Beyond affinity: selection of antibody variants with optimal biophysical properties and reduced immunogenicity from mammalian display libraries

Advancing Antibody Engineering through Synthetic Evolution and Machine Learning

Abs are versatile molecules with the potential to achieve exceptional binding to target Ags, while also possessing biophysical properties suitable for therapeutic drug development. Protein display and directed evolution systems have transformed synthetic Ab discovery, engineering, and optimization, vastly expanding the number of Ab clones able to be experimentally screened for binding. Moreover, the burgeoning integration of high-throughput screening, deep sequencing, and machine learning has further augmented in vitro Ab optimization, promising to accelerate the design process and massively expand the Ab sequence space interrogated. In this Brief Review, we discuss the experimental and computational tools employed in synthetic Ab engineering and optimization. We also explore the therapeutic challenges posed by developing Abs for infectious diseases, and the prospects for leveraging machine learning-guided protein engineering to prospectively design Abs resistant to viral escape.

Engineering hydrophobicity and manufacturability for optimized biparatopic antibody-drug conjugates targeting c-MET

Optimal combinations of paratopes assembled into a biparatopic antibody have the capacity to mediate high-grade target cross-linking on cell membranes, leading to degradation of the target, as well as antibody and payload delivery in the case of an antibody-drug conjugate (ADC). In the work presented here, molecular docking suggested a suitable paratope combination targeting c-MET, but hydrophobic patches in essential binding regions of one moiety necessitated engineering. In addition to rational design of HCDR2 and HCDR3 mutations, site-specific spiking libraries were generated and screened in yeast and mammalian surface display approaches. Comparative analyses revealed similar positions amendable for hydrophobicity reduction, with a broad combinatorial diversity obtained from library outputs. Optimized variants showed high stability, strongly reduced hydrophobicity, retained affinities supporting the desired functionality and enhanced producibility. The resulting biparatopic anti-c-MET ADCs were comparably active on c-MET expressing tumor cell lines as REGN5093 exatecan DAR6 ADC. Structural molecular modeling of paratope combinations for preferential inter-target binding combined with protein engineering for manufacturability yielded deep insights into the capabilities of rational and library approaches. The methodologies of in silico hydrophobicity identification and sequence optimization could serve as a blueprint for rapid development of optimal biparatopic ADCs targeting further tumor-associated antigens in the future.

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.

Systematic single amino acid affinity tuning of CD229 CAR T cells retains efficacy against multiple myeloma and eliminates on-target off-tumor toxicity

T cells expressing chimeric antigen receptors (CARs) have shown remarkable therapeutic activity against different types of cancer. However, the wider use of CAR T cells has been hindered by the potential for life-threatening toxicities due to on-target off-tumor killing of cells expressing low amounts of the target antigen. CD229, a signaling lymphocyte-activation molecule (SLAM) family member, has previously been identified as a target for CAR T cell-mediated treatment of multiple myeloma (MM) due to its high expression on the surfaces of MM cells. CD229 CAR T cells have shown effective clearance of MM cells in vitro and in vivo. However, healthy lymphocytes also express CD229, albeit at lower amounts than MM cells, causing their unintended targeting by CD229 CAR T cells. To increase the selectivity of CD229 CAR T cells for MM cells, we used a single amino acid substitution approach of the CAR binding domain to reduce CAR affinity. To identify CARs with increased selectivity, we screened variant binding domains using solid-phase binding assays and biolayer interferometry and determined the cytotoxic activity of variant CAR T cells against MM cells and healthy lymphocytes. We identified a CD229 CAR binding domain with micromolar affinity that, when combined with overexpression of c-Jun, confers antitumor activity comparable to parental CD229 CAR T cells but lacks the parental cells' cytotoxic activity toward healthy lymphocytes in vitro and in vivo. The results represent a promising strategy to improve the efficacy and safety of CAR T cell therapy that requires clinical validation.

Selection of biophysically favorable antibody variants using a modified Flp-In CHO mammalian display platform

Mammalian display enables the selection of biophysically favorable antibodies from a large IgG antibody library displayed on the plasma membrane of mammalian cells. We constructed and validated a novel mammalian display platform utilizing the commercially available Flp-In CHO cell line as a starting point. We introduced a single copy of a landing pad for Bxb1 integrase-driven recombinase-mediated cassette exchange into the FRT site of the Flp-In CHO line to facilitate the efficient single-copy integration of an antibody display cassette into the genome of the cell line. We then proceeded to demonstrate the ability of our platform to select biophysically favorable antibodies from a library of 1 × 10⁶ displayed antibodies designed to improve the biophysical properties of bococizumab via randomization of problematic hydrophobic surface residues of the antibody. Enrichment of bococizumab variants via fluorescence-activated cell sorting selections was followed by next generation sequencing and thorough characterization of biophysical properties of 10 bococizumab variants that subsequently allowed attribution of the mutations to the biophysical properties of the antibody variants. The mammalian displayed variants exhibited reduced aggregation propensity and polyreactivity, while critically retaining its target binding thereby demonstrating the utility of this valuable tool.

Tuning charge density of chimeric antigen receptor optimizes tonic signaling and CAR-T cell fitness

Tonic signaling of chimeric antigen receptor (CAR), i.e., the spontaneous CAR activation in the absence of tumor antigen stimulation, is considered to be a pivotal event controlling CAR-T efficacy. However, the molecular mechanism underlying the spontaneous CAR signals remains elusive. Here, we unveil that positively charged patches (PCPs) on the surface of the CAR antigen-binding domain mediate CAR clustering and result in CAR tonic signaling. For CARs with high tonic signaling (e.g., GD2.CAR and CSPG4.CAR), reducing PCPs on CARs or boosting ionic strength in the culture medium during ex vivo CAR-T cell expansion minimizes spontaneous CAR activation and alleviates CAR-T cell exhaustion. In contrast, introducing PCPs into the CAR with weak tonic signaling, such as CD19.CAR, results in improved in vivo persistence and superior antitumor function. These results demonstrate that CAR tonic signaling is induced and maintained by PCP-mediated CAR clustering. Notably, the mutations we generated to alter the PCPs maintain the antigen-binding affinity and specificity of the CAR. Therefore, our findings suggest that the rational tuning of PCPs to optimize tonic signaling and in vivo fitness of CAR-T cells is a promising design strategy for the next-generation CAR.

Discovery and optimization of a broadly-neutralizing human monoclonal antibody against long-chain α-neurotoxins from snakes

Snakebite envenoming continues to claim many lives across the globe, necessitating the development of improved therapies. To this end, broadly-neutralizing human monoclonal antibodies may possess advantages over current plasma-derived antivenoms by offering superior safety and high neutralization capacity. Here, we report the establishment of a pipeline based on phage display technology for the discovery and optimization of high affinity broadly-neutralizing human monoclonal antibodies. This approach yielded a recombinant human antibody with superior broadly-neutralizing capacities in vitro and in vivo against different long-chain α-neurotoxins from elapid snakes. This antibody prevents lethality induced by Naja kaouthia whole venom at an unprecedented low molar ratio of one antibody per toxin and prolongs the survival of mice injected with Dendroaspis polylepis or Ophiophagus hannah whole venoms.

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.

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.

10th antibody industrial symposium: new developments in antibody and adoptive cell therapies

The annual "Antibody Industrial Symposium", co-organized by LabEx MAbImprove and MabDesign, held its 10th anniversary edition in Montpellier, France, on June 28-29, 2022. The meeting focused on new results and concepts in antibody engineering (naked, mono- or multi-specific, conjugated to drugs or radioelements) and also on new cell-based therapies, such as chimeric antigenic receptor (CAR)-T cells. The symposium, which brought together scientists from academia and industry, also addressed issues concerning the production of these molecules and cells, and the necessary steps to ensure a strong intellectual property protection of these new molecules and approaches. These two days of exchanges allowed a rich discussion among the various actors in the field of therapeutic antibodies.

Mammalian display to secretion switchable libraries for antibody preselection and high throughput functional screening

Recently, there has been a co-evolution of mammalian libraries and diverse microfluidic approaches for therapeutic antibody hit discovery. Mammalian libraries enable the preservation of full immune repertoires, produce hit candidates in final format and facilitate broad combinatorial bispecific antibody screening, while several available microfluidic methodologies offer opportunities for rapid high-content screens. Here, we report proof-of-concept studies exploring the potential of combining microfluidic technologies with mammalian libraries for antibody discovery. First, antibody secretion, target co-expression and integration of appropriate reporter cell lines enabled the selection of in-trans acting agonistic bispecific antibodies. Second, a functional screen for internalization was established and comparison of autocrine versus co-encapsulation setups highlighted the advantages of an autocrine one cell approach. Third, synchronization of antibody-secreting cells prior to microfluidic screens reduced assay variability. Furthermore, a display to secretion switchable system was developed and applied for pre-enrichment of antibody clones with high manufacturability in conjunction with subsequent screening for functional properties. These case studies demonstrate the system's feasibility and may serve as basis for further development of integrated workflows combining manufacturability sorting and functional screens for the identification of optimal therapeutic antibody candidates.

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.

Application of recombinant antibodies for treatment of Clostridioides difficile infection: Current status and future perspective

Clostridioides difficile (C. difficile), known as the major cause of antibiotic-associated diarrhea, is regarded as one of the most common healthcare-associated bacterial infections worldwide. Due to the emergence of hypervirulent strains, development of new therapeutic methods for C. difficile infection (CDI) has become crucially important. In this context, antibodies have been introduced as valuable tools in the research and clinical environments, as far as the effectiveness of antibody therapy for CDI was reported in several clinical investigations. Hence, production of high-performance antibodies for treatment of CDI would be precious. Traditional approaches of antibody generation are based on hybridoma technology. Today, application of in vitro technologies for generating recombinant antibodies, like phage display, is considered as an appropriate alternative to hybridoma technology. These techniques can circumvent the limitations of the immune system and they can be exploited for production of antibodies against different types of biomolecules in particular active toxins. Additionally, DNA encoding antibodies is directly accessible in in vitro technologies, which enables the application of antibody engineering in order to increase their sensitivity and specificity. Here, we review the application of antibodies for CDI treatment with an emphasis on recombinant fragment antibodies. Also, this review highlights the current and future prospects of the aforementioned approaches for antibody-mediated therapy of CDI.

Designing antibodies as therapeutics

Antibody therapeutics are a large and rapidly expanding drug class providing major health benefits. We provide a snapshot of current antibody therapeutics including their formats, common targets, therapeutic areas, and routes of administration. Our focus is on selected emerging directions in antibody design where progress may provide a broad benefit. These topics include enhancing antibodies for cancer, antibody delivery to organs such as the brain, gastrointestinal tract, and lungs, plus antibody developability challenges including immunogenicity risk assessment and mitigation and subcutaneous delivery. Machine learning has the potential, albeit as yet largely unrealized, for a transformative future impact on antibody discovery and engineering.

Development and characterization of a camelid derived antibody targeting a linear epitope in the hinge domain of human PCSK9 protein

PCSK9 is an effective target for lowering LDL-c. Previously, a camelid-human chimeric heavy chain antibody VHH-B11-Fc targeting human PCSK9 was designed. It had a potent hypolipidemic effect. However, the nanobody VHH-B11 interacts with PCSK9 at low affinity, while camelid VHH exhibits some immunogenicity. Moreover, the interacting epitope is yet to be identified, although VHH-B11 was shown to have distinct hPCSK9-binding epitopes for Evolocumab. This might impede the molecule’s progress from bench to bedside. In the present study, we designed various configurations to improve the affinity of VHH-B11 with hPCSK9 (< 10 nM) that in turn enhanced the druggability of VHH-B11-Fc. Then, 17 amino acids were specifically mutated to increase the degree of humanization of the nanobody VHH-B11. Using phage display and sequencing technology, the linear epitope “STHGAGW” (amino acids 447–452) was identified in the hinge region of PCSK9 as the interacting site between VHH-B11-Fc and hPCSK9. Unlike the interaction epitope of Evolocumab, located in the catalytic region of PCSK9, the binding epitope of VHH-B11 is located in the hinge region of PCSK9, which is rarely reported. These findings indicated that a specific mechanism underlying this interaction needs to be explored.

Understanding and Modulating Antibody Fine Specificity: Lessons from Combinatorial Biology

Combinatorial biology methods such as phage and yeast display, suitable for the generation and screening of huge numbers of protein fragments and mutated variants, have been useful when dissecting the molecular details of the interactions between antibodies and their target antigens (mainly those of protein nature). The relevance of these studies goes far beyond the mere description of binding interfaces, as the information obtained has implications for the understanding of the chemistry of antibody–antigen binding reactions and the biological effects of antibodies. Further modification of the interactions through combinatorial methods to manipulate the key properties of antibodies (affinity and fine specificity) can result in the emergence of novel research tools and optimized therapeutics.

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

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

Periodontal ligament cells under mechanical force regulate local immune homeostasis by modulating Th17/Treg cell differentiation

OBJECTIVES

Improper orthodontic force often causes root resorption or destructive bone resorption. There is evidence that T helper 17 (Th17) cells and regulatory T (Treg) cells may be actively involved in bone remodeling during tooth movement. In a combination of in vitro and in vivo studies, we investigated the effect of human periodontal ligament cells (hPDLCs) on Th17/Treg cells under different orthodontic forces and corticotomy.

MATERIAL AND METHODS

hPDLCs were cultured in vitro and subjected to different mechanical forces. The expression of interleukin (IL)-6 and transforming growth factor (TGF)-β in the supernatant and the mRNA levels of hypoxia inducible factor (HIF)-1α, Notch1, and TGF-β in hPDLCs were investigated. Supernatants were collected and co-cultured with activated CD4⁺T cells, and the differentiation of Th17/Treg cells was analyzed by flow cytometry. We also established an animal model of tooth movement with or without corticotomy. The tooth movement distance, alveolar bone height, and root resorption were analyzed using micro-computed tomography. Expression of interleukin (IL)-17A, forkhead Box P3 (Foxp3), and IL-6 were analyzed using immunohistochemistry, while osteoclasts were evaluated by tartrate-resistant acid phosphatase (TRAP) staining. The mRNA levels of IL-17A, IL-6, Foxp3, IL-10, HIF-1α, notch1, and C-X-C motif chemokine ligand 12 (CXCL12) in alveolar bone and gingiva were investigated.

RESULTS

Heavy force repressed cell viability and increased the mortality rate of hPDLCs; it also improved the expression of IL-6, declined the expression of TGF-β, and promoted the mRNA expression level of HIF-1α. The expression of TGF-β and Notch1 mRNA decreased and then increased. The supernatant of hPDLCs under heavy force promotes the polarization of Th17 cells. The heavy force caused root resorption and decreased alveolar bone height and increased the positive area of IL-17A immunohistochemical staining and the expression of IL-17A, IL-6, HIF-1α, and Notch1 mRNA. Corticotomy accelerated tooth movement, increased the proportion of Foxp3-positive cells, and up-regulated the expression of Foxp3, IL-10, and CXCL12 mRNA.

CONCLUSIONS

During orthodontic tooth movement, the heavy force causes root resorption and inflammatory bone destruction, which could be associated with increased expression of Th17 cells and IL-6. Corticotomy can accelerate tooth movement without causing root resorption and periodontal bone loss, which may be related to the increased expression of Treg cells.

CLINICAL RELEVANCE

Altogether, this report provides a new perspective on the prevention of inflammatory injury via the regulation of Th17/Treg cells in orthodontics.

Reduction of therapeutic antibody self-association using yeast-display selections and machine learning

Self-association governs the viscosity and solubility of therapeutic antibodies in high-concentration formulations used for subcutaneous delivery, yet it is difficult to reliably identify candidates with low self-association during antibody discovery and early-stage optimization. Here, we report a high-throughput protein engineering method for rapidly identifying antibody candidates with both low self-association and high affinity. We find that conjugating quantum dots to IgGs that strongly self-associate (pH 7.4, PBS), such as lenzilumab and bococizumab, results in immunoconjugates that are highly sensitive for detecting other high self-association antibodies. Moreover, these conjugates can be used to rapidly enrich yeast-displayed bococizumab sub-libraries for variants with low levels of immunoconjugate binding. Deep sequencing and machine learning analysis of the enriched bococizumab libraries, along with similar library analysis for antibody affinity, enabled identification of extremely rare variants with co-optimized levels of low self-association and high affinity. This analysis revealed that co-optimizing bococizumab is difficult because most high-affinity variants possess positively charged variable domains and most low self-association variants possess negatively charged variable domains. Moreover, negatively charged mutations in the heavy chain CDR2 of bococizumab, adjacent to its paratope, were effective at reducing self-association without reducing affinity. Interestingly, most of the bococizumab variants with reduced self-association also displayed improved folding stability and reduced nonspecific binding, revealing that this approach may be particularly useful for identifying antibody candidates with attractive combinations of drug-like properties.Abbreviations: AC-SINS: affinity-capture self-interaction nanoparticle spectroscopy; CDR: complementarity-determining region; CS-SINS: charge-stabilized self-interaction nanoparticle spectroscopy; FACS: fluorescence-activated cell sorting; Fab: fragment antigen binding; Fv: fragment variable; IgG: immunoglobulin; QD: quantum dot; PBS: phosphate-buffered saline; V_H: variable heavy; V_L: variable light.

In vitro discovery of a human monoclonal antibody that neutralizes lethality of cobra snake venom

The monocled cobra (Naja kaouthia) is among the most feared snakes in Southeast Asia due to its toxicity, which is predominantly derived from long-chain α-neurotoxins. The only specific treatment for snakebite envenoming is antivenom based on animal-derived polyclonal antibodies. Despite the lifesaving importance of these medicines, major limitations in safety, supply consistency, and efficacy create a need for improved treatments. Here, we describe the discovery and subsequent optimization of a recombinant human monoclonal immunoglobulin G antibody against α-cobratoxin using phage display technology. Affinity maturation by light chain-shuffling resulted in a significant increase in in vitro neutralization potency and in vivo efficacy. The optimized antibody prevented lethality when incubated with N. kaouthia whole venom prior to intravenous injection. This study is the first to demonstrate neutralization of whole snake venom by a single recombinant monoclonal antibody, thus providing a tantalizing prospect of bringing recombinant antivenoms based on human monoclonal or oligoclonal antibodies to the clinic.

Deep mutational scanning for therapeutic antibody engineering

The biophysical and functional properties of monoclonal antibody (mAb) drug candidates are often improved by protein engineering methods to increase the probability of clinical efficacy. One emerging method is deep mutational scanning (DMS) which combines the power of exhaustive protein mutagenesis and functional screening with deep sequencing and bioinformatics. The application of DMS has yielded significant improvements to the affinity, specificity, and stability of several preclinical antibodies alongside novel applications such as introducing multi-specific binding properties. DMS has also been applied directly on target antigens to precisely map antibody-binding epitopes and notably to profile the mutational escape potential of viral targets (e.g., SARS-CoV-2 variants). Finally, DMS combined with machine learning is enabling advances in the computational screening and engineering of therapeutic antibodies.

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.

Structure-based charge calculations for predicting isoelectric point, viscosity, clearance, and profiling antibody therapeutics

The effect of hydrophobicity on antibody aggregation is well understood, and it has been shown that charge calculations can be useful for high-concentration viscosity and pharmacokinetic (PK) clearance predictions. In this work, structure-based charge descriptors are evaluated for their predictive performance on recently published antibody pI, viscosity, and clearance data. From this, we devised four rules for therapeutic antibody profiling which address developability issues arising from hydrophobicity and charged-based solution behavior, PK, and the ability to enrich for those that are approved by the U.S. Food and Drug Administration. Differences in strategy for optimizing the solution behavior of human IgG1 antibodies versus the IgG2 and IgG4 isotypes and the impact of pH alterations in formulation are discussed.

An end-to-end automated platform process for high-throughput engineering of next-generation multi-specific antibody therapeutics

Next-generation multi-specific antibody therapeutics (MSATs) are engineered to combine several functional activities into one molecule to provide higher efficacy compared to conventional, mono-specific antibody therapeutics. However, highly engineered MSATs frequently display poor yields and less favorable drug-like properties (DLPs), which can adversely affect their development. Systematic screening of a large panel of MSAT variants in very high throughput (HT) is thus critical to identify potent molecule candidates with good yield and DLPs early in the discovery process. Here we report on the establishment of a novel, format-agnostic platform process for the fast generation and multiparametric screening of tens of thousands of MSAT variants. To this end, we have introduced full automation across the entire value chain for MSAT engineering. Specifically, we have automated the in-silico design of very large MSAT panels such that it reflects precisely the wet-lab processes for MSAT DNA library generation. This includes mass saturation mutagenesis or bulk modular cloning technologies while, concomitantly, enabling library deconvolution approaches using HT Sanger DNA sequencing. These DNA workflows are tightly linked to fully automated downstream processes for compartmentalized mammalian cell transfection expression, and screening of multiple parameters. All sub-processes are seamlessly integrated with tailored workflow supporting bioinformatics. As described here, we used this platform to perform multifactor optimization of a next-generation bispecific, cross-over dual variable domain-Ig (CODV-Ig). Screening of more than 25,000 individual protein variants in mono- and bispecific format led to the identification of CODV-Ig variants with over 1,000-fold increased potency and significantly optimized production titers, demonstrating the power and versatility of the platform.

Animal Immunization, in Vitro Display Technologies, and Machine Learning for Antibody Discovery

For years, a discussion has persevered on the benefits and drawbacks of antibody discovery using animal immunization versus in vitro selection from non-animal-derived recombinant repertoires using display technologies. While it has been argued that using recombinant display libraries can reduce animal consumption, we hold that the number of animals used in immunization campaigns is dwarfed by the number sacrificed during preclinical studies. Thus, improving quality control of antibodies before entering in vivo studies will have a larger impact on animal consumption. Both animal immunization and recombinant repertoires present unique advantages for discovering antibodies that are fit for purpose. Furthermore, we anticipate that machine learning will play a significant role within discovery workflows, refining current antibody discovery practices.

Antibody display technologies: selecting the cream of the crop

Antibody display technologies enable the successful isolation of antigen-specific antibodies with therapeutic potential. The key feature that facilitates the selection of an antibody with prescribed properties is the coupling of the protein variant to its genetic information and is referred to as genotype phenotype coupling. There are several different platform technologies based on prokaryotic organisms as well as strategies employing higher eukaryotes. Among those, phage display is the most established system with more than a dozen of therapeutic antibodies approved for therapy that have been discovered or engineered using this approach. In recent years several other technologies gained a certain level of maturity, most strikingly mammalian display. In this review, we delineate the most important selection systems with respect to antibody generation with an emphasis on recent developments.

A single donor is sufficient to produce a highly functional in vitro antibody library

Antibody complementarity determining region diversity has been considered to be the most important metric for the production of a functional antibody library. Generally, the greater the antibody library diversity, the greater the probability of selecting a diverse array of high affinity leads. According to this paradigm, the primary means of elevating library diversity has been by increasing the number of donors. In the present study we explored the possibility of creating an in vitro antibody library from a single healthy individual, showing that the number of lymphocytes, rather than the number of donors, is the key criterion in the production of a diverse and functional antibody library. We describe the construction of a high-quality phage display library comprising 5 × 10⁹ human antibodies by applying an efficient B cell extraction protocol from a single donor and a targeted V-gene amplification strategy favoring specific antibody families for their improved developability profiles. Each step of the library generation process was followed and validated by next generation sequencing to monitor the library quality and diversity. The functionality of the library was tested using several therapeutically relevant targets for which a vast number of different antibodies with desired biophysical properties were obtained.

Versatile and rapid microfluidics-assisted antibody discovery

Recent years have seen unparalleled development of microfluidic applications for antibody discovery in both academic and pharmaceutical research. Microfluidics can support native chain-paired library generation as well as direct screening of antibody secreting cells obtained by rodent immunization or from the human peripheral blood. While broad diversities of neutralizing antibodies against infectious diseases such as HIV, Ebola, or COVID-19 have been identified from convalescent individuals, microfluidics can expedite therapeutic antibody discovery for cancer or immunological disease indications. In this study, a commercially available microfluidic device, Cyto-Mine, was used for the rapid identification of natively paired antibodies from rodents or human donors screened for specific binding to recombinant antigens, for direct screening with cells expressing the target of interest, and, to our knowledge for the first time, for direct broad functional IgG antibody screening in droplets. The process time from cell preparation to confirmed recombinant antibodies was four weeks. Application of this or similar microfluidic devices and methodologies can accelerate and enhance pharmaceutical antibody hit discovery.

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.