Deamidation and isomerization liability analysis of 131 clinical-stage antibodies

Alcohol-based solvents as mobile phases for LC-MS characterization of therapeutic proteins

Acetonitrile (ACN) is currently the preferred solvent in reversed phase (RP) chromatography for protein characterization through peptide mapping. Despite its effective performance, ACN poses toxicity risks to humans and has adverse effects on environmental sustainability. In the current work, we developed a novel alcohol-based solvent system for peptide mapping by systematic evaluation of parameters such as organic eluent composition, solvent gradient, flow rate, and column temperature. We compared the chromatographic performance and MS response of peptides between the standard (ACN based) and the new developed solvent systems (EtOH/IPA based). The results of our study show that the EtOH/IPA based solvent system improves selectivity factor (α) and resolution (R), while the standard ACN based solvent system provides a lower peak width and hence a higher peak height. The majority of the analysed peptides exhibited shorter retention times, whereas hydrophobic peptides eluted later when using the EtOH/IPA solvent system. Several critical quality attributes (CQA) of a monoclonal antibody (mAb) were successfully characterized by this method without compromising the chromatographic separations and MS response of the peptides. Furthermore, the suitability of the new approach for LC-UV assessment of a mAb as part of an identity test of therapeutic proteins was demonstrated. Our proposed approach, which prioritizes safety, non-toxicity, and compatibility with all LC-MS instruments, offers significant support to a broad community of academic and biopharmaceutical scientists in their pursuit of a bottom-up green strategy.

Revealing Unpredicted Aspartic Acid Isomerization Hotspots by Probing Diagnostic Fragmentation Propensities in Top-Down and Middle-Down Mass Spectrometry

Isomerization of aspartic acid residues is a relevant degradation pathway of protein biopharmaceuticals as it can impair their biological activity. However, the in silico prediction of isomerization hotspots and their consequences remains ambiguous and misleading. We have previously shown that all ion differential analysis (AiDA) of middle-down spectra can be used to reveal diagnostic terminal and internal fragments with more sensitivity than the conventional fragment ion mass matching methodology. In this study, we use AiDA to characterize the degradation of an antibody fragment at three aspartic acid isomerization sites including a novel DW motif directly with electron-transfer/higher-energy collisional dissociation top-down and middle-down mass spectrometry. We show that AiDA methodology is pivotal to probe diagnostic fragmentation propensities of terminal c and z fragments at the N-terminus and vicinity of isomerization sites in addition to the diagnostic c+57 terminal fragments. Furthermore, AiDA can probe remote structural changes in the loop of an antibody complementarity-determining region induced by isomerization and the succinimide intermediate, revealing interactions between residues in agreement with molecular simulations. This study shows that aspartic acid residues at noncanonical DW and DF motifs can be hotspots for isomerization despite being ranked as false positives in physics-based prediction models. We show that the enzyme-free, fast, and sensitive AiDA methodology can be used as an orthogonal technique to fractionation for online variant characterization.

Hierarchical clustering of therapeutic proteins based on agitation-induced aggregation propensity and its relation to physicochemical parameters

Physical stresses such as agitation induce protein aggregation, which causes adverse effects on the immune system of patients, leading to challenges in drug development. Aggregation induced by physical stresses can be minimized by formulation optimization. In this study, 120 combinations of 10 therapeutic proteins and 12 different formulations (4 pH conditions and 3 salt concentrations) were prepared. Subsequently, the agitation-induced aggregation propensity of each protein was investigated by evaluating its monomer recovery (%) using size exclusion chromatography. Hierarchical clustering was applied to categorize each protein according to its aggregation propensity, resulting in two groups of proteins: group A and B. The aggregation propensity of proteins in group A was insensitive to changes in formulation conditions because conformational, colloidal, and interfacial stabilities were minimally affected by changes in the pH and salt concentration and a compensation mechanism existed between conformational and colloidal stabilities. Thus, proteins in group A can be formulated with a relatively high degree of freedom. In contrast, the aggregation propensity of proteins in group B was sensitive to changes in formulation conditions. Multiple regression analysis of the physicochemical parameters and monomer recovery of proteins in group B clarified that changes in conformational stability in response to changes in formulations primarily contributed to the sensitivity of the monomer recovery to changes in formulation conditions. For all antibodies, there was a positive correlation between the monomer recovery after agitation and that after quiescent storage at 40 °C for 1 month, suggesting that a stable formulation can be obtained without the quiescent testing. Therefore, a proposed formulation optimization strategy based on the agitation-induced monomer recovery can improve the efficiency of formulating selected therapeutic proteins. This strategic approach is expected to accelerate the development of therapeutic proteins while reflecting the importance of aggregation factors and quiescent stability in the optimization of therapeutic protein formulations.

AI-enhanced profiling of phage-display-identified anti-TIM3 and anti-TIGIT novel antibodies

Antibody discovery is a lengthy and labor-intensive process, requiring extensive laboratory work to ensure that an antibody demonstrates the appropriate efficacy, production, and safety characteristics necessary for its use as a therapeutic agent in human patients. Traditionally, this process begins with phage display or B-cells isolation campaigns, where affinity serves as the primary selection criterion. However, the initial leads identified through this approach lack sufficient characterization in terms of developability and epitope definition, which are typically performed at late stages. In this study, we present a pipeline that integrates early-stage phage display screening with AI-based characterization, enabling more informed decision-making throughout the selection process. Using immune checkpoints TIM3 and TIGIT as targets, we identified five initial leads exhibiting similar binding properties. Two of these leads were predicted to have poor developability profiles due to unfavorable surface physicochemical properties. Of the remaining three candidates, structural models of the complexes formed with their respective targets were generated for 2: T4 (against TIGIT) and 6E9 (against TIM3). The predicted epitopes allowed us to anticipate a competition with TIM3 and TIGIT binding partners, and to infer the antagonistic functions expected from these antibodies. This study lays the foundations of a multidimensional AI-driven selection of lead candidates derived from high throughput analysis.

In vitro Stability Study of a Panel of Commercial Antibodies at Physiological pH and Temperature as a Guide to Screen Biologic Candidate Molecules for the Potential Risk of In vivo Asparagine Deamidation and Activity Loss

OBJECTIVE

Biologic drug molecules such as antibodies are exposed to the physiological stress conditions of pH 7.4 and 37°C during their long circulation lifetime in vivo. The stress on biologic molecules in vivo is more severe compared to that under typical storage conditions of low pH formulation and cold temperature. Chemical degradation of critical residues such as asparagine may occur in vivo, leading to potential loss of biological activity. This study describes a physiologically relevant and convenient in vitro PBS stress condition of pH 7.4 and 40°C for pre-clinical stability screening of biologic molecules.

METHODS

As benchmarks, multiple commercial antibodies (alirocumab, evolocumab, golimumab, ramucirumab, and trastuzumab) were tested in parallel for formulation stability at storage and accelerated temperature conditions and for physiological stability at pH 7.4 and 40°C stress both for 3-4 weeks. The stressed antibodies were monitored for chemical modification and target binding, without requiring affinity purification.

RESULTS

The major CDR chemical modifications observed in PBS-stressed commercial antibodies were deamidations of asparagine residues. Although slight decreases in target binding were observed for two antibodies, the affinities overall remained strong after PBS stress.

CONCLUSIONS

This benchmarking study of commercial antibodies would be useful as a guide to screen discovery-stage biologic molecules both for drug product stability at formulation pH under storage and accelerated temperature conditions and for physiological stability under in vivo-mimicking pH and temperature stress condition.

Simple endoglycosidase-assisted peptide mapping workflow for characterizing non-consensus n-glycosylation in therapeutic monoclonal antibodies

N-linked glycosylation, an extensively studied protein post-translational modification, was conventionally understood to occur at asparagine (Asn or N) sites with the consensus motif NXS/T, where X can be any amino acid residue except for proline, followed by serine or threonine. However, with advancements in characterization techniques and bioinformatic tools, increasing evidence indicates that Asn residues that are not located in the NXS/T consensus motif can also undergo N-glycosylation, which is also known as non-consensus or noncanonical N-glycosylation. Characterizing non-consensus N-glycosylation remains challenging because of the unpredictable sequon and its relatively low abundance. Here, we report an endoglycosidase-assisted peptide mapping workflow for mass spectrometry (MS) characterization of non-consensus N-glycosylation in monoclonal antibodies (mAbs). The feasibility of the workflow was demonstrated by a challenging case study, in which an atypical glycosite located within an NPNNXN sequence in a 25-residue tryptic peptide was identified in the fragment antigen-binding (Fab) region of a mAb. With the aids of endoglycosidase treatment, the resulting truncated glycan structures improved peptide ionization efficiency in MS and hence facilitated reliable quantitation of glycosite occupancy. Meanwhile, the remaining mono-/di-saccharides served as a large mass tag enabling differentiation between the glycopeptide and deamidated peptide, thus allowing for database searching for glycosite localization and semi-automation of the data processing workflow. This workflow offers a simple solution for characterizing non-consensus N-glycosylation for the development of therapeutic mAbs.

Utilization of Liquid Chromatography-Mass Spectrometry and High-Resolution Ion Mobility-Mass Spectrometry to Characterize Therapeutically Relevant Peptides with Asparagine Deamidation and Isoaspartate

Rapid identification of asparagine (Asn) deamidation and isoaspartate (isoAsp) in proteins remains a challenging analytical task during the development of biological therapeutics. For this study, 46 therapeutically relevant peptides corresponding to 13 peptide families (13 unmodified peptides and 33 modified peptides) were obtained; modified peptides included Asn deamidation and isoAsp. The peptide families were characterized by three methods: reversed-phase ultrahigh performance liquid chromatography-mass spectrometry (RP-UHPLC-MS); flow injection analysis high-resolution ion mobility-mass spectrometry (FIA-HRIM-MS); and shortened gradient RP-UHPLC-HRIM-MS. UHPLC-MS data acquisition was 2 h per injection, in contrast to high-throughput 1 min data acquisition of the FIA-HRIM-MS technique. A rapid 2D peptide map has been demonstrated by combining shortened gradient RP-UHPLC with HRIM, to optimize the resolution of the Asn-, Asp-, and isoAsp-containing peptides, increasing the likelihood of detecting peptides containing these quality attributes with expedited data acquisition. Additionally, this paper provides an ion mobility calibration data set for therapeutically relevant peptides (unmodified and modified) over an ion-neutral collisional cross-section range of 300-800 Å2.

Machine Learning Models for Predicting Monoclonal Antibody Biophysical Properties from Molecular Dynamics Simulations and Deep Learning-Based Surface Descriptors

Monoclonal antibodies (mAbs) have found extensive applications and development in treating various diseases. From the pharmaceutical industry's perspective, the journey from the design and development of mAbs to clinical testing and large-scale production is a highly time-consuming and resource-intensive process. During the research and development phase, assessing and optimizing the developability of mAbs is of paramount importance to ensure their success as candidates for therapeutic drugs. The critical factors influencing mAb development are their biophysical properties, such as aggregation propensity, solubility, and viscosity. This study utilized a data set comprising 12 biophysical properties of 137 antibodies from a previous study (Proc Natl Acad Sci USA. 114(5):944-949, 2017). We employed full-length antibody molecular dynamics simulations and machine learning techniques to predict experimental data for these 12 biophysical properties. Additionally, we utilized a newly developed deep learning model called DeepSP, which directly predicts the dynamical and structural properties of spatial aggregation propensity and spatial charge map in different antibody regions from sequences. Our research findings indicate that the machine learning models we developed outperform previous methods in predicting most biophysical properties. Furthermore, the DeepSP model yields similar predictive results compared to molecular dynamic simulations while significantly reducing computational time. The code and parameters are freely available at https://github.com/Lailabcode/AbDev. Also, the webapp, AbDev, for 12 biophysical properties prediction has been developed and provided at https://devpred.onrender.com/AbDev.

Biophysical Analysis of Therapeutic Antibodies in the Early Development Pipeline

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

The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning

Therapeutic antibodies such as monoclonal antibodies (mAbs), bispecific and multispecific antibodies are pivotal in therapeutic protein development and have transformed disease treatments across various therapeutic areas. The integrity of therapeutic antibodies, however, is compromised by sequence liabilities, notably deamidation, where asparagine (N) and glutamine (Q) residues undergo chemical degradations. Deamidation negatively impacts the efficacy, stability, and safety of diverse classes of antibodies, thus necessitating the critical need for the early and accurate identification of vulnerable sites. In this article, a comprehensive antibody deamidation-specific dataset (n = 2285) of varied modalities was created by using high-throughput automated peptide mapping followed by supervised machine learning to predict the deamidation propensities, as well as the extents, throughout the entire antibody sequences. We propose a novel chimeric deep learning model, integrating protein language model (pLM)-derived embeddings with local sequence information for enhanced deamidation predictions. Remarkably, this model requires only sequence inputs, eliminating the need for laborious feature engineering. Our approach demonstrates state-of-the-art performance, offering a streamlined workflow for high-throughput automated peptide mapping and deamidation prediction, with the potential of broader applicability to other antibody sequence liabilities.

Titer and charge-based heterogeneity multiattribute monitoring of mAbs in cell culture harvest using 2D ProA CEX MS

Robust monitoring of heterogeneity in biopharmaceutical development is crucial for producing safe and efficacious biotherapeutic products. Multiattribute monitoring (MAM) has emerged as an efficient tool for monitoring of mAb heterogeneities like deamidation, sialylation, glycosylation, and oxidation. Conventional biopharma analysis during mAb development relies on use of one-dimensional methods for monitoring titer and charge-based heterogeneity using non-volatile solvents without direct coupling with mass spectrometry (MS). This approach requires analysis of mAb harvest by ProA for titer estimation followed by separate cation exchange chromatography (CEX) analysis of the purified sample for estimating charge-based heterogeneity. This can take up to 60-90 min due to the required fraction collection and buffer exchange steps. In this work, a native two-dimensional liquid chromatography (2DLC) mass spectrometry method has been developed with Protein A chromatography in the first dimension for titer estimation and cation exchange chromatography (CEX) in the second dimension for charge variant analysis. The method uses volatile salts for both dimensions and enables easy coupling to MS. The proposed 2DLC method exhibits a charge variant profile that is similar to that observed via the traditional methods and takes only 15 min for mass identification of each variant. A total of six charge variants were separated by the CEX analysis after titer estimation, including linearity assessment from 5 μg to 160 μg of injected mAb sample. The proposed method successfully estimated charge variants for the mAb innovator and 4 of its biosimilars, showcasing its applicability for biosimilarity exercises. Hence, the 2D ProA CEX MS method allows direct titer and charge variant estimation of mAbs in a single workflow.

Development of a platform method for rapid detection and characterization of domain-specific post-translational modifications in bispecific antibodies

Charge heterogeneity is inherent to all therapeutic antibodies and arises from post-translational modifications (PTMs) and/or protein degradation events that may occur during manufacturing. Among therapeutic antibodies, the bispecific antibody (bsAb) containing two unique Fab arms directed against two different targets presents an additional layer of complexity to the charge profile. In the context of a bsAb, a single domain-specific PTM within one of the Fab domains may be sufficient to compromise target binding and could potentially impact the stability, safety, potency, and efficacy of the drug product. Therefore, characterization and routine monitoring of domain-specific modifications is critical to ensure the quality of therapeutic bispecific antibody products. We developed a Digestion-assisted imaged Capillary isoElectric focusing (DiCE) method to detect and quantitate domain-specific charge variants of therapeutic bispecific antibodies (bsAbs). The method involves enzymatic digestion using immunoglobulin G (IgG)-degrading enzyme of S. pyogenes (IdeS) to generate F(ab)2 and Fc fragments, followed by imaged capillary isoelectric focusing (icIEF) under reduced, denaturing conditions to separate the light chains (LCs) from the Fd domains. Our results suggest that DiCE is a highly sensitive method that is capable of quantitating domain-specific PTMs of a bsAb. In one case study, DiCE was used to quantitate unprocessed C-terminal lysine and site-specific glycation of Lys98 in the complementarity-determining region (CDR) of a bsAb that could not be accurately quantitated using conventional, platform-based charge variant analysis, such as intact icIEF. Quantitation of these PTMs by DiCE was comparable to results from peptide mapping, demonstrating that DiCE is a valuable orthogonal method for ensuring product quality. This method may also have potential applications for characterizing fusion proteins, antibody-drug conjugates, and co-formulated antibody cocktails.

Stability of Protein Pharmaceuticals: Recent Advances

There have been significant advances in the formulation and stabilization of proteins in the liquid state over the past years since our previous review. Our mechanistic understanding of protein-excipient interactions has increased, allowing one to develop formulations in a more rational fashion. The field has moved towards more complex and challenging formulations, such as high concentration formulations to allow for subcutaneous administration and co-formulation. While much of the published work has focused on mAbs, the principles appear to apply to any therapeutic protein, although mAbs clearly have some distinctive features. In this review, we first discuss chemical degradation reactions. This is followed by a section on physical instability issues. Then, more specific topics are addressed: instability induced by interactions with interfaces, predictive methods for physical stability and interplay between chemical and physical instability. The final parts are devoted to discussions how all the above impacts (co-)formulation strategies, in particular for high protein concentration solutions.'

Addressing common challenges of biotherapeutic protein peptide mapping using recombinant trypsin

Peptide mapping is the key method for characterization of primary structure of biotherapeutic proteins. This method relies on digestion of proteins into peptides that are then analyzed for amino acid sequence and post-translational modifications. Owing to its high activity and cleavage specificity, trypsin is the protease of choice for peptide mapping. In this study, we investigated critical requirements of peptide mapping and how trypsin affects these requirements. We found that the commonly used MS-grade trypsins contained non-specific, chymotryptic-like cleavage activity causing generation of semi-tryptic peptides and degradation of tryptic-specific peptides. Furthermore, MS-grade trypsins contained pre-existing autoproteolytic peptides and, moreover, additional autoproteolytic peptides were resulting from prominent autoproteolysis during digestion. In our long-standing quest to improve trypsin performance, we developed novel recombinant trypsin and evaluated whether it could address major trypsin drawbacks in peptide mapping. The study showed that the novel trypsin was free of detectable non-specific cleavage activity, had negligible level of autoproteolysis and maintained high activity over the course of digestion reaction. Taking advantage of the novel trypsin advanced properties, especially high cleavage specificity, we established the application for use of large trypsin quantities to digest proteolytically resistant protein sites without negative side effects. We also tested trypsin/Lys-C mix comprising the novel trypsin and showed elimination of non-specific cleavages observed in the digests with the commonly used trypsins. In addition, the improved features of the novel trypsin allowed us to establish the method for accurate and efficient non-enzymatic PTM analysis in biotherapeutic proteins.

Human antibodies neutralizing the alpha-latrotoxin of the European black widow

Poisoning by widow-spider (genus Latrodectus) bites occurs worldwide. The illness, termed latrodectism, can cause severe and persistent pain and can lead to muscle rigidity, respiratory complications, and cardiac problems. It is a global health challenge especially in developing countries. Equine serum-derived polyclonal anti-sera are commercially available as a medication for patients with latrodectism, but the use of sera imposes potential inherent risks related to its animal origin. The treatment may cause allergic reactions in humans (serum sickness), including anaphylactic shock. Furthermore, equine-derived antivenom is observed to have batch-to-batch variability and poor specificity, as it is always an undefined mix of antibodies. Because latrodectism can be extremely painful but is rarely fatal, the use of antivenom is controversial and only a small fraction of patients is treated. In this work, recombinant human antibodies were selected against alpha-latrotoxin of the European black widow (Latrodectus tredecimguttatus) by phage display from a naïve antibody gene library. Alpha-Latrotoxin (α-LTX) binding scFv were recloned and produced as fully human IgG. A novel alamarBlue assay for venom neutralization was developed and used to select neutralizing IgGs. The human antibodies showed in vitro neutralization efficacy both as single antibodies and antibody combinations. This was also confirmed by electrophysiological measurements of neuronal activity in cell culture. The best neutralizing antibodies showed nanomolar affinities. Antibody MRU44-4-A1 showed outstanding neutralization efficacy and affinity to L. tredecimguttatus α-LTX. Interestingly, only two of the neutralizing antibodies showed cross-neutralization of the venom of the Southern black widow (Latrodectus mactans). This was unexpected, because in the current literature the alpha-latrotoxins are described as highly conserved. The here-engineered antibodies are candidates for future development as potential therapeutics and diagnostic tools, as they for the first time would provide unlimited supply of a chemically completely defined drug of constant quality and efficacy, which is also made without the use of animals.

Heat-induced structural and chemical changes to a computationally designed miniprotein

The de novo design of miniprotein inhibitors has recently emerged as a new technology to create proteins that bind with high affinity to specific therapeutic targets. Their size, ease of expression, and apparent high stability makes them excellent candidates for a new class of protein drugs. However, beyond circular dichroism melts and hydrogen/deuterium exchange experiments, little is known about their dynamics, especially at the elevated temperatures they seemingly tolerate quite well. To address that and gain insight for future designs, we have focused on identifying unintended and previously overlooked heat-induced structural and chemical changes in a particularly stable model miniprotein, EHEE_rd2_0005. Nuclear magnetic resonance (NMR) studies suggest the presence of dynamics on multiple time and temperature scales. Transiently elevating the temperature results in spontaneous chemical deamidation visible in the NMR spectra, which we validate using both capillary electrophoresis and mass spectrometry (MS) experiments. High temperatures also result in greatly accelerated intrinsic rates of hydrogen exchange and signal loss in NMR heteronuclear single quantum coherence spectra from local unfolding. These losses are in excellent agreement with both room temperature hydrogen exchange experiments and hydrogen bond disruption in replica exchange molecular dynamics simulations. Our analysis reveals important principles for future miniprotein designs and the potential for high stability to result in long-lived alternate conformational states.

Preclinical development of humanized monoclonal antibodies against CD169 as a broad antiviral therapeutic strategy

New therapies to treat or prevent viral infections are essential, as recently observed during the COVID-19 pandemic. Here, we propose a therapeutic strategy based on monoclonal antibodies that block the specific interaction between the host receptor Siglec-1/CD169 and gangliosides embedded in the viral envelope. Antibodies are an excellent option for treating infectious diseases based on their high specificity, strong targeting affinity, and relatively low toxicity. Through a process of humanization, we optimized monoclonal antibodies to eliminate sequence liabilities and performed biophysical characterization. We demonstrated that they maintain their ability to block viral entry into myeloid cells. These molecular improvements during the discovery stage are key if we are to maximize efforts to develop new therapeutic strategies. Humanized monoclonal antibodies targeting CD169 provide new opportunities in the treatment of infections caused by ganglioside-containing enveloped viruses, which pose a constant threat to human health. In contrast with current neutralizing antibodies that bind antigens on the infectious particle, our antibodies can prevent several types of enveloped viruses interacting with host cells because they target the host CD169 protein, thus becoming a potential pan-antiviral therapy.

Good practices for 89Zr radiopharmaceutical production and quality control

BACKGROUND

During the previous two decades, PET imaging of biopharmaceuticals radiolabeled with zirconium-89 has become a consistent tool in preclinical and clinical drug development and patient selection, primarily due to its advantageous physical properties that allow straightforward radiolabeling of antibodies (89Zr-immuno-PET). The extended half-life of 78.4 h permits flexibility with respect to the logistics of tracer production, transportation, and imaging and allows imaging at later points in time. Additionally, its relatively low positron energy contributes to high-sensitivity, high-resolution PET imaging. Considering the growing interest in radiolabeling antibodies, antibody derivatives, and other compound classes with 89Zr in both clinical and pre-clinical settings, there is an urgent need to acquire valuable recommendations and guidelines towards standardization of labeling procedures.

MAIN BODY

This review provides an overview of the key aspects of 89Zr-radiochemistry and radiopharmaceuticals. Production of 89Zr, conjugation with the mostly used chelators and radiolabeling strategies, and quality control of the radiolabeled products are described in detail, together with discussions about alternative options and critical steps, as well as recommendations for troubleshooting. Moreover, some historical background on 89Zr-immuno-PET, coordination chemistry of 89Zr, and future perspectives are provided. This review aims to serve as a quick-start guide for scientists new to the field of 89Zr-immuno-PET and to suggest approaches for harmonization and standardization of current procedures.

CONCLUSION

The favorable PET imaging characteristics of 89Zr, its excellent availability due to relatively simple production and purification processes, and the development of suitable bifunctional chelators have led to the widespread use of 89Zr. The combination of antibodies and 89Zr, known as 89Zr-immuno-PET, has become a cornerstone in drug development and patient selection in recent years. Despite the advanced state of 89Zr-immuno-PET, new developments in chelator conjugation and radiolabeling procedures, application in novel compound classes, and improved PET scanner technology and quantification methods continue to reshape its landscape towards improving clinical outcomes.

Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins

Post-translational modifications (PTMs) of proteins play a vital role in their function and stability. These modifications influence protein folding, signaling, protein-protein interactions, enzyme activity, binding affinity, aggregation, degradation, and much more. To date, over 400 types of PTMs have been described, representing chemical diversity well beyond the genetically encoded amino acids. Such modifications pose a challenge to the successful design of proteins, but also represent a major opportunity to diversify the protein engineering toolbox. To this end, we first trained artificial neural networks (ANNs) to predict eighteen of the most abundant PTMs, including protein glycosylation, phosphorylation, methylation, and deamidation. In a second step, these models were implemented inside the computational protein modeling suite Rosetta, which allows flexible combination with existing protocols to model the modified sites and understand their impact on protein stability as well as function. Lastly, we developed a new design protocol that either maximizes or minimizes the predicted probability of a particular site being modified. We find that this combination of ANN prediction and structure-based design can enable the modification of existing, as well as the introduction of novel, PTMs. The potential applications of our work include, but are not limited to, glycan masking of epitopes, strengthening protein-protein interactions through phosphorylation, as well as protecting proteins from deamidation liabilities. These applications are especially important for the design of new protein therapeutics where PTMs can drastically change the therapeutic properties of a protein. Our work adds novel tools to Rosetta's protein engineering toolbox that allow for the rational design of PTMs.

LAP: Liability Antibody Profiler by sequence & structural mapping of natural and therapeutic antibodies

Antibody-based therapeutics must not undergo chemical modifications that would impair their efficacy or hinder their developability. A commonly used technique to de-risk lead biotherapeutic candidates annotates chemical liability motifs on their sequence. By analyzing sequences from all major sources of data (therapeutics, patents, GenBank, literature, and next-generation sequencing outputs), we find that almost all antibodies contain an average of 3-4 such liability motifs in their paratopes, irrespective of the source dataset. This is in line with the common wisdom that liability motif annotation is over-predictive. Therefore, we have compiled three computational flags to prioritize liability motifs for removal from lead drug candidates: 1. germline, to reflect naturally occurring motifs, 2. therapeutic, reflecting chemical liability motifs found in therapeutic antibodies, and 3. surface, indicative of structural accessibility for chemical modification. We show that these flags annotate approximately 60% of liability motifs as benign, that is, the flagged liabilities have a smaller probability of undergoing degradation as benchmarked on two experimental datasets covering deamidation, isomerization, and oxidation. We combined the liability detection and flags into a tool called Liability Antibody Profiler (LAP), publicly available at lap.naturalantibody.com. We anticipate that LAP will save time and effort in de-risking therapeutic molecules.

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.

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.

RUBY® - a tetravalent (2+2) bispecific antibody format with excellent functionality and IgG-like stability, pharmacology and developability properties

Despite the large number of existing bispecific antibody (bsAb) formats, the generation of novel bsAbs is still associated with development and bioprocessing challenges. Here, we present RUBY, a novel bispecific antibody format that allows rapid generation of bsAbs that fulfill key development criteria. The RUBYTM format has a 2 + 2 geometry, where two Fab fragments are linked via their light chains to the C-termini of an IgG, and carries mutations for optimal chain pairing. The unique design enables generation of bsAbs with mAb-like attributes. Our data demonstrate that RUBY bsAbs are compatible with small-scale production systems for screening purposes and can be produced at high yields (>3 g/L) from stable cell lines. The bsAbs produced are shown to, in general, contain low amounts of aggregates and display favorable solubility and stress endurance profiles. Further, compatibility with various IgG isotypes is shown and tailored Fc gamma receptor binding confirmed. Also, retained interaction with FcRn is demonstrated to translate into a pharmacokinetic profile in mice and non-human primates that is comparable to mAb controls. Functionality of conditional active RUBY bsAbs is confirmed in vitro. Anti-tumor effects in vivo have previously been demonstrated, and shown to be superior to a comparable mAb, and here it is further shown that RUBY bsAbs penetrate and localize to tumor tissue in vivo. In all, the RUBY format has attractive mAb-like attributes and offers the possibility to mitigate many of the development challenges linked to other bsAb formats, facilitating both high functionality and developability.

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.

Developability considerations for bispecific and multispecific antibodies

Bispecific antibodies (bsAb) and multispecific antibodies (msAb) encompass a diverse variety of formats that can concurrently bind multiple epitopes, unlocking mechanisms to address previously difficult-to-treat or incurable diseases. Early assessment of candidate developability enables demotion of antibodies with low potential and promotion of the most promising candidates for further development. Protein-based therapies have a stringent set of developability requirements in order to be competitive (e.g. high-concentration formulation, and long half-life) and their assessment requires a robust toolkit of methods, few of which are validated for interrogating bsAbs/msAbs. Important considerations when assessing the developability of bsAbs/msAbs include their molecular format, likelihood for immunogenicity, specificity, stability, and potential for high-volume production. Here, we summarize the critical aspects of developability assessment, and provide guidance on how to develop a comprehensive plan tailored to a given bsAb/msAb.

LC-MS Approach to Decipher a Light Chain Chromatographic Peak Splitting of a Monoclonal Antibody

PURPOSE

Monoclonal antibodies (mAbs), like other protein therapeutics, are prone to various forms of degradation, some of which are difficult to distinguish from the native form yet may alter potency. A generalizable LC-MS approach was developed to enable quantitative analysis of isoAsp. In-depth understanding of product quality attributes (PQAs) enables optimization of the manufacturing process, better formulation selection, and decreases risk associated with product handling in the clinic or during shipment.

METHODS

Reversed-phase chromatographic peak splitting was observed when a mAb was exposed to elevated temperatures. Multiple LC-MS based methods were applied to identify the reason for peak splitting. The approach involved the use of complementary HPLC columns, multiple enzymatic digestions and different MS/MS ion dissociation methods. In addition, mAb potency was measured by enzyme-linked immunosorbent assay (ELISA).

RESULTS

The split peaks had identical masses, and the root cause of the peak splitting was identified as isomerization of an aspartic acid located in the complementarity-determining region (CDR) of the light chain. And the early eluting and late eluting peaks were collected and performed enzymatic digestion to confirm the isoAsp enrichment in the early eluting peak. In addition, decreased potency was observed in the same heat-stressed sample, and the increased isoAsp levels in the CDR correlate well with a decrease of potency.

CONCLUSION

Liquid chromatography-mass spectrometry (LC-MS) has been utilized extensively to assess PQAs of biological therapeutics. In this study, a generalizable LC-MS-based approach was developed to enable identification and quantitation of the isoAsp-containing peptides.

Emerging trends in post-translational modification: Shedding light on Glioblastoma multiforme

Recent multi-omics studies, including proteomics, transcriptomics, genomics, and metabolomics have revealed the critical role of post-translational modifications (PTMs) in the progression and pathogenesis of Glioblastoma multiforme (GBM). Further, PTMs alter the oncogenic signaling events and offer a novel avenue in GBM therapeutics research through PTM enzymes as potential biomarkers for drug targeting. In addition, PTMs are critical regulators of chromatin architecture, gene expression, and tumor microenvironment (TME), that play a crucial function in tumorigenesis. Moreover, the implementation of artificial intelligence and machine learning algorithms enhances GBM therapeutics research through the identification of novel PTM enzymes and residues. Herein, we briefly explain the mechanism of protein modifications in GBM etiology, and in altering the biologics of GBM cells through chromatin remodeling, modulation of the TME, and signaling pathways. In addition, we highlighted the importance of PTM enzymes as therapeutic biomarkers and the role of artificial intelligence and machine learning in protein PTM prediction.

Atypical Asparagine Deamidation of NW Motif Significantly Attenuates the Biological Activities of an Antibody Drug Conjugate

Asparagine deamidation is a post-translational modification (PTM) that converts asparagine residues into iso-aspartate and/or aspartate. Non-enzymatic asparagine deamidation is observed frequently during the manufacturing, processing, and/or storage of biotherapeutic proteins. Depending on the site of deamidation, this PTM can significantly impact the therapeutic's potency, stability, and/or immunogenicity. Thus, deamidation is routinely monitored as a potential critical quality attribute. The initial evaluation of an asparagine's potential to deamidate begins with identifying sequence liabilities, in which the n + 1 amino acid is of particular interest. NW is one motif that occurs frequently within the complementarity-determining region (CDR) of therapeutic antibodies, but according to the published literature, has a very low risk of deamidating. Here we report an unusual case of this NW motif readily deamidating within the CDR of an antibody drug conjugate (ADC), which greatly impacts the ADC's biological activities. Furthermore, this NW motif solely deamidates into iso-aspartate, rather than the typical mixture of iso-aspartate and aspartate. Interestingly, biological activities are more severely impacted by the conversion of asparagine into iso-aspartate via deamidation than by conversion into aspartate via mutagenesis. Here, we detail the discovery of this unusual NW deamidation occurrence, characterize its impact on biological activities, and utilize structural data and modeling to explain why conversion to iso-aspartate is favored and impacts biological activities more severely.

AI/ML combined with next-generation sequencing of VHH immune repertoires enables the rapid identification of de novo humanized and sequence-optimized single domain antibodies: a prospective case study

Introduction: In this study, we demonstrate the feasibility of yeast surface display (YSD) and nextgeneration sequencing (NGS) in combination with artificial intelligence and machine learning methods (AI/ML) for the identification of de novo humanized single domain antibodies (sdAbs) with favorable early developability profiles. Methods: The display library was derived from a novel approach, in which VHH-based CDR3 regions obtained from a llama (Lama glama), immunized against NKp46, were grafted onto a humanized VHH backbone library that was diversified in CDR1 and CDR2. Following NGS analysis of sequence pools from two rounds of fluorescence-activated cell sorting we focused on four sequence clusters based on NGS frequency and enrichment analysis as well as in silico developability assessment. For each cluster, long short-term memory (LSTM) based deep generative models were trained and used for the in silico sampling of new sequences. Sequences were subjected to sequence- and structure-based in silico developability assessment to select a set of less than 10 sequences per cluster for production. Results: As demonstrated by binding kinetics and early developability assessment, this procedure represents a general strategy for the rapid and efficient design of potent and automatically humanized sdAb hits from screening selections with favorable early developability profiles.

The ice age - A review on formulation of Adeno-associated virus therapeutics

Gene therapies offer promising therapeutic alternatives for many disorders that currently lack efficient treatment options. Due to their chemical nature and physico-chemical properties, delivery of polynucleic acids into target cells and subcellular compartments remains a significant challenge. Adeno-associated viruses (AAV) have gained a lot of interest for the efficient delivery of therapeutic single-stranded DNA (ssDNA) genomes over the past decades. More than a hundred products have been tested in clinical settings and three products have received market authorization by the US FDA in recent years. A lot of effort is being made to generate potent recombinant AAV (rAAV) vectors that show favorable safety and immunogenicity profiles for either local or systemic administration. Manufacturing processes are gradually being optimized to deliver a consistently high product quality and to serve potential market needs beyond rare indications. In contrast to protein therapeutics, most rAAV products are still supplied as frozen liquids within rather simple formulation buffers to enable sufficient product shelf life, significantly hampering global distribution and access. In this review, we aim to outline the hurdles of rAAV drug product development and discuss critical formulation and composition aspects of rAAV products under clinical evaluation. Further, we highlight recent development efforts in order to achieve stable liquid or lyophilized products. This review therefore provides a comprehensive overview on current state-of-the-art rAAV formulations and can further serve as a map for rational formulation development activities in the future.

Strategies for overcoming protein and peptide instability in biodegradable drug delivery systems

The global pharmaceutical market has recently shifted its focus from small molecule drugs to peptide, protein, and nucleic acid drugs, which now comprise a majority of the top-selling pharmaceutical products on the market. Although these biologics often offer improved drug specificity, new mechanisms of action, and/or enhanced efficacy, they also present new challenges, including an increased potential for degradation and a need for frequent administration via more invasive administration routes, which can limit patient access, patient adherence, and ultimately the clinical impact of these drugs. Controlled-release systems have the potential to mitigate these challenges by offering superior control over in vivo drug levels, localizing these drugs to tissues of interest (e.g., tumors), and reducing administration frequency. Unfortunately, adapting controlled-release devices to release biologics has proven difficult due to the poor stability of biologics. In this review, we summarize the current state of controlled-release peptides and proteins, discuss existing techniques used to stabilize these drugs through encapsulation, storage, and in vivo release, and provide perspective on the most promising opportunities for the clinical translation of controlled-release peptides and proteins.

The Evolution of Commercial Antibody Formulations

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

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.

Characterization of 405B8H3(D-E), a newly engineered high affinity chimeric LAG-3 antibody with potent antitumor activity

Lymphocyte activation gene-3 (LAG-3) is a type I transmembrane protein with structural similarities to CD4. Overexpression of LAG-3 enables cancer cells to escape immune surveillance, while its blockade reinvigorates exhausted T cells and strengthens anti-infection immunity. Blockade of LAG-3 may have antitumor effects. Here, we generated a novel anti-LAG-3 chimeric antibody, 405B8H3(D-E), through hybridoma technology from monoclonal antibodies produced in mice. The heavy-chain variable region of the selected mouse antibody was grafted onto a human IgG4 scaffold, while a modified light-chain variable region was coupled to the human kappa light-chain constant region. 405B8H3(D-E) could effectively bind LAG-3-expressing HEK293 cells. Moreover, it could bind cynomolgus monkey (cyno) LAG-3 expressed on HEK293 cells with a higher affinity than the reference anti-LAG-3 antibody BMS-986016. Furthermore, 405B8H3(D-E) promoted interleukin-2 secretion and was able to block the interactions of LAG-3 with liver sinusoidal endothelial cell lectin and major histocompatibility complex II molecules. Finally, 405B8H3(D-E) combined with anti-mPD-1-antibody showed effective therapeutic potential in the MC38 tumor mouse model. Therefore, 405B8H3(D-E) is likely to be a promising candidate therapeutic antibody for immunotherapy.

Technical considerations for the implementation of the Multi-Attribute-Method by mass spectrometry in a Quality Control laboratory

Multi-attribute methods employing mass spectrometry are applied throughout the biopharmaceutical industry for product and process characterization purposes but are not yet widely accepted as a method for batch release and stability testing under good manufacturing practice (GMP) due to limited experience and level of comfort with the technical, compliance and regulatory aspects of its implementation at quality control (QC) laboratories. Here, current literature related to the development and application of the multi-attribute method by peptide mapping liquid chromatography mass spectrometry (MAM) is compiled with the aim of providing guidance for the implementation of MAM in a QC laboratory. This article, focusing on technical considerations, is the first part of a two-tiered publication, whereby the second part will focus on GMP compliance and regulatory aspects. This publication has been prepared by a group of industry experts representing 14 globally acting major biotechnology companies under the umbrella of the European Federation of Pharmaceutical Industries and Associations (EFPIA) Manufacturing & Quality Expert Group (MQEG).

Pertuzumab Charge Variant Analysis and Complementarity-Determining Region Stability Assessment to Deamidation

Pertuzumab is a monoclonal antibody used for the treatment of HER2-positive breast cancer in combination with trastuzumab. Charge variants of trastuzumab have been extensively described in the literature; however, little is known about the charge heterogeneity of pertuzumab. Here, changes in the ion-exchange profile of pertuzumab were evaluated by pH gradient cation-exchange chromatography after stressing it for up to 3 weeks at physiological and elevated pH and 37 °C. Isolated charge variants arising under stress conditions were characterized by peptide mapping. The results of peptide mapping showed that deamidation in the Fc domain and N-terminal pyroglutamate formation in the heavy chain are the main contributors to charge heterogeneity. The heavy chain CDR2, which is the only CDR containing asparagine residues, was quite resistant to deamidation under stress conditions according to peptide mapping results. Using surface plasmon resonance, it was shown that the affinity of pertuzumab for the HER2 target receptor does not change under stress conditions. Peptide mapping analysis of clinical samples showed an average of 2-3% deamidation in the heavy chain CDR2, 20-25% deamidation in the Fc domain, and 10-15% N-terminal pyroglutamate formation in the heavy chain. These findings suggest that in vitro stress studies are able to predict in vivo modifications.

Computing the Differences between Asn-X and Gln-X Deamidation and Their Impact on Pharmaceutical and Physiological Proteins: A Theoretical Investigation Using Model Dipeptides

Protein deamidation is a degradation mechanism that significantly impacts both pharmaceutical and physiological proteins. Deamidation impacts two amino acids, Asn and Gln, where the net neutral residues are converted into their acidic forms. While there are multiple similarities between the reaction mechanisms of the two residues, the impact of Gln deamidation has been noted to be most significant on physiological proteins while Asn deamidation has been linked to both pharmaceutical and physiological proteins. For this purpose, we sought to analyze the thermochemical and kinetic properties of the different reactions of Gln deamidation relative to Asn deamidation. In this study, we mapped the deamidation of Gln-X dipeptides into Glu-X dipeptides using density functional theory (DFT). Full network mapping facilitated the prediction of reaction selectivity between the two primary pathways, as well as between the two products of Gln-X deamidation as a function of solvent dielectric. To achieve this analysis, we studied a total of 77 dipeptide reactions per solvent dielectric (308 total reactions). Modeled at a neutral pH and using quantum chemical and statistical thermodynamic methods, we computed the following values: enthalpy of reaction (ΔH_RXN), entropy (ΔS_RXN), Gibbs free energy of reaction (ΔG_RXN), activation energy (E_A), and the Arrhenius preexponential factor (log(A)) for each dipeptide. Additionally, using chemical reaction principles, we generated a database of computed rate coefficients for all possible N-terminus Gln-X deamidation reactions at a neutral pH, predicted the most likely deamidation reaction mechanism for each dipeptide reaction, analyzed our results against our prior study on Asn-X deamidation, and matched our results against qualitative trends previously noted by experimental literature.

SUMO: In Silico Sequence Assessment Using Multiple Optimization Parameters

To select the most promising screening hits from antibody and VHH display campaigns for subsequent in-depth profiling and optimization, it is highly desirable to assess and select sequences on properties beyond only their binding signals from the sorting process. In addition, developability risk criteria, sequence diversity, and the anticipated complexity for sequence optimization are relevant attributes for hit selection and optimization. Here, we describe an approach for the in silico developability assessment of antibody and VHH sequences. This method not only allows for ranking and filtering multiple sequences with regard to their predicted developability properties and diversity, but also visualizes relevant sequence and structural features of potentially problematic regions and thereby provides rationales and starting points for multi-parameter sequence optimization.

Approaches to expand the conventional toolbox for discovery and selection of antibodies with drug-like physicochemical properties

Antibody drugs should exhibit not only high-binding affinity for their target antigens but also favorable physicochemical drug-like properties. Such drug-like biophysical properties are essential for the successful development of antibody drug products. The traditional approaches used in antibody drug development require significant experimentation to produce, optimize, and characterize many candidates. Therefore, it is attractive to integrate new methods that can optimize the process of selecting antibodies with both desired target-binding and drug-like biophysical properties. Here, we summarize a selection of techniques that can complement the conventional toolbox used to de-risk antibody drug development. These techniques can be integrated at different stages of the antibody development process to reduce the frequency of physicochemical liabilities in antibody libraries during initial discovery and to co-optimize multiple antibody features during early-stage antibody engineering and affinity maturation. Moreover, we highlight biophysical and computational approaches that can be used to predict physical degradation pathways relevant for long-term storage and in-use stability to reduce the need for extensive experimentation.

Identification and characterization of an unexpected isomerization motif in CDRH2 that affects antibody activity

Aspartic acid (Asp) isomerization is a spontaneous non-enzymatic post-translation modification causing a change in the structure of the protein backbone, which is commonly observed in therapeutic antibodies during manufacturing and storage. The Asps in Asp-Gly (DG), Asp-Ser (DS), and Asp-Thr (DT) motifs in the structurally flexible regions, such as complementarity-determining regions (CDRs) in antibodies, are often found to have high rate of isomerization, and they are considered "hot spots" in antibodies. In contrast, the Asp-His (DH) motif is usually considered a silent spot with low isomerization propensity. However, in monoclonal antibody mAb-a, the isomerization rate of an Asp residue, Asp55, in the aspartic acid-histidine-lysine (DHK) motif present in CDRH2 was found to be unexpectedly high. By determining the conformation of DHK motif in the crystal structure of mAb-a, we found that the Cgamma of the Asp side chain carbonyl group and the back bone amide nitrogen of successor His were in proximal contact, which facilitates the formation of succinimide intermediate, and the +2 Lys played an important role in stabilizing such conformation. The contributing roles of the His and Lys residues in DHK motif were also verified using a series of synthetic peptides. This study identified a novel Asp isomerization hot spot, DHK, and the structural-based molecular mechanism was revealed. When 20% Asp55 isomerization in this DHK motif occurred in mAb-a, antigen binding activity reduced to 54%, but the pharmacokinetics in rat was not affected significantly. Although Asp isomerization of DHK motif in CDR does not appear to have a negative impact on PK, DHK motifs in the CDRs of antibody therapeutics should be removed, considering the high propensity of isomerization and impact on antibody activity and stability.

A library approach for the de novo high-throughput isolation of humanized VHH domains with favorable developability properties following camelid immunization

In this study, we generated a novel library approach for high throughput de novo identification of humanized single-domain antibodies following camelid immunization. To achieve this, VHH-derived complementarity-determining regions-3 (CDR3s) obtained from an immunized llama (Lama glama) were grafted onto humanized VHH backbones comprising moderately sequence-diversified CDR1 and CDR2 regions similar to natural immunized and naïve antibody repertoires. Importantly, these CDRs were tailored toward favorable in silico developability properties, by considering human-likeness as well as excluding potential sequence liabilities and predicted immunogenic motifs. Target-specific humanized single-domain antibodies (sdAbs) were readily obtained by yeast surface display. We demonstrate that, by exploiting this approach, high affinity sdAbs with an optimized in silico developability profile can be generated. These sdAbs display favorable biophysical, biochemical, and functional attributes and do not require any further sequence optimization. This approach is generally applicable to any antigen upon camelid immunization and has the potential to significantly accelerate candidate selection and reduce risks and attrition rates in sdAb development.

Structure-Based Optimization of Antibody-Based Biotherapeutics for Improved Developability: A Practical Guide for Molecular Modelers

A great effort to avoid known developability risks is now more often being made earlier during the lead candidate discovery and optimization phase of biotherapeutic drug development. Predictive computational strategies, used in the early stages of antibody discovery and development, to mitigate the risk of late-stage failure of antibody candidates, are highly valuable. Various structure-based methods exist for accurately predicting properties critical to developability, and, in this chapter, we discuss the history of their development and demonstrate how they can be used to filter large sets of candidates arising from target affinity screening and to optimize lead candidates for developability. Methods for modeling antibody structures from sequence and detecting post-translational modifications and chemical degradation liabilities are also discussed.

Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies

Biologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computational methods to predict biophysical properties from protein sequence, potentially in combination with high throughput assays, could decrease timelines and increase the success rates for therapeutic developability engineering by eliminating lengthy and expensive cycles of recombinant protein production and testing. To support development of high-quality predictive models for antibody developability, we designed a sequence-diverse panel of 83 effector functionless IgG1 antibodies displaying a range of biophysical properties, produced and formulated each protein under standard platform conditions, and collected a comprehensive package of analytical data, including in vitro assays and in vivo mouse pharmacokinetics. We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. Our models predict with 87% accuracy whether viscosity at 150 mg/mL is above or below a threshold of 15 centipoise (cP) and with 75% accuracy whether the area under the plasma drug concentration-time curve (AUC0-672 h) in normal mouse is above or below a threshold of 3.9 × 106 h x ng/mL.

Developability profiling of a panel of Fc engineered SARS-CoV-2 neutralizing antibodies

To combat the COVID-19 pandemic, potential therapies have been developed and moved into clinical trials at an unprecedented pace. Some of the most promising therapies are neutralizing antibodies against SARS-CoV-2. In order to maximize the therapeutic effectiveness of such neutralizing antibodies, Fc engineering to modulate effector functions and to extend half-life is desirable. However, it is critical that Fc engineering does not negatively impact the developability properties of the antibodies, as these properties play a key role in ensuring rapid development, successful manufacturing, and improved overall chances of clinical success. In this study, we describe the biophysical characterization of a panel of Fc engineered ("TM-YTE") SARS-CoV-2 neutralizing antibodies, the same Fc modifications as those found in AstraZeneca's Evusheld (AZD7442; tixagevimab and cilgavimab), in which the TM modification (L234F/L235E/P331S) reduce binding to FcγR and C1q and the YTE modification (M252Y/S254T/T256E) extends serum half-life. We have previously shown that combining both the TM and YTE Fc modifications can reduce the thermal stability of the CH2 domain and possibly lead to developability challenges. Here we show, using a diverse panel of TM-YTE SARS-CoV-2 neutralizing antibodies, that despite lowering the thermal stability of the Fc CH2 domain, the TM-YTE platform does not have any inherent developability liabilities and shows an in vivo pharmacokinetic profile in human FcRn transgenic mice similar to the well-characterized YTE platform. The TM-YTE is therefore a developable, effector function reduced, half-life extended antibody platform.

Blueprint for antibody biologics developability

Large-molecule antibody biologics have revolutionized medicine owing to their superior target specificity, pharmacokinetic and pharmacodynamic properties, safety and toxicity profiles, and amenability to versatile engineering. In this review, we focus on preclinical antibody developability, including its definition, scope, and key activities from hit to lead optimization and selection. This includes generation, computational and in silico approaches, molecular engineering, production, analytical and biophysical characterization, stability and forced degradation studies, and process and formulation assessments. More recently, it is apparent these activities not only affect lead selection and manufacturability, but ultimately correlate with clinical progression and success. Emerging developability workflows and strategies are explored as part of a blueprint for developability success that includes an overview of the four major molecular properties that affect all developability outcomes: 1) conformational, 2) chemical, 3) colloidal, and 4) other interactions. We also examine risk assessment and mitigation strategies that increase the likelihood of success for moving the right candidate into the clinic.

Prediction of polyreactive and nonspecific single-chain fragment variables through structural biochemical features and protein language-based descriptors

BACKGROUND

Monoclonal antibodies (mAbs) have been used as therapeutic agents, which must overcome many developability issues after the discovery from in vitro display libraries. Especially, polyreactive mAbs can strongly bind to a specific target and weakly bind to off-target proteins, which leads to poor antibody pharmacokinetics in clinical development. Although early assessment of polyreactive mAbs is important in the early discovery stage, experimental assessments are usually time-consuming and expensive. Therefore, computational approaches for predicting the polyreactivity of single-chain fragment variables (scFvs) in the early discovery stage would be promising for reducing experimental efforts.

RESULTS

Here, we made prediction models for the polyreactivity of scFvs with the known polyreactive antibody features and natural language model descriptors. We predicted 19,426 protein structures of scFvs with trRosetta to calculate the polyreactive antibody features and investigated the classifying performance of each factor for polyreactivity. In the known polyreactive features, the net charge of the CDR2 loop, the tryptophan and glycine residues in CDR-H3, and the lengths of the CDR1 and CDR2 loops, importantly contributed to the performance of the models. Additionally, the hydrodynamic features, such as partial specific volume, gyration radius, and isoelectric points of CDR loops and scFvs, were newly added to improve model performance. Finally, we made the prediction model with a robust performance ([Formula: see text]) with an ensemble learning of the top 3 best models.

CONCLUSION

The prediction models for polyreactivity would help assess polyreactive scFvs in the early discovery stage and our approaches would be promising to develop machine learning models with quantitative data from high throughput assays for antibody screening.

Advancing humanized monoclonal antibody for counteracting fentanyl toxicity towards clinical development

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

Developability assessment at early-stage discovery to enable development of antibody-derived therapeutics

Developability refers to the likelihood that an antibody candidate will become a manufacturable, safe and efficacious drug. Although the safety and efficacy of a drug candidate will be well considered by sponsors and regulatory agencies, developability in the narrow sense can be defined as the likelihood that an antibody candidate will go smoothly through the chemistry, manufacturing and control (CMC) process at a reasonable cost and within a reasonable timeline. Developability in this sense is the focus of this review. To lower the risk that an antibody candidate with poor developability will move to the CMC stage, the candidate's developability-related properties should be screened, assessed and optimized as early as possible. Assessment of developability at the early discovery stage should be performed in a rapid and high-throughput manner while consuming small amounts of testing materials. In addition to monoclonal antibodies, bispecific antibodies, multispecific antibodies and antibody-drug conjugates, as the derivatives of monoclonal antibodies, should also be assessed for developability. Moreover, we propose that the criterion of developability is relative: expected clinical indication, and the dosage and administration route of the antibody could affect this criterion. We also recommend a general screening process during the early discovery stage of antibody-derived therapeutics. With the advance of artificial intelligence-aided prediction of protein structures and features, computational tools can be used to predict, screen and optimize the developability of antibody candidates and greatly reduce the risk of moving a suboptimal candidate to the development stage.

Physical Instabilities of Therapeutic Monoclonal Antibodies: A Critical Review

The proteinaceous nature of monoclonal antibodies (mAbs) makes them highly sensitive to various physical and chemical conditions, thus leading to instabilities that are classified as physical and chemical instabilities. In this review, we are discussing in detail the physical instability of mAbs because a large number of articles previously published solely focus on the chemical aspect of the instability with little coverage on the physical side. The physical instabilities of mAbs are classified into denaturation and aggregation (precipitation, visible and subvisible particles). The mechanism involved in their formation is discussed in the article, along with the pathways correlating the denaturation of mAb or the formation of aggregates to immunogenicity. Further equations like Gibbs-Helmholtz involved in detecting and quantifying denaturation are discussed, along with various factors causing the denaturation. Moreover, questions related to aggregation like the types of aggregates and the pathway involved in their formation are answered in this article. Factors influencing the physical stability of the mAbs by causing denaturation or formation of aggregates involving the structure of the protein, concentration of mAbs, pH of the protein and the formulations, excipients involved in the formulations, salts added to the formulations, storage temperature, light and UV radiation exposure and processing factors are mentioned in this article. Finally, the analytical approaches used for detecting and quantifying the physical instability of mAbs at all levels of structural conformation like far and near UV, infrared spectroscopy, capillary electrophoresis, LC-MS, microflow imagining, circular dichroism and peptide mapping are discussed.

A dataset comprised of binding interactions for 104,972 antibodies against a SARS-CoV-2 peptide

The dataset presented here contains quantitative binding scores of scFv-format antibodies against a SARS-CoV-2 target peptide collected via an AlphaSeq assay that can be used in the development and benchmarking of machine learning models. Starting from three seed sequences identified from a phage display campaign using a human naïve library, four sets of 29,900 antibodies were designed in silico by creating all k = 1 mutations and random k = 2 and k = 3 mutations throughout the complementary-determining regions (CDRs). Of the 119,600 designs, 104,972 were successfully built in to the AlphaSeq library and target binding was subsequently measured with 71,384 designs resulting in a predicted affinity value for at least one of the triplicate measurements. Data include antibodies with predicted affinity measurements ranging from 37 pM to 22 mM. To our knowledge, this dataset is the largest, publicly available dataset that contains antibody sequences, antigen sequence and quantitative measurements of binding scores and provides an opportunity to serve as a benchmark to evaluate antibody-specific representation models for machine learning.

Measurement(s)	Antibody Binding
Technology Type(s)	AlphaSeq
Factor Type(s)	Antibody sequence
Sample Characteristic - Organism	Homo sapiens