Non-specificity as the sticky problem in therapeutic antibody development

Ratiometric electrochemical aptasensor with strand displacement for insulin detection in blood samples

BACKGROUND

Diabetes patients suffer either from insulin deficiency or resistance with a high risk of severe long-term complications, thus the quantitative assessment of insulin level is highly desired for diabetes surveillance and management. Utilizing insulin-capturing aptamers may facilitate the development of affordable biosensors however, their rigid G-quadruplex structures impair conformational changes of the aptamers and diminish the sensor signals.

RESULTS

Here we report on a ratiometric, electrochemical insulin aptasensor which is achieved by hybridization of an insulin-capturing aptamer and a partially complementary ssDNA to break the rigid G-quadruplex structures. To improve the durability of the aptasensor, the capturing aptamer was immobilized on gold electrodes via two dithiol-phosphoramidite functional groups while methoxy-polyethylene glycol thiol was used as a blocking molecule. The exposure of the sensor to insulin-containing solutions induced the dissociation of the hybridized DNA accompanied by a conformational rearrangement of the capturing aptamer back into a G-quadruplex structure. The reliability of sensor readout was improved by the adoption of an AND logic gate utilizing anthraquinone and methylene blue redox probes associated to the aptamer and complementary strand, respectively. Our aptasensor possessed an improved detection limit of 0.15 nM in comparison to aptasensors without strand displacement.

SIGNIFICANCE

The sensor was adapted for detection in real blood and is ready for future PoC diagnostics. The capability of monitoring the insulin level in an affordably manner can improve the treatment for an increasing number of patients in developed and developing nations. The utilization of low-cost and versatile aptamer receptors together with the engineering of ratiometric electrochemical signal recording has the potential to considerably advance the current insulin detection technology toward multi-analyte diabetes sensors.

Utility of Cellular Measurements of Non-Specific Endocytosis to Assess the Target-Independent Clearance of Monoclonal Antibodies

Past studies have demonstrated higher clearance for monoclonal antibodies possessing increased rates of non-specific endocytosis. However, this metric is oftentimes evaluated indirectly using biophysical techniques or cell surface binding studies that may not provide insight into the specific rates of cellular turnover. Furthermore, few examples evaluating non-specific endocytosis have been reported for a therapeutic antibody that reached clinical assessment. In the current report, we evaluated a therapeutic human immunoglobulin G2 monoclonal antibody targeted against the interleukin-4 receptor alpha chain (IL-4Rα) that exhibited elevated target independent clearance in previous Phase 1 and 2 studies. We confirmed high non-specific clearance of the anti-IL-4Rα antibody as compared to a reference antibody during pharmacokinetic assessments in wild type mice where target-mediated disposition was absent. We then developed a cell-based method capable of measuring cellular protein endocytosis and demonstrated the anti-IL-4Rα antibody exhibited marked non-specific uptake relative to the reference compound. Antibody homology modeling identified the anti-IL-4Rα antibody possessed positive charge patches whose removal via targeted mutations substantially reduced its non-specific endocytosis. We then expanded the scope of the study by evaluating panels of both preclinical and clinically relevant monoclonal antibodies and demonstrate those with the highest rates of non-specific uptake in vitro exhibit elevated target independent clearance, low subcutaneous bioavailability, or both. Our results support the observation that high non-specific endocytosis is a negative attribute in monoclonal antibody development and demonstrate the utility of a generic cell-based screen as a quantitative tool to measure non-specific endocytosis of protein therapeutics at the single-cell level.

Fit-for-purpose heterodivalent single-domain antibody for gastrointestinal targeting of toxin B from Clostridium difficile

Single-domain antibodies (sdAbs), such as VHHs, are increasingly being developed for gastrointestinal (GI) applications against pathogens to strengthen gut health. However, what constitutes a suitable developability profile for applying these proteins in a gastrointestinal setting remains poorly explored. Here, we describe an in vitro methodology for the identification of sdAb derivatives, more specifically divalent VHH constructs, that display extraordinary developability properties for oral delivery and functionality in the GI environment. We showcase this by developing a heterodivalent VHH construct that cross-inhibits the toxic activity of the glycosyltransferase domains (GTDs) from three different toxinotypes of cytotoxin B (TcdB) from lineages of Clostridium difficile. We show that the VHH construct possesses high stability and binding activity under gastric conditions, in the presence of bile salts, and at high temperatures. We suggest that the incorporation of early developability assessment could significantly aid in the efficient discovery of VHHs and related constructs fit for oral delivery and GI applications.

Tools to investigate the cell surface: Proximity as a central concept in glycoRNA biology

Proximity is a fundamental concept in chemistry and biology, referring to the convergence of molecules to facilitate new molecular interactions or reactions. Hybrid biopolymers like glycosylphosphatidylinositol (GPI)-anchored proteins, ubiquitinated proteins, glycosylated RNAs (glycoRNAs), and RNAylated proteins exemplify this by covalent bonding of moieties that are often orthogonally active. Hybrid molecules like glycoRNAs are localized to new physical spaces, generating new interfaces for biological functions. To fully investigate the compositional and spatial features of molecules like glycoRNAs, flexible genetic and chemical tools that encompass different encoding and targeting biopolymers are required. Here we discuss concepts of molecular proximity and explore newer proximity labeling technologies that facilitate applications in RNA biology, cell surface biology, and the interface therein with a particular focus on glycoRNA biology. We review the advantages and disadvantages of methods pertaining to cell surface RNA identification and provide insights into the vast opportunities for method development in this area.

Structural, Biophysical, and Computational Studies of a Murine Light Chain Dimer

Antibodies are widely used in medicinal and scientific research due to their ability to bind to a specific antigen. Most often, antibodies are composed of heavy and light chain domains. Under physiological conditions, light chains are produced in excess, as compared to the heavy chain. It is now known that light chains are not silent partners of the heavy chain and can modulate the immune response independently. In this work, the first crystal structure of a light chain dimer originating from mice is described. It represents the light chain dimer of 6A8, a monoclonal antibody specific to the allergen Der f 1. Building on the unexpected occurrence of this kind of dimer, we have demonstrated that this light chain is stable in solution alone. Moreover, enzyme-linked immunosorbent assays (ELISA) have revealed that, when the light chain is not partnered to its corresponding heavy chain, it interacts non-specifically with a wide range of proteins. Computational studies were used to provide insight on the role of the 6A8 heavy chain domain in the specific binding to Der f 1. Overall, this work demonstrates and supports the ongoing notion that light chains can function by themselves and are not silent partners of heavy chains.

Development of a Selective and Stable Antimicrobial Peptide

Antimicrobial peptides (AMPs) are presented as potential scaffolds for antibiotic development due to their desirable qualities including broad-spectrum activity, rapid action, and general lack of susceptibility to current resistance mechanisms. However, they often lose antibacterial activity under physiological conditions and/or display mammalian cell toxicity, which limits their potential use. Identification of AMPs that overcome these barriers will help develop rules for how this antibacterial class can be developed to treat infection. Here we describe the development of our novel synthetic AMP, from discovery through in vivo application. Our evolved AMP, DTr18-dab, has broad-spectrum antibacterial activity and is nonhemolytic. It is active against planktonic bacteria and biofilm, is unaffected by colistin resistance, and importantly is active in both human serum and a Galleria mellonella infection model. Several modifications, including the incorporation of noncanonical amino acids, were used to arrive at this robust sequence. We observed that the impact on antibacterial activity with noncanonical amino acids was dependent on assay conditions and therefore not entirely predictable. Overall, our results demonstrate how a relatively weak lead can be developed into a robust AMP with qualities important for potential therapeutic translation.

Homogeneous multi-payload antibody-drug conjugates

Many systemic cancer chemotherapies comprise a combination of drugs, yet all clinically used antibody-drug conjugates (ADCs) contain a single-drug payload. These combination regimens improve treatment outcomes by producing synergistic anticancer effects and slowing the development of drug-resistant cell populations. In an attempt to replicate these regimens and improve the efficacy of targeted therapy, the field of ADCs has moved towards developing techniques that allow for multiple unique payloads to be attached to a single antibody molecule with high homogeneity. However, the methods for generating such constructs-homogeneous multi-payload ADCs-are both numerous and complex owing to the plethora of reactive functional groups that make up the surface of an antibody. Here, by summarizing and comparing the methods of both single- and multi-payload ADC generation and their key preclinical and clinical results, we provide a timely overview of this relatively new area of research. The methods discussed range from branched linker installation to the incorporation of unnatural amino acids, with a generalized comparison tool of the most promising modification strategies also provided. Finally, the successes and challenges of this rapidly growing field are critically evaluated, and from this, future areas of research and development are proposed.

AttABseq: an attention-based deep learning prediction method for antigen-antibody binding affinity changes based on protein sequences

The optimization of therapeutic antibodies through traditional techniques, such as candidate screening via hybridoma or phage display, is resource-intensive and time-consuming. In recent years, computational and artificial intelligence-based methods have been actively developed to accelerate and improve the development of therapeutic antibodies. In this study, we developed an end-to-end sequence-based deep learning model, termed AttABseq, for the predictions of the antigen-antibody binding affinity changes connected with antibody mutations. AttABseq is a highly efficient and generic attention-based model by utilizing diverse antigen-antibody complex sequences as the input to predict the binding affinity changes of residue mutations. The assessment on the three benchmark datasets illustrates that AttABseq is 120% more accurate than other sequence-based models in terms of the Pearson correlation coefficient between the predicted and experimental binding affinity changes. Moreover, AttABseq also either outperforms or competes favorably with the structure-based approaches. Furthermore, AttABseq consistently demonstrates robust predictive capabilities across a diverse array of conditions, underscoring its remarkable capacity for generalization across a wide spectrum of antigen-antibody complexes. It imposes no constraints on the quantity of altered residues, rendering it particularly applicable in scenarios where crystallographic structures remain unavailable. The attention-based interpretability analysis indicates that the causal effects of point mutations on antibody-antigen binding affinity changes can be visualized at the residue level, which might assist automated antibody sequence optimization. We believe that AttABseq provides a fiercely competitive answer to therapeutic antibody optimization.

Benchmarking of methods for DNA methylome deconvolution

Defining the number and abundance of different cell types in tissues is important for understanding disease mechanisms as well as for diagnostic and prognostic purposes. Typically, this is achieved by immunohistological analyses, cell sorting, or single-cell RNA-sequencing. Alternatively, cell-specific DNA methylome information can be leveraged to deconvolve cell fractions from a bulk DNA mixture. However, comprehensive benchmarking of deconvolution methods and modalities was not yet performed. Here we evaluate 16 deconvolution algorithms, developed either specifically for DNA methylome data or more generically. We assess the performance of these algorithms, and the effect of normalization methods, while modeling variables that impact deconvolution performance, including cell abundance, cell type similarity, reference panel size, method for methylome profiling (array or sequencing), and technical variation. We observe differences in algorithm performance depending on each these variables, emphasizing the need for tailoring deconvolution analyses. The complexity of the reference, marker selection method, number of marker loci and, for sequencing-based assays, sequencing depth have a marked influence on performance. By developing handles to select the optimal analysis configuration, we provide a valuable source of information for studies aiming to deconvolve array- or sequencing-based methylation data.

Towards a structural and functional analysis of the immunoglobulin-fold proteome

The immunoglobulin fold (Ig fold) domain is a super-secondary structural motif consisting of a sandwich with two layers of β-sheets that is present in many proteins with very diverse biological functions covering a wide range of physiological processes. This domain presents a modular architecture built with β strands connected by variable length loops that has a highly conserved structural core of four β-strands and quite variable β-sheet extensions in the two sandwich layers that enable both divergent and convergent evolutionary mechanisms in the known Ig fold proteome. The central role of this Ig fold's structural plasticity in the evolutionary success of antibodies in our immune system is well established. Nature has also utilized this Ig fold in all domains of life in many different physiological contexts that go way beyond the immune system. Here we will present a structural and functional overview of the utilization of the Ig fold in different biological processes and in different cellular contexts to highlight some of the innumerable ways that this structural motif can interact in multidomain proteins to enable their diversity of functions. This includes shareable specific protein structure visualizations behind those functions that serve as starting points for further explorations of the biomolecular interactions spanning the Ig fold proteome. This overview also highlights how this Ig fold is being utilized through natural adaptation, engineering, and even building from scratch for a range of biotechnological applications.

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.

Cross-Reactivity of Human, Wild Boar, and Farm Animal Sera from Pre- and Post-Pandemic Periods with Alpha- and Βeta-Coronaviruses (CoV), including SARS-CoV-2

Panels of pre- and post-pandemic farm animals, wild boar and human sera, including human sera able to neutralize SARS-CoV-2 in vitro, were tested in serological tests to determine their cross-reactivity with β- and α-CoV originating from farm animals. Sera were tested in neutralization assays with high ascending concentrations (up to 1 × 104 TCID50 units/well) of β-CoV Bovine coronavirus (BCV), SARS-CoV-2, and porcine α-CoV-transmissible gastroenteritis virus (TGEV). In addition, sera were tested for immunostaining of cells infected with β-CoV porcine hemagglutinating encephalomyelitis (PHEV). Testing revealed a significantly higher percentage of BCV neutralization (78%) for sera of humans that had experienced a SARS-CoV-2 infection (SARS-CoV-2 convalescent sera) than was observed for human pre-pandemic sera (37%). Also, 46% of these human SARS-CoV-2 convalescent sera neutralized the highest concentration of BCV (5 × 103 TCID50/well) tested, whereas only 9.6% of the pre-pandemic sera did. Largely similar percentages were observed for staining of PHEV-infected cells by these panels of human sera. Furthermore, post-pandemic sera collected from wild boars living near a densely populated area in The Netherlands also showed a higher percentage (43%) and stronger BCV neutralization than was observed for pre-pandemic sera from this area (21%) and for pre- (28%) and post-pandemic (20%) sera collected from wild boars living in a nature reserve park with limited access for the public. High percentages of BCV neutralization were observed for pre- and post-pandemic sera of cows (100%), pigs (up to 45%), sheep (36%) and rabbits (60%). However, this cross-neutralization was restricted to sera collected from specific herds or farms. TGEV was neutralized only by sera of pigs (68%) and a few wild boar sera (4.6%). None of the BCV and PHEV cross-reacting human pre-pandemic, wild boar and farm animal sera effectively neutralized SARS-CoV-2 in vitro. Preexisting antibodies in human sera effectively neutralized the animal β-CoV BCV in vitro. This cross-neutralization was boosted after humans had experienced a SARS-CoV-2 infection, indicating that SARS-CoV-2 activated a "memory" antibody response against structurally related epitopes expressed on the surface of a broad range of heterologous CoV, including β-CoV isolated from farm animals. Further research is needed to elucidate if a symptomless infection or environmental exposure to SARS-CoV-2 or another β-CoV also triggers such a "memory" antibody response in wild boars and other free-living animals.

Protocol for the production and purification of an i-Motif-specific nanobody

Intercalated motifs or i-Motifs (iMs) are nucleic acid structures formed by cytosine-rich sequences, which may regulate cellular processes and have broad applications in nanotechnology due to their pH-dependent nature. We have developed an iM-specific nanobody (iMbody) that can recognize iM DNA structures regardless of their sequences, making it a versatile research tool for studying iMs in various contexts. Here, we provide a protocol for the bacterial expression and His-tag purification of iMbody. We then describe procedures for performing ELISA and immunostaining using iMbody.

Phage display assisted discovery of a pH-dependent anti-α-cobratoxin antibody from a natural variable domain library

Recycling IgG antibodies bind to their target antigen at physiological pH in the blood stream and release them upon endocytosis when pH levels drop, allowing the IgG antibodies to be recycled into circulation via FcRn-mediated cellular pathways, while the antigens undergo lysosomal degradation. This enables recycling antibodies to achieve comparable therapeutic effect at lower doses than their non-recycling counterparts. The development of such antibodies is typically achieved by histidine doping of their variable regions or by performing in vitro antibody selection campaigns utilizing histidine doped libraries. Both are strategies that may introduce sequence liabilities. Here, we present a methodology that employs a naïve antibody phage display library, consisting of natural variable domains, to discover antibodies that bind α-cobratoxin from the venom of Naja kaouthia in a pH-dependent manner. As a result, an antibody was discovered that exhibits a 7-fold higher off-rate at pH 5.5 than pH 7.4 in bio-layer interferometry experiments. Interestingly, no histidine residues were found in its variable domains, and in addition, the antibody showed pH-dependent binding to a histidine-devoid antigen mutant. As such, the results demonstrate that pH-dependent antigen-antibody binding may not always be driven by histidine residues. By employing molecular dynamics simulations, different protonation states of titratable residues were found, which potentially could be responsible for the observed pH-dependent antigen binding properties of the antibody. Finally, given the typically high diversity of naïve antibody libraries, the methodology presented here can likely be applied to discover recycling antibodies against different targets ab initio without the need for histidine doping.

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.

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.

The Evolution of Commercial Antibody Formulations

It has been nearly four decades since the first therapeutic monoclonal antibodies were approved and made available for widespread human use. Herein, US and EU approved antibody formulations are reviewed, and their nature and compositions are evaluated over time. From 1986 through Jan 2023, significant formulation trends have occurred and to represent this, 165 commercial antibody therapeutic formulations were binned into 5 different periods of time. Overall, we have observed the following: 1) The average formulation pH has decreased in recent years by over 0.5 units along with a decrease in variability that is largely driven by non-high concentration liquid in vial presentations for IV administration, 2) The use of certain excipients and buffers such as histidine, sucrose, metal chelators, arginine and methionine has become significantly more common, whereas formulations that contain phosphate, salt, no sugar or no surfactant have fallen out of favor, 3) Overall formulation space has increasingly become more homogenous and has converged in terms of formulation pH and excipient preferences regardless of formulation concentration, drug product presentation, and route of administration, 4) The average calculated isoelectric point (pI) has decreased 0.26 units, and 5) Overall, the average formulation pH and calculated pI for all commercial antibodies surveyed was 6.0 and 8.4, respectively. These trends and formulation convergence may be driven by multiple factors such as advancements in high-throughput computational and analytical technologies, the increased emphasis and understanding of certain developability attributes and formulation principles during lead selection and formulation development, and the adoption of low-risk development platform approaches.

Accelerating therapeutic protein design with computational approaches toward the clinical stage

Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.

Surface patches induce nonspecific binding and phase separation of antibodies

Nonspecific interactions are a key challenge in the successful development of therapeutic antibodies. The tendency for nonspecific binding of antibodies is often difficult to reduce by rational design, and instead, it is necessary to rely on comprehensive screening campaigns. To address this issue, we performed a systematic analysis of the impact of surface patch properties on antibody nonspecificity using a designer antibody library as a model system and single-stranded DNA as a nonspecificity ligand. Using an in-solution microfluidic approach, we find that the antibodies tested bind to single-stranded DNA with affinities as high as KD = 1 µM. We show that DNA binding is driven primarily by a hydrophobic patch in the complementarity-determining regions. By quantifying the surface patches across the library, the nonspecific binding affinity is shown to correlate with a trade-off between the hydrophobic and total charged patch areas. Moreover, we show that a change in formulation conditions at low ionic strengths leads to DNA-induced antibody phase separation as a manifestation of nonspecific binding at low micromolar antibody concentrations. We highlight that phase separation is driven by a cooperative electrostatic network assembly mechanism of antibodies with DNA, which correlates with a balance between positive and negative charged patches. Importantly, our study demonstrates that both nonspecific binding and phase separation are controlled by the size of the surface patches. Taken together, these findings highlight the importance of surface patches and their role in conferring antibody nonspecificity and its macroscopic manifestation in phase separation.

Immunomodulation with Nucleic Acid Nanodevices

The precise regulation of interactions of specific immunological components is crucial for controllable immunomodulation, yet it remains a great challenge. With the assistance of advanced computer design, programmable nucleic acid nanotechnology enables the customization of synthetic nucleic acid nanodevices with unprecedented geometrical and functional precision, which have shown promising potential for precise immunoengineering. Notably, the inherently immunologic functions of nucleic acids endow these nucleic acid-based assemblies with innate advantages in immunomodulatory engagement. In this review, the roles of nucleic acids in innate immunity are discussed, focusing on the definition, immunologic modularity, and enhanced bioavailability of structural nucleic acid nanodevices. In light of this, molecular programming and precise organization of functional modules with nucleic acid nanodevices for immunomodulation are emphatically reviewed. At last, the present challenges and future perspectives of nucleic acid nanodevices for immunomodulation are discussed.

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

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

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.

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.