Early determination of potential critical quality attributes of therapeutic antibodies in developability studies through surface plasmon resonance-based relative binding activity assessment.

Precise measurement of the binding activity changes of therapeutic antibodies is important to determine the potential critical quality attributes (CQAs) in developability assessment at the early stage of antibody development. Here, we report a surface plasmon resonance (SPR)-based relative binding activity method, which incorporates both binding affinity and binding response and allows us to determine relative binding activity of antibodies with high accuracy and precision. We applied the SPR-based relative binding activity method in multiple forced degradation studies of antibody developability assessment. The current developability assessment strategy provided comprehensive, precise characterization of antibody binding activity in the stability studies, enabling us to perform correlation analysis and establish the structure-function relationship between relative binding activity and quality attributes. The impact of a given quality attribute on binding activity could be confidently determined without isolating antibody variants. We identified several potential CQAs, including Asp isomerization, Asn deamidation, and fragmentation. Some potential CQAs affected binding affinity of antibody and resulted in a reduction of binding activity. Certain potential CQAs impaired antibody binding to antigen and led to a loss of binding activity. A few potential CQAs could influence both binding affinity and binding response and cause a substantial decrease in antibody binding activity. Specifically, we identified low abundance Asn33 deamidation in the light chain complementarity-determining region as a potential CQA, in which all the stressed antibody samples showed Asn33 deamidation abundances ranging from 4.2% to 27.5% and a mild binding affinity change from 1.76 nM to 2.16 nM.

Minimal purification method enables developability assessment of recombinant proteins.

Analytical characterization of proteins is a critical task for developing therapeutics and subunit vaccine candidates. Assessing candidates with a battery of biophysical assays can inform the selection of one that exhibits properties consistent with a given target product profile (TPP). Such assessments, however, require several milligrams of purified protein, and ideal assessments of the physicochemical attributes of the proteins should not include unnatural modifications like peptide tags for purification. Here, we describe a fast two-stage minimal purification process for recombinant proteins secreted by the yeast host Komagataella phaffii from a 20 mL culture supernatant. This method comprises a buffer exchange and filtration with a Q-membrane filter and we demonstrate sufficient removal of key supernatant impurities including host-cell proteins (HCPs) and DNA with yields of 1-2 mg and >60% purity. This degree of purity enables characterizing the resulting proteins using affinity binding, mass spectrometry, and differential scanning calorimetry. We first evaluated this method to purify an engineered SARS-CoV-2 subunit protein antigen and compared the purified protein to a conventional two-step chromatographic process. We then applied this method to compare several SARS-CoV-2 RBD sequences. Finally, we show this simple process can be applied to a range of other proteins, including a single-domain antibody, a rotavirus protein subunit, and a human growth hormone. This simple and fast developability methodology obviates the need for genetic tagging or full chromatographic development when assessing and comparing early-stage protein therapeutics and vaccine candidates produced in K. phaffii.

Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics.

Therapeutic monoclonal antibodies and their derivatives are key components of clinical pipelines in the global biopharmaceutical industry. The availability of large datasets of antibody sequences, structures, and biophysical properties is increasingly enabling the development of predictive models and computational tools for the "developability assessment" of antibody drug candidates. Here, we provide an overview of the antibody informatics tools applicable to the prediction of developability issues such as stability, aggregation, immunogenicity, and chemical degradation. We further evaluate the opportunities and challenges of using biopharmaceutical informatics for drug discovery and optimization. Finally, we discuss the potential of developability guidelines based on in silico metrics that can be used for the assessment of antibody stability and manufacturability.

The Therapeutic Antibody Profiler for Computational Developability Assessment.

The need to consider an antibody's "developability" (immunogenicity, solubility, specificity, stability, manufacturability, and storability) is now well understood in therapeutic antibody design. Predicting these properties rapidly and inexpensively is critical to industrial workflows, to avoid devoting resources to non-productive candidates. Here, we describe a high-throughput computational developability assessment tool, the Therapeutic Antibody Profiler (TAP), which assesses the physicochemical "druglikeness" of an antibody candidate. Input variable domain sequences are converted to three-dimensional structural models, and then five developability-linked molecular surface descriptors are calculated and compared to advanced-stage clinical therapeutics. Values at the extremes of/outside of the distributions seen in therapeutics imply an increased risk of developability issues. Therefore, TAP, starting only from sequence information, provides a route to rapidly identifying drug candidate antibodies that are likely to have poor developability. Our web application ( opig.stats.ox.ac.uk/webapps/tap ) profiles input antibody sequences against a continually updated reference set of clinical therapeutics.

Combining Unfolding Reversibility Studies and Molecular Dynamics Simulations to Select Aggregation-Resistant Antibodies.

The efficient development of new therapeutic antibodies relies on developability assessment with biophysical and computational methods to find molecules with drug-like properties such as resistance to aggregation. Despite the many novel approaches to select well-behaved proteins, antibody aggregation during storage is still challenging to predict. For this reason, there is a high demand for methods that can identify aggregation-resistant antibodies. Here, we show that three straightforward techniques can select the aggregation-resistant antibodies from a dataset with 13 molecules. The ReFOLD assay provided information about the ability of the antibodies to refold to monomers after unfolding with chemical denaturants. Modulated scanning fluorimetry (MSF) yielded the temperatures that start causing irreversible unfolding of the proteins. Aggregation was the main reason for poor unfolding reversibility in both ReFOLD and MSF experiments. We therefore performed temperature ramps in molecular dynamics (MD) simulations to obtain partially unfolded antibody domains in silico and used CamSol to assess their aggregation potential. We compared the information from ReFOLD, MSF, and MD to size-exclusion chromatography (SEC) data that shows whether the antibodies aggregated during storage at 4, 25, and 40 °C. Contrary to the aggregation-prone molecules, the antibodies that were resistant to aggregation during storage at 40 °C shared three common features: (i) higher tendency to refold to monomers after unfolding with chemical denaturants, (ii) higher onset temperature of nonreversible unfolding, and (iii) unfolding of regions containing aggregation-prone sequences at higher temperatures in MD simulations.

Modeling the impact of amino acid substitution in a monoclonal antibody on cation exchange chromatography.

A vital part of biopharmaceutical research is decision making around which lead candidate should be progressed in early-phase development. When multiple antibody candidates show similar biological activity, developability aspects are taken into account to ease the challenges of manufacturing the potential drug candidate. While current strategies for developability assessment mainly focus on drug product stability, only limited information is available on how antibody candidates with minimal differences in their primary structure behave during downstream processing. With increasing time-to-market pressure and an abundance of monoclonal antibodies (mAbs) in development pipelines, developability assessments should also consider the ability of mAbs to integrate into the downstream platform. This study investigates the influence of amino acid substitutions in the complementarity-determining region (CDR) of a full-length IgG1 mAb on the elution behavior in preparative cation exchange chromatography. Single amino acid substitutions within the investigated mAb resulted in an additional positive charge in the light chain (L) and heavy chain (H) CDR, respectively. The mAb variants showed an increased retention volume in linear gradient elution compared with the wild-type antibody. Furthermore, the substitution of tryptophan with lysine in the H-CDR3 increased charge heterogeneity of the product. A multiscale in silico analysis, consisting of homology modeling, protein surface analysis, and mechanistic chromatography modeling increased understanding of the adsorption mechanism. The results reveal the potential effects of lead optimization during antibody drug discovery on downstream processing.

Stability liabilities of biotherapeutic proteins: Early assessment as mitigation strategy.

Identification of molecular liabilities and implementation of mitigation strategies are key aspects that need to be considered by pharmaceutical companies developing therapeutic proteins. In the field of monoclonal antibodies, an efficient and streamlined process known as developability assessment is used for the selection of the "fittest" candidate. Other protein modalities, have in most cases only a limited number of possible candidates, requiring a paradigm change to a concept of candidate enabling. The assessment of liabilities at early project phases with the possibility to re-engineer candidates becomes essential for the success of these projects. Each protein possesses a unique stability profile resulting from the interplay of conformational, colloidal, chemical and physical stability attributes. All of these attributes strongly depend on external factors. Conformational and colloidal stability profiles of three non-immunoglobulin domain based proteins, namely Carbonic anhydrase, Ovalbumin and Thyroglobulin, and of two monoclonal antibodies were assessed in dependence of solution pH, ionic strength and varying buffering agents. The impact of screened external factors on proteins' stability attributes varied significantly, indicating presence of molecule specific liabilities. Screening of such a broad space of conditions at early project phases is only feasible using low-material consuming, high-throughput analytical methods as exemplified in this study.

Advancing Therapeutic Protein Discovery and Development through Comprehensive Computational and Biophysical Characterization.

Therapeutic protein candidates should exhibit favorable properties that render them suitable to become drugs. Nevertheless, there are no well-established guidelines for the efficient selection of proteinaceous molecules with desired features during early stage development. Such guidelines can emerge only from a large body of published research that employs orthogonal techniques to characterize therapeutic proteins in different formulations. In this work, we share a study on a diverse group of proteins, including their primary sequences, purity data, and computational and biophysical characterization at different pH and ionic strength. We report weak linear correlations between many of the biophysical parameters. We suggest that a stability comparison of diverse therapeutic protein candidates should be based on a computational and biophysical characterization in multiple formulation conditions, as the latter can largely determine whether a protein is above or below a certain stability threshold. We use the presented data set to calculate several stability risk scores obtained with an increasing level of analytical effort and show how they correlate with protein aggregation during storage. Our work highlights the importance of developing combined risk scores that can be used for early stage developability assessment. We suggest that such scores can have high prediction accuracy only when they are based on protein stability characterization in different solution conditions.

Development of a high-throughput solubility screening assay for use in antibody discovery.

Poor solubility is a common challenge encountered during the development of high concentration monoclonal antibody (mAb) formulations, but there are currently no methods that can provide predictive information on high-concentration behavior of mAbs in early discovery. We explored the utility of methodologies used for determining extrapolated solubility as a way to rank-order mAbs based on their relative solubility properties. We devised two approaches to accomplish this: 1) vapor diffusion technique utilized in traditional protein crystallization practice, and 2) polyethylene glycol (PEG)-induced precipitation and quantitation by turbidity. Using a variety of in-house mAbs with known high-concentration behavior, we demonstrated that both approaches exhibited reliable predictability of the relative solubility properties of these mAbs. Optimizing the latter approach, we developed a format that is capable of screening a large panel of mAbs in multiple pH and buffer conditions. This simple, material-saving, high-throughput approach enables the selection of superior molecules and optimal formulation conditions much earlier in the antibody discovery process, prior to time-consuming and material intensive high-concentration studies.

Developability Assessment of Physicochemical Properties and Stability Profiles of HIV-1 BG505 SOSIP.664 and BG505 SOSIP.v4.1-GT1.1 gp140 Envelope Glycoprotein Trimers as Candidate Vaccine Antigens.

The induction of broadly neutralizing antibodies (bNAbs) is a major goal in the development of an effective vaccine against HIV-1. A soluble, trimeric, germline (gI) bNAb-targeting variant of the HIV-1 envelope glycoprotein (termed BG505 SOSIP.v4.1-GT1.1 gp140, abbreviated to GT1.1) has recently been developed. Here, we have compared this new immunogen with the parental trimer from which it was derived, BG505 SOSIP.664 gp140. We used a comprehensive suite of biochemical and biophysical methods to determine physicochemical similarities and differences between the 2 trimers, and thereby assessed whether additional formulation development efforts were needed for the GT1.1 vaccine candidate. The overall higher order structure and oligomeric states of the 2 vaccine antigens were quite similar, as were their thermal, chemical, and colloidal stability profiles, as evaluated during accelerated stability studies. Overall, we conclude that the primary sequence changes made to create the gl bNAb-targeting GT1.1 trimer did not detrimentally affect its physicochemical properties or stability profiles from a pharmaceutical perspective. This developability assessment of the BG505 GT1.1 vaccine antigen supports using the formulation and storage conditions previously identified for the parental SOSIP.664 trimer and enables the development of GT1.1 for phase I clinical studies.

In vitro and in silico assessment of the developability of a designed monoclonal antibody library.

Despite major advances in antibody discovery technologies, the successful development of monoclonal antibodies (mAbs) into effective therapeutic and diagnostic agents can often be impeded by developability liabilities, such as poor expression, low solubility, high viscosity and aggregation. Therefore, strategies to predict at the early phases of antibody development the risk of late-stage failure of antibody candidates are highly valuable. In this work, we employ the in silico solubility predictor CamSol to design a library of 17 variants of a humanized mAb predicted to span a broad range of solubility values, and we examine their developability potential with a battery of commonly used in vitro and in silico assays. Our results demonstrate the ability of CamSol to rationally enhance mAb developability, and provide a quantitative comparison of in vitro developability measurements with each other and with more resource-intensive solubility measurements, as well as with in silico predictors that offer a potentially faster and cheaper alternative. We observed a strong correlation between predicted and experimentally determined solubility values, as well as with measurements obtained using a panel of in vitro developability assays that probe non-specific interactions. These results indicate that computational methods have the potential to reduce or eliminate the need of carrying out laborious in vitro quality controls for large numbers of lead candidates. Overall, our study provides support to the emerging view that the implementation of in silico tools in antibody discovery campaigns can ensure rapid and early selection of antibodies with optimal developability potential.

High-throughput screening of antibody variants for chemical stability: identification of deamidation-resistant mutants.

Developability assessment of therapeutic antibody candidates assists drug discovery by enabling early identification of undesirable instabilities. Rapid chemical stability screening of antibody variants can accelerate the identification of potential solutions. We describe here the development of a high-throughput assay to characterize asparagine deamidation. We applied the assay to identify a mutation that unexpectedly stabilizes a critical asparagine. Ninety antibody variants were incubated under thermal stress in order to induce deamidation and screened for both affinity and total binding capacity. Surprisingly, a mutation five residues downstream from the unstable asparagine greatly reduced deamidation. Detailed assessment by LC-MS analysis confirmed the predicted improvement. This work describes both a high-throughput method for antibody stability screening during the early stages of antibody discovery and highlights the value of broad searches of antibody sequence space.

Susceptibility of Antibody CDR Residues to Chemical Modifications Can Be Revealed Prior to Antibody Humanization and Aid in the Lead Selection Process.

A critical part of the clinical development path for a therapeutic antibody involves evaluating the physical and chemical stability of candidate molecules throughout the manufacturing process. In particular, the risks of chemical liabilities that can impact antigen binding, such as deamidation, oxidation, and isomerization in the antibody CDR sequences, need to be controlled through formulation development or eliminated by replacing the amino acid motif displaying the chemical instability. Commonly, the antibody CDR sequence contains multiple sequence motifs (potential hotspots) for chemical instability. However, only a subset of these motifs results in actual chemical modification, and thus, experimental assessment of the extent of instability is necessary to identify positions for potential sequence engineering. Ideally, this information should be available prior to antibody humanization at the stage of parental rodent antibody identification. Early knowledge of liabilities allows for ranking of clones or the mitigation of liabilities by concurrent engineering with the antibody humanization process instead of time-consuming sequential activities. However, concurrent engineering of chemical liabilities and humanization requires translatability of the chemical modifications from the rodent parental antibody to the humanized. We experimentally compared the stability of all sequence motifs by mass spectrometric peptide mapping between the rodent parental antibody and the final humanized antibody and observed a linear correlation. These results have enabled a streamlined developability assessment process for therapeutic antibodies from lead discovery to clinical development.

Multi-criteria manufacturability indices for ranking high-concentration monoclonal antibody formulations.

The need for high-concentration formulations for subcutaneous delivery of therapeutic monoclonal antibodies (mAbs) can present manufacturability challenges for the final ultrafiltration/diafiltration (UF/DF) step. Viscosity levels and the propensity to aggregate are key considerations for high-concentration formulations. This work presents novel frameworks for deriving a set of manufacturability indices related to viscosity and thermostability to rank high-concentration mAb formulation conditions in terms of their ease of manufacture. This is illustrated by analyzing published high-throughput biophysical screening data that explores the influence of different formulation conditions (pH, ions, and excipients) on the solution viscosity and product thermostability. A decision tree classification method, CART (Classification and Regression Tree) is used to identify the critical formulation conditions that influence the viscosity and thermostability. In this work, three different multi-criteria data analysis frameworks were investigated to derive manufacturability indices from analysis of the stress maps and the process conditions experienced in the final UF/DF step. Polynomial regression techniques were used to transform the experimental data into a set of stress maps that show viscosity and thermostability as functions of the formulation conditions. A mathematical filtrate flux model was used to capture the time profiles of protein concentration and flux decay behavior during UF/DF. Multi-criteria decision-making analysis was used to identify the optimal formulation conditions that minimize the potential for both viscosity and aggregation issues during UF/DF. Biotechnol. Bioeng. 2017;114: 2043-2056. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Perodicals, Inc.

Aggregation risk prediction for antibodies and its application to biotherapeutic development.

Aggregation is a common problem affecting biopharmaceutical development that can have a significant effect on the quality of the product, as well as the safety to patients, particularly because of the increased risk of immune reactions. Here, we describe a new high-throughput screening algorithm developed to classify antibody molecules based on their propensity to aggregate. The tool, constructed and validated on experimental aggregation data for over 500 antibodies, is able to discern molecules with a high aggregation propensity as defined by experimental criteria relevant to bioprocessing and manufacturing of these molecules. Furthermore, we show how this tool can be combined with other computational approaches during early drug development to select molecules with reduced risk of aggregation and optimal developability properties.

Early developability screen of therapeutic antibody candidates using Taylor dispersion analysis and UV area imaging detection.

Therapeutic antibodies represent one of the fastest growing segments in the pharmaceutical market. They are used in a broad range of disease fields, such as autoimmune diseases, cancer, inflammation and infectious diseases. The growth of the segment has necessitated development of new analytical platforms for faster and better antibody selection and characterization. Early quality control and risk assessment of biophysical parameters help prevent failure in later stages of antibody development, and thus can reduce costs and save time. Critical parameters such as aggregation, conformational stability, colloidal stability and hydrophilicity, are measured during the early phase of antibody generation and guide the selection process of the best lead candidates in terms of technical developability. We report on the use of a novel instrument (ActiPix/Viscosizer) for measuring both the hydrodynamic radius and the absolute viscosity of antibodies based on Taylor dispersion analysis and UV area imaging. The looped microcapillary-based method combines low sample consumption, fast throughput and high precision compared to other conventional methods. From a random panel of 130 antibodies in the early selection process, we identified some with large hydrodynamic radius outside the normal distribution and others with non-Gaussian Taylor dispersion profiles. The antibodies with such abnormal properties were confirmed later in the selection process to show poor developability profiles. Moreover, combining these results with those of the viscosity measurements at high antibody concentrations allows screening, with limited amounts of materials, candidates with potential issues in pre-formulation development.