An alternative assay to hydrophobic interaction chromatography for high-throughput characterization of monoclonal antibodies

Biophysical Analysis of Therapeutic Antibodies in the Early Development Pipeline

The successful progression of therapeutic antibodies and other biologics from the laboratory to the clinic depends on their possession of "drug-like" biophysical properties. The techniques and the resultant biophysical and biochemical parameters used to characterize their ease of manufacture can be broadly defined as developability. Focusing on antibodies, this review firstly discusses established and emerging biophysical techniques used to probe the early-stage developability of biologics, aimed towards those new to the field. Secondly, we describe the inter-relationships and redundancies amongst developability assays and how in silico methods aid the efficient deployment of developability to bring a new generation of cost-effective therapeutic proteins from bench to bedside more quickly and sustainably.

A comprehensive overview of recent advances in generative models for antibodies

Therapeutic antibodies are an important class of biopharmaceuticals. With the rapid development of deep learning methods and the increasing amount of antibody data, antibody generative models have made great progress recently. They aim to solve the antibody space searching problems and are widely incorporated into the antibody development process. Therefore, a comprehensive introduction to the development methods in this field is imperative. Here, we collected 34 representative antibody generative models published recently and all generative models can be divided into three categories: sequence-generating models, structure-generating models, and hybrid models, based on their principles and algorithms. We further studied their performance and contributions to antibody sequence prediction, structure optimization, and affinity enhancement. Our manuscript will provide a comprehensive overview of the status of antibody generative models and also offer guidance for selecting different approaches.

DOTAD: A Database of Therapeutic Antibody Developability

The development of therapeutic antibodies is an important aspect of new drug discovery pipelines. The assessment of an antibody's developability-its suitability for large-scale production and therapeutic use-is a particularly important step in this process. Given that experimental assays to assess antibody developability in large scale are expensive and time-consuming, computational methods have been a more efficient alternative. However, the antibody research community faces significant challenges due to the scarcity of readily accessible data on antibody developability, which is essential for training and validating computational models. To address this gap, DOTAD (Database Of Therapeutic Antibody Developability) has been built as the first database dedicated exclusively to the curation of therapeutic antibody developability information. DOTAD aggregates all available therapeutic antibody sequence data along with various developability metrics from the scientific literature, offering researchers a robust platform for data storage, retrieval, exploration, and downloading. In addition to serving as a comprehensive repository, DOTAD enhances its utility by integrating a web-based interface that features state-of-the-art tools for the assessment of antibody developability. This ensures that users not only have access to critical data but also have the convenience of analyzing and interpreting this information. The DOTAD database represents a valuable resource for the scientific community, facilitating the advancement of therapeutic antibody research. It is freely accessible at http://i.uestc.edu.cn/DOTAD/ , providing an open data platform that supports the continuous growth and evolution of computational methods in the field of antibody development.

Crimean-Congo hemorrhagic fever survivors elicit protective non-neutralizing antibodies that target 11 overlapping regions on glycoprotein GP38

Crimean-Congo hemorrhagic fever virus can cause lethal disease in humans yet there are no approved medical countermeasures. Viral glycoprotein GP38, exclusive to Nairoviridae, is a target of protective antibodies and is a key antigen in preclinical vaccine candidates. Here, we isolate 188 GP38-specific antibodies from human survivors of infection. Competition experiments show that these antibodies bind across 5 distinct antigenic sites, encompassing 11 overlapping regions. Additionally, we show structures of GP38 bound with 9 of these antibodies targeting different antigenic sites. Although these GP38-specific antibodies are non-neutralizing, several display protective efficacy equal to or better than murine antibody 13G8 in two highly stringent rodent models of infection. Together, these data expand our understanding regarding this important viral protein and may inform the development of broadly effective CCHFV antibody therapeutics.

Crimean-Congo Hemorrhagic Fever Survivors Elicit Protective Non-Neutralizing Antibodies that Target 11 Overlapping Regions on Viral Glycoprotein GP38

Crimean-Congo hemorrhagic fever virus can cause lethal disease in humans yet there are no approved medical countermeasures. Viral glycoprotein GP38, unique to Nairoviridae, is a target of protective antibodies, but extensive mapping of the human antibody response to GP38 has not been previously performed. Here, we isolated 188 GP38-specific antibodies from human survivors of infection. Competition experiments showed that these antibodies bind across five distinct antigenic sites, encompassing eleven overlapping regions. Additionally, we reveal structures of GP38 bound with nine of these antibodies targeting different antigenic sites. Although GP38-specific antibodies were non-neutralizing, several antibodies were found to have protection equal to or better than murine antibody 13G8 in two highly stringent rodent models of infection. Together, these data expand our understanding regarding this important viral protein and inform the development of broadly effective CCHFV antibody therapeutics.

A next-generation Fab library platform directly yielding drug-like antibodies with high affinity, diversity, and developability

We previously described an in vitro single-chain fragment (scFv) library platform originally designed to generate antibodies with excellent developability properties. The platform design was based on the use of clinical antibodies as scaffolds into which replicated natural complementarity-determining regions purged of sequence liabilities were inserted, and the use of phage and yeast display to carry out antibody selection. In addition to being developable, antibodies generated using our platform were extremely diverse, with most campaigns yielding sub-nanomolar binders. Here, we describe a platform advancement that incorporates Fab phage display followed by single-chain antibody-binding fragment Fab (scFab) yeast display. The scFab single-gene format provides balanced expression of light and heavy chains, with enhanced conversion to IgG, thereby combining the advantages of scFvs and Fabs. A meticulously engineered, quality-controlled Fab phage library was created using design principles similar to those used to create the scFv library. A diverse panel of binding scFabs, with high conversion efficiency to IgG, was isolated against two targets. This study highlights the compatibility of phage and yeast display with a Fab semi-synthetic library design, offering an efficient approach to generate drug-like antibodies directly, facilitating their conversion to potential therapeutic candidates.

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.

Assessment and incorporation of in vitro correlates to pharmacokinetic outcomes in antibody developability workflows

In vitro assessments for the prediction of pharmacokinetic (PK) behavior of biotherapeutics can help identify corresponding liabilities significantly earlier in the discovery timeline. This can minimize the need for extensive early in vivo PK characterization, thereby reducing animal usage and optimizing resources. In this study, we recommend bolstering classical developability workflows with in vitro measures correlated with PK. In agreement with current literature, in vitro measures assessing nonspecific interactions, self-interaction, and FcRn interaction are demonstrated to have the highest correlations to clearance in hFcRn Tg32 mice. Crucially, the dataset used in this study has broad sequence diversity and a range of physicochemical properties, adding robustness to our recommendations. Finally, we demonstrate a computational approach that combines multiple in vitro measurements with a multivariate regression model to improve the correlation to PK compared to any individual assessment. Our work demonstrates that a judicious choice of high throughput in vitro measurements and computational predictions enables the prioritization of candidate molecules with desired PK properties.

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.

Profiling the Biophysical Developability Properties of Common IgG1 Fc Effector Silencing Variants

Therapeutic antibodies represent the most significant modality in biologics, with around 150 approved drugs on the market. In addition to specific target binding mediated by the variable fragments (Fvs) of the heavy and light chains, antibodies possess effector functions through binding of the constant region (Fc) to Fcγ receptors (FcγR), which allow immune cells to attack and kill target cells using a variety of mechanisms. However, for some applications, including T-cell-engaging bispecifics, this effector function is typically undesired. Mutations within the lower hinge and the second constant domain (CH2) of IgG1 that comprise the FcγR binding interface reduce or eliminate effector function ("Fc silencing") while retaining binding to the neonatal Fc receptor (FcRn), important for normal antibody pharmacokinetics (PKs). Comprehensive profiling of biophysical developability properties would benefit the choice of constant region variants for development. Here, we produce a large panel of representative mutations previously described in the literature and in many cases in clinical or approved molecules, generate select combinations thereof, and characterize their binding and biophysical properties. We find that some commonly used CH2 mutations, including D265A and P331S, are effective in reducing binding to FcγR but significantly reduce stability, promoting aggregation, particularly under acidic conditions commonly employed in manufacturing. We highlight mutation sets that are particularly effective for eliminating Fc effector function with the retention of WT-like stability, including L234A, L235A, and S267K (LALA-S267K), L234A, L235E, and S267K (LALE-S267K), L234A, L235A, and P329A (LALA-P329A), and L234A, L235E, and P329G (LALE-P329G).

High-Throughput and Format-Agnostic Mispairing Assay for Multispecific Antibodies Using Intact Mass Spectrometry

Multispecific antibodies have gained significant importance in a broad indication space due to their ability to engage multiple epitopes simultaneously and to thereby overcome therapeutic barriers. With growing therapeutic potential, however, the molecular complexity increases, thus intensifying the demand for innovative protein engineering and analytical strategies. A major challenge for multispecific antibodies is the correct assembly of light and heavy chains. Engineering strategies exist to stabilize the correct pairing, but typically individual engineering campaigns are required to arrive at the anticipated format. Mass spectrometry has proven to be a versatile tool to identify mispaired species. However, due to manual data analysis procedures, mass spectrometry is limited to lower throughputs. To keep pace with increasing sample numbers, we developed a high-throughput-capable mispairing workflow based on intact mass spectrometry with automated data analysis, peak detection, and relative quantification using Genedata Expressionist. This workflow is capable of detecting mispaired species of ∼1000 multispecific antibodies in three weeks and thus is applicable to complex screening campaigns. As a proof of concept, the assay was applied to engineering a trispecific antibody. Strikingly, the new setup has not only proved successful in mispairing analysis but has also revealed its potential to automatically annotate other product-related impurities. Furthermore, we could confirm the assay to be format-agnostic, as shown by analyzing several different multispecific formats in one run. With these comprehensive capabilities, the new automated intact mass workflow can be applied as a universal tool to detect and annotate peaks in a format-agnostic approach and in high-throughput, thus enabling complex discovery campaigns.

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.

Advancing humanized monoclonal antibody for counteracting fentanyl toxicity towards clinical development

Innovative therapies to complement current treatments are needed to curb the growing incidence of fatal overdoses related to synthetic opioids. Murine and chimeric monoclonal antibodies (mAb) specific for fentanyl and its analogs have demonstrated pre-clinical efficacy in preventing and reversing drug-induced toxicity in rodent models. However, mAb-based therapeutics require extensive engineering as well as in vitro and in vivo characterization to advance to first-in-human clinical trials. Here, novel murine anti-fentanyl mAbs were selected for development based on affinity for fentanyl, and efficacy in counteracting the pharmacological effects of fentanyl in mice. Humanization and evaluation of mutations designed to eliminate predicted post-translational modifications resulted in two humanized mAbs that were effective at preventing fentanyl-induced pharmacological effects in rats. These humanized mAbs showed favorable biophysical properties with respect to aggregation and hydrophobicity by chromatography-based assays, and thermostability by dynamic scanning fluorimetry. These results collectively support that the humanized anti-fentanyl mAbs developed herein warrant further clinical development for treatment of fentanyl toxicity.

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.

Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods

Although antibodies have become the fastest-growing class of therapeutics on the market, it is still challenging to develop them for therapeutic applications, which often require these molecules to withstand stresses that are not present in vivo. We define developability as the likelihood of an antibody candidate with suitable functionality to be developed into a manufacturable, stable, safe, and effective drug that can be formulated to high concentrations while retaining a long shelf life. The implementation of reliable developability assessments from the early stages of antibody discovery enables flagging and deselection of potentially problematic candidates, while focussing available resources on the development of the most promising ones. Currently, however, thorough developability assessment requires multiple in vitro assays, which makes it labor intensive and time consuming to implement at early stages. Furthermore, accurate in vitro analysis at the early stage is compromised by the high number of potential candidates that are often prepared at low quantities and purity. Recent improvements in the performance of computational predictors of developability potential are beginning to change this scenario. Many computational methods only require the knowledge of the amino acid sequences and can be used to identify possible developability issues or to rank available candidates according to a range of biophysical properties. Here, we describe how the implementation of in silico tools into antibody discovery pipelines is increasingly offering time- and cost-effective alternatives to in vitro experimental screening, thus streamlining the drug development process. We discuss in particular the biophysical and biochemical properties that underpin developability potential and their trade-offs, review various in vitro assays to measure such properties or parameters that are predictive of developability, and give an overview of the growing number of in silico tools available to predict properties important for antibody development, including the CamSol method developed in our laboratory.

Improving antibody drug development using bionanotechnology

Monoclonal antibodies are being used to treat a remarkable breadth of human disorders. Nevertheless, there are several key challenges at the earliest stages of antibody drug development that need to be addressed using simple and widely accessible methods, especially related to generating antibodies against membrane proteins and identifying antibody candidates with drug-like biophysical properties (high solubility and low viscosity). Here we highlight key bionanotechnologies for preparing functional and stable membrane proteins in diverse types of lipoparticles that are being used to improve antibody discovery and engineering efforts. We also highlight key bionanotechnologies for high-throughput and ultra-dilute screening of antibody biophysical properties during antibody discovery and optimization that are being used for identifying antibodies with superior combinations of in vitro (formulation) and in vivo (half-life) properties.

An accelerated surface-mediated stress assay of antibody instability for developability studies

High physical stability is required for the development of monoclonal antibodies (mAbs) into successful therapeutic products. Developability assays are used to predict physical stability issues such as high viscosity and poor conformational stability, but protein aggregation remains a challenging property to predict. Among different types of stresses, air–water and solid–liquid interfaces are well known to potentially trigger protein instability and induce aggregation. Yet, in contrast to the increasing number of developability assays to evaluate bulk properties, there is still a lack of experimental methods to evaluate antibody stability against interfaces. Here, we investigate the potential of a hydrophobic nanoparticle surface-mediated stress assay to assess the stability of mAbs during the early stages of development. We evaluate this surface-mediated accelerated stability assay on a rationally designed library of 14 variants of a humanized IgG4, featuring a broad span of solubility values and other developability properties. The assay could identify variants characterized by high instability against agitation in the presence of air–water interfaces. Remarkably, for the set of investigated molecules, we observe strong correlations between the extent of aggregation induced by the surface-mediated stress assay and other developability properties of the molecules, such as aggregation upon storage at 45°C, self-association (evaluated by affinity-capture self-interaction nanoparticle spectroscopy) and nonspecific interactions (estimated by cross-interaction chromatography, stand-up monolayer chromatography (SMAC), SMAC*). This highly controlled surface-mediated stress assay has the potential to complement and increase the ability of the current set of screening techniques to assess protein aggregation and developability potential of mAbs during the early stages of drug development.

Abbreviations:AC-SINS: Affinity-Capture Self-Interaction Nanoparticle Spectroscopy; AMS: Ammonium sulfate precipitation; ANS: 1-anilinonaphtalene-8-sulfonate; CIC: Cross-interaction chromatography; DLS: Dynamic light scattering; HIC: Hydrophobic interaction chromatography; HNSSA: Hydrophobic nanoparticles surface-stress assay; mAb: Monoclonal antibody; NP: Nanoparticle; SEC: Size exclusion chromatography; SMAC: Stand-up monolayer chromatography; WT: Wild type

Caffeine as a Viscosity Reducer for Highly Concentrated Monoclonal Antibody Solutions

Many monoclonal antibody (mAb) solutions exhibit high viscosity at elevated concentrations, which prevents manufacturing and injecting of concentrated mAb drug products at the small volumes needed for subcutaneous (SC) administration. Addition of excipients that interrupt intermolecular interactions is a common approach to reduce viscosity of high concentration mAb formulations. However, in some cases widely used excipients can fail to lower viscosity. Here, using infliximab and ipilimumab as model proteins, we show that caffeine effectively lowers the viscosity of both mAb formulations, whereas other common viscosity-reducing excipients, sodium chloride and arginine, do not. Furthermore, stability studies under accelerated conditions show that caffeine has no impact on stability of lyophilized infliximab or liquid ipilimumab formulations. In addition, presence of caffeine in the formulations does not affect in vitro bioactivities of infliximab or ipilimumab. Results from this study suggest that caffeine could be a useful viscosity reducing agent that complements other traditional excipients and provides viscosity reduction to a wider range of mAb drug products.

Ultradilute Measurements of Self-Association for the Identification of Antibodies with Favorable High-Concentration Solution Properties

There is significant interest in formulating antibody therapeutics as concentrated liquid solutions, but early identification of developable antibodies with optimal manufacturability, stability, and delivery attributes remains challenging. Traditional methods of identifying developable mAbs with low self-association in common antibody formulations require relatively concentrated protein solutions (>1 mg/mL), and this single challenge has frustrated early-stage and large-scale identification of antibody candidates with drug-like colloidal properties. Here, we describe charge-stabilized self-interaction nanoparticle spectroscopy (CS-SINS), an affinity-capture nanoparticle assay that measures colloidal self-interactions at ultradilute antibody concentrations (0.01 mg/mL), and is predictive of antibody developability issues of high viscosity and opalescence that manifest at four orders of magnitude higher concentrations (>100 mg/mL). CS-SINS enables large-scale, high-throughput selection of developable antibodies during early discovery.

High-throughput developability assays enable library-scale identification of producible protein scaffold variants

Proteins require high developability-quantified by expression, solubility, and stability-for robust utility as therapeutics, diagnostics, and in other biotechnological applications. Measuring traditional developability metrics is low throughput in nature, often slowing the developmental pipeline. We evaluated the ability of 10 variations of three high-throughput developability assays to predict the bacterial recombinant expression of paratope variants of the protein scaffold Gp2. Enabled by a phenotype/genotype linkage, assay performance for 105 variants was calculated via deep sequencing of populations sorted by proxied developability. We identified the most informative assay combination via cross-validation accuracy and correlation feature selection and demonstrated the ability of machine learning models to exploit nonlinear mutual information to increase the assays' predictive utility. We trained a random forest model that predicts expression from assay performance that is 35% closer to the experimental variance and trains 80% more efficiently than a model predicting from sequence information alone. Utilizing the predicted expression, we performed a site-wise analysis and predicted mutations consistent with enhanced developability. The validated assays offer the ability to identify developable proteins at unprecedented scales, reducing the bottleneck of protein commercialization.

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

PURPOSE

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

METHOD

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

RESULT

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

CONCLUSION

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

Broad and potent activity against SARS-like viruses by an engineered human monoclonal antibody

The recurrent zoonotic spillover of coronaviruses (CoVs) into the human population underscores the need for broadly active countermeasures. We employed a directed evolution approach to engineer three severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies for enhanced neutralization breadth and potency. One of the affinity-matured variants, ADG-2, displays strong binding activity to a large panel of sarbecovirus receptor binding domains and neutralizes representative epidemic sarbecoviruses with high potency. Structural and biochemical studies demonstrate that ADG-2 employs a distinct angle of approach to recognize a highly conserved epitope that overlaps the receptor binding site. In immunocompetent mouse models of SARS and COVID-19, prophylactic administration of ADG-2 provided complete protection against respiratory burden, viral replication in the lungs, and lung pathology. Altogether, ADG-2 represents a promising broad-spectrum therapeutic candidate against clade 1 sarbecoviruses.

Predicting Antibody Developability Profiles Through Early Stage Discovery Screening

Monoclonal antibodies play an increasingly important role for the development of new drugs across multiple therapy areas. The term 'developability' encompasses the feasibility of molecules to successfully progress from discovery to development via evaluation of their physicochemical properties. These properties include the tendency for self-interaction and aggregation, thermal stability, colloidal stability, and optimization of their properties through sequence engineering. Selection of the best antibody molecule based on biological function, efficacy, safety, and developability allows for a streamlined and successful CMC phase. An efficient and practical high-throughput developability workflow (100 s-1,000 s of molecules) implemented during early antibody generation and screening is crucial to select the best lead candidates. This involves careful assessment of critical developability parameters, combined with binding affinity and biological properties evaluation using small amounts of purified material (<1 mg), as well as an efficient data management and database system. Herein, a panel of 152 various human or humanized monoclonal antibodies was analyzed in biophysical property assays. Correlations between assays for different sets of properties were established. We demonstrated in two case studies that physicochemical properties and key assay endpoints correlate with key downstream process parameters. The workflow allows the elimination of antibodies with suboptimal properties and a rank ordering of molecules for further evaluation early in the candidate selection process. This enables any further engineering for problematic sequence attributes without affecting program timelines.

Highly sensitive detection of antibody nonspecific interactions using flow cytometry

The rapidly evolving nature of antibody drug development has resulted in technologies that generate vast numbers (hundreds to thousands) of lead antibody candidates during early discovery. These candidates must be rapidly pared down to identify the most drug-like candidates for in-depth analysis of their safety and efficacy, which can only be performed on a limited number of antibodies due to time and resource requirements. One key biophysical property of successful antibody therapeutics is high specificity, defined as low levels of nonspecific binding or polyspecificity. Although there has been some progress in developing assays for detecting antibody polyspecificity, most of these assays are limited by poor sensitivity or assay formats that require proprietary antibody surface display methods, and some of these assays use complex and poorly defined polyspecificity reagents. Here we report the PolySpecificity Particle (PSP) assay, a sensitive flow cytometry assay for evaluating antibody nonspecific interactions that overcomes previous limitations and can be used for evaluating diverse types of IgGs, multispecific antibodies and Fc-fusion proteins. Our approach uses micron-sized magnetic beads coated with Protein A to capture antibodies at extremely dilute concentrations (<0.02 mg/mL). Flow cytometry analysis of polyspecificity reagent binding to these conjugates results in sensitive detection of differences in nonspecific interactions for clinical-stage antibodies. Our PSP assay strongly discriminates between antibodies with different levels of polyspecificity using previously reported polyspecificity reagents that are either well-defined proteins or highly complex protein mixtures. Moreover, we also find that a unique reagent, namely ovalbumin, results in the best assay sensitivity and specificity. Importantly, our assay is much more sensitive than standard assays such as ELISAs. We expect that our simple, sensitive, and high-throughput PSP assay will accelerate the development of safe and effective antibody therapeutics.

Drug-like antibodies with high affinity, diversity and developability directly from next-generation antibody libraries

Therapeutic antibodies must have "drug-like" properties. These include high affinity and specificity for the intended target, biological activity, and additional characteristics now known as "developability properties": long-term stability and resistance to aggregation when in solution, thermodynamic stability to prevent unfolding, high expression yields to facilitate manufacturing, low self-interaction, among others. Sequence-based liabilities may affect one or more of these characteristics. Improving the stability and developability of a lead antibody is typically achieved by modifying its sequence, a time-consuming process that often results in reduced affinity. Here we present a new antibody library format that yields high-affinity binders with drug-like developability properties directly from initial selections, reducing the need for further engineering or affinity maturation. The innovative semi-synthetic design involves grafting natural complementarity-determining regions (CDRs) from human antibodies into scaffolds based on well-behaved clinical antibodies. HCDR3s were amplified directly from B cells, while the remaining CDRs, from which all sequence liabilities had been purged, were replicated from a large next-generation sequencing dataset. By combining two in vitro display techniques, phage and yeast display, we were able to routinely recover a large number of unique, highly developable antibodies against clinically relevant targets with affinities in the subnanomolar to low nanomolar range. We anticipate that the designs and approaches presented here will accelerate the drug development process by reducing the failure rate of leads due to poor antibody affinities and developability.Abbreviations: AC-SINS: affinity-capture self-interaction nanoparticle spectroscopy; CDR: complementarity-determining region; CQA: critical quality attribute; ELISA: enzyme-linked immunoassay; FACS: fluorescence-activated cell sorting; Fv: fragment variable; GM-CSF: granulocyte-macrophage colony-stimulating factor; HCDR3: heavy chain CDR3; IFN2a: interferon α-2; IL6: interleukin-6; MACS: magnetic-activated cell sorting; NGS: next generation sequencing; PCR: polymerase chain reaction; SEC: size-exclusion chromatography; SPR: surface plasmon resonance; TGFβ-R2: transforming growth factor β-R2; VH: variable heavy; VK: variable kappa; VL: variable light; Vl: variable lambda.

Particle Detection and Characterization for Biopharmaceutical Applications: Current Principles of Established and Alternative Techniques

Detection and characterization of particles in the visible and subvisible size range is critical in many fields of industrial research. Commercial particle analysis systems have proliferated over the last decade. Despite that growth, most systems continue to be based on well-established principles, and only a handful of new approaches have emerged. Identifying the right particle-analysis approach remains a challenge in research and development. The choice depends on each individual application, the sample, and the information the operator needs to obtain. In biopharmaceutical applications, particle analysis decisions must take product safety, product quality, and regulatory requirements into account. Biopharmaceutical process samples and formulations are dynamic, polydisperse, and very susceptible to chemical and physical degradation: improperly handled product can degrade, becoming inactive or in specific cases immunogenic. This article reviews current methods for detecting, analyzing, and characterizing particles in the biopharmaceutical context. The first part of our article represents an overview about current particle detection and characterization principles, which are in part the base of the emerging techniques. It is very important to understand the measuring principle, in order to be adequately able to judge the outcome of the used assay. Typical principles used in all application fields, including particle–light interactions, the Coulter principle, suspended microchannel resonators, sedimentation processes, and further separation principles, are summarized to illustrate their potentials and limitations considering the investigated samples. In the second part, we describe potential technical approaches for biopharmaceutical particle analysis as some promising techniques, such as nanoparticle tracking analysis (NTA), micro flow imaging (MFI), tunable resistive pulse sensing (TRPS), flow cytometry, and the space- and time-resolved extinction profile (STEP^®) technology.

Conformational Ensembles of Antibodies Determine Their Hydrophobicity

A major challenge in the development of antibody biotherapeutics is their tendency to aggregate. One root cause for aggregation is exposure of hydrophobic surface regions to the solvent. Many current techniques predict the relative aggregation propensity of antibodies via precalculated scales for the hydrophobicity or aggregation propensity of single amino acids. However, those scales cannot describe the nonadditive effects of a residue’s surrounding on its hydrophobicity. Therefore, they are inherently limited in their ability to describe the impact of subtle differences in molecular structure on the overall hydrophobicity. Here, we introduce a physics-based approach to describe hydrophobicity in terms of the hydration free energy using grid inhomogeneous solvation theory (GIST). We apply this method to assess the effects of starting structures, conformational sampling, and protonation states on the hydrophobicity of antibodies. Our results reveal that high-quality starting structures, i.e., crystal structures, are crucial for the prediction of hydrophobicity and that conformational sampling can compensate errors introduced by the starting structure. On the other hand, sampling of protonation states only leads to good results when combined with high-quality structures, whereas it can even be detrimental otherwise. We conclude by pointing out that a single static homology model may not be adequate for predicting hydrophobicity.

An Engineered Antibody with Broad Protective Efficacy in Murine Models of SARS and COVID-19

The recurrent zoonotic spillover of coronaviruses (CoVs) into the human population underscores the need for broadly active countermeasures. Here, we employed a directed evolution approach to engineer three SARS-CoV-2 antibodies for enhanced neutralization breadth and potency. One of the affinity-matured variants, ADG-2, displays strong binding activity to a large panel of sarbecovirus receptor binding domains (RBDs) and neutralizes representative epidemic sarbecoviruses with remarkable potency. Structural and biochemical studies demonstrate that ADG-2 employs a unique angle of approach to recognize a highly conserved epitope overlapping the receptor binding site. In murine models of SARS-CoV and SARS-CoV-2 infection, passive transfer of ADG-2 provided complete protection against respiratory burden, viral replication in the lungs, and lung pathology. Altogether, ADG-2 represents a promising broad-spectrum therapeutic candidate for the treatment and prevention of SARS-CoV-2 and future emerging SARS-like CoVs.

Toward Drug-Like Multispecific Antibodies by Design

The success of antibody therapeutics is strongly influenced by their multifunctional nature that couples antigen recognition mediated by their variable regions with effector functions and half-life extension mediated by a subset of their constant regions. Nevertheless, the monospecific IgG format is not optimal for many therapeutic applications, and this has led to the design of a vast number of unique multispecific antibody formats that enable targeting of multiple antigens or multiple epitopes on the same antigen. Despite the diversity of these formats, a common challenge in generating multispecific antibodies is that they display suboptimal physical and chemical properties relative to conventional IgGs and are more difficult to develop into therapeutics. Here we review advances in the design and engineering of multispecific antibodies with drug-like properties, including favorable stability, solubility, viscosity, specificity and pharmacokinetic properties. We also highlight emerging experimental and computational methods for improving the next generation of multispecific antibodies, as well as their constituent antibody fragments, with natural IgG-like properties. Finally, we identify several outstanding challenges that need to be addressed to increase the success of multispecific antibodies in the clinic.

SSH: A Tool for Predicting Hydrophobic Interaction of Monoclonal Antibodies Using Sequences

Therapeutic antibodies are one of the most important parts of the pharmaceutical industry. They are widely used in treating various diseases such as autoimmune diseases, cancer, inflammation, and infectious diseases. Their development process however is often brought to a standstill or takes a longer time and is then more expensive due to their hydrophobicity problems. Hydrophobic interactions can cause problems on half-life, drug administration, and immunogenicity at all stages of antibody drug development. Some of the most widely accepted and used technologies for determining the hydrophobic interactions of antibodies include standup monolayer adsorption chromatography (SMAC), salt-gradient affinity-capture self-interaction nanoparticle spectroscopy (SGAC-SINS), and hydrophobic interaction chromatography (HIC). However, to measure SMAC, SGAC-SINS, and HIC for hundreds of antibody drug candidates is time-consuming and costly. To save time and money, a predictor called SSH is developed. Based on the antibody's sequence only, it can predict the hydrophobic interactions of monoclonal antibodies (mAbs). Using the leave-one-out crossvalidation, SSH achieved 91.226% accuracy, 96.396% sensitivity or recall, 84.196% specificity, 87.754% precision, 0.828 Mathew correlation coefficient (MCC), 0.919 f-score, and 0.961 area under the receiver operating characteristic (ROC) curve (AUC).

The uniqueness of flow in probing the aggregation behavior of clinically relevant antibodies

The development of therapeutic monoclonal antibodies (mAbs) can be hindered by their tendency to aggregate throughout their lifetime, which can illicit immunogenic responses and render mAb manufacturing unfeasible. Consequently, there is a need to identify mAbs with desirable thermodynamic stability, solubility, and lack of self-association. These behaviors are assessed using an array of in silico and in vitro assays, as no single assay can predict aggregation and developability. We have developed an extensional and shear flow device (EFD), which subjects proteins to defined hydrodynamic forces which mimic those experienced in bioprocessing. Here, we utilize the EFD to explore the aggregation propensity of 33 IgG1 mAbs, whose variable domains are derived from clinical antibodies. Using submilligram quantities of material per replicate, wide-ranging EFD-induced aggregation (9-81% protein in pellet) was observed for these mAbs, highlighting the EFD as a sensitive method to assess aggregation propensity. By comparing the EFD-induced aggregation data to those obtained previously from 12 other biophysical assays, we show that the EFD provides distinct information compared with current measures of adverse biophysical behavior. Assessing a candidate's liability to hydrodynamic force thus adds novel insight into the rational selection of developable mAbs that complements other assays.

ADC Analysis by Hydrophobic Interaction Chromatography

Hydrophobic interaction chromatography (HIC) is a traditional technique used for the separation, purification, and characterization of proteins. As the number of antibody-drug conjugates (ADCs) continues to increase in clinical trials, HIC and other orthogonal methods utilizing changes in hydrophobicity are being used for ADC characterization and analysis. Unlike other techniques, HIC uniquely allows for protein analysis under mild nondenaturing conditions that preserve the native structure and activity of the molecules. Analysis of the ADC in its native form is advantageous. Herein, we describe a generic HIC protocol for the screening, analysis, and characterization of ADCs using an ammonium sulfate buffer and a high-pressure liquid chromatography system. Parameters affecting data quality and interpretation are addressed. In addition, several recommendations are included for method optimization and troubleshooting.

Determination of Protein-Protein Interactions in a Mixture of Two Monoclonal Antibodies

The coformulation of monoclonal antibody (mAb) mixtures provides an attractive route to achieving therapeutic efficacy where the targeting of multiple epitopes is necessary. Controlling and predicting the behavior of such mixtures requires elucidating the molecular basis for the self- and cross-protein-protein interactions and how they depend on solution variables. While self-interactions are now beginning to be well understood, systematic studies of cross-interactions between mAbs in solution do not exist. Here, we have used static light scattering to measure the set of self- and cross-osmotic second virial coefficients in a solution containing a mixture of two mAbs, mAbA and mAbB, as a function of ionic strength and pH. mAbB exhibits strong association at a low ionic strength, which is attributed to an electrostatic attraction that is enhanced by the presence of a strong short-ranged attraction of nonelectrostatic origin. Under all solution conditions, the measured cross-interactions are intermediate self-interactions and follow similar patterns of behavior. There is a strong electrostatic attraction at higher pH values, reflecting the behavior of mAbB. Protein-protein interactions become more attractive with an increasing pH due to reducing the overall protein net charges, an effect that is attenuated with an increasing ionic strength due to the screening of electrostatic interactions. Under moderate ionic strength conditions, the reduced cross-virial coefficient, which reflects only the energetic contribution to protein-protein interactions, is given by a geometric average of the corresponding self-coefficients. We show the relationship can be rationalized using a patchy sphere model, where the interaction energy between sites i and j is given by the arithmetic mean of the i-i and j-j interactions. The geometric mean does not necessarily apply to all mAb mixtures and is expected to break down at a lower ionic strength due to the nonadditivity of electrostatic interactions.

Affinity Maturation Enhances Antibody Specificity but Compromises Conformational Stability

Monoclonal antibodies (mAbs) have recently emerged as one of the most promising classes of biotherapeutics. A potential advantage of B cell-derived mAbs as therapeutic agents is that they have been subjected to natural filtering mechanisms, which may enrich for B cell receptors (BCRs) with favorable biophysical properties. Here, we evaluated 400 human mAbs for polyreactivity, hydrophobicity, and thermal stability using high-throughput screening assays. Overall, mAbs derived from memory B cells and long-lived plasma cells (LLPCs) display reduced levels of polyreactivity, hydrophobicity, and thermal stability compared with naive B cell-derived mAbs. Somatic hypermutation (SHM) is inversely associated with all three biophysical properties, as well as BCR expression levels. Finally, the developability profiles of the human B cell-derived mAbs are comparable with those observed for clinical mAbs, suggesting their high therapeutic potential. The results provide insight into the biophysical consequences of affinity maturation and have implications for therapeutic antibody engineering and development.

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.

Engineering an EGFR-binding Gp2 domain for increased hydrophilicity

The Gp2 domain is a 45 amino-acid scaffold that has been evolved for specific, high-affinity binding towards multiple targets and was proven useful in molecular imaging and biological antagonism. It was hypothesized that Gp2 may benefit from increased hydrophilicity for improved physiological distribution as well as for physicochemical robustness. We identified seven exposed hydrophobic sites for hydrophilic mutations and experimentally evaluated single mutants, which yielded six mutations that do not substantially hinder expression, binding affinity or specificity (to epidermal growth factor receptor), and thermal stability. Eight combinations of these mutations improved hydrophilicity relative to the parental Gp2 clone as assessed by reverse-phase high-performance liquid chromatography (p < 0.05). Secondary structures and refolding abilities of the selected single mutants and all multimutants were unchanged relative to the parental ligand. A variant with five hydrophobic-to-hydrophilic mutations was identified with enhanced solubility as well as reasonable binding affinity ( K d  = 53-63 nM), recombinant yield (1.3 ± 0.8 mg/L), and thermal stability ( T m  = 53 ± 3°C). An alternative variant with a cluster of three leucine-to-hydrophilic mutations was identified with increased solubility, nominally increased binding affinity ( K d  = 13-28 nM) and reasonable thermal stability ( T m  = 54.0 ± 0.6°C) but reduced yield (0.4 ± 0.3 mg/L). In addition, a ≥7°C increase in the midpoint of thermal denaturation was observed in one of the single mutants (T21N). These mutants highlight the physicochemical tradeoffs associated with hydrophobic-to-hydrophilic mutation within a small protein, improve the solubility and hydrophilicity of an existent molecular imaging probe, and provide a more hydrophilic starting point for discovery of new Gp2 ligands towards additional targets.

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

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

Structure, heterogeneity and developability assessment of therapeutic antibodies

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

AggScore: Prediction of aggregation-prone regions in proteins based on the distribution of surface patches

Protein aggregation is a phenomenon that has attracted considerable attention within the pharmaceutical industry from both a developability standpoint (to ensure stability of protein formulations) and from a research perspective for neurodegenerative diseases. Experimental identification of aggregation behavior in proteins can be expensive; and hence, the development of accurate computational approaches is crucial. The existing methods for predicting protein aggregation rely mostly on the primary sequence and are typically trained on amyloid-like proteins. However, the training bias toward beta amyloid peptides may worsen prediction accuracy of such models when applied to larger protein systems. Here, we present a novel algorithm to identify aggregation-prone regions in proteins termed "AggScore" that is based entirely on three-dimensional structure input. The method uses the distribution of hydrophobic and electrostatic patches on the surface of the protein, factoring in the intensity and relative orientation of the respective surface patches into an aggregation propensity function that has been trained on a benchmark set of 31 adnectin proteins. AggScore can accurately identify aggregation-prone regions in several well-studied proteins and also reliably predict changes in aggregation behavior upon residue mutation. The method is agnostic to an amyloid-specific aggregation context and thus may be applied to globular proteins, small peptides and antibodies.

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.

Measurements of Monoclonal Antibody Self-Association Are Correlated with Complex Biophysical Properties

Successful development of monoclonal antibodies (mAbs) for therapeutic applications requires identification of mAbs with favorable biophysical properties (high solubility and low viscosity) in addition to potent bioactivities. Nevertheless, mAbs can also display complex, nonconventional biophysical properties that impede their development such as formation of soluble aggregates and subvisible particles as well as nonspecific interactions with various types of surfaces such as nonadsorptive chromatography columns. Here we have investigated the potential of using antibody self-interaction measurements obtained via affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS) at dilute concentrations (0.01 mg/mL) for ranking a panel of 12 mAbs in terms of their expected biophysical properties at higher concentrations (1-30 mg/mL). Several mAb properties (solubility, % monomer, size-exclusion elution time and % recovery) displayed modest correlation with each other, as some mAbs with deficiencies in one or more properties (e.g., solubility) failed to show deficiencies in other properties (e.g., % monomer). The ranking of mAbs in terms of their level of self-association was correlated with their solubility ranking. However, the correlation was even stronger between the average ranking of the four biophysical properties and the AC-SINS measurements. This finding suggests that weak self-interactions detected via AC-SINS can manifest themselves in different ways and lead to complex biophysical properties. Our findings highlight the potential for using high-throughput self-interaction measurements to improve the identification of mAbs that possess a collection of excellent biophysical properties without the need for cumbersome analysis of each individual property during early candidate selection.