Polyreactivity and polyspecificity in therapeutic antibody development: risk factors for failure in preclinical and clinical development campaigns

Linguistics-based formalization of the antibody language as a basis for antibody language models

Apparent parallels between natural language and antibody sequences have led to a surge in deep language models applied to antibody sequences for predicting cognate antigen recognition. However, a linguistic formal definition of antibody language does not exist, and insight into how antibody language models capture antibody-specific binding features remains largely uninterpretable. Here we describe how a linguistic formalization of the antibody language, by characterizing its tokens and grammar, could address current challenges in antibody language model rule mining.

Immunoglobulin constant regions provide stabilization to the paratope and enforce epitope specificity

Constant domains in antibody molecules at the level of the Fab (CH1 and CL) have long been considered to be simple scaffolding elements that physically separate the paratope-defining variable (V) region from the effector function-mediating constant (C) regions. However, due to recent findings that C domains of different isotypes can modulate the fine specificity encoded in the V region, elucidating the role of C domains in shaping the paratope and influencing specificity is a critical area of interest. To dissect the relative contributions of each C domain to this phenomenon, we generated antibody fragments with different C regions omitted, using a set of antibodies targeting capsular polysaccharides from the fungal pathogen, Cryptococcus neoformans. Antigen specificity mapping and functional activity measurements revealed that V region-only antibody fragments exhibited poly-specificity to antigenic variants and extended to recognition of self-antigens, while measurable hydrolytic activity of the capsule was greatly attenuated. To better understand the mechanistic origins of the remarkable loss of specificity that accompanies the removal of C domains from identical paratopes, we performed molecular dynamics simulations which revealed increased paratope plasticity in the scFv relative to the corresponding Fab. Together, our results provide insight into how the remarkable specificity of immunoglobulins is governed and maintained at the level of the Fab through the enforcement of structural restrictions on the paratope by CH1 domains.

Site-Specific Conjugation of Native Antibody: Transglutaminase-Mediated Modification of a Conserved Glutamine While Maintaining the Primary Sequence and Core Fc Glycan via Trimming with an Endoglycosidase

A versatile chemo-enzymatic tool to site-specifically modify native (nonengineered) antibodies is using transglutaminase (TGase, E.C. 2.3.2.13). With various amines as cosubstrates, this enzyme converts the unsubstituted side chain amide of glutamine (Gln or Q) in peptides and proteins into substituted amides (i.e., conjugates). A pleasant surprise is that only a single conserved glutamine (Gln295) in the Fc region of IgG is modified by microbial TGase (mTGase, EC 2.3.2.13), thereby providing a highly specific and generally applicable conjugation method. However, prior to the transamidation (access to the glutamine residue by mTGase), the steric hindrance from the nearby conserved N-glycan (Asn297 in IgG1) must be reduced. In previous approaches, amidase (PNGase F, EC 3.5.1.52) was used to completely remove the N-glycan. However, PNGase F also converts a net neutral asparagine (Asn297) to a negatively charged aspartic acid (Asp297). This charge alteration may markedly change the structure, function, and immunogenicity of an IgG antibody. In contrast, in our new method presented herein, the N-glycan is trimmed by an endoglycosidase (EndoS2, EC 3.2.1.96), hence retaining both the core N-acetylglucosamine (GlcNAc) moiety and the neutral asparaginyl amide. The trimmed glycan also reduces or abolishes Fc receptor-mediated functions, which results in better imaging agents by decreasing nonspecific binding to other cells (e.g., immune cells). Moreover, the remaining core glycan allows further derivatization such as glycan remodeling and dual conjugation. Practical and robust, our method generates conjugates in near quantitative yields, and both enzymes are commercially available.

Engineered polyspecific antibodies: A new frontier in the field of immunotherapeutics

The 21st-century beginning remarked with the huge success of monospecific MAbs, however, in the last couple of years, polyspecific MAbs (PsAbs) have been an interesting topic and show promise of being biobetter than monospecific MAbs. Polyspecificity, in which a single antibody serves multiple specific target binding, has been hypothesized to contribute to the development of a highly effective antibody repertoire for immune defence. This polyspecific MAb trend represents an explosion that is gripping the whole pharmaceutical industry. This review is concerned with the current development and quality enforcement of PsAbs. All provided literature on monospecific MAbs and polyspecific MAbs (PsAbs) were searched using various electronic databases such as PubMed, Google Scholar, Web of Science, Elsevier, Springer, ACS, Google Patent and books via the keywords Antibody engineering, Polyspecific antibody, Conventional antibody, non-conventional antibody, and Single domain antibody. In the literature, there are more than 100 different formats to construct PsAb by quadroma technology, chemical conjugation and genetic engineering. Till March 2023, nine PsAb have been approved around the world, and around 330 are in advanced developmental stages, showing the dominancy of PsAb in the growing health sector. Recent advancements in protein engineering techniques and the fusion of non-conventional antibodies have made it possible to create complex PsAbs that demonstrate higher stability and enhanced potency. This marks the most significant achievement for cancer immunotherapy, in which PsAbs have immense promise. It is worth mentioning that seven out of the nine PsAbs have been approved as anti-cancer therapy. As PsAbs continue to acquire prominence, they could pave the way for the development of novel immunotherapies for multiple diseases.

Antibodies, repertoires and microdevices in antibody discovery and characterization

Therapeutic antibodies are paramount in treating a wide range of diseases, particularly in auto-immunity, inflammation and cancer, and novel antibody candidates recognizing a vast array of novel antigens are needed to expand the usefulness and applications of these powerful molecules. Microdevices play an essential role in this challenging endeavor at various stages since many general requirements of the overall process overlap nicely with the general advantages of microfluidics. Therefore, microfluidic devices are rapidly taking over various steps in the process of new candidate isolation, such as antibody characterization and discovery workflows. Such technologies can allow for vast improvements in time-lines and incorporate conservative antibody stability and characterization assays, but most prominently screenings and functional characterization within integrated workflows due to high throughput and standardized workflows. First, we aim to provide an overview of the challenges of developing new therapeutic candidates, their repertoires and requirements. Afterward, this review focuses on the discovery of antibodies using microfluidic systems, technological aspects of micro devices and small-scale antibody protein characterization and selection, as well as their integration and implementation into antibody discovery workflows. We close with future developments in microfluidic detection and antibody isolation principles and the field in general.

Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling

In silico assessment of antibody developability during early lead candidate selection and optimization is of paramount importance, offering a rapid and material-free screening approach. However, the predictive power and reproducibility of such methods depend heavily on the selection of molecular descriptors, model parameters, accuracy of predicted structure models, and conformational sampling techniques. Here, we present a set of molecular surface descriptors specifically designed for predicting antibody developability. We assess the performance of these descriptors by benchmarking their correlations with an extensive array of experimentally determined biophysical properties, including viscosity, aggregation, hydrophobic interaction chromatography, human pharmacokinetic clearance, heparin retention time, and polyspecificity. Further, we investigate the sensitivity of these surface descriptors to methodological nuances, such as the choice of interior dielectric constant, hydrophobicity scales, structure prediction methods, and the impact of conformational sampling. Notably, we observe systematic shifts in the distribution of surface descriptors depending on the structure prediction method used, driving weak correlations of surface descriptors across structure models. Averaging the descriptor values over conformational distributions from molecular dynamics mitigates the systematic shifts and improves the consistency across different structure prediction methods, albeit with inconsistent improvements in correlations with biophysical data. Based on our benchmarking analysis, we propose six in silico developability risk flags and assess their effectiveness in predicting potential developability issues for a set of case study molecules.

Electrostatic properties of human germlines and biodistribution of small biologics

Off-target biodistribution of biologics bears important toxicological consequences. Antibody fragments intended for use as vectors of cytotoxic payloads (e.g. antibody-drug conjugates, radiotherapy) can accumulate at clearance organs like kidneys and liver, where they can cause dose-limiting toxicities. Renal and hepatic uptakes are known to be affected by protein electrostatics, which promote protein internalization through pinocytosis. Using minibodies as a model of an antibody fragment lacking FcRn recycling, we compared the biodistributions of leads with different degrees of accumulation at the kidney and liver. We identified a positive electrostatic patch highly conserved in a germline family very commonly used in the humanization of approved biologics. Neutralization of this patch led to a drastic reduction in the kidney uptake, leading to a biodistribution more favorable to the delivery of highly cytotoxic payloads. Next, we conducted a high throughput study of the electrostatic properties for all combinations of VH and VL germlines. This analysis shows how different VH/VL combinations exhibit varying tendencies to create electrostatic patches, resulting in Fv variants with different isoelectric points. Our work emphasizes the importance of carefully selecting germlines for humanization with optimal electrostatic properties in order to control the unspecific tissue uptake of low molecular weight biologics.

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.

Recombinant human endostatin as a potential anti-angiogenic agent: therapeutic perspective and current status

Angiogenesis is the physiological process that results in the formation of new blood vessels develop from pre-existing vasculature and plays a significant role in several physiological and pathological processes. Inhibiting angiogenesis, a crucial mechanism in the growth and metastasis of cancer, has been proposed as a potential anticancer therapy. Different studies showed the beneficial effects of angiogenesis inhibitors either in patients suffering from different cancers, alone or in combination with conventional therapies. Even though there are currently a number of efficient anti-angiogenic drugs, including monoclonal antibodies and kinase inhibitors, the associated toxicity profile and their affordability constraints are prompting researchers to search for a safe and affordable angiostatic agent for cancer treatment. Endostatin is one of the endogenous anti-angiogenic candidates that have been extensively pursued for the treatment of cancer, but even over three decades after its discovery, we have not made much advancement in employing it as an anticancer therapeutic despite of its remarkable anti-angiogenic effect with low toxicity profile. A recombinant human endostatin (rh-Es) variant for non-small cell lung cancer was approved by China in 2006 and has since been used effectively. Several other successful clinical trials related to endostatin for various malignancies are either ongoing or have already been completed with promising results. Thus, in this review, we have provided an overview of existing anti-angiogenic drugs developed for cancer therapy, with a summary of tumour angiogenesis in the context of Endostatin, and clinical status of rh-Es in cancer treatment. Furthermore, we briefly discuss the various strategies to improve endostatin features (poor pharmacokinetic properties) for developing rh-Es as a safe and effective agent for cancer treatment.

Polyreactivity of antibodies from different B-cell subpopulations is determined by distinct sequence patterns of variable region

An antibody molecule that can bind to multiple distinct antigens is defined as polyreactive. In the present study, we performed statistical analyses to assess sequence correlates of polyreactivity of >600 antibodies cloned from different B-cell types of healthy humans. The data revealed several sequence patterns of variable regions of heavy and light immunoglobulin chains that determine polyreactivity. The most prominent identified patterns were increased number of basic amino acid residues, reduced frequency of acidic residues, increased number of aromatic and hydrophobic residues, and longer length of CDR L1. Importantly, our study revealed that antibodies isolated from different B-cell populations used distinct sequence patterns (or combinations of them) for polyreactive antigen binding. Furthermore, we combined the data from sequence analyses with molecular modeling of selected polyreactive antibodies and demonstrated that human antibodies can use multiple pathways for achieving antigen-binding promiscuity. These data reconcile some contradictions in the literature regarding the determinants of antibody polyreactivity. Moreover, our study demonstrates that the mechanism of polyreactivity of antibodies evolves during immune response and might be tailored to specific functional properties of different B-cell compartments. Finally, these data can be of use for efforts in the development and engineering of therapeutic antibodies.

Mechanistic Insight into Poly-Reactivity of Immune Antibodies upon Acid Denaturation or Arginine Mutation in Antigen-Binding Regions

The poly-reactivity of antibodies is defined as their binding to specific antigens as well as to related proteins and also to unrelated targets. Poly-reactivity can occur in individual molecules of natural serum antibodies, likely due to their conformation flexibility, and, for therapeutic antibodies, it plays a critical role in their clinical development. On the one hand, it can enhance their binding to target antigens and cognate receptors, but, on the other hand, it may lead to a loss of antibody function by binding to off-target proteins. Notably, poly-reactivity has been observed in antibodies subjected to treatments with dissociating, destabilizing or denaturing agents, in particular acidic pH, a common step in the therapeutic antibody production process involving the elution of Protein-A bound antibodies and viral clearance using low pH buffers. Additionally, poly-reactivity can emerge during the affinity maturation in the immune system, such as the germinal center. This review delves into the underlying potential causes of poly-reactivity, highlighting the importance of conformational flexibility, which can be further augmented by the acid denaturation of antibodies and the introduction of arginine mutations into the complementary regions of antibody-variable domains. The focus is placed on a particular antibody's acid conformation, meticulously characterized through circular dichroism, differential scanning calorimetry, and sedimentation velocity analyses. By gaining a deeper understanding of these mechanisms, we aim to shed light on the complexities of antibody poly-reactivity and its implications for therapeutic applications.

Non-Immunoglobulin Synthetic Binding Proteins for Oncology

Extensive application of technologies like phage display in screening peptide and protein combinatorial libraries has not only facilitated creation of new recombinant antibodies but has also significantly enriched repertoire of the protein binders that have polypeptide scaffolds without homology to immunoglobulins. These innovative synthetic binding protein (SBP) platforms have grown in number and now encompass monobodies/adnectins, DARPins, lipocalins/anticalins, and a variety of miniproteins such as affibodies and knottins, among others. They serve as versatile modules for developing complex affinity tools that hold promise in both diagnostic and therapeutic settings. An optimal scaffold typically has low molecular weight, minimal immunogenicity, and demonstrates resistance against various challenging conditions, including proteolysis - making it potentially suitable for peroral administration. Retaining functionality under reducing intracellular milieu is also advantageous. However, paramount to its functionality is the scaffold's ability to tolerate mutations across numerous positions, allowing for the formation of a sufficiently large target binding region. This is achieved through the library construction, screening, and subsequent expression in an appropriate system. Scaffolds that exhibit high thermodynamic stability are especially coveted by the developers of new SBPs. These are steadily making their way into clinical settings, notably as antagonists of oncoproteins in signaling pathways. This review surveys the diverse landscape of SBPs, placing particular emphasis on the inhibitors targeting the oncoprotein KRAS, and highlights groundbreaking opportunities for SBPs in oncology.

Defining and Studying B Cell Receptor and TCR Interactions

BCRs (Abs) and TCRs (or adaptive immune receptors [AIRs]) are the means by which the adaptive immune system recognizes foreign and self-antigens, playing an integral part in host defense, as well as the emergence of autoimmunity. Importantly, the interaction between AIRs and their cognate Ags defies a simple key-in-lock paradigm and is instead a complex many-to-many mapping between an individual's massively diverse AIR repertoire, and a similarly diverse antigenic space. Understanding how adaptive immunity balances specificity with epitopic coverage is a key challenge for the field, and terms such as broad specificity, cross-reactivity, and polyreactivity remain ill-defined and are used inconsistently. In this Immunology Notes and Resources article, a group of experimental, structural, and computational immunologists define commonly used terms associated with AIR binding, describe methodologies to study these binding modes, as well as highlight the implications of these different binding modes for therapeutic design.

Polyspecificity - An emerging trend in the development of clinical antibodies

The essence of the growth and development of therapeutic conventional monoclonal antibodies (MAbs) for the treatment of various disorders is the aptitude of MAbs to precisely bind a target antigen and neutralise or promote its activity. However, the conventional antibodies are monoclonal i.e., both paratopes bind to the same epitope. But most of the pathophysiological conditions are multifaceted, hence targeting/blocking/inhibition of more than one epitope/antigen is more promising than one epitope/antigen. Polyspecific antibodies (PsAbs) have the potential to concurrently bind to more than one target and are the next-generation antibodies that augment efficacy in both clinical and non-clinical contexts. Thus, the trend of engineering and developing various formats of PsAbs is emerging. In this review, we have briefly discussed the importance of antibody polyspecificity and PsAbs approved for clinical use. Subsequently, we have discussed the role of TNF-α and IL-23 in inflammatory diseases and stressed the need for developing anti-TNF-α and anti-IL-23 bispecific antibodies.

Non-Affinity Purification of Antibodies

Currently, purification of antibodies is mainly carried out using a platform technology composed primarily of Protein A chromatography as a capture step, regardless of the scale. However, Protein A chromatography has a number of drawbacks, which are summarized in this review. As an alternative, we propose a simple small-scale purification protocol without Protein A that uses novel agarose native gel electrophoresis and protein extraction. For large-scale antibody purification, we suggest mixed-mode chromatography that can in part mimic the properties of Protein A resin, focusing on 4-Mercapto-ethyl-pyridine (MEP) column chromatography.

Approaches to expand the conventional toolbox for discovery and selection of antibodies with drug-like physicochemical properties

Antibody drugs should exhibit not only high-binding affinity for their target antigens but also favorable physicochemical drug-like properties. Such drug-like biophysical properties are essential for the successful development of antibody drug products. The traditional approaches used in antibody drug development require significant experimentation to produce, optimize, and characterize many candidates. Therefore, it is attractive to integrate new methods that can optimize the process of selecting antibodies with both desired target-binding and drug-like biophysical properties. Here, we summarize a selection of techniques that can complement the conventional toolbox used to de-risk antibody drug development. These techniques can be integrated at different stages of the antibody development process to reduce the frequency of physicochemical liabilities in antibody libraries during initial discovery and to co-optimize multiple antibody features during early-stage antibody engineering and affinity maturation. Moreover, we highlight biophysical and computational approaches that can be used to predict physical degradation pathways relevant for long-term storage and in-use stability to reduce the need for extensive experimentation.

Assessing developability early in the discovery process for novel biologics

Beyond potency, a good developability profile is a key attribute of a biological drug. Selecting and screening for such attributes early in the drug development process can save resources and avoid costly late-stage failures. Here, we review some of the most important developability properties that can be assessed early on for biologics. These include the influence of the source of the biologic, its biophysical and pharmacokinetic properties, and how well it can be expressed recombinantly. We furthermore present in silico, in vitro, and in vivo methods and techniques that can be exploited at different stages of the discovery process to identify molecules with liabilities and thereby facilitate the selection of the most optimal drug leads. Finally, we reflect on the most relevant developability parameters for injectable versus orally delivered biologics and provide an outlook toward what general trends are expected to rise in the development of biologics.

Blueprint for antibody biologics developability

Large-molecule antibody biologics have revolutionized medicine owing to their superior target specificity, pharmacokinetic and pharmacodynamic properties, safety and toxicity profiles, and amenability to versatile engineering. In this review, we focus on preclinical antibody developability, including its definition, scope, and key activities from hit to lead optimization and selection. This includes generation, computational and in silico approaches, molecular engineering, production, analytical and biophysical characterization, stability and forced degradation studies, and process and formulation assessments. More recently, it is apparent these activities not only affect lead selection and manufacturability, but ultimately correlate with clinical progression and success. Emerging developability workflows and strategies are explored as part of a blueprint for developability success that includes an overview of the four major molecular properties that affect all developability outcomes: 1) conformational, 2) chemical, 3) colloidal, and 4) other interactions. We also examine risk assessment and mitigation strategies that increase the likelihood of success for moving the right candidate into the clinic.

An in silico method to assess antibody fragment polyreactivity

Antibodies are essential biological research tools and important therapeutic agents, but some exhibit non-specific binding to off-target proteins and other biomolecules. Such polyreactive antibodies compromise screening pipelines, lead to incorrect and irreproducible experimental results, and are generally intractable for clinical development. Here, we design a set of experiments using a diverse naïve synthetic camelid antibody fragment (nanobody) library to enable machine learning models to accurately assess polyreactivity from protein sequence (AUC > 0.8). Moreover, our models provide quantitative scoring metrics that predict the effect of amino acid substitutions on polyreactivity. We experimentally test our models' performance on three independent nanobody scaffolds, where over 90% of predicted substitutions successfully reduced polyreactivity. Importantly, the models allow us to diminish the polyreactivity of an angiotensin II type I receptor antagonist nanobody, without compromising its functional properties. We provide a companion web-server that offers a straightforward means of predicting polyreactivity and polyreactivity-reducing mutations for any given nanobody sequence.

The High-Resolution Structure Reveals Remarkable Similarity in PD-1 Binding of Cemiplimab and Dostarlimab, the FDA-Approved Antibodies for Cancer Immunotherapy

Multiple tumors have responded well to immunotherapies, which use monoclonal antibodies to block the immune checkpoint proteins and reactivate the T-cell immune response to cancer cells. Significantly, the anti-PD-1 antibodies pembrolizumab and nivolumab, which were approved in 2014, have revolutionized cancer therapy, demonstrating dramatic improvement and longer duration. The US FDA authorized the third anti-PD-1 medication, cemiplimab, in 2018 for use in patients with cutaneous squamous cell carcinoma. To further understand the molecular mechanism of the antibody drug, we now reveal the intricate structure of PD-1 in complex with the cemiplimab Fab at a resolution of 1.98 Å. The cemiplimab-PD-1 interaction preoccupies the space for PD-L1 binding with a greater binding affinity than the PD-1/PD-L1 interaction, which is the basis for the PD-1 blocking mechanism. The structure reveals that cemiplimab and dostarlimab are significantly similar in PD-1 binding, although the precise interactions differ. A comparative investigation of PD-1 interactions with the four FDA-approved antibodies reveals that the BC, C'D, and FG loops of PD-1 adopt distinct conformations for optimal interaction with the antibodies. The structural characteristics in this work could be helpful information for developing more potent anti-PD-1 biologics against cancer.

Non-specificity as the sticky problem in therapeutic antibody development

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

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

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

A New in Silico Antibody Similarity Measure Both Identifies Large Sets of Epitope Binders with Distinct CDRs and Accurately Predicts Off-Target Reactivity

Developing a therapeutic antibody is a long, tedious, and expensive process. Many obstacles need to be overcome, such as biophysical properties (issues of solubility, stability, weak production yields, etc.), as well as cross-reactivity and subsequent toxicity, which are major issues. No in silico method exists today to solve such issues. We hypothesized that if we were able to properly measure the similarity between the CDRs of antibodies (Ab) by considering not only their evolutionary proximity (sequence identity) but also their structural features, we would be able to identify families of Ab recognizing similar epitopes. As a consequence, Ab within the family would share the property to recognize their targets, which would allow (i) to identify off-targets and forecast the cross-reactions, and (ii) to identify new Ab specific for a given target. Testing our method on 238D2, an antagonistic anti-CXCR4 nanobody, we were able to find new nanobodies against CXCR4 and to identify influenza hemagglutinin as an off-target of 238D2.

Computational counterselection identifies nonspecific therapeutic biologic candidates

Effective biologics require high specificity and limited off-target binding, but these properties are not guaranteed by current affinity-selection-based discovery methods. Molecular counterselection against off targets is a technique for identifying nonspecific sequences but is experimentally costly and can fail to eliminate a large fraction of nonspecific sequences. Here, we introduce computational counterselection, a framework for removing nonspecific sequences from pools of candidate biologics using machine learning models. We demonstrate the method using sequencing data from single-target affinity selection of antibodies, bypassing combinatorial experiments. We show that computational counterselection outperforms molecular counterselection by performing cross-target selection and individual binding assays to determine the performance of each method at retaining on-target, specific antibodies and identifying and eliminating off-target, nonspecific antibodies. Further, we show that one can identify generally polyspecific antibody sequences using a general model trained on affinity data from unrelated targets with potential affinity for a broad range of sequences.