Predicting Antibody Developability Profiles Through Early Stage Discovery Screening

An antibody developability triaging pipeline exploiting protein language models

Therapeutic monoclonal antibodies (mAbs) are a successful class of biologic drugs that are frequently selected from phage display libraries and transgenic mice that produce fully human antibodies. However, binding affinity to the correct epitope is necessary, but not sufficient, for a mAb to have therapeutic potential. Sequence and structural features affect the developability of an antibody, which influences its ability to be produced at scale and enter trials, or can cause late-stage failures. Using data on paired human antibody sequences, we introduce a pipeline using a machine learning approach that exploits protein language models to identify antibodies which cluster with antibodies that have entered the clinic and are therefore expected to have developability features similar to clinically acceptable antibodies, and triage out those without these features. We propose this pipeline as a useful tool in candidate selection from large libraries, reducing the cost of exploration of the antibody space, and pursuing new therapeutics.

Understanding IgM Structure and Biology to Engineer New Antibody Therapeutics

Immunoglobulin M (IgM) antibodies are an essential and conserved part of adaptive immunity. IgMs assemble into pentamers and hexamers that bind to antigens with high avidity. Pentamers incorporate a small protein called J-chain (JC) that is important for their transcytosis via the poly-immunoglobulin receptor (pIgR). IgM antibodies can efficiently activate complement and interact with different Fc receptors (FcμR, Fcα/μR, pIgR) that trigger distinct effector functions and biodistribution. Even if these features have made the clinical use of IgM attractive over the past decades, there are currently no approved therapeutic IgMs on the market. In this review, we summarize the recent advances in the knowledge of IgM biogenesis and structure and discuss the therapeutic opportunities of IgM over IgG arising from high avidity, target clustering, binding to distinct IgM receptors, complement activation, transcytosis, and protein engineering opportunities. In addition, we summarize possibilities and outstanding challenges in the production of therapeutic IgM, including available technologies for IgM purification. Finally, we review recent preclinical and clinical data showing that IgM outperforms IgG in various in vitro assays but still fails to pass through clinical trials successfully. Challenges remain for IgM development, such as the need for a better understanding of IgM biology to facilitate a smoother transition from the preclinic to successful clinical trials.

Integration of QSAR models with high throughput screening to accelerate the development of polishing chromatography unit operations

The development of robust polishing chromatographic processes is a critical step in downstream bioprocess development that can be time-consuming and resource intensive. Recently, there has been an increase in diverse protein constructs that are not amenable to platform approaches, increasing the need for novel processes to be developed for effective purification. High throughput screening (HTS) is an important tool to parse chromatographic design space and identify promising conditions to continue development. Despite its utility, HTS capabilities are challenged by tight development timelines, material scarcity, and an increasingly complex pipeline of biotherapeutics. Predictive modeling can augment HTS by leveraging historical screening data to rapidly explore and prioritize process design space, effectively expanding the range of conditions considered without the need for additional experimental screening. Here we present the development of a quantitative structure activity relationship (QSAR) model, trained from internal HTS data, that predicts protein partitioning as a function of resin and mobile phase conditions. The training dataset contains a diverse collection of screening data and has more than 8000 datapoints, covering 29 therapeutic proteins and 44 resins. The model encodes partitioning by building descriptors of the mobile phase, parameters that describe the resin, and biophysical properties of the protein. Overall, the regression model has an R2=0.92 and shows 95% and 93% classification accuracy for predicting elution and strong binding conditions, respectively. Here, we highlight the model predictiveness and describe how in silico screening can be used as a first step in the HTS workflow to reduce design space and accelerate process development.

Prediction of Self-Association and Solution Behavior of Monoclonal Antibodies Using the QCM-D Metric of Loosely Interacting Layer

Despite the increasing availability and success of monoclonal antibodies (mAb), early identification of candidate molecules with desirable developability attributes remains challenging due to self-association and poor solution behavior. Measuring these phenomena experimentally using the available methods is complicated in mAbs development. Quartz crystal microbalance with dissipation monitoring (QCM-D) detects a loosely interacting layer on top of the irreversibly adsorbed layer of molecules, providing information about the mAbs interaction. This work aimed to explore whether the characteristics of this layer can be used as a reliable self-association metric. QCM-D experiments showed a large frequency shift (Δf) associated with loosely interacting layers for omalizumab but a small or absent layer for tocilizumab. Accordingly, the viscosity of omalizumab increased exponentially at high concentrations compared to tocilizumab. Testing eight mAbs with different self-association behaviors revealed a strong rank order correlation between the mostly used metric of self-association, i.e., diffusion interaction parameter (kD-DLS), and Δf, indicating Δf's potential for predicting mAb solution behavior. The study also highlighted the robustness of the metric to impurities and temperature variations compared to the sensitive kD-DLS. Overall, we demonstrate that the loosely interacting layer provides valuable information about mAb self-association, predicting the colloidal stability and solution behavior in therapeutic development.

Flycodes enable simultaneous preclinical analysis for dozens of antibodies in single cassette-dosed mice

Protein therapeutics such as antibodies require in-depth in vivo characterization during development and consequently account for a large proportion of laboratory animal consumption in the pharmaceutical industry. Currently, antibody candidates are exhaustively tested one-by-one in animal models to determine pharmacokinetic and pharmacodynamic (PK/PD) profiles. The simultaneous analysis of antibody mixtures in single animals, called cassette-dosing, could in principle overcome this bottleneck, but is currently limited to small cassette sizes. Here, we demonstrate how the use of genetically encoded peptide tags (flycodes), designed for maximal detectability in liquid chromatography-mass spectrometry, can allow for the simultaneous characterization of large pools of drug candidates, from single cassette-dosed mice. We demonstrate the simultaneous assessment of PK parameters for a group of >20 marketed/development-stage antibodies. Biodistribution experiments in mice bearing EGFR-expressing tumors correctly identified the two pool members recognizing EGFR, while organ analysis registered liver accumulation of an antibody targeting glucagon receptor, a protein profoundly expressed in that organ. In analogy to an early-phase drug development campaign, we performed biophysical and PK analysis for a cassette of 80 unique bispecific DARPin-sybody molecules. The data shown in this study originate from only 18 cassette-dosed mice, thereby demonstrating how flycode technology efficiently advances preclinical discovery pipelines allowing a direct comparison of drug candidates under identical experimental conditions.

Trends and challenges in bispecific antibody production

Bispecific antibodies (bsAbs) represent a rapidly growing field of therapeutic agents. More bsAbs are being approved worldwide and are in various stages of clinical trials. However, the discovery and production of novel bsAbs presents significant challenges due to their complex structure. Thus, precise control of assembly and stability is required, given the many formats developed. This review examines recent trends in bsAb production, focusing on advancements in engineering platforms, production strategies, and challenges in large-scale manufacturing. Key developments include improvements in modular antibody design, novel expression systems, and optimization of bioprocessing techniques to enhance stability, yield, and efficacy. Additionally, the article explores the future potential of bsAbs as next-generation therapeutics, underscoring the growing impact of these innovations on expanding treatment options for patients with unmet medical needs.

Rapid In Vivo Screening of Monoclonal Antibody Cocktails Using Hydrodynamic Delivery of DNA-Encoded Modified Antibodies

Background: Monoclonal antibodies (mAbs) are potent treatment options for infectious diseases. The rapid isolation and in vivo validation of therapeutic mAb candidates, including mAb cocktails, are essential to combat novel or rapidly mutating pathogens. The rapid selection and production of mAb candidates in sufficient amount and quality for preclinical studies are a major limiting step in the mAb development pipeline. Methods: Here, we developed a method to facilitate the screening of therapeutic mAbs in mouse models. Four conventional mAbs were transformed into single-chain variable fragments fused to the fragment crystallizable (Fc) region of a human IgG1 (scFv-IgG). These scFv-IgG were expressed individually or as a cocktail in vitro and in mice following transfection or hydrodynamic delivery of the corresponding plasmids. Results: This method induced high expression of all scFv-IgG and provided protection in two murine infection models. Conclusions: This study highlights the benefits of this approach for the rapid, low-cost screening of therapeutic mAb candidates.

Large-scale transcriptomics analysis reveals a novel stress biomarker in CHO cells producing difficult to express mAbs

Monoclonal antibodies (mAbs) are considered one of the most game-changing products of the biopharmaceutical industry. The introduction of several diverse and complex formats consisting of several polypeptide chains and engineered with multiple antigen-binding domains has made the manufacturability process particularly challenging, especially in the context of assessing expression levels and yields of the formats. Here we present the largest and most diversified CHO transcriptomics analysis consisting of data derived from 892 different monoclonal cell lines, producing 11 different mAbs with various non-standard, highly complex formats. We apply three robust feature selection methods, one traditional differential expression analysis and two machine learning approaches to identify genes correlated to high product titer and quality. Cnpy3 gene is identified as a novel gene biomarker, showing a very strong negative correlation (Pearson r2 = 0.94) to the overall format productivity. These results were validated by a hold-out data set from cell lines expressing two different antibody formats. Additionally, the expression of Cnpy3 gene is positively correlated to the structural complexity of the examined mAbs. As complexity increases, cellular stress escalates leading to reduced productivity, implicating Cnpy3 as a strong CHO cell lines stress indicator. Thus, we conclude that Cnpy3 gene has the potential to be used as a screening biomarker for assessing format manufacturability and selecting formats and pools with a high potential to deliver subsequent higher productivity rates, resulting in a substantially smarter cell line and process development.

Structure and sequence engineering approaches to improve in vivo expression of nucleic acid-delivered antibodies

Monoclonal antibodies are an important class of biologics with over 160 Food and Drug Administration/European Union-approved drugs. A significant bottleneck to global accessibility of recombinant monoclonal antibodies stems from complexities related to their production, storage, and distribution. Recently, gene-encoded approaches such as mRNA, DNA, or viral delivery have gained popularity, but ensuring biologically relevant levels of antibody expression in the host remains a critical issue. Using a synthetic DNA platform, we investigated the role of antibody structure and sequence toward in vivo expression. SARS-CoV-2 antibody 2196 was recently engineered as a DNA-encoded monoclonal antibody (DMAb-2196). Utilizing an immunoglobulin heavy and light chain "chain-swap" methodology, we interrogated features of DMAb-2196 that can modulate in vivo expression through rational design and structural modeling. Comparing these results to natural variation of antibody sequences resulted in development of an antibody frequency score that aids in the prediction of expression-improving mutations by leveraging antibody repertoire datasets. We demonstrate that a single amino acid mutation identified through this score increases in vivo expression up to 2-fold and that combinations of mutations can also enhance expression. This analysis has led to a generalized pipeline that can unlock the potential for in vivo delivery of therapeutic antibodies across many indications.

Hybrid Mass Spectrometry Applied across the Production of Antibody Biotherapeutics

Post expression from the host cells, biotherapeutics undergo downstream processing steps before final formulation. Mass spectrometry and biophysical characterization methods are valuable for examining conformational and stoichiometric changes at these stages, although typically not used in biomanufacturing, where stability is assessed via bulk property studies. Here we apply hybrid MS methods to understand how solution condition changes impact the structural integrity of a biopharmaceutical across the processing pipeline. As an exemplar product, we use the model IgG1 antibody, mAb4. Flexibility, stability, aggregation propensity, and bulk properties are evaluated in relation to perfusion media, purification stages, and formulation solutions. Comparisons with Herceptin, an extensively studied IgG1 antibody, were conducted in a mass spectrometry-compatible solution. Despite presenting similar charge state distributions (CSD) in native MS, mAb4, and Herceptin show distinct unfolding patterns in activated ion mobility mass spectrometry (aIM-MS) and differential scanning fluorimetry (DSF). Herceptin's greater structural stability and aggregation onset temperature (Tagg) are attributed to heavier glycosylation and kappa-class light chains, unlike the lambda-class light chains in mAb4. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) revealed that mAb4 undergoes substantial structural changes during purification, marked by high flexibility, low melting temperature (Tm), and prevalent repulsive protein-protein interactions but transitions to a compact and stable structure in high-salt and formulated environments. Notably, in formulation, the third constant domain (CH3) of the heavy chain retains flexibility and is a region of interest for aggregation. Future work could translate features of interest from comprehensive studies like this to targeted approaches that could be utilized early in the development stage to aid in decision-making regarding targeted mutations or to guide the design space of bioprocesses and formulation choices.

A framework for the biophysical screening of antibody mutations targeting solvent-accessible hydrophobic and electrostatic patches for enhanced viscosity profiles

The formulation of high-concentration monoclonal antibody (mAb) solutions in low dose volumes for autoinjector devices poses challenges in manufacturability and patient administration due to elevated solution viscosity. Often many therapeutically potent mAbs are discovered, but their commercial development is stalled by unfavourable developability challenges. In this work, we present a systematic experimental framework for the computational screening of molecular descriptors to guide the design of 24 mutants with modified viscosity profiles accompanied by experimental evaluation. Our experimental observations using a model anti-IL8 mAb and eight engineered mutant variants reveal that viscosity reduction is influenced by the location of hydrophobic interactions, while targeting positively charged patches significantly increases viscosity in comparison to wild-type anti-IL-8 mAb. We conclude that most predicted in silico physicochemical properties exhibit poor correlation with measured experimental parameters for antibodies with suboptimal developability characteristics, emphasizing the need for comprehensive case-by-case evaluation of mAbs. This framework combining molecular design and triage via computational predictions with experimental evaluation aids the agile and rational design of mAbs with tailored solution viscosities, ensuring improved manufacturability and patient convenience in self-administration scenarios.

Predictive Model Building for Aggregation Kinetics Based on Molecular Dynamics Simulations of an Antibody Fragment

Computational methods including machine learning and molecular dynamics simulations have strong potential to characterize, understand, and ultimately predict the properties of proteins relevant to their stability and function as therapeutics. Such methods would streamline the development pathway by minimizing the current experimental testing required for many protein variants and formulations. The molecular understanding of thermostability and aggregation propensity has advanced significantly along with predictive algorithms based on the sequence-level or structural-level information on a protein. However, these approaches focus largely on a comparison of protein sequence variations to correlate the properties of proteins to their stability, solubility, and aggregation propensity. For therapeutic protein development, it is of equal importance to take into account the impact of the formulation conditions to elucidate and predict the stability of the antibody drugs. At the macroscopic level, changing temperature, pH, ionic strength, and the addition of excipients can significantly alter the kinetics of protein aggregation. The mechanisms controlling aggregation kinetics have been traced back to a combination of molecular features, including conformational stability, partial unfolding to aggregation-prone states, and the colloidal stability governed by surface charges and hydrophobicity. However, very little has been done to evaluate these features in the context of protein dynamics in different formulations. In this work, we have combined a range of molecular features calculated from the Fab A33 protein sequence and molecular dynamics simulations. Using the power of advanced, yet interpretable, statistical tools, it has been possible to uncover greater insights into the mechanisms behind protein stability, validating previous findings, and also develop models that can predict the aggregation kinetics within a range of 49 different solution conditions.

Molecular Dynamics Simulations Reveal How Competing Protein-Surface Interactions for Glycine, Citrate, and Water Modulate Stability in Antibody Fragment Formulations

The design of stable formulations remains a major challenge for protein therapeutics, particularly the need to minimize aggregation. Experimental formulation screens are typically based on thermal transition midpoints (Tm), and forced degradation studies at elevated temperatures. Both approaches give limited predictions of long-term storage stability, particularly at low temperatures. Better understanding of the mechanisms of action for formulation of excipients and buffers could lead to improved strategies for formulation design. Here, we identified a complex impact of glycine concentration on the experimentally determined stability of an antibody Fab fragment and then used molecular dynamics simulations to reveal mechanisms that underpin these complex behaviors. Tm values increased monotonically with glycine concentration, but associated ΔSvh measurements revealed more complex changes in the native ensemble dynamics, which reached a maximum at 30 mg/mL. The aggregation kinetics at 65 °C were similar at 0 and 20 mg/mL glycine, but then significantly slower at 50 mg/mL. These complex behaviors indicated changes in the dominant stabilizing mechanisms as the glycine concentration was increased. MD revealed a complex balance of glycine self-interaction, and differentially preferred interactions of glycine with the Fab as it displaced hydration-shell water, and surface-bound water and citrate buffer molecules. As a result, glycine binding to the Fab surface had different effects at different concentrations, and led from preferential interactions at low concentrations to preferential exclusion at higher concentrations. During preferential interaction, glycine displaced water from the Fab hydration shell, and a small number of water and citrate molecules from the Fab surface, which reduced the protein dynamics as measured by root-mean-square fluctuation (RMSF) on the short time scales of MD. By contrast, the native ensemble dynamics increased according to ΔSvh, suggesting increased conformational changes on longer time scales. The aggregation kinetics did not change at low glycine concentrations, and so the opposing dynamics effects either canceled out or were not directly relevant to aggregation. During preferential exclusion at higher glycine concentrations, glycine could only bind to the Fab surface through the displacement of citrate buffer molecules already favorably bound on the Fab surface. Displacement of citrate increased the flexibility (RMSF) of the Fab, as glycine formed fewer bridging hydrogen bonds to the Fab surface. Overall, the slowing of aggregation kinetics coincided with reduced flexibility in the Fab ensemble at the very highest glycine concentrations, as determined by both RMSF and ΔSvh, and occurred at a point where glycine binding displaced neither water nor citrate. These final interactions with the Fab surface were driven by mass action and were the least favorable, leading to a macromolecular crowding effect under the regime of preferential exclusion that stabilized the dynamics of Fab.

Thermal and pH stress dictate distinct mechanisms of monoclonal antibody aggregation

Protein aggregation is a significant challenge in the development of monoclonal antibodies (mAbs), which can be exacerbated by stress conditions encountered along its production pipeline. In this study, we examine how thermal and pH stress conditions influence mAb aggregation mechanisms. We observe a complex interplay between these factors that significantly affects mAb stability, particularly under combined stress conditions. The mAb aggregates formed also varied distinctly in size and properties depending on the pH and thermal conditions, suggesting differences in their underlying mechanisms. Using a combination of experimental methods and kinetic modelling, we found that acidic pH conditions primarily promoted aggregation via the mAb unfolding step, while higher temperature conditions facilitated the formation of larger aggregates via monomer-independent cluster-cluster aggregation steps. These insights underscore the importance of extrinsic stress conditions in determining mAb aggregation propensity, and potentially provides a quantitative framework to holistically assess this across various accelerated stress conditions for the development of stable biologics.

A comprehensive engineering strategy improves potency and manufacturability of a near pan-neutralizing antibody against HIV

Anti-HIV envelope broadly neutralizing antibodies (bnAbs) are alternatives to conventional antiretrovirals with the potential to prevent and treat infection, reduce latent reservoirs, and/or mediate a functional cure. Clinical trials with "first generation" bnAbs used alone or in combination show promising antiviral effects but also highlight that additional engineering of "enhanced" antibodies will be required for optimal clinical utility, while preserving or enhancing cGMP manufacturing capability. Here we report the engineering of an anti-CD4 binding-site (CD4bs) bnAb, N49P9.3, purified from the plasma of an HIV elite-neutralizer. Through a series of rational modifications we produced a variant that demonstrates: enhanced potency; superior antiviral activity in combination with other bnAbs; low polyreactivity; and longer circulating half-life. Additional engineering for manufacturing produced a final variant, eN49P9, with properties conducive to cGMP production. Overall, these efforts demonstrate the feasibility of developing enhanced anti-CD4bs bnAbs with greatly improved antiviral properties as well as potential translational value.

Engineering and physicochemical characterization of a novel, stable, symmetric bispecific antibody with dual target-binding using a common light chain

Bispecific antibodies (BsAbs) have emerged as a major class of antibody therapeutics owing to their substantial potential in disease treatment. While several BsAbs have been successfully approved in recent years, ongoing development efforts continue to focus on optimizing various BsAbs tailored to particular antigens and action mechanisms, aiming to achieve favorable physicochemical properties. BsAbs generally encounter challenges due to their unfavorable physicochemical characteristics and poor manufacturing efficiencies, highlighting the need for optimization to achieve reliable productivity and developability. Herein, we describe the development of a novel symmetric BsAb, REGULGENT™ (N-term/C-term), comprising two Fab domains, using a common light chain. The heavy chain fragment encoded two antigen-binding determinants in one chain. The design and production of REGULGENT™ (N-term/C-term) are simple owing to the use of the same light chain, which does not induce heavy and light chain mispairing, frequently observed with the asymmetric BsAb format. REGULGENT™ (N-term/C-term) exhibited high expression and low aggregation characteristics during cell culture and stress treatment under low pH conditions. Differential scanning calorimetric data indicated that REGULGENT™ molecules had high conformational stability, similar to that of stabilized monoclonal antibodies. Surface plasmon resonance data showed that REGULGENT™ (N-term/C-term) could bind to two antigens simultaneously and exhibited a high affinity for two antigens. In summary, the symmetric BsAb format of REGULGENT™ confers its desirable IgG-like physicochemical properties, thus making it an excellent candidate for commercial development. The findings demonstrate a novel BsAb with substantial development potential for clinical applications.

AI-based antibody discovery platform identifies novel, diverse, and pharmacologically active therapeutic antibodies against multiple SARS-CoV-2 strains

Background

We are entering a new era of antibody discovery and optimization where machine learning (ML) processes will become indispensable for the design and development of therapeutics.

Methods

We have constructed a Humanoid Antibody Library for the discovery of therapeutics that is an initial step towards leveraging the utility of artificial intelligence and ML. We describe how we began our validation of the library for antibody discovery by isolating antibodies against a target of pandemic concern, SARS-CoV-2. The two main antibody quality aspects that we focused on were functional and biophysical characterization.

Results

The applicability of our platform for effective therapeutic antibody discovery is demonstrated here with the identification of a panel of human monoclonal antibodies that are novel, diverse, and pharmacologically active.

Conclusions

These first-generation antibodies, without the need for affinity maturation, exhibited neutralization of SARS-CoV-2 viral infectivity across multiple strains and indicated high developability potential.

Advancements in mammalian display technology for therapeutic antibody development and beyond: current landscape, challenges, and future prospects

The evolving development landscape of biotherapeutics and their growing complexity from simple antibodies into bi- and multi-specific molecules necessitates sophisticated discovery and engineering platforms. This review focuses on mammalian display technology as a potential solution to the pressing challenges in biotherapeutic development. We provide a comparative analysis with established methodologies, highlighting key aspects of mammalian display technology, including genetic engineering, construction of display libraries, and its pivotal role in hit selection and/or developability engineering. The review delves into the mechanisms underpinning developability-driven selection via mammalian display and their broader implications. Applications beyond antibody discovery are also explored, alongside advancements towards function-first screening technologies, precision genome engineering and AI/ML-enhanced libraries, situating them in the context of mammalian display. Overall, the review provides a comprehensive overview of the current mammalian display technology landscape, underscores the expansive potential of the technology for biotherapeutic development, addresses the critical challenges for the full realisation of this potential, and examines advances in related disciplines that might impact the future application of mammalian display technologies.

AbMelt: Learning antibody thermostability from molecular dynamics

Antibody thermostability is challenging to predict from sequence and/or structure. This difficulty is likely due to the absence of direct entropic information. Herein, we present AbMelt where we model the inherent flexibility of homologous antibody structures using molecular dynamics simulations at three temperatures and learn the relevant descriptors to predict the temperatures of aggregation (Tagg), melt onset (Tm,on), and melt (Tm). We observed that the radius of gyration deviation of the complementarity determining regions at 400 K is the highest Pearson correlated descriptor with aggregation temperature (rp = -0.68 ± 0.23) and the deviation of internal molecular contacts at 350 K is the highest correlated descriptor with both Tm,on (rp = -0.74 ± 0.04) as well as Tm (rp = -0.69 ± 0.03). Moreover, after descriptor selection and machine learning regression, we predict on a held-out test set containing both internal and public data and achieve robust performance for all endpoints compared with baseline models (Tagg R2 = 0.57 ± 0.11, Tm,on R2 = 0.56 ± 0.01, and Tm R2 = 0.60 ± 0.06). In addition, the robustness of the AbMelt molecular dynamics methodology is demonstrated by only training on <5% of the data and outperforming more traditional machine learning models trained on the entire data set of more than 500 internal antibodies. Users can predict thermostability measurements for antibody variable fragments by collecting descriptors and using AbMelt, which has been made available.

Elucidation of the Reversible Self-Association Interface of a Diabody-Interleukin Fusion Protein Using Hydrogen-Exchange Mass Spectrometry and In Silico Modeling

Reversible self-association (RSA) of therapeutic proteins presents major challenges in the development of high-concentration formulations, especially those intended for subcutaneous administration. Understanding self-association mechanisms is therefore critical to the design and selection of candidates with acceptable developability to advance to clinical trials. The combination of experiments and in silico modeling presents a powerful tool to elucidate the interface of self-association. RSA of monoclonal antibodies has been studied extensively under different solution conditions and have been shown to involve interactions for both the antigen-binding fragment and the crystallizable fragment. Novel modalities such as bispecific antibodies, antigen-binding fragments, single-chain-variable fragments, and diabodies constitute a fast-growing class of antibody-based therapeutics that have unique physiochemical properties compared to monoclonal antibodies. In this study, the RSA interface of a diabody-interleukin 22 fusion protein (FP-1) was studied using hydrogen-deuterium exchange coupled with mass spectrometry (HDX-MS) in combination with in silico modeling. Taken together, the results show that a complex solution behavior underlies the self-association of FP-1 and that the interface thereof can be attributed to a specific segment in the variable light chain of the diabody. These findings also demonstrate that the combination of HDX-MS with in silico modeling is a powerful tool to guide the design and candidate selection of novel biotherapeutic modalities.

A systemic approach to identifying sequence frameworks that decrease mAb production in a transient Chinese hamster ovary cell expression system

Monoclonal antibodies (mAbs) are often engineered at the sequence level for improved clinical performance yet are rarely evaluated prior to candidate selection for their "developability" characteristics, namely expression, which can necessitate additional resource investments to improve the manufacturing processes for problematic mAbs. A strong relationship between primary sequence and expression has emerged, with slight differences in amino acid sequence resulting in titers differing by up to an order of magnitude. Previous work on these "difficult-to-express" (DTE) mAbs has shown that these phenotypes are driven by post-translational bottlenecks in antibody folding, assembly, and secretion processes. However, it has been difficult to translate these findings across cell lines and products. This work presents a systematic approach to study the impact of sequence variation on mAb expression at a larger scale and under more industrially relevant conditions. The analysis found 91 mutations that decreased transient expression of an IgG1κ in Chinese hamster ovary (CHO) cells and revealed that mutations at inaccessible residues, especially those leading to decreases in residue hydrophobicity, are not favorable for high expression. This workflow can be used to better understand sequence determinants of mAb expression to improve candidate selection procedures and reduce process development timelines.

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.

Current state of implementation of in silico tools in the biopharmaceutical industry-Proceedings of the 5th modeling workshop

The fifth modeling workshop (5MW) was held in June 2023 at Favrholm, Denmark and sponsored by Recovery of Biological Products Conference Series. The goal of the workshop was to assemble modeling practitioners to review and discuss the current state, progress since the last fourth mini modeling workshop (4MMW), gaps and opportunities for development, deployment and maintenance of models in bioprocess applications. Areas of focus were four categories: biophysics and molecular modeling, mechanistic modeling, computational fluid dynamics (CFD) and plant modeling. Highlights of the workshop included significant advancements in biophysical/molecular modeling to novel protein constructs, mechanistic models for filtration and initial forays into modeling of multiphase systems using CFD for a bioreactor and mapped strategically to cell line selection/facility fit. A significant impediment to more fully quantitative and calibrated models for biophysics is the lack of large, anonymized datasets. A potential solution would be the use of specific descriptors in a database that would allow for detailed analyzes without sharing proprietary information. Another gap identified was the lack of a consistent framework for use of models that are included or support a regulatory filing beyond the high-level guidance in ICH Q8-Q11. One perspective is that modeling can be viewed as a component or precursor of machine learning (ML) and artificial intelligence (AI). Another outcome was alignment on a key definition for "mechanistic modeling." Feedback from participants was that there was progression in all of the fields of modeling within scope of the conference. Some areas (e.g., biophysics and molecular modeling) have opportunities for significant research investment to realize full impact. However, the need for ongoing research and development for all model types does not preclude the application to support process development, manufacturing and use in regulatory filings. Analogous to ML and AI, given the current state of the four modeling types, a prospective investment in educating inter-disciplinary subject matter experts (e.g., data science, chromatography) is essential to advancing the modeling community.

Holistic in silico developability assessment of novel classes of small proteins using publicly available sequence-based predictors

The development of novel therapeutic proteins is a lengthy and costly process, with an average attrition rate of 91% (Thomas et al. Clinical Development Success Rates and Contributing Factors 2011-2020, 2021). To increase the probability of success and ensure robust drug supply beyond approval, it is essential to assess the developability profile of new potential drug candidates as early and broadly as possible in development (Jain et al. MAbs, 2023. https://doi.org/10.1016/j.copbio.2011.06.002 ). Predicting these properties in silico is expected to be the next leap in innovation as it would enable significantly reduced development timelines combined with broader screens at lower costs. However, developing predictive algorithms typically requires substantial datasets generated under very defined conditions, a limiting factor especially for new classes of therapeutic proteins that hold immense clinical promise. Here we describe a strategy for assessing the developability of a novel class of small therapeutic Anticalin® proteins using machine learning in conjunction with a knowledge-driven approach. The knowledge-driven approach considers developability attributes such as aggregation propensity, charge variants, immunogenicity, specificity, thermal stability, hydrophobicity, and potential post-translational modifications, to calculate a holistic developability score. Based on sequence-derived descriptors as input parameters we established novel statistical models designed to predict the developability scores for Anticalin proteins. The best models yielded low root mean square errors across the entire dataset and were further validated by removing input data from individual screening campaigns and predicting developability scores for those drug candidates. The adoption of the described workflow will enable significantly streamlined preclinical development of Anticalin drug candidates and could potentially be applied to other therapeutic protein scaffolds.

A First Insight into the Developability of an Immunoglobulin G3: A Combined Computational and Experimental Approach

Immunoglobulin G 3 (IgG3) monoclonal antibodies (mAbs) are high-value scaffolds for developing novel therapies. Despite their wide-ranging therapeutic potential, IgG3 physicochemical properties and developability characteristics remain largely under-characterized. Protein-protein interactions elevate solution viscosity in high-concentration formulations, impacting physicochemical stability, manufacturability, and the injectability of mAbs. Therefore, in this manuscript, the key molecular descriptors and biophysical properties of a model anti-IL-8 IgG1 and its IgG3 ortholog are characterized. A computational and experimental framework was applied to measure molecular descriptors impacting their downstream developability. Findings from this approach underpin a detailed understanding of the molecular characteristics of IgG3 mAbs as potential therapeutic entities. This work is the first report examining the manufacturability of IgG3 for high-concentration mAb formulations. While poorer conformational and colloidal stability and elevated solution viscosity were observed for IgG3, future efforts controlling surface potential through sequence-engineering of solvent-accessible patches can be used to improve biophysical parameters that dictate mAb developability.

Insulin purification-Innovation continuum via synthesis of fundamentals, technology, and modeling

Advancement in all disciplines (art, science, education, and engineering) requires a careful balance of disruption and advancement of classical techniques. Often technologies are created with a limited understanding of fundamental principles and are prematurely abandoned. Over time, knowledge improves, new opportunities are identified, and technology is reassessed in a different light leading to a renaissance. Recovery of biological products is currently experiencing such a renaissance. Crystallization is one example of an elegant and ancient technology that has been applied in many fields and was employed to purify insulins from naturally occurring sources. Crystallization can also be utilized to determine protein structures. However, a multitude of parameters can impact protein crystallization and the "hit rate" for identifying protein crystals is relatively low, so much so that the development of a crystallization process is often viewed as a combination of art and science even today. Supplying the worldwide requirement for insulin (and associated variants) requires significant advances in process intensification to support scale of production and to minimize the overall cost to enable broader access. Expanding beyond insulin, the increasing complexity and diversity of biologics agents challenge the current purification methodologies. To harness the full potential of biologics, there is a need to fully explore a broader range of purification technologies, including nonchromatographic approaches. This impetus requires one to challenge and revisit the classical techniques including crystallization, chromatography, and filtration from a different vantage point and with a new set of tools, including molecular modeling. Fortunately, computational biophysics tools now exist to provide insights into mechanisms of protein/ligand interactions and molecular assembly processes (including crystallization) that can be used to support de novo process development. For example, specific regions or motifs of insulins and ligands can be identified and used as targets to support crystallization or purification development. Although the modeling tools have been developed and validated for insulin systems, the same tools can be applied to more complex modalities and to other areas including formulation, where the issue of aggregation and concentration-dependent oligomerization could be mechanistically modeled. This paper will illustrate a case study juxtaposing historical approaches to insulin downstream processes to a recent production process highlighting the application and evolution of technologies. Insulin production from Escherichia coli via inclusion bodies is an elegant example since it incorporates virtually all the unit operations associated with protein production-recovery of cells, lysis, solubilization, refolding, purification, and crystallization. The case study will include an example of an innovative application of existing membrane technology to combine three-unit operations into one, significantly reducing solids handling and buffer consumption. Ironically, a new separations technology was developed over the course of the case study that could further simplify and intensify the downstream process, emphasizing and highlighting the ever-accelerating pace of innovation in downstream processing. Molecular biophysics modeling was also employed to enhance the mechanistic understanding of the crystallization and purification processes.

Designing Robust Monoclonal Antibody Drug Products: Pitfalls of Simplistic Approaches for Stability Prediction

Thermal stability attributes including unfolding onset (Tonset) and mid-point (Tm) are often utilized for efficient development of monoclonal antibody (mAb) products during lead selection and formulation screening workflows. An assumption of direct correlation between thermal and kinetic physical stability underpins this basic approach. While literature reports have substantiated this general approach under specific conditions, clear exceptions have been highlighted alongside. Herein, a set of mAbs formulated under diverse solution conditions to generate a broad array of thermal and kinetic stability profiles were systematically analyzed. Sequence modifications in the Fc region were purposefully engineered to generate a set of low-melting mAbs. A diverse set of excipients were subsequently utilized and shown to modulate the Tm over a wide range. While a general correlation between high Tm and low aggregation rate was observed under accelerated conditions, the predictive utility of Tm under relevant product storage conditions was inadequate at best. Critically, Tm data did not correlate with long-term aggregation rates under refrigerated or room temperature conditions. Even under accelerated conditions, Tm appeared to be a poor predictor of aggregation once it exceeded the solution storage temperature (40°C) by ∼15°C, similar to conditions routinely encountered in the development of canonical mAbs (Tm > 60°C). Pitfalls of simplistic correlative approaches are discussed in the context of practical biologics product development.

Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability

Designing effective monoclonal antibody (mAb) therapeutics faces a multi-parameter optimization challenge known as "developability", which reflects an antibody's ability to progress through development stages based on its physicochemical properties. While natural antibodies may provide valuable guidance for mAb selection, we lack a comprehensive understanding of natural developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes of human-engineered and natural antibodies relate to one another. These gaps hinder fundamental developability profile cartography. To chart natural and engineered DP landscapes, we computed 40 sequence- and 46 structure-based DPs of over two million native and human-engineered single-chain antibody sequences. We find lower redundancy among structure-based compared to sequence-based DPs. Sequence DP sensitivity to single amino acid substitutions varied by antibody region and DP, and structure DP values varied across the conformational ensemble of antibody structures. We show that sequence DPs are more predictable than structure-based ones across different machine-learning tasks and embeddings, indicating a constrained sequence-based design space. Human-engineered antibodies localize within the developability and sequence landscapes of natural antibodies, suggesting that human-engineered antibodies explore mere subspaces of the natural one. Our work quantifies the plasticity of antibody developability, providing a fundamental resource for multi-parameter therapeutic mAb design.

When will we have a clone? An industry perspective on the typical CLD timeline

Cell line development (CLD) represents a complex but highly critical process during the development of a biological drug. To shed light on this crucial workflow, a team of BioPhorum members (authors) has developed and executed surveys focused on the activities and effort involved in a typical CLD campaign. An average of 27 members from different companies that participate in the BioPhorum CLD working group answered surveys covering three distinguishable stages of a standard CLD process: (1) Pre-transfection, including vector design and construction; (2) Transfection, spanning the initial introduction of vector into cells and subsequent selection and analysis of the pools; and (3) Single Cell Cloning and Lead Clone Selection, comprising methods of isolating single cells and confirming clonal origin, subsequent expansion and screening processes, and methods for identifying and banking lead clones. The surveys were very extensive, including a total of 341 questions split between antibody and complex molecule CLD processes. In this survey review, the authors interpret and highlight responses for antibody development and, where relevant, contrast complex molecule development challenges to provide a comprehensive industry perspective on the typical time and effort required to develop a CHO production cell line.

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

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

Druggability properties of a L309K mutation in the antibody CH2 domain

In the early stages of antibody drug development, it is imperative to conduct a comprehensive assessment and enhancement of the druggability attributes of potential molecules by considering their fundamental physicochemical properties. This study specifically concentrates on the surface-exposed hydrophobic region of the candidate antibody aPDL1-WT and explores the effectiveness of the L309K mutation strategy. The resulting aPDL1-LK variant demonstrates a notable enhancement over the original antibody in addressing the issue of aggregation and formation of large molecular impurities under accelerated high-temperature conditions. The mutated molecule, aPDL1-LK, exhibits excellent physicochemical properties such as hydrophilicity, conformational stability, charge variant stability, post-translational modifications, and serum stability. In terms of biological function, aPDL1-LK maintains the same glycosylation pattern as the original antibody and shows no significant difference in affinity for antigen hPDL1 protein, CD16a-F158, CD64, CD32a-H131, and complement C1q, compared to aPDL1-WT. The L309K mutation results in an approximately twofold reduction in its affinity for CD16a-V158 and CD32a-R131. In vitro biological assays, including antibody-dependent cell-mediated cytotoxicity (ADCC), antibody-dependent cellular phagocytosis (ADCP), and complement-dependent cytotoxicity (CDC), reveal that the L309K mutation may decrease CD16a-V158-mediated ADCC activity due to the mutation-induced decrease in ligand affinity, while not affect CD32a-R131-mediated ADCP activity. In conclusion, the L309K mutation offers a promising strategy to enhance the druggability properties of candidate antibodies.

Sequence-Based Viscosity Prediction for Rapid Antibody Engineering

Through machine learning, identifying correlations between amino acid sequences of antibodies and their observed characteristics, we developed an internal viscosity prediction model to empower the rapid engineering of therapeutic antibody candidates. For a highly viscous anti-IL-13 monoclonal antibody, we used a structure-based rational design strategy to generate a list of variants that were hypothesized to mitigate viscosity. Our viscosity prediction tool was then used as a screen to cull virtually engineered variants with a probability of high viscosity while advancing those with a probability of low viscosity to production and testing. By combining the rational design engineering strategy with the in silico viscosity prediction screening step, we were able to efficiently improve the highly viscous anti-IL-13 candidate, successfully decreasing the viscosity at 150 mg/mL from 34 cP to 13 cP in a panel of 16 variants.

AbLEF: antibody language ensemble fusion for thermodynamically empowered property predictions

MOTIVATION

Pre-trained protein language and/or structural models are often fine-tuned on drug development properties (i.e. developability properties) to accelerate drug discovery initiatives. However, these models generally rely on a single structural conformation and/or a single sequence as a molecular representation. We present a physics-based model, whereby 3D conformational ensemble representations are fused by a transformer-based architecture and concatenated to a language representation to predict antibody protein properties. Antibody language ensemble fusion enables the direct infusion of thermodynamic information into latent space and this enhances property prediction by explicitly infusing dynamic molecular behavior that occurs during experimental measurement.

RESULTS

We showcase the antibody language ensemble fusion model on two developability properties: hydrophobic interaction chromatography retention time and temperature of aggregation (Tagg). We find that (i) 3D conformational ensembles that are generated from molecular simulation can further improve antibody property prediction for small datasets, (ii) the performance benefit from 3D conformational ensembles matches shallow machine learning methods in the small data regime, and (iii) fine-tuned large protein language models can match smaller antibody-specific language models at predicting antibody properties.

AVAILABILITY AND IMPLEMENTATION

AbLEF codebase is available at https://github.com/merck/AbLEF.

For antibody sequence generative modeling, mixture models may be all you need

MOTIVATION

Antibody therapeutic candidates must exhibit not only tight binding to their target but also good developability properties, especially low risk of immunogenicity.

RESULTS

In this work, we fit a simple generative model, SAM, to sixty million human heavy and seventy million human light chains. We show that the probability of a sequence calculated by the model distinguishes human sequences from other species with the same or better accuracy on a variety of benchmark datasets containing >400 million sequences than any other model in the literature, outperforming large language models (LLMs) by large margins. SAM can humanize sequences, generate new sequences, and score sequences for humanness. It is both fast and fully interpretable. Our results highlight the importance of using simple models as baselines for protein engineering tasks. We additionally introduce a new tool for numbering antibody sequences which is orders of magnitude faster than existing tools in the literature.

AVAILABILITY AND IMPLEMENTATION

All tools developed in this study are available at https://github.com/Wang-lab-UCSD/AntPack.

Thermal segment microwell plate control for automated liquid handling setups

Automated high-throughput liquid handling operations in biolabs necessitate miniaturised and automatised equipment for effective space utilisation and system integration. This paper presents a thermal segment microwell plate control unit designed for enhanced microwell-based experimentation in liquid handling setups. The development of this device stems from the need to move towards geometry standardization and system integration of automated lab equipment. It incorporates features based on Smart Sensor and Sensor 4.0 concepts. An enzymatic activity assay is implemented with the developed device on a liquid handling station, allowing fast characterisation via a high-throughput approach. The device outperforms other comparable devices in certain metrics based on automated liquid handling requirements and addresses the needs of future biolabs in automation, especially in high-throughput screening.

Innovations and trends in antibody repertoire analysis

Monoclonal antibodies have revolutionized the treatment of human diseases, which has made them the fastest-growing class of therapeutics, with global sales expected to reach $346.6 billion USD by 2028. Advances in antibody engineering and development have led to the creation of increasingly sophisticated antibody-based therapeutics (e.g. bispecific antibodies and chimeric antigen receptor T cells). However, approaches for antibody discovery have remained comparatively grounded in conventional yet reliable in vitro assays. Breakthrough developments in high-throughput single B-cell sequencing and immunoglobulin proteomic serology, however, have enabled the identification of high-affinity antibodies directly from endogenous B cells or circulating immunoglobulin produced in vivo. Moreover, advances in artificial intelligence offer vast potential for antibody discovery and design with large-scale repertoire datasets positioned as the optimal source of training data for such applications. We highlight advances and recent trends in how these technologies are being applied to antibody repertoire analysis.

Electrostatically Mediated Attractive Self-Interactions and Reversible Self-Association of Fc-Fusion Proteins

Attractive self-interactions and reversible self-association are implicated in many problematic solution behaviors for therapeutic proteins, such as irreversible aggregation, elevated viscosity, phase separation, and opalescence. Protein self-interactions and reversible oligomerization of two Fc-fusion proteins (monovalent and bivalent) and the corresponding fusion partner protein were characterized experimentally with static and dynamic light scattering as a function of pH (5 and 6.5) and ionic strength (10 mM to at least 300 mM). The fusion partner protein and monovalent Fc-fusion each displayed net attractive electrostatic self-interactions at pH 6.5 and net repulsive electrostatic self-interactions at pH 5. Solutions of the bivalent Fc-fusion contained higher molecular weight species that prevented quantification of typical interaction parameters (B22 and kD). All three of the proteins displayed reversible self-association at pH 6.5, where oligomers dissociated with increased ionic strength. Coarse-grained molecular simulations were used to model the self-interactions measured experimentally, assess net self-interactions for the bivalent Fc-fusion, and probe the specific electrostatic interactions between charged amino acids that were involved in attractive electrostatic self-interactions. Mayer-weighted pairwise electrostatic energies from the simulations suggested that attractive electrostatic self-interactions at pH 6.5 for the two Fc-fusion proteins were due to cross-domain interactions between the fusion partner domain(s) and the Fc domain.

Recent advances in generative biology for biotherapeutic discovery

Generative biology combines artificial intelligence (AI), advanced life sciences technologies, and automation to revolutionize the process of designing novel biomolecules with prescribed properties, giving drug discoverers the ability to escape the limitations of biology during the design of next-generation protein therapeutics. Significant hurdles remain, namely: (i) the inherently complex nature of drug discovery, (ii) the bewildering number of promising computational and experimental techniques that have emerged in the past several years, and (iii) the limited availability of relevant protein sequence-function data for drug-like molecules. There is a need to focus on computational methods that will be most practically effective for protein drug discovery and on building experimental platforms to generate the data most appropriate for these methods. Here, we discuss recent advances in computational and experimental life sciences that are most crucial for impacting the pace and success of protein drug discovery.

Engineering therapeutic monoclonal antibodies

The use of human antibodies as biologic therapeutics has revolutionized patient care throughout fields of medicine. As our understanding of the many roles antibodies play within our natural immune responses continues to advance, so will the number of therapeutic indications for which an mAb will be developed. The great breadth of function, long half-life, and modular structure allow for nearly limitless therapeutic possibilities. Human antibodies can be rationally engineered to enhance their desired immune functions and eliminate those that may result in unwanted effects. Antibody therapeutics now often start with fully human variable regions, either acquired from genetically engineered humanized mice or from the actual human B cells. These variable genes can be further engineered by widely used methods for optimization of their specificity through affinity maturation, random mutagenesis, targeted mutagenesis, and use of in silico approaches. Antibody isotype selection and deliberate mutations are also used to improve efficacy and tolerability by purposeful fine-tuning of their immune effector functions. Finally, improvements directed at binding to the neonatal Fc receptor can endow therapeutic antibodies with unbelievable extensions in their circulating half-life. The future of engineered antibody therapeutics is bright, with the global mAb market projected to exhibit compound annual growth, forecasted to reach a revenue of nearly half a trillion dollars in 2030.

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

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

Antibodies, repertoires and microdevices in antibody discovery and characterization

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

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

Quinaldine Red as a fluorescent probe for determining the melting temperature (Tm) of proteins: a simple, rapid and high-throughput assay

Proteins play an important role in biological systems and several proteins are used in diagnosis, therapy, food industry etc. Thus, knowledge about the physical properties of the proteins is of utmost importance, which will aid in understanding their function and subsequent applications. The melting temperature (Tm) of a protein is one of the essential parameters which gives information about the stability of a protein under different conditions. In the present study, we have demonstrated a method for determining the Tm of proteins using the supramolecular interaction between Quinaldine Red (QR) and proteins. Using this method, we have determined the Tm of 5 proteins and compared our results with established protocols. Our results showed good agreement with the other methods and published values. The method developed in this study is inexpensive, quick, and devoid of complex instruments and pre/post-treatment of the samples. In addition, this method can be adopted for high throughput in multi-plate mode. Thus, this study projects a new methodology for Tm determination of various proteins with user friendly operation.

Roadmap for Drug Product Development and Manufacturing of Biologics

Therapeutic biology encompasses different modalities, and their manufacturing processes may be vastly different. However, there are many similarities that run across the different modalities during the drug product (DP) development process and manufacturing. Similarities include the need for Quality Target Product Profile (QTTP), analytical development, formulation development, container/closure studies, drug product process development, manufacturing and technical requirements set out by numerous regulatory documents such as the FDA, EMA, and ICH for pharmaceuticals for human use and other country specific requirements. While there is a plethora of knowledge on studies needed for development of a drug product, there is no specific guidance set out in a phase dependent manner delineating what studies should be completed in alignment with the different phases of clinical development from pre-clinical through commercialization. Because of this reason, we assembled a high-level drug product development and manufacturing roadmap. The roadmap is applicable across the different modalities with the intention of providing a unified framework from early phase development to commercialization of biologic drug products.

De novo design of high-affinity binders of bioactive helical peptides

Many peptide hormones form an α-helix on binding their receptors1-4, and sensitive methods for their detection could contribute to better clinical management of disease5. De novo protein design can now generate binders with high affinity and specificity to structured proteins6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.

Sequence-developability mapping of affibody and fibronectin paratopes via library-scale variant characterization

Protein developability is requisite for use in therapeutic, diagnostic, or industrial applications. Many developability assays are low throughput, which limits their utility to the later stages of protein discovery and evolution. Recent approaches enable experimental or computational assessment of many more variants, yet the breadth of applicability across protein families and developability metrics is uncertain. Here, three library-scale assays-on-yeast protease, split green fluorescent protein (GFP), and non-specific binding-were evaluated for their ability to predict two key developability outcomes (thermal stability and recombinant expression) for the small protein scaffolds affibody and fibronectin. The assays' predictive capabilities were assessed via both linear correlation and machine learning models trained on the library-scale assay data. The on-yeast protease assay is highly predictive of thermal stability for both scaffolds, and the split-GFP assay is informative of affibody thermal stability and expression. The library-scale data was used to map sequence-developability landscapes for affibody and fibronectin binding paratopes, which guides future design of variants and libraries.

Design and engineering of bispecific antibodies: insights and practical considerations

Bispecific antibodies (bsAbs) have attracted significant attention due to their dual binding activity, which permits simultaneous targeting of antigens and synergistic binding effects beyond what can be obtained even with combinations of conventional monospecific antibodies. Despite the tremendous therapeutic potential, the design and construction of bsAbs are often hampered by practical issues arising from the increased structural complexity as compared to conventional monospecific antibodies. The issues are diverse in nature, spanning from decreased biophysical stability from fusion of exogenous antigen-binding domains to antibody chain mispairing leading to formation of antibody-related impurities that are very difficult to remove. The added complexity requires judicious design considerations as well as extensive molecular engineering to ensure formation of high quality bsAbs with the intended mode of action and favorable drug-like qualities. In this review, we highlight and summarize some of the key considerations in design of bsAbs as well as state-of-the-art engineering principles that can be applied in efficient construction of bsAbs with diverse molecular formats.

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.

A comparative study of the developability of full-length antibodies, fragments, and bispecific formats reveals higher stability risks for engineered constructs

Engineered antibody formats, such as antibody fragments and bispecifics, have the potential to offer improved therapeutic efficacy compared to traditional full-length monoclonal antibodies (mAbs). However, the translation of these non-natural molecules into successful therapeutics can be hampered by developability challenges. Here, we systematically analyzed 64 different antibody constructs targeting Tumor Necrosis Factor (TNF) which cover 8 distinct molecular format families, encompassing full-length antibodies, various types of single chain variable fragments, and bispecifics. We measured 15 biophysical properties related to activity, manufacturing, and stability, scoring variants with a flag-based risk approach and a recent in silico developability profiler. Our comparative assessment revealed that overall developability is higher for the natural full-length antibody format. Bispecific antibodies, antibodies with scFv fragments at the C-terminus of the light chain, and single-chain Fv antibody fragments (scFvs) have intermediate developability properties, while more complicated formats, such as scFv- scFv, bispecific mAbs with one Fab exchanged with a scFv, and diabody formats are collectively more challenging. In particular, our study highlights the propensity for fragmentation and aggregation, both in bulk and at interfaces, for many current engineered formats.

Predicting deamidation and isomerization sites in therapeutic antibodies using structure-based in silico approaches

Asparagine (Asn) deamidation and aspartic acid (Asp) isomerization are common degradation pathways that affect the stability of therapeutic antibodies. These modifications can pose a significant challenge in the development of biopharmaceuticals. As such, the early engineering and selection of chemically stable monoclonal antibodies (mAbs) can substantially mitigate the risk of subsequent failure. In this study, we introduce a novel in silico approach for predicting deamidation and isomerization sites in therapeutic antibodies by analyzing the structural environment surrounding asparagine and aspartate residues. The resulting quantitative structure-activity relationship (QSAR) model was trained using previously published forced degradation data from 57 clinical-stage mAbs. The predictive accuracy of the model was evaluated for four different states of the protein structure: (1) static homology models, (2) enhancing low-frequency vibrational modes during short molecular dynamics (MD) runs, (3) a combination of (2) with a protonation state reassignment, and (4) conventional full-atomistic MD simulations. The most effective QSAR model considered the accessible surface area (ASA) of the residue, the pKa value of the backbone amide, and the root mean square deviations of both the alpha carbon and the side chain. The accuracy was further enhanced by incorporating the QSAR model into a decision tree, which also includes empirical information about the sequential successor and the position in the protein. The resulting model has been implemented as a plugin named "Forecasting Reactivity of Isomerization and Deamidation in Antibodies" in MOE software, completed with a user-friendly graphical interface to facilitate its use.