ng/mL.

Optimizing Antibody-Antigen Binding Affinities with the ADAPT Platform

The ADAPT (Assisted Design of Antibody and Protein Therapeutics) platform guides the selection of mutants that improve/modulate the affinity of antibodies and other biologics. Predicted affinities are based on a consensus z-score from three scoring functions. Computational predictions are interleaved with experimental validation, significantly enhancing the robustness of the design and selection of mutants. A key step is an initial exhaustive virtual single-mutant scan that identifies hot spots and the mutations predicted to improve affinity. A small number of proposed single mutants are then produced and assayed. Only the validated single mutants (i.e., having improved affinity) are used to design double and higher-order mutants in subsequent rounds of design, avoiding the combinatorial explosion that arises from random mutagenesis. Typically, with a total of about 30-50 designed single, double, and triple mutants, affinity improvements of 10- to 100-fold are obtained.

A machine learning strategy for the identification of key in silico descriptors and prediction models for IgG monoclonal antibody developability properties

Identification of favorable biophysical properties for protein therapeutics as part of developability assessment is a crucial part of the preclinical development process. Successful prediction of such properties and bioassay results from calculated in silico features has potential to reduce the time and cost of delivering clinical-grade material to patients, but nevertheless has remained an ongoing challenge to the field. Here, we demonstrate an automated and flexible machine learning workflow designed to compare and identify the most powerful features from computationally derived physiochemical feature sets, generated from popular commercial software packages. We implement this workflow with medium-sized datasets of human and humanized IgG molecules to generate predictive regression models for two key developability endpoints, hydrophobicity and poly-specificity. The most important features discovered through the automated workflow corroborate several previous literature reports, and newly discovered features suggest directions for further research and potential model improvement.

Prediction of polyreactive and nonspecific single-chain fragment variables through structural biochemical features and protein language-based descriptors

BACKGROUND

Monoclonal antibodies (mAbs) have been used as therapeutic agents, which must overcome many developability issues after the discovery from in vitro display libraries. Especially, polyreactive mAbs can strongly bind to a specific target and weakly bind to off-target proteins, which leads to poor antibody pharmacokinetics in clinical development. Although early assessment of polyreactive mAbs is important in the early discovery stage, experimental assessments are usually time-consuming and expensive. Therefore, computational approaches for predicting the polyreactivity of single-chain fragment variables (scFvs) in the early discovery stage would be promising for reducing experimental efforts.

RESULTS

Here, we made prediction models for the polyreactivity of scFvs with the known polyreactive antibody features and natural language model descriptors. We predicted 19,426 protein structures of scFvs with trRosetta to calculate the polyreactive antibody features and investigated the classifying performance of each factor for polyreactivity. In the known polyreactive features, the net charge of the CDR2 loop, the tryptophan and glycine residues in CDR-H3, and the lengths of the CDR1 and CDR2 loops, importantly contributed to the performance of the models. Additionally, the hydrodynamic features, such as partial specific volume, gyration radius, and isoelectric points of CDR loops and scFvs, were newly added to improve model performance. Finally, we made the prediction model with a robust performance ([Formula: see text]) with an ensemble learning of the top 3 best models.

CONCLUSION

The prediction models for polyreactivity would help assess polyreactive scFvs in the early discovery stage and our approaches would be promising to develop machine learning models with quantitative data from high throughput assays for antibody screening.

Non-specificity as the sticky problem in therapeutic antibody development

Antibodies are highly potent therapeutic scaffolds with more than a hundred different products approved on the market. Successful development of antibody-based drugs requires a trade-off between high target specificity and target binding affinity. In order to better understand this problem, we here review non-specific interactions and explore their fundamental physicochemical origins. We discuss the role of surface patches - clusters of surface-exposed amino acid residues with similar physicochemical properties - as inducers of non-specific interactions. These patches collectively drive interactions including dipole-dipole, π-stacking and hydrophobic interactions to complementary moieties. We elucidate links between these supramolecular assembly processes and macroscopic development issues, such as decreased physical stability and poor in vivo half-life. Finally, we highlight challenges and opportunities for optimizing protein binding specificity and minimizing non-specificity for future generations of therapeutics.

Developability assessment at early-stage discovery to enable development of antibody-derived therapeutics

Developability refers to the likelihood that an antibody candidate will become a manufacturable, safe and efficacious drug. Although the safety and efficacy of a drug candidate will be well considered by sponsors and regulatory agencies, developability in the narrow sense can be defined as the likelihood that an antibody candidate will go smoothly through the chemistry, manufacturing and control (CMC) process at a reasonable cost and within a reasonable timeline. Developability in this sense is the focus of this review. To lower the risk that an antibody candidate with poor developability will move to the CMC stage, the candidate's developability-related properties should be screened, assessed and optimized as early as possible. Assessment of developability at the early discovery stage should be performed in a rapid and high-throughput manner while consuming small amounts of testing materials. In addition to monoclonal antibodies, bispecific antibodies, multispecific antibodies and antibody-drug conjugates, as the derivatives of monoclonal antibodies, should also be assessed for developability. Moreover, we propose that the criterion of developability is relative: expected clinical indication, and the dosage and administration route of the antibody could affect this criterion. We also recommend a general screening process during the early discovery stage of antibody-derived therapeutics. With the advance of artificial intelligence-aided prediction of protein structures and features, computational tools can be used to predict, screen and optimize the developability of antibody candidates and greatly reduce the risk of moving a suboptimal candidate to the development stage.

Comparison of hydrophobicity scales for predicting biophysical properties of antibodies

While antibody-based therapeutics have grown to be one of the major classes of novel medicines, some antibody development candidates face significant challenges regarding expression levels, solubility, as well as stability and aggregation, under physiological and storage conditions. A major determinant of those properties is surface hydrophobicity, which promotes unspecific interactions and has repeatedly proven problematic in the development of novel antibody-based drugs. Multiple computational methods have been devised for in-silico prediction of antibody hydrophobicity, often using hydrophobicity scales to assign values to each amino acid. Those approaches are usually validated by their ability to rank potential therapeutic antibodies in terms of their experimental hydrophobicity. However, there is significant diversity both in the hydrophobicity scales and in the experimental methods, and consequently in the performance of in-silico methods to predict experimental results. In this work, we investigate hydrophobicity of monoclonal antibodies using hydrophobicity scales. We implement several scoring schemes based on the solvent-accessibility and the assigned hydrophobicity values, and compare the different scores and scales based on their ability to predict retention times from hydrophobic interaction chromatography. We provide an overview of the strengths and weaknesses of several commonly employed hydrophobicity scales, thereby improving the understanding of hydrophobicity in antibody development. Furthermore, we test several datasets, both publicly available and proprietary, and find that the diversity of the dataset affects the performance of hydrophobicity scores. We expect that this work will provide valuable guidelines for the optimization of biophysical properties in future drug discovery campaigns.

Understanding the Stabilizing Effect of Histidine on mAb Aggregation: A Molecular Dynamics Study

Histidine, a widely used buffer in monoclonal antibody (mAb) formulations, is known to reduce antibody aggregation. While experimental studies suggest a nonelectrostatic, nonstructural (relating to secondary structure preservation) origin of the phenomenon, the underlying microscopic mechanism behind the histidine action is still unknown. Understanding this mechanism will help evaluate and predict the stabilizing effect of this buffer under different experimental conditions and for different mAbs. We have used all-atom molecular dynamics simulations and contact-based free energy calculations to investigate molecular-level interactions between the histidine buffer and mAbs, which lead to the observed stability of therapeutic formulations in the presence of histidine. We reformulate the Spatial Aggregation Propensity index by including the buffer–protein interactions. The buffer adsorption on the protein surface leads to lower exposure of the hydrophobic regions to water. Our analysis indicates that the mechanism behind the stabilizing action of histidine is connected to the shielding of the solvent-exposed hydrophobic regions on the protein surface by the buffer molecules.

Two physics-based models for pH-dependent calculations of protein solubility

When engineering a protein for its biological function, many physicochemical properties are also optimized throughout the engineering process, and the protein's solubility is among the most important properties to consider. Here, we report two novel computational methods to calculate the pH-dependent protein solubility, and to rank the solubility of mutants. The first is an empirical method developed for fast ranking of the solubility of a large number of mutants of a protein. It takes into account electrostatic solvation energy term calculated using Generalized Born approximation, hydrophobic patches, protein charge, and charge asymmetry, as well as the changes of protein stability upon mutation. This method has been tested on over 100 mutations for 17 globular proteins, as well as on 44 variants of five different antibodies. The prediction rate is over 80%. The antibody tests showed a Pearson correlation coefficient, R, with experimental data from .83 to .91. The second method is based on a novel, completely force-field-based approach using CHARMm program modules to calculate the binding energy of the protein to a part of the crystal lattice, generated from X-ray structure. The method predicted with very high accuracy the solubility of Ribonuclease SA and its 3K and 5K mutants as a function of pH without any parameter adjustments of the existing BIOVIA Discovery Studio binding affinity model. Our methods can be used for rapid screening of large numbers of design candidates based on solubility, and to guide the design of solution conditions for antibody formulation.

Investigation of the pH-dependent aggregation mechanisms of GCSF using low resolution protein characterization techniques and advanced molecular dynamics simulations

Granulocyte-colony stimulating factor (GCSF) is a widely used therapeutic protein to treat neutropenia. GCSF has an increased propensity to aggregate if the pH is increased above 5.0. Although GCSF is very well experimentally characterized, the exact pH-dependent aggregation mechanism of GCSF is still under debate. This study aimed to model the complex pH-dependent aggregation behavior of GCSF using state-of-the-art simulation techniques. The conformational stability of GCSF was investigated by performing metadynamics simulations, while the protein-protein interactions were investigated using coarse-grained (CG) simulations of multiple GCSF monomers. The CG simulations were directly compared with small-angle X-ray (SAXS) data. The metadynamics simulations demonstrated that the orientations of Trp residues in GCSF are dependent on pH. The conformational change of Trp residues is due to the loss of Trp-His interactions at the physiological pH, which in turn may increase protein flexibility. The helical structure of GCSF was not affected by the pH conditions of the simulations. Our CG simulations indicate that at pH 4.0, the colloidal stability may be more important than the conformational stability of GCSF. The electrostatic potential surface and CG simulations suggested that the basic residues are mainly responsible for colloidal stability as deprotonation of these residues causes a reduction of the highly positively charged electrostatic barrier close to the aggregation-prone long loop regions.

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.

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).

Expanding the toolbox for predictive parameters describing antibody stability considering thermodynamic and kinetic determinants

PURPOSE

Introduction of the activation energy (E_a) as a kinetic parameter to describe and discriminate monoclonal antibody (mAb) stability.

METHODS

E_a is derived from intrinsic fluorescence (IF) unfolding thermograms. An apparent irreversible three-state fit model based on the Arrhenius integral is developed to determine E_a of respective unfolding transitions. These activation energies are compared to the thermodynamic parameter of van´t Hoff enthalpies (∆H_vh). Using a set of 34 mAbs formulated in four different formulations, both the apparent thermodynamic and kinetic parameters together with apparent melting temperatures are correlated collectively with each other to storage stabilities to evaluate its predictive power with respect to long-term effects potentially reflected in shelf-life.

RESULTS

E_a allows for the discrimination of (i) different parent mAbs, (ii) different variants that originate from parent mAbs, and (iii) different formulations. Interestingly, we observed that the E_a of the CH₂ unfolding transition shows strongest correlations with monomer and aggregate content after storage at accelerated and stress conditions when collectively compared to ∆H_vh and T_m of the CH₂ transition. Moreover, the predictive parameters determined for the CH₂ domain show generally stronger correlations with monomer and aggregate content than those derived for the Fab. Qualitative assessment by ranking E_a of the Fab domain showed good agreement with monomer content in storage stabilities of individual mAb sub-sets.

CONCLUSION

E_a from IF unfolding transitions can be used in addition to other commonly used thermodynamic predictive parameters to discriminate and characterize thermal stability of different mAbs in different formulations. Hence, it shows great potential for antibody engineering and formulation scientists.

Systematic workflow for studying domain contributions of bispecific antibodies to selectivity in multimodal chromatography

In this article, a systematic workflow was formulated and implemented to understand selectivity differences and preferred binding patches for bispecific monoclonal antibodies (mAbs) and their parental mAbs on three multimodal cation exchange resin systems. This workflow incorporates chromatographic screening of the parent mAbs and their fragments at various pH followed by surface property mapping and protein footprinting using covalent labeling followed by liquid chromatography-mass spectrometry analysis. The chromatography screens on multimodal resins with the intact mAbs indicated enhanced selectivity as compared to single-mode interaction systems. While the bispecific antibody (bsAb) eluted between the two parental mAbs on most of the resins, the retention of the bispecific transitioned from co-eluting with one parental mAb to the other parental mAb on Capto MMC. To investigate the contribution of different domains, mAb fragments were evaluated and the results indicated that the interactions were likely dominated by the Fab domain at higher pH. Protein surface property maps were then employed to hypothesize the potential preferred binding patches in the solvent-exposed regions of the parental Fabs. Finally, protein footprinting was carried out with the parental mAbs and the bsAb in the bound and unbound states at pH 7.5 to identify the preferred binding patches. Results with the intact mAb analysis supported the hypothesis that interactions with the resins were primarily driven by the residues in the Fab fragments and not the Fc. Furthermore, peptide mapping data indicated that the light chain may be playing a more important role in the higher binding of Parent A as compared with Parent B in these resin systems. Finally, results with the bsAb indicated that both halves of the molecule contributed to binding with the resins, albeit with subtle differences as compared to the parental mAbs. The workflow presented in this paper lays the foundation to systematically study the chromatographic selectivity of large multidomain molecules which can provide insights into improved biomanufacturability and expedited downstream bioprocess development.

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.

Challenges for design of aggregation-resistant variants of granulocyte colony-stimulating factor

Non-native protein aggregation is a long-standing issue in pharmaceutical biotechnology. A rational design approach was used in order to identify variants of recombinant human granulocyte colony-stimulating factor (rhG-CSF) with lower aggregation propensity at solution conditions that are typical of commercial formulation. The approach used aggregation-prone-region (APR) predictors to select single amino acid substitutions that were predicted to decrease intrinsic aggregation propensity (IAP). The results of static light scattering temperature-ramps and chemical unfolding experiments demonstrated that none of the selected variants exhibited improved aggregation resistance, and the apparent conformational stability of each variant was lower than that of WT. Aggregation studies under partly denaturing conditions suggested that the IAP of at least one variant remained unaltered. Overall, this study highlights a general challenge in designing aggregation resistance for proteins, due to the need to accurately predict both APRs and conformational stability.

An accelerated surface-mediated stress assay of antibody instability for developability studies

High physical stability is required for the development of monoclonal antibodies (mAbs) into successful therapeutic products. Developability assays are used to predict physical stability issues such as high viscosity and poor conformational stability, but protein aggregation remains a challenging property to predict. Among different types of stresses, air–water and solid–liquid interfaces are well known to potentially trigger protein instability and induce aggregation. Yet, in contrast to the increasing number of developability assays to evaluate bulk properties, there is still a lack of experimental methods to evaluate antibody stability against interfaces. Here, we investigate the potential of a hydrophobic nanoparticle surface-mediated stress assay to assess the stability of mAbs during the early stages of development. We evaluate this surface-mediated accelerated stability assay on a rationally designed library of 14 variants of a humanized IgG4, featuring a broad span of solubility values and other developability properties. The assay could identify variants characterized by high instability against agitation in the presence of air–water interfaces. Remarkably, for the set of investigated molecules, we observe strong correlations between the extent of aggregation induced by the surface-mediated stress assay and other developability properties of the molecules, such as aggregation upon storage at 45°C, self-association (evaluated by affinity-capture self-interaction nanoparticle spectroscopy) and nonspecific interactions (estimated by cross-interaction chromatography, stand-up monolayer chromatography (SMAC), SMAC*). This highly controlled surface-mediated stress assay has the potential to complement and increase the ability of the current set of screening techniques to assess protein aggregation and developability potential of mAbs during the early stages of drug development.

Abbreviations:AC-SINS: Affinity-Capture Self-Interaction Nanoparticle Spectroscopy; AMS: Ammonium sulfate precipitation; ANS: 1-anilinonaphtalene-8-sulfonate; CIC: Cross-interaction chromatography; DLS: Dynamic light scattering; HIC: Hydrophobic interaction chromatography; HNSSA: Hydrophobic nanoparticles surface-stress assay; mAb: Monoclonal antibody; NP: Nanoparticle; SEC: Size exclusion chromatography; SMAC: Stand-up monolayer chromatography; WT: Wild type

Evaluating parameters affecting drug fate at the intramuscular injection site

Intramuscular (IM) injections are a well-established method of delivering a variety of therapeutics formulated for parenteral administration. While the wide range of commercial IM pharmaceuticals provide a wealth of pharmacokinetic (PK) information following injection, there remains an inadequate understanding of drug fate at the IM injection site that could dictate these PK outcomes. An improved understanding of injection site events could improve approaches taken by formulation scientists to identify therapeutically effective and consistent drug PK outcomes. Interplay between the typically non-physiological aspects of drug formulations and the homeostatic IM environment may provide insights into the fate of drugs at the IM injection site, leading to predictions of how a drug will behave post-injection in vivo. Immune responses occur by design after e.g. vaccine administration, however immune responses post-injection are not in the scope of this article. Taking cues from existing in vitro modelling technologies, the purpose of this article is to propose "critical parameters" of the IM environment that could be examined in hypothesis-driven studies. Outcomes of such studies might ultimately be useful in predicting and improving in vivo PK performance of IM injected drugs.

Potential aggregation hot spots in recombinant human keratinocyte growth factor: a computational study

The recombinant human keratinocyte growth factor (rhKGF) is a highly aggregation-prone therapeutic protein. The high aggregation liability of rhKGF is manifested by loss of the monomeric state, and accumulation of the aggregated species even at moderate temperatures. Here, we analyzed the rhKGF for its vulnerability toward aggregation by detection of aggregation-prone regions (APRs) using several sequence-based computational tools including TANGO, ZipperDB, AGGRESCAN, Zyggregator, Camsol, PASTA, SALSA, WALTZ, SODA, Amylpred, AMYPDB, and structure-based tools including SolubiS, CamSol structurally corrected, Aggrescan3D and spatial aggregation propensity (SAP) algorithm. The sequence-based prediction of APRs in rhKGF indicated that they are mainly located at positions 10-30, 40-60, 61-66, 88-120, and 130-140. Mapping on the rhKGF structure revealed that most of these residues including F16-R25, I43, E45, R47-I56, F61, Y62, N66, L88-E91, E108-F110, A112, N114, T131, and H133-T140 are surface-exposed in the native state which can promote aggregation without major unfolding event, or the conformational change may occur in the oligomers. The other regions are buried in the native state and their contribution to non-native aggregation is mediated by a preceding unfolding event. The structure-based prediction of APRs using the SAP tool limited the number of identified APRs to the dynamically-exposed hydrophobic residues including V12, A50, V51, L88, I89, L90, I118, L135, and I139 mediating the native-state aggregation. Our analysis of APRs in rhKGF identified the regions determining the intrinsic aggregation propensity of the rhKGF which are the candidate positions for engineering the rhKGF to reduce its aggregation tendency.Communicated by Ramaswamy H. Sarma.

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.

Aggregation Prone Regions in Antibody Sequences Raised Against Vibrio cholerae: A Bioinformatic Approach

Background:

Cholera, a diarrheal illness, causes millions of deaths worldwide due to large outbreaks. The monoclonal antibody used as therapeutic purposes of cholera is prone to be unstable due to various factors including self-aggregation.

Objectives:

In this bioinformatic analysis, we identified the aggregation prone regions (APRs) of antibody sequences of different immunogens (i.e., CTB, ZnM-CTB, ZnP-CTB, TcpA-CT-CTB, ZnM-TcpA-CT-CTB, ZnP-TcpA-CT-CTB, ZnM-TcpA, ZnP-TcpA, TcpA-CT-TcpA, ZnM-TcpACT- TcpA, ZnP-TcpA-CT-TcpA, Ogawa, Inaba and ZnM-Inaba) raised against Vibrio cholerae.

Methods:

To determine APRs in antibody sequences that were generated after immunizing Vibrio cholerae immunogens on Mus musculus, a total of 94 sequences were downloaded as FASTA format from a protein database and the algorithms such as Tango, Waltz, PASTA 2.0, and AGGRESCAN were followed to analyze probable APRs in all of the sequences.

Results:

A remarkably high number of regions in the monoclonal antibodies were identified to be APRs which could explain a cause of instability/short term protection of the anticholera vaccine.

Conclusion:

To increase the stability, it would be interesting to eliminate the APR residues from the therapeutic antibodies in such a way that the antigen-binding sites or the complementarity determining region loops involved in antigen recognition are not disrupted.

Modeling and simulation in medical sciences: an overview of specific applications based on research experience in EMRI (Endocrinology and Metabolism Research Institute of Tehran University of Medical Sciences)

The concomitant use of various types of models (in silico, in vitro, and in vivo) has been exemplified here within the context of biomedical researches performed in the Endocrinology and Metabolism Research Institute (EMRI) of Tehran University of Medical Sciences. Two main research aeras have been discussed: the search for new small molecules as therapeutics for diabetes and related metabolic conditions, and diseases related to protein aggregation. Due to their multidisciplinary nature, the majority of these studies have needed the collaboration of different specialties. In both cases, a brief overview of the subject is provided through literature examples, and sequential use of these methods is described.

The impact of proline isomerization on antigen binding and the analytical profile of a trispecific anti-HIV antibody

Proline cis-trans conformational isomerization is a mechanism that affects different types of protein functions and behaviors. Using analytical characterization, structural analysis, and molecular dynamics simulations, we studied the causes of an aberrant two-peak size-exclusion chromatography profile observed for a trispecific anti-HIV antibody. We found that proline isomerization in the tyrosine-proline-proline (YPP) motif in the heavy chain complementarity-determining region (CDR)3 domain of one of the antibody arms (10e8v4) was a component of this profile. The pH effect on the conformational equilibrium that led to these two populations was presumably caused by a histidine residue (H147) in the light chain that is in direct contact with the YPP motif. Finally, we demonstrated that, due to chemical equilibrium between the cis and trans proline conformers, the antigen-binding potency of the trispecific anti-HIV antibody was not significantly affected in spite of a potential structural clash of 10e8v4 YP_transP_trans conformers with the membrane-proximal ectodomain region epitope in the GP41 antigen. Altogether, these results reveal at mechanistic and molecular levels the effect of proline isomerization in the CDR on the antibody binding and analytical profiles, and support further development of the trispecific anti-HIV antibody.

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.

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.

Impact of virus-antibody interactions on viral clearance in anion exchange chromatography

Viral clearance is an important performance metric for the downstream process of monoclonal antibodies (mAbs) due to its impact on patient safety. Anion exchange chromatography (AEX) has been well-accepted in the industry as one of the workhorse techniques for removing viruses, and is considered to be able to achieve high log clearance values under most operating conditions. However, it is not uncommon for viral clearance results on AEX to fall below the desired level despite operating under conditions that should achieve high clearance levels according to conventional wisdom of how this mode of chromatography operates. In this study, a design of experiment (DoE) approach was used to develop a more fundamental understanding of viral clearance during AEX chromatography using Minute Virus of Mice (MVM) on POROS HQ resin. Load pH, conductivity and virus concentration were evaluated as design factors for three mAbs with varying physical and chemical properties. The hydrophobicity and surface charge distributions of the molecules were found to be the most significant factors in influencing viral clearance performance, and the viral clearance trends did not seem to fit with conventional wisdom. To explain this seemingly unconventional behavior, we propose a new mechanism that suggests that interactions between the mAb and the virus have a major contribution on retention of the virus on the resin. This furthered understanding may help improve the predictability, performance and robustness of viral clearance during AEX chromatography.

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.

Toward Drug-Like Multispecific Antibodies by Design

The success of antibody therapeutics is strongly influenced by their multifunctional nature that couples antigen recognition mediated by their variable regions with effector functions and half-life extension mediated by a subset of their constant regions. Nevertheless, the monospecific IgG format is not optimal for many therapeutic applications, and this has led to the design of a vast number of unique multispecific antibody formats that enable targeting of multiple antigens or multiple epitopes on the same antigen. Despite the diversity of these formats, a common challenge in generating multispecific antibodies is that they display suboptimal physical and chemical properties relative to conventional IgGs and are more difficult to develop into therapeutics. Here we review advances in the design and engineering of multispecific antibodies with drug-like properties, including favorable stability, solubility, viscosity, specificity and pharmacokinetic properties. We also highlight emerging experimental and computational methods for improving the next generation of multispecific antibodies, as well as their constituent antibody fragments, with natural IgG-like properties. Finally, we identify several outstanding challenges that need to be addressed to increase the success of multispecific antibodies in the clinic.

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.

Phage Display Derived Monoclonal Antibodies: From Bench to Bedside

Monoclonal antibodies (mAbs) have become one of the most important classes of biopharmaceutical products, and they continue to dominate the universe of biopharmaceutical markets in terms of approval and sales. They are the most profitable single product class, where they represent six of the top ten selling drugs. At the beginning of the 1990s, an in vitro antibody selection technology known as antibody phage display was developed by John McCafferty and Sir. Gregory Winter that enabled the discovery of human antibodies for diverse applications, particularly antibody-based drugs. They created combinatorial antibody libraries on filamentous phage to be utilized for generating antigen specific antibodies in a matter of weeks. Since then, more than 70 phage–derived antibodies entered clinical studies and 14 of them have been approved. These antibodies are indicated for cancer, and non-cancer medical conditions, such as inflammatory, optical, infectious, or immunological diseases. This review will illustrate the utility of phage display as a powerful platform for therapeutic antibodies discovery and describe in detail all the approved mAbs derived from phage display.

Improving the analytical toolbox to investigate copurifying host cell proteins presence: N-(4)-(β-acetylglucosaminyl)- l-asparaginase case study

Levels of host cell proteins (HCPs) in purification intermediates and drug substances (DS) of monoclonal antibodies (mAbs) must be carefully monitored for the production of safe and efficacious biotherapeutics. During the development of mAb1, an immunoglobulin G1 product, unexpected results generated with HCP Enzyme‐Linked Immunosorbent Assay (ELISA) kit triggered an investigation which led to the identification of a copurifying HCP called N‐(4)‐(β‐acetylglucosaminyl)‐l‐asparaginase (AGA, EC3.5.1.26) by liquid chromatography–tandem mass spectrometry (LC‐MS/MS). The risk assessment performed indicated a low immunogenicity risk for the copurifying HCP and an ad hoc stability study demonstrated no mAb glycan cleavage and thus no impact on product quality. Fractionation studies performed on polishing steps revealed that AGA was coeluted with the mAb. Very interestingly, the native digestion protocol implemented to go deeper in the MS–HCP profiling was found to be incompatible with correct AGA detection in last purification intermediate and DS, further suggesting a hitchhiking behavior of AGA. In silico surface characterization of AGA also supports this hypothesis. Finally, the combined support of HCP ELISA results and MS allowed process optimization and removal of this copurifying HCP.

[Developability assessment]

The therapeutic antibodies and their by-products (antibody fragments, conjugated, etc.) establish one of the most dynamic biopharmaceutical market segments today. Due to their intrinsic properties of specificity towards their target, towards their flexible affinity and due to their stability, antibodies became therapeutic agents of the very first choice. One of the challenges of this sector is to create antibodies of very good quality, more and more quickly, while having less and less consequent development costs in fine.