Predicting Antibody Developability Profiles Through Early Stage Discovery Screening

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Comprehensive characterization of higher order structure changes in methionine oxidized monoclonal antibodies via NMR chemometric analysis and biophysical approaches

The higher order structure (HOS) of monoclonal antibodies (mAbs) is an important quality attribute with strong contribution to clinically relevant biological functions and drug safety. Due to the multi-faceted nature of HOS, the synergy of multiple complementary analytical approaches can substantially improve the understanding, accuracy, and resolution of HOS characterization. In this study, we applied one- and two-dimensional (1D and 2D) nuclear magnetic resonance (NMR) spectroscopy coupled with chemometric analysis, as well as circular dichroism (CD), differential scanning calorimetry (DSC), and fluorescence spectroscopy as orthogonal methods, to characterize the impact of methionine (Met) oxidation on the HOS of an IgG1 mAb. We used a forced degradation method involving concentration-dependent oxidation by peracetic acid, in which Met oxidation is site-specifically quantified by liquid chromatography-mass spectrometry. Conventional biophysical techniques report nuanced results, in which CD detects no change to the secondary structure and little change in the tertiary structure. Yet, DSC measurements show the destabilization of Fab and Fc domains due to Met oxidation. More importantly, our study demonstrates that 1D and 2D NMR and chemometric analysis can provide semi-quantitative analysis of chemical modifications and resolve localized conformational changes with high sensitivity. Furthermore, we leveraged a novel 15N-Met labeling technique of the antibody to directly observe structural perturbations at the oxidation sites. The NMR methods described here to probe HOS changes are highly reliable and practical in biopharmaceutical characterization.

Matrixed CDR grafting: A neoclassical framework for antibody humanization and developability

Discovery and optimization of a biotherapeutic monoclonal antibody requires a careful balance of target engagement and physicochemical developability properties. To take full advantage of the sequence diversity provided by different antibody discovery platforms, a rapid and reliable process for humanization of antibodies from nonhuman sources is required. Canonically, maximizing homology of the human variable region (V-region) to the original germline was believed to result in preservation of binding, often without much consideration for inherent molecular properties. We expand on this approach by grafting the complementary determining regions (CDRs) of a mouse anti-LAG3 antibody into an extensive matrix of human variable heavy chain (VH) and variable light chain (VL) framework regions with substantially broader sequence homology to assess the impact on complementary determining region-framework compatibility through progressive evaluation of expression, affinity, biophysical developability, and function. Specific VH and VL framework sequences were associated with major expression and purification phenotypes. Greater VL sequence conservation was correlated with retained or improved affinity. Analysis of grafts that bound the target demonstrated that initial developability criteria were significantly impacted by VH, but not VL. In contrast, cell binding and functional characteristics were significantly impacted by VL, but not VH. Principal component analysis of all factors identified multiple grafts that exhibited more favorable antibody properties, notably with nonoptimal sequence conservation. Overall, this study demonstrates that modern throughput systems enable a more thorough, customizable, and systematic analysis of graft-framework combinations, resulting in humanized antibodies with improved global properties that may progress through development more quickly and with a greater probability of success.

Next generation of multispecific antibody engineering

Multispecific antibodies recognize two or more epitopes located on the same or distinct targets. This added capability through protein design allows these man-made molecules to address unmet medical needs that are no longer possible with single targeting such as with monoclonal antibodies or cytokines alone. However, the approach to the development of these multispecific molecules has been met with numerous road bumps, which suggests that a new workflow for multispecific molecules is required. The investigation of the molecular basis that mediates the successful assembly of the building blocks into non-native quaternary structures will lead to the writing of a playbook for multispecifics. This is a must do if we are to design workflows that we can control and in turn predict success. Here, we reflect on the current state-of-the-art of therapeutic biologics and look at the building blocks, in terms of proteins, and tools that can be used to build the foundations of such a next-generation workflow.

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.

Improved Diffusion Interaction Parameter Measurement to Predict the Viscosity of Concentrated mAb Solutions

During the developability assessment of therapeutic monoclonal antibody (mAb) candidates, utilization of robust high-throughput predictive assays enables rapid selection of top candidates with low risks for late-stage development. Predicting the viscosities of highly concentrated mAbs using limited materials is an important aspect of developability assessment because high viscosity can complicate manufacturability, stability, and administration. Here, we report a high-throughput assay measuring protein-protein interactions to predict mAb viscosity. The diffusion interaction parameter (kD) measures colloidal self-association in dilute solutions and has been reported to be predictive of the mAb viscosity at high concentrations. However, kD of Amgen early stage IgG1 mAb candidates measured in 10 mM acetate at pH 5.2 containing sucrose and polysorbate (denoted A52SuT) shows only weak correlation to their viscosities at 140 mg/mL in A52SuT. We hypothesize that kD measured in A52SuT reflects primarily long-range electrostatic repulsions because most of these mAb candidates carry strong net positive charges in this low ionic strength formulation with pH (5.2) well below pI values of mAb candidates. However, the viscosities of high concentration mAbs depend heavily on short-range molecular interactions. We propose an improved kD method in which salt is added to suppress charge repulsions and to allow for detection of key short-range interactions in dilute solutions. Salt types and salt concentrations were screened, and an optimal salt condition was identified. This optimized method was further validated using two test mAb sets. Overall, the method improves the Pearson R2 between kD and viscosity (6-230 cP) from 0.24 to 0.80 for a data set consisting of 37 mAbs.

Prediction of Antibody Viscosity from Dilute Solution Measurements

The high antibody doses required to achieve a therapeutic effect often necessitate high-concentration products that can lead to challenging viscosity issues in production and delivery. Predicting antibody viscosity in early development can play a pivotal role in reducing late-stage development costs. In recent years, numerous efforts have been made to predict antibody viscosity through dilute solution measurements. A key finding is that the entanglement of long, flexible complexes contributes to the sharp rise in antibody viscosity at the required dosing. This entanglement model establishes a connection between the two-body binding affinity and the many-body viscosity. Exploiting this insight, this study connects dilute solution measurements of self-association to high-concentration viscosity profiles to quantify the relationship between these regimes. The resulting model has exhibited success in predicting viscosity at high concentrations (around 150 mg/mL) from dilute solution measurements, with only a few outliers remaining. Our physics-based approach provides an understanding of fundamental physics, interpretable connections to experimental data, the potential to extrapolate beyond training conditions, and the capacity to effectively explain the physical mechanics behind these outliers. Conducting hypothesis-driven experiments that specifically target the viscosity and relaxation mechanisms of outlier molecules may allow us to unravel the intricacies of their behavior and, in turn, enhance the performance of our model.

Antibody sequence-based prediction of pH gradient elution in multimodal chromatography

Multimodal chromatography has emerged as a promising technique for antibody purification, owing to its capacity to selectively capture and separate target molecules. However, the optimization of chromatography parameters remains a challenge due to the intricate nature of protein-ligand interactions. To tackle this issue, efficient predictive tools are essential for the development and optimization of multimodal chromatography processes. In this study, we introduce a methodology that predicts the elution behavior of antibodies in multimodal chromatography based on their amino acid sequences. We analyzed a total of 64 full-length antibodies, including IgG1, IgG4, and IgG-like multispecific formats, which were eluted using linear pH gradients from pH 9.0 to 4.0 on the anionic mixed-mode resin Capto adhere. Homology models were constructed, and 1312 antibody-specific physicochemical descriptors were calculated for each molecule. Our analysis identified six key structural features of the multimodal antibody interaction, which were correlated with the elution behavior, emphasizing the antibody variable region. The results show that our methodology can predict pH gradient elution for a diverse range of antibodies and antibody formats, with a test set R² of 0.898. The developed model can inform process development by predicting initial conditions for multimodal elution, thereby reducing trial and error during process optimization. Furthermore, the model holds the potential to enable an in silico manufacturability assessment by screening target antibodies that adhere to standardized purification conditions. In conclusion, this study highlights the feasibility of using structure-based prediction to enhance antibody purification in the biopharmaceutical industry. This approach can lead to more efficient and cost-effective process development while increasing process understanding.

A Research Journey: Over a Decade of Denaturing and Native-MS Analyses of Hydrophobic and Membrane Proteins in Amgen Therapeutic Discovery

Membrane proteins and associated complexes currently comprise the majority of therapeutic targets and remain among the most challenging classes of proteins for analytical characterization. Through long-term strategic collaborations forged between industrial and academic research groups, there has been tremendous progress in advancing membrane protein mass spectrometry (MS) analytical methods and their concomitant application to Amgen therapeutic project progression. Herein, I will describe a detailed and personal account of how electrospray ionization (ESI) native mass spectrometry (nMS), ion mobility-MS (IM-MS), reversed phase liquid chromatographic mass spectrometry (RPLC-MS), high-throughput solid phase extraction mass spectrometry, and matrix-assisted laser desorption ionization mass spectrometry methods were developed, optimized, and validated within Amgen Research, and importantly, how these analytical methods were applied for membrane and hydrophobic protein analyses and ultimately therapeutic project support and progression. Additionally, I will discuss all the highly important and productive collaborative efforts, both internal Amgen and external academic, which were key in generating the samples, methods, and associated data described herein. I will also describe some early and previously unpublished nano-ESI (nESI) native-MS data from Amgen Research and the highly productive University of California Los Angeles (UCLA) collaboration. I will also present previously unpublished examples of real-life Amgen biotherapeutic membrane protein projects that were supported by all the MS (and IM) analytical techniques described herein. I will start by describing the initial nESI nMS experiments performed at Amgen in 2011 on empty nanodisc molecules, using a quadrupole time-of-flight MS, and how these experiments progressed on to the 15 Tesla Fourier transform ion cyclotron resonance MS at UCLA. Then described are monomeric and multimeric membrane protein data acquired in both nESI nMS and tandem-MS modes, using multiple methods of ion activation, resulting in dramatic spectral simplification. Also described is how we investigated the far less established and less published subject, that is denaturing RPLC-MS analysis of membrane proteins, and how we developed a highly robust and reproducible RPLC-MS method capable of effective separation of membrane proteins differing in only the presence or absence of an N-terminal post translational modification. Also described is the evolution of the aforementioned RPLC-MS method into a high-throughput solid phase extraction MS method. Finally, I will give my opinion on key developments and how the area of nMS of membrane proteins needs to evolve to a state where it can be applied within the biopharmaceutical research environment for routine therapeutic project support.

Deep learning in preclinical antibody drug discovery and development

Antibody drugs have become a key part of biotherapeutics. Patients suffering from various diseases have benefited from antibody therapies. However, its development process is rather long, expensive and risky. To speed up the process, reduce cost and improve success rate, artificial intelligence, especially deep learning methods, have been widely used in all aspects of preclinical antibody drug development, from library generation to hit identification, developability screening, lead selection and optimization. In this review, we systematically summarize antibody encodings, deep learning architectures and models used in preclinical antibody drug discovery and development. We also critically discuss challenges and opportunities, problems and possible solutions, current applications and future directions of deep learning in antibody drug development.

Integration of a perfusion reactor and continuous precipitation in an entirely membrane-based process for antibody capture

Continuous precipitation coupled with continuous tangential flow filtration is a cost-effective alternative for the capture of recombinant antibodies from crude cell culture supernatant. The removal of surge tanks between unit operations, by the adoption of tubular reactors, maintains a continuous harvest and mass flow of product with the advantage of a narrow residence time distribution (RTD). We developed a continuous process implementing two orthogonal precipitation methods, CaCl2 precipitation for removal of host-cell DNA and polyethylene glycol (PEG) for capturing the recombinant antibody, with no influence on the glycosylation profile. Our lab-scale prototype consisting of two tubular reactors and two stages of tangential flow microfiltration was continuously operated for up to 8 days in a truly continuous fashion and without any product flow interruption, both as a stand-alone capture and as an integrated perfusion-capture. Furthermore, we explored the use of a negatively charged membrane adsorber for flow-through anion exchange as first polishing step. We obtained a product recovery of approximately 80% and constant product quality, with more than two logarithmic reduction values (LRVs) for both host-cell proteins and host-cell DNA by the combination of the precipitation-based capture and the first polishing step.

Predicting and Interpreting Protein Developability Via Transfer of Convolutional Sequence Representation

Engineered proteins have emerged as novel diagnostics, therapeutics, and catalysts. Often, poor protein developability─quantified by expression, solubility, and stability─hinders utility. The ability to predict protein developability from amino acid sequence would reduce the experimental burden when selecting candidates. Recent advances in screening technologies enabled a high-throughput (HT) developability dataset for 105 of 1020 possible variants of protein ligand scaffold Gp2. In this work, we evaluate the ability of neural networks to learn a developability representation from a HT dataset and transfer this knowledge to predict recombinant expression beyond observed sequences. The model convolves learned amino acid properties to predict expression levels 44% closer to the experimental variance compared to a non-embedded control. Analysis of learned amino acid embeddings highlights the uniqueness of cysteine, the importance of hydrophobicity and charge, and the unimportance of aromaticity, when aiming to improve the developability of small proteins. We identify clusters of similar sequences with increased recombinant expression through nonlinear dimensionality reduction and we explore the inferred expression landscape via nested sampling. The analysis enables the first direct visualization of the fitness landscape and highlights the existence of evolutionary bottlenecks in sequence space giving rise to competing subpopulations of sequences with different developability. The work advances applied protein engineering efforts by predicting and interpreting protein scaffold expression from a limited dataset. Furthermore, our statistical mechanical treatment of the problem advances foundational efforts to characterize the structure of the protein fitness landscape and the amino acid characteristics that influence protein developability.

Discovery of ABBV-154, an anti-TNF Glucocorticoid Receptor Modulator Immunology Antibody-Drug Conjugate (iADC)

Stable attachment of drug-linkers to the antibody is a critical requirement, and for maleimide conjugation to cysteine, it is achieved by ring hydrolysis of the succinimide ring. During ADC profiling in our in-house property screening funnel, we discovered that the succinimide ring open form is in equilibrium with the ring closed succinimide. Bromoacetamide (BrAc) was identified as the optimal replacement, as it affords stable attachment of the drug-linker to the antibody while completely removing the undesired ring open-closed equilibrium. Additionally, BrAc also offers multiple benefits over maleimide, especially with respect to homogeneity of the ADC structure. In combination with a short, hydrophilic linker and phosphate prodrug on the payload, this afforded a stable ADC (ABBV-154) with the desired properties to enable long-term stability to facilitate subcutaneous self-administration.

How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

Co-crystallisation and humanisation of an anti-HER2 single-domain antibody as a theranostic tool

Human epidermal growth factor receptor-2 (HER2) is a well-recognised biomarker associated with 25% of breast cancers. In most cases, early detection and/or treatment correlates with an increased chance of survival. This study, has identified and characterised a highly specific anti-HER2 single-domain antibody (sdAb), NM-02, as a potential theranostic tool. Complete structural description by X-ray crystallography has revealed a non-overlapping epitope with current anti-HER2 antibodies. To reduce the immunogenicity risk, NM-02 underwent a humanisation process and retained wild type-like binding properties. To further de-risk the progression towards chemistry, manufacturing and control (CMC) we performed full developability profiling revealing favourable thermal and physical biochemical 'drug-like' properties. Finally, the application of the lead humanised NM-02 candidate (variant K) for HER2-specific imaging purposes was demonstrated using breast cancer HER2+/BT474 xenograft mice.

Predictive Nature of High-Throughput Assays in ADC Formulation Screening

Utilization of high-throughput biophysical screening techniques during early screening studies is warranted due to the limited amount of material and large number of samples. But the predictability of the data to longer-term storage stability is critical as the high-throughput methods assist in defining the design space for the longer-term studies. In this study, the biophysical properties of two ADCs in 16 formulation conditions were evaluated using high-throughput techniques. Conformational stability and colloidal stability were evaluated by determining Tm values, kD, B22, and Tagg. In addition, the samples were placed on stability and the extent of aggregate formation over the 8-week interval was determined. The rank order of the 16 different formulations in the high-throughput assays was compared to the rank order observed during the stability studies to assess the predictive capabilities of the screening methods. It was demonstrated that similar rank orders can be expected between high-throughput physical stability indicating assays such as Tagg and B22 and traditional aggregation by SEC data, whereas conformational stability read-outs (Tm) are less predictive. In addition, the high-throughput assays appropriately identified the poor performing formulation conditions, which is ultimately what is desired of screening assays.

Optimization of Drug-Linker to Enable Long-term Storage of Antibody-Drug Conjugate for Subcutaneous Dosing

To facilitate subcutaneous dosing, biotherapeutics need to exhibit properties that enable high-concentration formulation and long-term stability in the formulation buffer. For antibody-drug conjugates (ADCs), the introduction of drug-linkers can lead to increased hydrophobicity and higher levels of aggregation, which are both detrimental to the properties required for subcutaneous dosing. Herein we show how the physicochemical properties of ADCs could be controlled through the drug-linker chemistry in combination with prodrug chemistry of the payload, and how optimization of these combinations could afford ADCs with significantly improved solution stability. Key to achieving this optimization is the use of an accelerated stress test performed in a minimal formulation buffer.

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.

Subclass Effects on Self-Association and Viscosity of Monoclonal Antibodies at High Concentrations

The effects of a subclass of monoclonal antibodies (mAbs) on protein-protein interactions, formation of reversible oligomers (clusters), and viscosity (η) are not well understood at high concentrations. Herein, we quantify a short-range anisotropic attraction between the complementarity-determining region (CDR) and CH3 domains (K_CDR-CH3) for vedolizumab IgG1, IgG2, or IgG4 subclasses by fitting small-angle X-ray scattering (SAXS) structure factor S_eff(q) data with an extensive library of 12-bead coarse-grained (CG) molecular dynamics simulations. The K_CDR-CH3 bead attraction strength was isolated from the strength of long-range electrostatic repulsion for the full mAb, which was determined from the theoretical net charge and a scaling parameter ψ to account for solvent accessibility and ion pairing. At low ionic strength (IS), the strongest short-range attraction (K_CDR-CH3) and consequently the largest clusters and highest η were observed with IgG1, the subclass with the most positively charged CH3 domain. Furthermore, the trend in K_CDR-CH3 with the subclass followed the electrostatic interaction energy between the CDR and CH3 regions calculated with the BioLuminate software using the 3D mAb structure and molecular interaction potentials. Whereas the equilibrium cluster size distributions and fractal dimensions were determined from fits of SAXS with the MD simulations, the degree of cluster rigidity under flow was estimated from the experimental η with a phenomenological model. For the systems with the largest clusters, especially IgG1, the inefficient packing of mAbs in the clusters played the largest role in increasing η, whereas for other systems, the relative contribution from stress produced by the clusters was more significant. The ability to relate η to short-range attraction from SAXS measurements at high concentrations and to theoretical characterization of electrostatic patches on the 3D surface is not only of fundamental interest but also of practical value for mAb discovery, processing, formulation, and subcutaneous delivery.

Accelerating therapeutic protein design with computational approaches toward the clinical stage

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

Application of germline antibody features to vaccine development, antibody discovery, antibody optimization and disease diagnosis

Although the efficacy and commercial success of vaccines and therapeutic antibodies have been tremendous, designing and discovering new drug candidates remains a labor-, time- and cost-intensive endeavor with high risks. The main challenges of vaccine development are inducing a strong immune response in broad populations and providing effective prevention against a group of highly variable pathogens. Meanwhile, antibody discovery faces several great obstacles, especially the blindness in antibody screening and the unpredictability of the developability and druggability of antibody drugs. These challenges are largely due to poorly understanding of germline antibodies and the antibody responses to pathogen invasions. Thanks to the recent developments in high-throughput sequencing and structural biology, we have gained insight into the germline immunoglobulin (Ig) genes and germline antibodies and then the germline antibody features associated with antigens and disease manifestation. In this review, we firstly outline the broad associations between germline antibodies and antigens. Moreover, we comprehensively review the recent applications of antigen-specific germline antibody features, physicochemical properties-associated germline antibody features, and disease manifestation-associated germline antibody features on vaccine development, antibody discovery, antibody optimization, and disease diagnosis. Lastly, we discuss the bottlenecks and perspectives of current and potential applications of germline antibody features in the biotechnology field.

Multidimensional Protein Solubility Optimization with an Ultrahigh-Throughput Microfluidic Platform

Protein-based biologics are highly suitable for drug development as they exhibit low toxicity and high specificity for their targets. However, for therapeutic applications, biologics must often be formulated to elevated concentrations, making insufficient solubility a critical bottleneck in the drug development pipeline. Here, we report an ultrahigh-throughput microfluidic platform for protein solubility screening. In comparison with previous methods, this microfluidic platform can make, incubate, and measure samples in a few minutes, uses just 20 μg of protein (>10-fold improvement), and yields 10,000 data points (1000-fold improvement). This allows quantitative comparison of formulation excipients, such as sodium chloride, polysorbate, histidine, arginine, and sucrose. Additionally, we can measure how solubility is affected by the combinatorial effect of multiple additives, find a suitable pH for the formulation, and measure the impact of mutations on solubility, thus enabling the screening of large libraries. By reducing material and time costs, this approach makes detailed multidimensional solubility optimization experiments possible, streamlining drug development and increasing our understanding of biotherapeutic solubility and the effects of excipients.

Computational and artificial intelligence-based methods for antibody development

Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has significant limitations. Substantial recent advances in computational and artificial intelligence (AI) technologies are now starting to overcome many of these limitations and are increasingly integrated into development pipelines. Here, we provide an overview of AI methods relevant for antibody development, including databases, computational predictors of antibody properties and structure, and computational antibody design methods with an emphasis on machine learning (ML) models, and the design of complementarity-determining region (CDR) loops, antibody structural components critical for binding.