Clinical-stage Antibodies Research Articles

Facile generation of biepitopic antibodies with intrinsic agonism for activating tumor necrosis factor receptors

Agonist antibodies are being pursued for therapeutic applications ranging from neurodegenerative diseases to cancer. For the tumor necrosis factor (TNF) receptor superfamily, higher-order clustering of three or more receptors is key to their activation, which can be achieved using antibodies that recognize two unique epitopes. However, the generation of biepitopic (i.e., biparatopic) antibodies typically requires animal immunization and is laborious and unpredictable. Here, we report a simple method for identifying biepitopic antibodies that potently activate TNF receptors without the need for additional animal immunization. Our approach uses existing, receptor-specific IgGs, which lack intrinsic agonist activity, to block their corresponding epitopes, then selects single-chain antibodies that bind accessible epitopes. The selected antibodies are fused to the light chains of IgGs to generate human tetravalent antibodies. We highlight the broad utility of this approach by converting several clinical-stage antibodies against OX40 and CD137 (4-1BB) into biepitopic antibodies with potent agonist activity.

Streamlined Multi-Attribute Assessment of an Array of Clinical-Stage Antibodies: Relationship Between Degradation and Stability.

Clinical antibodies are an important class of drugs for the treatment of both chronic and acute diseases. Their manufacturability is subject to evaluation to ensure product quality and efficacy. One critical quality attribute is deamidation, a non-enzymatic process that is observed to occur during thermal stress, at low or high pH, or a combination thereof. Deamidation may induce antibody instability and lead to aggregation, which may pose immunogenicity concerns. The introduction of a negative charge via deamidation may impact the desired therapeutic function (i) within the complementarity-determining region, potentially causing loss of efficacy; or (ii) within the fragment crystallizable region, limiting the effector function involving antibody-dependent cellular cytotoxicity. Here we describe a transformative solution that allows for a comparative assessment of deamidation and its impact on stability and aggregation. The innovative streamlined method evaluates the intact protein in its formulation conditions. This breakthrough platform technology is comprised of a quantum cascade laser microscope, a slide cell array that allows for flexibility in the design of experiments, and dedicated software. The enhanced spectral resolution is achieved using two-dimensional correlation, co-distribution, and two-trace two-dimensional correlation spectroscopies that reveal the molecular impact of deamidation. Eight re-engineered immunoglobulin G4 scaffold clinical antibodies under control and forced degradation conditions were evaluated for deamidation and aggregation. We determined the site of deamidation, the overall extent of deamidation, and where applicable, whether the deamidation event led to self-association or aggregation of the clinical antibody and the molecular events that led to the instability. The results were confirmed using orthogonal techniques for four of the samples.

Characterization of antibody surface properties and selection of a diverse subset for development of a generic size-exclusion chromatography method

Aggregates are an important quality attribute for biotherapeutics because they can affect the safety and efficacy of the drug and they are therefore routinely monitored, mainly with size-exclusion chromatography (SEC). However, there is often a need to tailor a SEC method to a specific molecule. This study describes the development of a generic SEC method tested on 138 antibodies with the variable domains taken from clinical stage antibodies. We report on the discovery of a subset of 12 antibodies that represents the full range of physiochemical properties found in the 138 antibodies. This subset is shown to be an efficient and reliable test set when developing chromatographic methods for antibodies. An understanding of the nature of the analyte-stationary phase interactions was gained when using this set with its wide range of physiochemical properties. Highly hydrophobic antibodies interact strongly with some modern silica hybrid materials causing the elution time to increase significantly, while a hydrophilically modified hybrid surface showed highly reduced interactions for the hydrophobic antibodies. Highly hydrophilic antibodies, on the other hand, exhibited asymmetric peaks to a certain extent on all stationary phases, while the elution time was not affected. The developed SEC method was shown to have satisfactory performance in terms of linearity, repeatability, range, and accuracy and exhibit very narrow distributions of elution time and peak symmetry when testing the 138 antibodies indicating its generic performance.

Nonspecificity fingerprints for clinical-stage antibodies in solution.

Monoclonal antibodies (mAbs) have successfully been developed for the treatment of a wide range of diseases. The clinical success of mAbs does not solely rely on optimal potency and safety but also require good biophysical properties to ensure a high developability potential. In particular, nonspecific interactions are a key developability parameter to monitor during discovery and development. Despite an increased focus on the detection of nonspecific interactions, their underlying physicochemical origins remain poorly understood. Here, we employ solution-based microfluidic technologies to characterize a set of clinical-stage mAbs and their interactions with commonly used nonspecificity ligands to generate nonspecificity fingerprints, providing quantitative data on the underlying physical chemistry. Furthermore, the solution-based analysis enables us to measure binding affinities directly, and we evaluate the contribution of avidity in nonspecific binding by mAbs. We find that avidity can increase the apparent affinity by two orders of magnitude. Notably, we find that a subset of these highly developed mAbs show nonspecific electrostatic interactions, even at physiological pH and ionic strength, and that they can form microscale particles with charge-complementary polymers. The group of mAb constructs flagged here for nonspecificity are among the worst performers in independent reports of surface and column-based screens. The solution measurements improve on the state-of-the-art by providing a stand-alone result for individual mAbs without the need to benchmark against cohort data. Based on our findings, we propose a quantitative solution-based nonspecificity score, which can be integrated in the development workflow for biological therapeutics and more widely in protein engineering.

Optimization of therapeutic antibodies for reduced self-association and non-specific binding via interpretable machine learning.

Antibody development, delivery, and efficacy are influenced by antibody-antigen affinity interactions, off-target interactions that reduce antibody bioavailability and pharmacokinetics, and repulsive self-interactions that increase the stability of concentrated antibody formulations and reduce their corresponding viscosity. Yet identifying antibody variants with optimal combinations of these three types of interactions is challenging. Here we show that interpretable machine-learning classifiers, leveraging antibody structural features descriptive of their variable regions and trained on experimental data for a panel of 80 clinical-stage monoclonal antibodies, can identify antibodies with optimal combinations of low off-target binding in a common physiological-solution condition and low self-association in a common antibody-formulation condition. For three clinical-stage antibodies with suboptimal combinations of off-target binding and self-association, the classifiers predicted variable-region mutations that optimized non-affinity interactions while maintaining high-affinity antibody-antigen interactions. Interpretable machine-learning models may facilitate the optimization of antibody candidates for therapeutic applications.

Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data.

Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity, and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries. Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns. All deep sequencing datasets and code to perform the analyses presented within are available via https://github.com/Tessier-Lab-UMich/PSERM_paper.

Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space

Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. Here we evaluate the use of machine learning to simplify antibody co-optimization for a clinical-stage antibody (emibetuzumab) that displays high levels of both on-target (antigen) and off-target (non-specific) binding. We mutate sites in the antibody complementarity-determining regions, sort the antibody libraries for high and low levels of affinity and non-specific binding, and deep sequence the enriched libraries. Interestingly, machine learning models trained on datasets with binary labels enable predictions of continuous metrics that are strongly correlated with antibody affinity and non-specific binding. These models illustrate strong tradeoffs between these two properties, as increases in affinity along the co-optimal (Pareto) frontier require progressive reductions in specificity. Notably, models trained with deep learning features enable prediction of novel antibody mutations that co-optimize affinity and specificity beyond what is possible for the original antibody library. These findings demonstrate the power of machine learning models to greatly expand the exploration of novel antibody sequence space and accelerate the development of highly potent, drug-like antibodies.

Antibodies with Weakly Basic Isoelectric Points Minimize Trade-offs between Formulation and Physiological Colloidal Properties.

The widespread interest in antibody therapeutics has led to much focus on identifying antibody candidates with favorable developability properties. In particular, there is broad interest in identifying antibody candidates with highly repulsive self-interactions in standard formulations (e.g., low ionic strength buffers at pH 5-6) for high solubility and low viscosity. Likewise, there is also broad interest in identifying antibody candidates with low levels of non-specific interactions in physiological solution conditions (PBS, pH 7.4) to promote favorable pharmacokinetic properties. To what extent antibodies that possess both highly repulsive self-interactions in standard formulations and weak non-specific interactions in physiological solution conditions can be systematically identified remains unclear and is a potential impediment to successful therapeutic drug development. Here, we evaluate these two properties for 42 IgG1 variants based on the variable fragments (Fvs) from four clinical-stage antibodies and complementarity-determining regions from 10 clinical-stage antibodies. Interestingly, we find that antibodies with the strongest repulsive self-interactions in a standard formulation (pH 6 and 10 mM histidine) display the strongest non-specific interactions in physiological solution conditions. Conversely, antibodies with the weakest non-specific interactions under physiological conditions display the least repulsive self-interactions in standard formulations. This behavior can be largely explained by the antibody isoelectric point, as highly basic antibodies that are highly positively charged under standard formulation conditions (pH 5-6) promote repulsive self-interactions that mediate high colloidal stability but also mediate strong non-specific interactions with negatively charged biomolecules at physiological pH and vice versa for antibodies with negatively charged Fv regions. Therefore, IgG1s with weakly basic isoelectric points between 8 and 8.5 and Fv isoelectric points between 7.5 and 9 typically display the best combinations of strong repulsive self-interactions and weak non-specific interactions. We expect that these findings will improve the identification and engineering of antibody candidates with drug-like biophysical properties.

Functional Changes of Therapeutic Antibodies upon Exposure to Pro-Oxidative Agents.

Therapeutic monoclonal antibodies have exerted a transformative impact on clinical practice in last two decades. However, development of a therapeutic antibody remains a complex process. Various physiochemical and functional liabilities can compromise the production or the therapeutic efficacy of antibodies. One of these liabilities is the susceptibility to oxidation. In the present study, we portrayed an oxidation-dependent vulnerability of immunoglobulins that can be of concern for therapeutic antibodies. By using a library of 119 monoclonal IgG1 molecules, containing variable domain matching clinical-stage antibodies, we demonstrated that a substantial number of these molecules acquired antigen-binding polyreactivity upon exposure to ferrous ions. Statistical analyses revealed that the potential for induction of polyreactivity by the redox-active metal ions correlated with a higher number of somatic mutations in V genes encoding variable domains of heavy and light immunoglobulin chains. Moreover, the sensitive antibodies used with biased frequencies particular V gene families encoding variable domains of their light chains. Besides the exposure to ferrous ions the induction of polyreactivity of therapeutic antibodies occurred after contact with an unrelated pro-oxidative substance—hypochlorite ions. Our data also revealed that induction of polyreactivity by pro-oxidative agents did not impact the binding of antibodies to their cognate antigens. The results from this study may contribute for better selection of antibody therapeutics with suitable developability profiles.

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.

Tandem-Cleavage Linkers Improve the In Vivo Stability and Tolerability of Antibody-Drug Conjugates.

Although peptide motifs represent the majority of cleavable linkers used in clinical-stage antibody-drug conjugates (ADCs), the sequences are often sensitive to cleavage by extracellular enzymes, such as elastase, which leads to systemic release of the cytotoxic payload. This action reduces the therapeutic index by causing off-target toxicities that can be dose-limiting. For example, a common side-effect of ADCs made using peptide-cleavable linkers is myelosuppression, including neutropenia. Only a few reports describe methods for optimizing peptide linkers to maintain efficient and potent tumor payload delivery while enhancing circulating stability. Herein, we address these critical limitations through the development of a tandem-cleavage linker strategy, where two sequential enzymatic cleavage events mediate payload release. We prepared dipeptides that are protected from degradation in the circulation by a sterically encumbering glucuronide moiety. Upon ADC internalization and lysosomal degradation, the monosaccharide is removed and the exposed dipeptide is degraded, which liberates the attached payload inside the target cell. We used CD79b-targeted monomethyl auristatin E (MMAE) conjugates as our model system and compared the stability, efficacy, and tolerability of ADCs made with tandem-cleavage linkers to ADCs made using standard technology with the vedotin linker. The results, where rat studies showed dramatically improved tolerability in the hematopoietic compartment, highlight the role that linker stability plays in efficacy and tolerability and also offer a means of improving an ADC's therapeutic index for improved patient outcomes.

MDKN-01, a Novel Anti-DKK1 mAb, Enhances Innate Immune Responses in the Tumor Microenvironment.

Dickkopf-1 (DKK1), a secreted modulator of Wnt signaling, is overexpressed in many cancers, is often associated with worse clinical outcomes, and has been shown to have immunosuppressive effects. DKN-01 is an IgG4 clinical stage antibody that potently and specifically neutralizes human and murine DKK1 and has recently completed a promising study in combination with pembrolizumab in patients with gastric/gastroesophageal junction cancer. The purpose of this study is to characterize a murine version of DKN-01 (mDKN-01) and to better understand its mechanism of action. We examined the efficacy of mDKN-01 in both melanoma and metastatic breast cancer models. Immune depletion experiments revealed a requirement for natural killer (NK) but not B and T cells for tumor growth inhibition. mDKN-01 treatment promotes the induction of the NK-activating cytokines IL15 and IL33 as well as an enhanced recruitment of CD45+ cells. Other treatment-related changes include a reduction of Gr-1+CD11b+ myeloid-derived suppressor cells (MDSC) in the tumor and spleen and the upregulation of PD-L1 on MDSCs. In addition, mDKN-01 has a marked effect at reducing pulmonary metastases in the mouse 4T1 breast cancer model. Finally, the mDKN-01/anti-PD-1 combination was more effective at inhibiting melanoma growth than mDKN-01 alone. Taken together, our data demonstrate that mDKN-01 has efficacy by blocking the immunosuppressive effects of DKK1 in the tumor microenvironment (TME) and provides insight into the clinical activity observed with DKN-01-based treatment. IMPLICATIONS: mDKN-01 reverses a DKK1-mediated innate immune suppression in the TME and has additive efficacy with a PD-1 inhibitor.

Epitope Mapping and Computational Analysis of Anti-HPV16 E6 and E7 Antibodies in Single-Chain Format for Clinical Development as Antitumor Drugs.

Human Papillomavirus 16-associated cancer, affecting primarily the uterine cervix but, increasingly, other body districts, including the head–neck area, will long be a public health problem, despite there being a vaccine. Since the virus oncogenic activity is fully ascribed to the viral E6 and E7 oncoproteins, one of the therapeutic approaches for HPV16 cancer is based on specific antibodies in single-chain format targeting the E6/E7 activity. We analyzed the Complementarity Determining Regions, repositories of antigen-binding activity, of four anti-HPV16 E6 and -HPV16 E7 scFvs, to highlight possible conformity to biophysical properties, recognized to be advantageous for therapeutic use. By epitope mapping, using E7 mutants with amino acid deletions or variations, we investigated differences among the anti-16E7 scFvs in terms of antigen-binding capacity. We also performed computational analyses to determine whether length, total net charge, surface hydrophobicity, polarity and charge distribution conformed well to those of the antibodies that had already reached clinical use, through the application of developability guidelines derived from recent literature on clinical-stage antibodies, and the Therapeutic Antibodies Profiler software. Overall, our findings show that the scFvs investigated may represent valid candidates to be developed as therapeutic molecules for clinical use, and highlight characteristics that could be improved by molecular engineering.

Physicochemical Rules for Identifying Monoclonal Antibodies with Drug-like Specificity.

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

Charge and hydrophobicity are key features in sequence-trained machine learning models for predicting the biophysical properties of clinical-stage antibodies.

Improved understanding of properties that mediate protein solubility and resistance to aggregation are important for developing biopharmaceuticals, and more generally in biotechnology and synthetic biology. Recent acquisition of large datasets for antibody biophysical properties enables the search for predictive models. In this report, machine learning methods are used to derive models for 12 biophysical properties. A physicochemical perspective is maintained in analysing the models, leading to the observation that models cluster largely according to charge (cross-interaction measurements) and hydrophobicity (self-interaction methods). These two properties also overlap in some cases, for example in a new interpretation of variation in hydrophobic interaction chromatography. Since the models are developed from differences of antibody variable loops, the next stage is to extend models to more diverse protein sets.AvailabilityThe web application for the sequence-based algorithms are available on the protein-sol webserver, at https://protein-sol.manchester.ac.uk/abpred, with models and virtualisation software available at https://protein-sol.manchester.ac.uk/software.

Five computational developability guidelines for therapeutic antibody profiling

Therapeutic mAbs must not only bind to their target but must also be free from "developability issues" such as poor stability or high levels of aggregation. While small-molecule drug discovery benefits from Lipinski's rule of five to guide the selection of molecules with appropriate biophysical properties, there is currently no in silico analog for antibody design. Here, we model the variable domain structures of a large set of post-phase-I clinical-stage antibody therapeutics (CSTs) and calculate in silico metrics to estimate their typical properties. In each case, we contextualize the CST distribution against a snapshot of the human antibody gene repertoire. We describe guideline values for five metrics thought to be implicated in poor developability: the total length of the complementarity-determining regions (CDRs), the extent and magnitude of surface hydrophobicity, positive charge and negative charge in the CDRs, and asymmetry in the net heavy- and light-chain surface charges. The guideline cutoffs for each property were derived from the values seen in CSTs, and a flagging system is proposed to identify nonconforming candidates. On two mAb drug discovery sets, we were able to selectively highlight sequences with developability issues. We make available the Therapeutic Antibody Profiler (TAP), a computational tool that builds downloadable homology models of variable domain sequences, tests them against our five developability guidelines, and reports potential sequence liabilities and canonical forms. TAP is freely available at opig.stats.ox.ac.uk/webapps/sabdab-sabpred/TAP.php.

Net charge of antibody complementarity-determining regions is a key predictor of specificity.

Specificity is one of the most important and complex properties that is central to both natural antibody function and therapeutic antibody efficacy. However, it has proven extremely challenging to define robust guidelines for predicting antibody specificity. Here we evaluated the physicochemical determinants of antibody specificity for multiple panels of antibodies, including >100 clinical-stage antibodies. Surprisingly, we find that the theoretical net charge of the complementarity-determining regions (CDRs) is a strong predictor of antibody specificity. Antibodies with positively charged CDRs have a much higher risk of low specificity than antibodies with negatively charged CDRs. Moreover, the charge of the entire set of six CDRs is a much better predictor of antibody specificity than the charge of individual CDRs, variable domains (VH or VL) or the entire variable fragment (Fv). The best indicators of antibody specificity in terms of CDR amino acid composition are reduced levels of arginine and lysine and increased levels of aspartic and glutamic acid. Interestingly, clinical-stage antibodies with negatively charged CDRs also have a lower risk for poor biophysical properties in general, including a reduced risk for high levels of self-association. These findings provide powerful guidelines for predicting antibody specificity and for identifying safe and potent antibody therapeutics.

Abstract 3822: Characterization of the first chicken-derived anti-PD-1 clinical stage antibody with a unique epitope and promising anticancer activity

Abstract Inhibition of immunologic checkpoints like Programmed Cell Death 1 (PD-1) has shown clinical efficacy in a broad range of cancers by improving or restoring T-cell activity. Anti-PD-1 antibodies show great promise in treating cancer malignances when administered alone or in combination with other immune activating approaches. However, high protein sequence identity between human and mammalian species used for antibody generation often disfavor generation of antibodies against functionally conserved epitopes, or prevents isolating antibodies cross reacting with ortholog species used for evaluating potential toxicity. Chickens are phylogenetically distant from mammals and are better at generating antibodies against epitopes that are conserved in mammals. Because chickens generate antibodies from a very restricted set of V-gene germline genes that are diversified by “gene conversion”, we envisioned that high throughput humanization of antibody frameworks was achievable by “mass CDR grafting” after recovering antibodies by immunization and B-cell cloning. Wild type chickens were immunized with PD-1 antigen, and a repertoire of 120 antibodies was generated with Symplex™ technology, by combining single B-cell FACS sorting and high throughput RT-PCR cloning of cognate VH and VL chains. The isolated PD-1 repertoire was cloned with an inert Fc backbone and humanized by a combination of in silico CDR grafting and gene synthesis. Humanized antibodies were expressed and screened for retained binding affinity and functionality in T-cell based assays. We successfully generated a humanized PD-1 antibody repertoire and found that most antibodies retained affinity and functionality similar to that of parental chicken antibodies. Furthermore, the antibody repertoire displayed broad binding epitope coverage on PD-1, often with strong pM affinity, and showed biophysical properties acceptable for drug development. Our lead antibody Sym021 blocked PD-L1 and PD-L2 ligand binding and downstream PD-1 signaling, resulting in elevated T-cell cytokine production in vitro. Moreover, Sym021 bound human PD-1 with much stronger affinity of 30 pM compared to clinical PD-1 mAbs nivolumab and pembrolizumab, while also cross reacting to cynomolgous and mouse PD-1. This enabled direct testing of Sym021 in syngenic mouse in vivo models and evaluation of preclinical toxicology in cynomolgus monkeys. Detailed epitope mapping showed that the epitope recognized by Sym021 was unique compared to clinical antibodies pembrolizumab and nivolumab. These results supported entry of PD-1 targeting Sym021 into clinical trials. Citation Format: Torben Gjetting, Monika Gad, Camilla Fröhlich, Maria C. Melander, Gunther Galler, Johan Lantto, Thomas Bouquin, Ivan D. Horak, Michael Kragh, Mikkel W. Pedersen, Klaus Koefoed. Characterization of the first chicken-derived anti-PD-1 clinical stage antibody with a unique epitope and promising anticancer activity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3822.

Biophysical and Sequence-Based Methods for Identifying Monovalent and Bivalent Antibodies with High Colloidal Stability.

In vitro antibody discovery and/or affinity maturation are often performed using antibody fragments (Fabs), but most monovalent Fabs are reformatted as bivalent IgGs (monoclonal antibodies, mAbs) for therapeutic applications. One problem related to reformatting antibodies is that the bivalency of mAbs can lead to increased antibody self-association and poor biophysical properties (e.g., reduced antibody solubility and increased viscosity). Therefore, it is important to identify monovalent Fabs early in the discovery and/or optimization process that will display favorable biophysical properties when reformatted as bivalent mAbs. Here we demonstrate a facile approach for evaluating Fab self-association in a multivalent assay format that is capable of identifying antibodies with low self-association and favorable colloidal properties when reformatted as bivalent mAbs. Our approach (self-interaction nanoparticle spectroscopy, SINS) involves immobilizing Fabs on gold nanoparticles in a multivalent format (multiple Fabs per nanoparticle) and evaluating their self-association behavior via shifts in the plasmon wavelength or changes in the absorbance values. Importantly, we find that SINS measurements of Fab self-association are correlated with self-interaction measurements of bivalent mAbs and are useful for identifying antibodies with favorable biophysical properties. Moreover, the significant differences in the levels of self-association detected for Fabs and mAbs with similar frameworks can be largely explained by the physicochemical properties of the complementarity-determining regions (CDRs). Comparison of the properties of the CDRs in this study relative to those of approved therapeutic antibodies reveals several key factors (net charge, fraction of charged residues, and presence of self-interaction motifs) that strongly influence antibody self-association behavior. Increased positive charge in the CDRs was observed to correlate with increased risk of high self-association for the mAbs in this study and clinical-stage antibodies. We expect that these findings will be useful for improving the development of therapeutic antibodies that are well suited for high concentration applications.

Biophysical properties of the clinical-stage antibody landscape

Antibodies are a highly successful class of biological drugs, with over 50 such molecules approved for therapeutic use and hundreds more currently in clinical development. Improvements in technology for the discovery and optimization of high-potency antibodies have greatly increased the chances for finding binding molecules with desired biological properties; however, achieving drug-like properties at the same time is an additional requirement that is receiving increased attention. In this work, we attempt to quantify the historical limits of acceptability for multiple biophysical metrics of "developability." Amino acid sequences from 137 antibodies in advanced clinical stages, including 48 approved for therapeutic use, were collected and used to construct isotype-matched IgG1 antibodies, which were then expressed in mammalian cells. The resulting material for each source antibody was evaluated in a dozen biophysical property assays. The distributions of the observed metrics are used to empirically define boundaries of drug-like behavior that can represent practical guidelines for future antibody drug candidates.