Potential aggregation-prone regions in complementarity-determining regions of antibodies and their contribution towards antigen recognition: a computational analysis

Inhibiting Protein Aggregation Using Cellulose Nanocrystal in MALDI-TOF MS Analysis: Improving the Sensitivity and Repeatability of Intact Protein in Pueraria

Protein aggregation can induce low sensitivity and poor repeatability of matrix-assisted laser desorption/ionization time-of-fight mass spectrometry (MALDI-TOF MS) analysis for intact protein. Herein, we introduced a strategy to decrease protein aggregation in the sample solution by using cellulose nanocrystal (CNC). The results indicated that protein granule size was effectively reduced by adding CNC to the sample solution. Through MALDI-TOF MS analysis, the signal-to-noise ratio of [M + H]+ peak increased 2-fold, and the detection of limit was <10 μg/mL for intact protein. The CNC also contributed to excellent point-to-point repeatability for MALDI-TOF MS analysis with the coefficient of variation (CV) of 10.0% with CNC vs 48.9% without CNC in Hb solution. Also, the repeatability of Pueraria protein ion signals was improved by using CNC, and the CV with and without CNC was 16.1% and 39.6%, respectively. Moreover, protein ion intensity exhibited great linear relationship (y = 53.04x - 3.474, R2 = 0.9936) with the concentrations (ranging from 0.1 to 10 mg/mL) when using CNC. Further investigation revealed that m/z 19,000 and m/z 21,000 peaks of Pueraria could be used for the adulteration analysis and post-translational modification research, demonstrating our method has the potential for broad applications.

Enhancement of solubility of recombinant alcohol dehydrogenase from Rhodococcus ruber using predictive tool

Solubility is one of key factors influencing the heterologous production of recombinant proteins in biotechnology. Among many aggregation-prone proteins, alcohol dehydrogenase (ADH-A) from Rhodococcus ruber (in this work abbreviated RrADH) shows a great potential in processes involved in the biotransformation of natural compounds. As ADH-A is a potentially high value asset in industrial biotransformation processes, improvement of its solubility would be of major commercial benefit. Predictive tools and in silico analysis provide a fast means for improving protein properties, for selecting appropriate changes, and ultimately for saving costs. We have therefore focused on enhancement of the solubility of RrADH using an online accesible predictive tool Aggrescan 3D 2.0. Selected mutations were introduced into the protein amino acid sequence by using site-directed PCR. This led to a 17% increase in the protein solubility of RrADHmut1 and a 98% increase for RrADHmut2. Moreover, the basic kinetics of the enzyme reaction were positively affected, further optimizing the overall performance of the production process.

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.

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.

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.

Effect of charged mutation on aggregation of a pentapeptide: Insights from molecular dynamics simulations

Aggregation of therapeutic monoclonal antibodies (mAbs) can negatively affect their chemistry, manufacturing, and control attributes and lead to undesirable immune responses in patients. Therefore, optimization of lead mAb drug candidates during discovery stages to mitigate aggregation is increasingly becoming an integral part of their developability assessments. The disruption of short sequence motifs called aggregation prone regions (APRs) found in amino acid sequences of mAb candidates can potentially mitigate their aggregation. In this work, we have performed molecular dynamics simulations to study the aggregation of an APR (VLVIY) found in λ light chains of human antibodies and its single point mutant KLVIY. Eighteen different multicopy peptide simulation systems of "VLVIY" and "KLVIY" were constructed by varying their concentrations, temperatures, termini capping, and flanking gate-keeper regions. Within 20 ns of the simulation, peptide "VLVIY" formed an aggregate of 100 peptides at ~0.1 M concentration with a 60% reduction in solvent accessible surface area (SASA). Furthermore, analysis of the SASA change, peptide cluster distribution, and water residence time demonstrated how Val ➔ Lys mutation resists aggregation and improves solubility. Presence of Lys slows down aggregation kinetics via charge-charge repulsions and by raising the kinetic barrier to formation of large oligomers. However, the effect of the Val ➔ Lys mutation is dependent on sequence and structural contexts around the APR. This mutation also alters the solvation shell around the peptide by favoring solute-solvent interactions, thereby increasing its solubility. This work has provided a detailed mechanistic explanation of how APR disruption can mitigate aggregation in biotherapeutics and improve their developability.

Exploring the sequence features determining amyloidosis in human antibody light chains

The light chain (AL) amyloidosis is caused by the aggregation of light chain of antibodies into amyloid fibrils. There are plenty of computational resources available for the prediction of short aggregation-prone regions within proteins. However, it is still a challenging task to predict the amyloidogenic nature of the whole protein using sequence/structure information. In the case of antibody light chains, common architecture and known binding sites can provide vital information for the prediction of amyloidogenicity at physiological conditions. Here, in this work, we have compared classical sequence-based, aggregation-related features (such as hydrophobicity, presence of gatekeeper residues, disorderness, β-propensity, etc.) calculated for the CDR, FR or V_L regions of amyloidogenic and non-amyloidogenic antibody light chains and implemented the insights gained in a machine learning-based webserver called "V_LAmY-Pred" ( https://web.iitm.ac.in/bioinfo2/vlamy-pred/ ). The model shows prediction accuracy of 79.7% (sensitivity: 78.7% and specificity: 79.9%) with a ROC value of 0.88 on a dataset of 1828 variable region sequences of the antibody light chains. This model will be helpful towards improved prognosis for patients that may likely suffer from diseases caused by light chain amyloidosis, understanding origins of aggregation in antibody-based biotherapeutics, large-scale in-silico analysis of antibody sequences generated by next generation sequencing, and finally towards rational engineering of aggregation resistant antibodies.

Evaluation of in silico tools for the prediction of protein and peptide aggregation on diverse datasets

Several prediction algorithms and tools have been developed in the last two decades to predict protein and peptide aggregation. These in silico tools aid to predict the aggregation propensity and amyloidogenicity as well as the identification of aggregation-prone regions. Despite the immense interest in the field, it is of prime importance to systematically compare these algorithms for their performance. In this review, we have provided a rigorous performance analysis of nine prediction tools using a variety of assessments. The assessments were carried out on several non-redundant datasets ranging from hexapeptides to protein sequences as well as amyloidogenic antibody light chains to soluble protein sequences. Our analysis reveals the robustness of the current prediction tools and the scope for improvement in their predictive performances. Insights gained from this work provide critical guidance to the scientific community on advantages and limitations of different aggregation prediction methods and make informed decisions about their research needs.

Interaction of clinical-stage antibodies with heme predicts their physiochemical and binding qualities

Immunoglobulin repertoires contain a fraction of antibodies that recognize low molecular weight compounds, including some enzymes' cofactors, such as heme. Here, by using a set of 113 samples with variable region sequences matching clinical-stage antibodies, we demonstrated that a considerable number of these antibodies interact with heme. Antibodies that interact with heme possess specific sequence traits of their antigen-binding regions. Moreover they manifest particular physicochemical and functional qualities i.e. increased hydrophobicity, higher propensity of self-binding, higher intrinsic polyreactivity and reduced expression yields. Thus, interaction with heme is a strong predictor of different molecular and functional qualities of antibodies. Notably, these qualities are of high importance for therapeutic antibodies, as their presence was associated with failure of drug candidates to reach clinic. Our study reveled an important facet of information about relationship sequence-function in antibodies. It also offers a convenient tool for detection of liabilities of therapeutic antibodies.

Application of Affinity-Capture Self-Interaction Nanoparticle Spectroscopy in Predicting Protein Stability, Especially for Co-Formulated Antibodies

PURPOSE

From traditional monoclonal antibodies to more and more complex mAb-based formulations, biopharmaceutical faces one challenge after another. To avoid these issues, identification of therapeutic proteins in the initial discovery process that has high stability and low self-interaction would simplify the development of safe and effective antibody therapeutics.

METHOD

Affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS) is a new prediction method capable of identifying mAbs with different self-association propensity. In this study, 10 formulated monoclonal antibody (mAb) therapeutics include different mAb isotypes and co-formulated antibodies were measured by AC-SINS and some biophysical methods to predict protein stability. The prediction results of all 10 mAbs were compared to their stability data (Δ%monomer and Δ%HMWs) at accelerated (25°C and 40°C) and long-term storage conditions (4°C) as measured by size exclusion chromatography.

RESULT

AC-SINS method has a good predictive correlation with each mAbs and co-formulated antibodies. There were no physicochemical, intermolecular, or biological interactions that occurred between the two components of co-formulated antibodies which confirmed by Analytical ultracentrifugation (AUC).

CONCLUSION

Here we discuss the correlation between each method and protein stability, and also use AC-SINS assay to predict the stability of co-formulated antibodies for the first time. This may be an effective way to predict the stability of these complex mAb-based formulations such as co-formulated mAbs.

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.

Local environment effects on charged mutations for developing aggregation-resistant monoclonal antibodies

Protein aggregation is a major concern in biotherapeutic applications of monoclonal antibodies. Introducing charged mutations is among the promising strategies to improve aggregation resistance. However, the impact of such mutations on solubilizing activity depends largely on the inserting location, whose mechanism is still not well understood. Here, we address this issue from a solvation viewpoint, and this is done by analyzing how the change in solvation free energy upon charged mutation is composed of individual contributions from constituent residues. To this end, we perform molecular dynamics simulations for a number of antibody mutants and carry out the residue-wise decomposition of the solvation free energy. We find that, in addition to the previously identified “global” principle emphasizing the key role played by the protein total net charge, a local net charge within \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim$$\end{document}∼15 Å from the mutation site exerts significant effects. For example, when the net charge of an antibody is positive, the global principle states that introducing a positively charged mutation will lead to more favorable solvation. Our finding further adds that an even more optimal mutation can be done at the site around which more positively charged residues and fewer negatively charged residues are present. Such a “local” design principle accounts for the location dependence of charged mutations, and will be useful in producing aggregation-resistant antibodies.

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.

Long-Term Stability of Anti-Vascular Endothelial Growth Factor (a-VEGF) Biologics Under Physiologically Relevant Conditions and Its Impact on the Development of Long-Acting Delivery Systems

The port delivery system with ranibizumab (PDS) is an investigational long-acting drug delivery system for the continuous release of ranibizumab, an anti-VEGF biologic, in the vitreous humor. The efficacy of the PDS implant relies on the maintenance of long-term drug stability under physiological conditions. Herein, the long-term stability of three anti-VEGF biologics - ranibizumab, bevacizumab and aflibercept - was investigated in phosphate buffered saline (PBS) at 37 °C for several months. Comparison of stability profiles shows that bevacizumab and aflibercept are increasingly prone to aggregation whereas ranibizumab undergoes minimal aggregation. Ranibizumab also shows the smallest loss in antigen binding capacity after long-term incubation in PBS. Even though the aggregated forms of bevacizumab and aflibercept bind to VEGF, the consequences of aggregation on immunogenicity, implant function and efficacy are unknown. These results highlight the importance of maintaining long-term drug stability under physiologically relevant conditions which is necessary for achieving efficacy with an in vivo continuous drug delivery device such as the PDS implant.

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.

Protein engineering approaches for antibody fragments: directed evolution and rational design approaches

The number of therapeutic antibodies in preclinical, clinical, or approved phases has been increasing exponentially, mostly due to their known successes. Development of antibody engineering methods has substantially hastened the development of therapeutic antibodies. A variety of protein engineering techniques can be applied to antibodies to improve their afinity and/or biophysical properties such as solubility and stability. Antibody fragments (where all or some parts of constant regions are eliminated while the essential antigen binding region is preserved) are more suitable for protein engineering techniques because there are many in vitro screening technologies available for antibody fragments but not full-length antibodies. Improvement of biophysical characteristics is important in the early development phase because most antibodies fail at the later stage of development and this leads to loss of resources and time. Here, we review directed evolution and rational design methods to improve antibody properties. Recent developments in rational design approaches and antibody display technologies, and especially phage display, which was recently awarded the 2018 Nobel Prize, are discussed to be used in antibody research and development.

SolubiS: Optimizing Protein Solubility by Minimal Point Mutations

Protein solubility is adapted to endogeneous protein abundance in the cell where protein folding is also assisted by multiple chaperones. During recombinant protein production, purification and storage proteins are frequently handled at concentrations that are several orders of magnitude above their physiological concentration, often resulting in protein aggregation. Here we describe SolubiS, a method allowing for (1) detection of aggregation prone linear segments within a protein sequence and (2) identification of mutations that abolish the aggregation propensity of these segments without affecting the thermodynamic stability of the protein. Provided the availability of structural information this method is applicable to all globular proteins including antibodies, resulting both in increased in vitro protein solubility and in better protein production yields.

Structure, heterogeneity and developability assessment of therapeutic antibodies

Increasing attention has been paid to developability assessment with the understanding that thorough evaluation of monoclonal antibody lead candidates at an early stage can avoid delays during late-stage development. The concept of developability is based on the knowledge gained from the successful development of approximately 80 marketed antibody and Fc-fusion protein drug products and from the lessons learned from many failed development programs over the last three decades. Here, we reviewed antibody quality attributes that are critical to development and traditional and state-of-the-art analytical methods to monitor those attributes. Based on our collective experiences, a practical workflow is proposed as a best practice for developability assessment including in silico evaluation, extended characterization and forced degradation using appropriate analytical methods that allow characterization with limited material consumption and fast turnaround time.

In Silico Methods in Antibody Design

Antibody therapies with high efficiency and low toxicity are becoming one of the major approaches in antibody therapeutics. Based on high-throughput sequencing and increasing experimental structures of antibodies/antibody-antigen complexes, computational approaches can predict antibody/antigen structures, engineering the function of antibodies and design antibody-antigen complexes with improved properties. This review summarizes recent progress in the field of in silico design of antibodies, including antibody structure modeling, antibody-antigen complex prediction, antibody stability evaluation, and allosteric effects in antibodies and functions. We listed the cases in which these methods have helped experimental studies to improve the affinities and physicochemical properties of antibodies. We emphasized how the molecular dynamics unveiled the allosteric effects during antibody-antigen recognition and antibody-effector recognition.

Understanding and overcoming trade-offs between antibody affinity, specificity, stability and solubility

The widespread use of monoclonal antibodies for therapeutic applications has led to intense interest in optimizing several of their natural properties (affinity, specificity, stability, solubility and effector functions) as well as introducing non-natural activities (bispecificity and cytotoxicity mediated by conjugated drugs). A common challenge during antibody optimization is that improvements in one property (e.g., affinity) can lead to deficits in other properties (e.g., stability). Here we review recent advances in understanding trade-offs between different antibody properties, including affinity, specificity, stability and solubility. We also review new approaches for co-optimizing multiple antibody properties and discuss how these methods can be used to rapidly and systematically generate antibodies for a wide range of applications.

Prediction and Reduction of the Aggregation of Monoclonal Antibodies

Protein aggregation remains a major area of focus in the production of monoclonal antibodies. Improving the intrinsic properties of antibodies can improve manufacturability, attrition rates, safety, formulation, titers, immunogenicity, and solubility. Here, we explore the potential of predicting and reducing the aggregation propensity of monoclonal antibodies, based on the identification of aggregation-prone regions and their contribution to the thermodynamic stability of the protein. Although aggregation-prone regions are thought to occur in the antigen binding region to drive hydrophobic binding with antigen, we were able to rationally design variants that display a marked decrease in aggregation propensity while retaining antigen binding through the introduction of artificial aggregation gatekeeper residues. The reduction in aggregation propensity was accompanied by an increase in expression titer, showing that reducing protein aggregation is beneficial throughout the development process. The data presented show that this approach can significantly reduce liabilities in novel therapeutic antibodies and proteins, leading to a more efficient path to clinical studies.

Solubis: a webserver to reduce protein aggregation through mutation

Protein aggregation is a major factor limiting the biotechnological and therapeutic application of many proteins, including enzymes and monoclonal antibodies. The molecular principles underlying aggregation are by now sufficiently understood to allow rational redesign of natural polypeptide sequences for decreased aggregation tendency, and hence potentially increased expression and solubility. Given that aggregation-prone regions (APRs) tend to contribute to the stability of the hydrophobic core or to functional sites of the protein, mutations in these regions have to be carefully selected in order not to disrupt protein structure or function. Therefore, we here provide access to an automated pipeline to identify mutations that reduce protein aggregation by reducing the intrinsic aggregation propensity of the sequence (using the TANGO algorithm), while taking care not to disrupt the thermodynamic stability of the native structure (using the empirical force-field FoldX). Moreover, by providing a plot of the intrinsic aggregation propensity score of APRs corrected by the local stability of that region in the folded structure, we allow users to prioritize those regions in the protein that are most in need of improvement through protein engineering. The method can be accessed at http://solubis.switchlab.org/.

De Novo Sequencing and Resurrection of a Human Astrovirus-Neutralizing Antibody

Monoclonal antibody (mAb) therapeutics targeting cancer, autoimmune diseases, inflammatory diseases, and infectious diseases are growing exponentially. Although numerous panels of mAbs targeting infectious disease agents have been developed, their progression into clinically useful mAbs is often hindered by the lack of sequence information and/or loss of hybridoma cells that produce them. Here we combine the power of crystallography and mass spectrometry to determine the amino acid sequence and glycosylation modification of the Fab fragment of a potent human astrovirus-neutralizing mAb. We used this information to engineer a recombinant antibody single-chain variable fragment that has the same specificity as the parent monoclonal antibody to bind to the astrovirus capsid protein. This antibody can now potentially be developed as a therapeutic and diagnostic agent.

Rational design of viscosity reducing mutants of a monoclonal antibody: hydrophobic versus electrostatic inter-molecular interactions

High viscosity of monoclonal antibody formulations at concentrations ≥100 mg/mL can impede their development as products suitable for subcutaneous delivery. The effects of hydrophobic and electrostatic intermolecular interactions on the solution behavior of MAB 1, which becomes unacceptably viscous at high concentrations, was studied by testing 5 single point mutants. The mutations were designed to reduce viscosity by disrupting either an aggregation prone region (APR), which also participates in 2 hydrophobic surface patches, or a negatively charged surface patch in the variable region. The disruption of an APR that lies at the interface of light and heavy chain variable domains, VH and VL, via L45K mutation destabilized MAB 1 and abolished antigen binding. However, mutation at the preceding residue (V44K), which also lies in the same APR, increased apparent solubility and reduced viscosity of MAB 1 without sacrificing antigen binding or thermal stability. Neutralizing the negatively charged surface patch (E59Y) also increased apparent solubility and reduced viscosity of MAB 1, but charge reversal at the same position (E59K/R) caused destabilization, decreased solubility and led to difficulties in sample manipulation that precluded their viscosity measurements at high concentrations. Both V44K and E59Y mutations showed similar increase in apparent solubility. However, the viscosity profile of E59Y was considerably better than that of the V44K, providing evidence that inter-molecular interactions in MAB 1 are electrostatically driven. In conclusion, neutralizing negatively charged surface patches may be more beneficial toward reducing viscosity of highly concentrated antibody solutions than charge reversal or aggregation prone motif disruption.

Early implementation of QbD in biopharmaceutical development: a practical example

In drug development, the “onus” of the low R&D efficiency has been put traditionally onto the drug discovery process (i.e., finding the right target or “binding” functionality). Here, we show that manufacturing is not only a central component of product success, but also that, by integrating manufacturing and discovery activities in a “holistic” interpretation of QbD methodologies, we could expect to increase the efficiency of the drug discovery process as a whole. In this new context, early risk assessment, using developability methodologies and computational methods in particular, can assist in reducing risks during development in a cost-effective way. We define specific areas of risk and how they can impact product quality in a broad sense, including essential aspects such as product efficacy and patient safety. Emerging industry practices around developability are introduced, including some specific examples of applications to biotherapeutics. Furthermore, we suggest some potential workflows to illustrate how developability strategies can be introduced in practical terms during early drug development in order to mitigate risks, reduce drug attrition and ultimately increase the robustness of the biopharmaceutical supply chain. Finally, we also discuss how the implementation of such methodologies could accelerate the access of new therapeutic treatments to patients in the clinic.

Antibodies: Computer-Aided Prediction of Structure and Design of Function

With the advent of high-throughput sequencing, and the increased availability of experimental structures of antibodies and antibody-antigen complexes, comes the improvement of computational approaches to predict the structure and design the function of antibodies and antibody-antigen complexes. While antibodies pose formidable challenges for protein structure prediction and design due to their large size and highly flexible loops in the complementarity-determining regions, they also offer exciting opportunities: the central importance of antibodies for human health results in a wealth of structural and sequence information that—as a knowledge base—can drive the modeling algorithms by limiting the conformational and sequence search space to likely regions of success. Further, efficient experimental platforms exist to test predicted antibody structure or designed antibody function, thereby leading to an iterative feedback loop between computation and experiment. We briefly review the history of computer-aided prediction of structure and design of function in the antibody field before we focus on recent methodological developments and the most exciting application examples.

Concentration dependent viscosity of monoclonal antibody solutions: explaining experimental behavior in terms of molecular properties

PURPOSE

Early identification of monoclonal antibody candidates whose development, as high concentration (≥100 mg/mL) drug products, could prove challenging, due to high viscosity, can help define strategies for candidate engineering and selection.

METHODS

Concentration dependent viscosities of 11 proprietary mAbs were measured. Sequence and structural features of the variable (Fv) regions were analyzed to understand viscosity behavior of the mAbs. Coarse-grained molecular simulations of two problematic mAbs were compared with that of a well behaved mAb.

RESULTS

Net charge, ξ-potential and pI of Fv regions were found to correlate with viscosities of highly concentrated antibody solutions. Negative net charges on the Fv regions of two mAbs with poor viscosity behaviors facilitate attractive self-associations, causing them to diffuse slower than a well-behaved mAb with positive net charge on its Fv region. An empirically derived equation that connects aggregation propensity and pI of the Fv region with high concentration viscosity of the whole mAb was developed.

CONCLUSIONS

An Fv region-based qualitative screening profile was devised to flag mAb candidates whose development, as high concentration drug products, could prove challenging. This screen can facilitate developability risk assessment and mitigation strategies for antibody based therapeutics via rapid high throughput material-free screening.

Optimal charged mutations in the complementarity-determining regions that prevent domain antibody aggregation are dependent on the antibody scaffold

Therapeutic antibodies need to be highly resistant to aggregation due to the high concentrations required for subcutaneous delivery and the potential immunogenicity of antibody aggregates. Human antibody fragments-such as single-domain antibodies (VH or VL)-are typically much less soluble than full-length antibodies. Nevertheless, some aggregation-resistant VH domains have been discovered that are negatively charged at neutral pH and/or enriched in negatively charged residues within the complementarity-determining regions (CDRs). To better understand how to engineer diverse domain antibodies to resist aggregation, we have investigated the solubilizing activity of positively and negatively charged mutations within hydrophobic CDRs of multiple VH scaffolds that differ in their net charge. We find that negatively charged mutations inserted near the edges of hydrophobic CDRs are more effective than positively charged ones at inhibiting aggregation for VH scaffolds that are negatively or near-neutrally charged. In contrast, positively charged CDR mutations prevent aggregation better than negatively charged ones for a VH scaffold that is highly positively charged. Our findings suggest that the net charge of the antibody scaffold is a key determinant of the optimal CDR mutations for preventing aggregation. We expect that our findings will improve the design of aggregation-resistant antibodies with single- and multidomain scaffolds.

High-throughput screening for developability during early-stage antibody discovery using self-interaction nanoparticle spectroscopy

The discovery of monoclonal antibodies (mAbs) that bind to a particular molecular target is now regarded a routine exercise. However, the successful development of mAbs that (1) express well, (2) elicit a desirable biological effect upon binding, and (3) remain soluble and display low viscosity at high concentrations is often far more challenging. Therefore, high throughput screening assays that assess self-association and aggregation early in the selection process are likely to yield mAbs with superior biophysical properties. Here, we report an improved version of affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS) that is capable of screening large panels of antibodies for their propensity to self-associate. AC-SINS is based on concentrating mAbs from dilute solutions around gold nanoparticles pre-coated with polyclonal capture (e.g., anti-Fc) antibodies. Interactions between immobilized mAbs lead to reduced inter-particle distances and increased plasmon wavelengths (wavelengths of maximum absorbance), which can be readily measured by optical means. This method is attractive because it is compatible with dilute and unpurified mAb solutions that are typical during early antibody discovery. In addition, we have improved multiple aspects of this assay for increased throughput and reproducibility. A data set comprising over 400 mAbs suggests that our modified assay yields self-interaction measurements that are well-correlated with other lower throughput assays such as cross-interaction chromatography. We expect that the simplicity and throughput of our improved AC-SINS method will lead to improved selection of mAbs with excellent biophysical properties during early antibody discovery.

On the role of aggregation prone regions in protein evolution, stability, and enzymatic catalysis: insights from diverse analyses

The various roles that aggregation prone regions (APRs) are capable of playing in proteins are investigated here via comprehensive analyses of multiple non-redundant datasets containing randomly generated amino acid sequences, monomeric proteins, intrinsically disordered proteins (IDPs) and catalytic residues. Results from this study indicate that the aggregation propensities of monomeric protein sequences have been minimized compared to random sequences with uniform and natural amino acid compositions, as observed by a lower average aggregation propensity and fewer APRs that are shorter in length and more often punctuated by gate-keeper residues. However, evidence for evolutionary selective pressure to disrupt these sequence regions among homologous proteins is inconsistent. APRs are less conserved than average sequence identity among closely related homologues (≥80% sequence identity with a parent) but APRs are more conserved than average sequence identity among homologues that have at least 50% sequence identity with a parent. Structural analyses of APRs indicate that APRs are three times more likely to contain ordered versus disordered residues and that APRs frequently contribute more towards stabilizing proteins than equal length segments from the same protein. Catalytic residues and APRs were also found to be in structural contact significantly more often than expected by random chance. Our findings suggest that proteins have evolved by optimizing their risk of aggregation for cellular environments by both minimizing aggregation prone regions and by conserving those that are important for folding and function. In many cases, these sequence optimizations are insufficient to develop recombinant proteins into commercial products. Rational design strategies aimed at improving protein solubility for biotechnological purposes should carefully evaluate the contributions made by candidate APRs, targeted for disruption, towards protein structure and activity.

Biotechnology requires the large-scale expression, yield, and storage of recombinant proteins. Each step in protein production has the potential to cause aggregation as proteins, not evolved to exist outside the cell, endure the various steps involved in commercial manufacturing processes. Mechanistic studies into protein aggregation have revealed that certain sequence regions contribute more to the aggregation propensity of a protein than other sequence regions do. Efforts to disrupt these regions have thus far indicated that rational sequence engineering is a useful technique to reduce the aggregation of biotechnologically relevant proteins. To improve our ability to rationally engineer proteins with enhanced expression, solubility, and shelf-life we conducted extensive analyses of aggregation prone regions (APRs) within protein sequences to characterize the various roles these regions play in proteins. Findings from this work indicate that protein sequences have evolved by minimizing their aggregation propensities. However, we also found that many APRs are conserved in protein families and are essential to maintain protein stability and function. Therefore, the contributions that APRs, targeted for disruption, make towards protein stability and function should be carefully evaluated when improving protein solubility via rational design.

High throughput detection of antibody self-interaction by bio-layer interferometry

Self-interaction of an antibody may lead to aggregation, low solubility or high viscosity. Rapid identification of highly developable leads remains challenging, even though progress has been made with the introduction of techniques such as self-interaction chromatography (SIC) and cross-interaction chromatography (CIC). Here, we report a high throughput method to detect antibody clone self-interaction (CSI) using bio-layer interferometry (BLI) technology. Antibodies with strong self-interaction responses in the CSI-BLI assay also show delayed retention times in SIC and CIC. This method allows hundreds of candidates to be screened in a matter of hours with minimal material consumption.

Correct primary structure assessment and extensive glyco-profiling of cetuximab by a combination of intact, middle-up, middle-down and bottom-up ESI and MALDI mass spectrometry techniques

The European Medicines Agency received recently the first marketing authorization application for a biosimilar monoclonal antibody (mAb) and adopted the final guidelines on biosimilar mAbs and Fc-fusion proteins. The agency requires high similarity between biosimilar and reference products for approval. Specifically, the amino acid sequences must be identical. The glycosylation pattern of the antibody is also often considered to be a very important quality attribute due to its strong effect on quality, safety, immunogenicity, pharmacokinetics and potency. Here, we describe a case study of cetuximab, which has been marketed since 2004. Biosimilar versions of the product are now in the pipelines of numerous therapeutic antibody biosimilar developers. We applied a combination of intact, middle-down, middle-up and bottom-up electrospray ionization and matrix assisted laser desorption ionization mass spectrometry techniques to characterize the amino acid sequence and major post-translational modifications of the marketed cetuximab product, with special emphasis on glycosylation. Our results revealed a sequence error in the reported sequence of the light chain in databases and in publications, thus highlighting the potency of mass spectrometry to establish correct antibody sequences. We were also able to achieve a comprehensive identification of cetuximab's glycoforms and glycosylation profile assessment on both Fab and Fc domains. Taken together, the reported approaches and data form a solid framework for the comparability of antibodies and their biosimilar candidates that could be further applied to routine structural assessments of these and other antibody-based products.

Proteins behaving badly: emerging technologies in profiling biopharmaceutical aggregation

Over recent decades biotechnology has made significant advances owing to the emergence of powerful biochemical and biophysical instrumentation. The development of such technologies has enabled high-throughput assessment of compounds, the implementation of recombinant DNA technology, and large-scale manufacture of monoclonal antibodies. Such innovations have ultimately resulted in the current experienced biopharmaceutical stronghold in the therapeutic market. Yet aggregate prediction and profiling remains a challenge in the formulation of biopharmaceuticals due to artifacts associated with each analytical method. We review some emerging trends and novel technologies that offer a promising potential for accurately predicting and profiling protein aggregation at various stages of biopharmaceutical product design.

Rapid analysis of antibody self-association in complex mixtures using immunogold conjugates

A key challenge in developing therapeutic antibodies is their highly variable propensities to self-associate at high antibody concentrations (>50 mg/mL) required for subcutaneous delivery. Identification of monoclonal antibodies (mAbs) in the initial discovery process that not only have high binding affinity but also have high solubility and low viscosity would simplify the development of safe and effective antibody therapeutics. Unfortunately, the low purities, small quantities and large numbers of antibody candidates during the early discovery process are incompatible with current methods of measuring antibody self-association. We report a method (affinity-capture self-interaction nanoparticle spectroscopy, AC-SINS) capable of identifying mAbs with low self-association propensity that is robust even at low mAb concentrations (5-50 μg/mL) and in the presence of cell culture media. Gold nanoparticles are coated with polyclonal antibodies specific for human antibodies, and then human mAbs are captured from dilute antibody solutions. We find that the wavelength of maximum absorbance (plasmon wavelength) of antibody-gold conjugates--which red-shifts as the distance between particles is reduced due to attractive mAb self-interactions--is well correlated with light scattering measurements conducted at several orders of magnitude higher antibody concentrations. The generality of AC-SINS makes it well suited for use in diverse settings ranging from antibody discovery to formulation development.

Insights into the potential aggregation liabilities of the b12 Fab fragment via elevated temperature molecular dynamics

Aggregation is a common hurdle faced during the development of antibody therapeutics. In this study, we explore the potential aggregation liabilities of the Fab (fragment antigen-binding) from a human IgG1κ antibody via multiple elevated temperature molecular dynamic simulations, analogous to accelerated stability studies performed during formulation development. Deformation and solvent exposure changes in response to thermal stress were monitored for individual structural domains (V(H), V(L), C(H)1 and C(L)), their interfaces (V(H):V(L) and C(H)1:C(L)), edge beta-strands and sequence-predicted aggregation-prone regions (APRs). During simulations, domain interfaces deformed prior to the unfolding of individual domains. However, interfacial beta-strands retained their secondary structure and remained solvent protected longer than all other strands or loops. Thus, APRs located in interfacial beta-strands are effectively blocked from self-association. Structural deformations were also observed in complementarity-determining regions, edge beta-strands and adjoining framework beta-strands, which increased their solvent-accessible surface area and exposed APRs in these regions. From the analysis of these structural changes, two potential aggregation liabilities were identified in the V(H) domain of this Fab. Insights gained from this investigation should be useful in devising a rational structure-based strategy for the design and selection of antibody candidates with high potency and improved developability.

IgG Fab fragments forming bivalent complexes by a conformational mechanism that is reversible by osmolytes

Generated by proteolytic cleavage of immunoglobulin, Fab fragments possess great promise as blocking reagents, able to bind receptors or other targets without inducing cross-linking. However, aggregation of Fab preparations is a common occurrence, which generates intrinsic stimulatory capacity and thwarts signal blockade strategies. Using a panel of biochemical approaches, including size exclusion chromatography, SDS-PAGE, mass spectrometry, and cell stimulation followed by flow cytometry, we have measured the oligomerization and acquisition of stimulatory capacity that occurs in four monoclonal IgG Fabs specific for TCR/CD3. Unexpectedly, we observed that all Fabs spontaneously formed complexes that were precisely bivalent, and these bivalent complexes possessed most of the stimulatory activity of each Fab preparation. Fabs composing bivalent complexes were more susceptible to proteolysis than monovalent Fabs, indicating a difference in conformation between the Fabs involved in these two different states of valency. Because osmolytes represent a class of compounds that stabilize protein folding and conformation, we sought to determine the extent to which the amino acid osmolyte l-proline might impact bivalent Fab complexation. We found that l-proline (i) inhibited the adoption of the conformation associated with bivalent complexation, (ii) preserved Fab monovalency, (iii) reversed the conformation of preformed bivalent Fabs to that of monovalent Fabs, and (iv) separated a significant percentage of preformed bivalent complexes into monovalent species. Thus, Fab fragments can adopt a conformation that is compatible with folding or packing of a bivalent complex in a process that can be inhibited by osmolytes.

Computer-aided antibody design

Recent clinical trials using antibodies with low toxicity and high efficiency have raised expectations for the development of next-generation protein therapeutics. However, the process of obtaining therapeutic antibodies remains time consuming and empirical. This review summarizes recent progresses in the field of computer-aided antibody development mainly focusing on antibody modeling, which is divided essentially into two parts: (i) modeling the antigen-binding site, also called the complementarity determining regions (CDRs), and (ii) predicting the relative orientations of the variable heavy (V(H)) and light (V(L)) chains. Among the six CDR loops, the greatest challenge is predicting the conformation of CDR-H3, which is the most important in antigen recognition. Further computational methods could be used in drug development based on crystal structures or homology models, including antibody-antigen dockings and energy calculations with approximate potential functions. These methods should guide experimental studies to improve the affinities and physicochemical properties of antibodies. Finally, several successful examples of in silico structure-based antibody designs are reviewed. We also briefly review structure-based antigen or immunogen design, with application to rational vaccine development.

Mechanisms of self-association of a human monoclonal antibody CNTO607

Some antibodies have a tendency to self-associate leading to precipitation at relatively low concentrations. CNTO607, a monoclonal antibody, precipitates irreversibly in phosphate-buffered saline at concentrations above 13 mg/ml. Previous mutagenesis work based on the Fab crystal structure pinpointed a three residue fragment in the heavy chain CDR-3, (99)FHW(100a), as an aggregation epitope that is anchored by two salt bridges. Biophysical characterization of variants reveals that F99 and W100a, but not H100, contribute to the intermolecular interaction. A K210T/K215T mutant designed to disrupt the charge interactions in the aggregation model yielded an antibody that does not precipitate but forms reversible aggregates. An isotype change from IgG1 to IgG4 prevents the antibody from precipitating at low concentration yet the solution viscosity is elevated. To further understand the nature of the antibody self-association, studies on the Fab fragment found high solubility but significant self- and cross-interactions remain. Dynamic light scattering data provides evidence for higher order Fab structure at increased concentrations. Our results provide direct support for the aggregation model that CNTO607 precipitation results primarily from the specific interaction of the Fab arms of neighboring antibodies followed by the development of an extensive network of antibodies inducing large-scale aggregation and precipitation.

Biosimilars and biobetters as tools for understanding and mitigating the immunogenicity of biotherapeutics

In this article, we review key steps for the development of biosimilars and biobetters and related bioanalytical challenges, with a focus on how they are associated with immunogenicity. We analyze the factors that can impact antidrug antibody (ADA) responses and their correlations with preclinical and clinical outcomes to provide relevant insights and to answer questions, including what types of aggregate are immunogenic. We also address strategies for developing less-immunogenic biotherapeutics. Using interferon-β (IFN-β) as a case study, we explore the correlation between aggregation and immunogenicity. We dissect and integrate with clinical data the IFN-β preclinical immunogenicity and aggregation predictions and discuss the feasibility of developing an IFN-β with lower aggregation and/or immunogenicity.

Biophysical characterisation of thermal-induced precipitates of recombinant anthrax protective antigen: evidence for kinetically trapped unfolding domains in solid-state

Insoluble aggregation or precipitation is one of the most common degradation pathways observed for biotherapeutics; despite this, the structural mechanisms by which this occurs remain poorly understood due to difficulties associated with biophysical characterisation of protein particulates. To address this knowledge gap, we developed a solid-state circular dichroism (CD) technique, which allows in situ measurements of the secondary and tertiary structural changes associated with the formation of visible therapeutic protein aggregates. We demonstrate how solid-state CD, in conjunction with other biophysical and computational methods can aid in gaining valuable insights into the mechanisms and pathways of thermal-induced precipitation of Bacillus anthracis recombinant protective antigen (rPA), the primary immunogen of anthrax subunit vaccine. Using these methods, we show the domains d3 and d4 are the most labile of the four structurally distinct domains of rPA and play the critical role in nucleating the cascade of unfolding and aggregation. During the assembly process, the domains d1 and d2 become kinetically trapped within the insoluble aggregate and reveal previously intractable distinct tertiary structural elements of the rPA native structure. These findings reveal a uniquely detailed insight into the role of rPA domains on protein stability and provide a mechanistic framework for thermal-induced unfolding and precipitation. It also shows that solid-state CD provides a novel approach in characterising protein precipitation that may facilitate rational improvements to the stability of biopharmaceuticals.