Structure-based prediction of asparagine and aspartate degradation sites in antibody variable regions

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.

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.

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.

Molecular Modeling of the Deamidation Reaction in Solution: A Theoretical-Computational Study

In this work, a theoretical-computational method is applied to study the deamidation reaction, a critical post-translational modification in proteins, using a simple model molecule in solution. The method allows one to comprehensively address the environmental effect, thereby enabling one to accurately derive the kinetic rate constants for the three main steps of the deamidation process. The results presented, in rather good agreement with the available experimental data, underline the necessity for a rigorous treatment of environmental factors and a precise kinetic model to correctly assess the overall kinetics of the deamidation reaction.

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.

Multiplex Bioanalytical Methods for Comprehensive Characterization and Quantification of the Unique Complementarity-Determining-Region Deamidation of MEDI7247, an Anti-ASCT2 Pyrrolobenzodiazepine Antibody-Drug Conjugate

Deamidation, a common post-translational modification, may impact multiple physiochemical properties of a therapeutic protein. MEDI7247, a pyrrolobenzodiazepine (PBD) antibody-drug conjugate (ADC), contains a unique deamidation site, N102, located within the complementarity-determining region (CDR), impacting the affinity of MEDI7247 to its target. Therefore, it was necessary to monitor MEDI7247 deamidation status in vivo. Due to the low dose, a sensitive absolute quantification method using immunocapture coupled with liquid chromatography-tandem mass spectrometry (LBA-LC-MS/MS) was developed and qualified. We characterized the isomerization via Electron-Activated Dissociation (EAD), revealing that deamidation resulted in iso-aspartic acid. The absolute quantification of deamidation requires careful assay optimization in order not to perturb the balance of the deamidated and nondeamidated forms. Moreover, the selection of capture reagents essential for the correct quantitative assessment of deamidation was evaluated. The final assay was qualified with 50 ng/mL LLOQ for ADC for total and nondeamidated antibody quantification, with qualitative monitoring of the deamidated antibody. The impact of deamidation on the pharmacokinetic characteristics of MEDI7247 from clinical trial NCT03106428 was analyzed, revealing a gradual reduction in the nondeamidated form of MEDI7247 in vivo. Careful quantitative biotransformation analyses of complex biotherapeutic conjugates help us understand changes in product PTMs after administration, thus providing a more complete view of in vivo pharmacology.

Structural mechanism of Fab domain dissociation as a measure of interface stability

Therapeutic antibodies should not only recognize antigens specifically, but also need to be free from developability issues, such as poor stability. Thus, the mechanistic understanding and characterization of stability are critical determinants for rational antibody design. In this study, we use molecular dynamics simulations to investigate the melting process of 16 antigen binding fragments (Fabs). We describe the Fab dissociation mechanisms, showing a separation in the V_H-V_L and in the C_H1-C_L domains. We found that the depths of the minima in the free energy curve, corresponding to the bound states, correlate with the experimentally determined melting temperatures. Additionally, we provide a detailed structural description of the dissociation mechanism and identify key interactions in the CDR loops and in the C_H1-C_L interface that contribute to stabilization. The dissociation of the V_H-V_L or C_H1-C_L domains can be represented by conformational changes in the bend angles between the domains. Our findings elucidate the melting process of antigen binding fragments and highlight critical residues in both the variable and constant domains, which are also strongly germline dependent. Thus, our proposed mechanisms have broad implications in the development and design of new and more stable antigen binding fragments.

Bioanalytical strategy for the characterization and bioanalysis of biologics: a global, nonregulated bioanalytical lab perspective

Over the past two decades, we have seen an increase in the complexity and diversity of biotherapeutic modalities pursued by biopharmaceutical companies. These biologics are multifaceted and susceptible to post-translational modifications and in vivo biotransformation that could impose challenges for bioanalysis. It is vital to characterize the functionality, stability and biotransformation products of these molecules to enable screening, identify potential liabilities at an early stage and devise a bioanalytical strategy. This article highlights our perspective on characterization and bioanalysis of biologics using hybrid LC-MS in our global nonregulated bioanalytical laboratories. AbbVie's suite of versatile, stage-appropriate characterization assays and quantitative bioanalytical approaches are discussed, along with guidance on their utility in answering project-specific questions to aid in decision-making.

Conformational Control of Fast Asparagine Deamidation in a Norovirus Capsid Protein

Accelerated spontaneous deamidation of asparagine 373 and subsequent conversion into an isoaspartate has been shown to attenuate the binding of histo blood group antigens (HBGAs) to the protruding domain (P-domain) of the capsid protein of a prevalent norovirus strain (GII.4). Here, we link an unusual backbone conformation of asparagine 373 to its fast site-specific deamidation. NMR spectroscopy and ion exchange chromatography have been used to monitor the deamidation reaction of P-domains of two closely related GII.4 norovirus strains, specific point mutants, and control peptides. MD simulations over several microseconds have been instrumental to rationalize the experimental findings. While conventional descriptors such as available surface area, root-mean-square fluctuations, or nucleophilic attack distance fail as explanations, the population of a rare syn-backbone conformation distinguishes asparagine 373 from all other asparagine residues. We suggest that stabilization of this unusual conformation enhances the nucleophilicity of the backbone nitrogen of aspartate 374, in turn accelerating the deamidation of asparagine 373. This finding should be relevant to the development of reliable prediction algorithms for sites of rapid asparagine deamidation in proteins.

Low-data interpretable deep learning prediction of antibody viscosity using a biophysically meaningful representation

Deep learning, aided by the availability of big data sets, has led to substantial advances across many disciplines. However, many scientific problems of practical interest lack sufficiently large datasets amenable to deep learning. Prediction of antibody viscosity is one such problem where deep learning methods have not yet been explored due to the relative scarcity of relevant training data. In this work, we overcome this limitation using a biophysically meaningful representation that enables us to develop generalizable models even under limited training data. We present, PfAbNet-viscosity, a 3D convolutional neural network architecture, to predict high-concentration viscosity of therapeutic antibodies. We show that with the electrostatic potential surface of the antibody variable region as the only input to the network, the models trained on as few as couple dozen datapoints can generalize with high accuracy. Our feature attribution analysis shows that PfAbNet-viscosity has learned key biophysical drivers of viscosity. The applicability of our approach to other biological systems is discussed.

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.

Identifying developability risks for clinical progression of antibodies using high-throughput in vitro and in silico approaches

With the growing significance of antibodies as a therapeutic class, identifying developability risks early during development is of paramount importance. Several high-throughput in vitro assays and in silico approaches have been proposed to de-risk antibodies during early stages of the discovery process. In this review, we have compiled and collectively analyzed published experimental assessments and computational metrics for clinical antibodies. We show that flags assigned based on in vitro measurements of polyspecificity and hydrophobicity are more predictive of clinical progression than their in silico counterparts. Additionally, we assessed the performance of published models for developability predictions on molecules not used during model training. We find that generalization to data outside of those used for training remains a challenge for models. Finally, we highlight the challenges of reproducibility in computed metrics arising from differences in homology modeling, in vitro assessments relying on complex reagents, as well as curation of experimental data often used to assess the utility of high-throughput approaches. We end with a recommendation to enable assay reproducibility by inclusion of controls with disclosed sequences, as well as sharing of structural models to enable the critical assessment and improvement of in silico predictions.

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.

Efficient production of bispecific antibody by FAST-IgTM and its application to NXT007 for the treatment of hemophilia A

Efficient production of bispecific antibodies (BsAbs) in single mammalian cells is essential for basic research and industrial manufacturing. However, preventing unwanted pairing of heavy chains (HCs) and light chains (LCs) is a challenging task. To address this, we created an engineering technology for preferential cognate HC/LC and HC/HC paring called FAST-Ig (Four-chain Assembly by electrostatic Steering Technology - Immunoglobulin), and applied it to NXT007, a BsAb for the treatment of hemophilia A. We introduced charged amino-acid substitutions at the HC/LC interface to facilitate the proper assembly for manufacturing a standard IgG-type BsAb. We generated CH1/CL interface-engineered antibody variants that achieved > 95% correct HC/LC pairing efficiency with favorable pharmacological properties and developability. Among these, we selected a design (C3) that allowed us to separate the mis-paired species with an unintended pharmacological profile using ion-exchange chromatography. Crystal structure analysis demonstrated that the C3 design did not affect the overall structure of both Fabs. To determine the final design for HCs-heterodimerization, we compared the stability of charge-based and knobs into hole-based Fc formats in acidic conditions and selected the more stable charge-based format. FAST-Ig was also applicable to stable CHO cell lines for industrial production and demonstrated robust chain pairing with different subclasses of parent BsAbs. Thus, it can be applied to a wide variety of BsAbs both preclinically and clinically.

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.

In Silico Prediction Method for Protein Asparagine Deamidation

In silico prediction methods were developed to predict protein asparagine (Asn) deamidation. The method is based on understanding deamidation mechanism on structural level with machine learning. Our structure-based method is more accurate than the sequence-based method which is still widely used in protein engineering process. In addition, molecular dynamics simulation was applied to study the time occupancy of nucleophilic attack distance, which is hypothesized as the most important step toward the rate-limiting succinimide intermediate formation. A more accurate prediction method for distinguishing potentially liable amino acid residues would allow their elimination or reduction as early as possible in the drug discovery process. It is possible that such quantitative protein structure-property relationship tools can also be applied to other protein hotspot predictions.

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.

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.

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.

Task-Based Visual Interactive Modeling: Decision Trees and Rule-Based Classifiers

Visual analytics enables the coupling of machine learning models and humans in a tightly integrated workflow, addressing various analysis tasks. Each task poses distinct demands to analysts and decision-makers. In this survey, we focus on one canonical technique for rule-based classification, namely decision tree classifiers. We provide an overview of available visualizations for decision trees with a focus on how visualizations differ with respect to 16 tasks. Further, we investigate the types of visual designs employed, and the quality measures presented. We find that (i) interactive visual analytics systems for classifier development offer a variety of visual designs, (ii) utilization tasks are sparsely covered, (iii) beyond classifier development, node-link diagrams are omnipresent, (iv) even systems designed for machine learning experts rarely feature visual representations of quality measures other than accuracy. In conclusion, we see a potential for integrating algorithmic techniques, mathematical quality measures, and tailored interactive visualizations to enable human experts to utilize their knowledge more effectively.

Structural and Functional Analysis of CEX Fractions Collected from a Novel Avastin® Biosimilar Candidate and Its Innovator: A Comparative Study

Avastin^® is a humanized recombinant monoclonal antibody used to treat cancer by targeting VEGF-A to inhibit angiogenesis. SIMAB054, an Avastin^® biosimilar candidate developed in this study, showed a different charge variant profile than its innovator. Thus, it is fractionated into acidic, main, and basic isoforms and collected physically by Cation Exchange Chromatography (CEX) for a comprehensive structural and functional analysis. The innovator product, fractionated into the same species and collected by the same method, is used as a reference for comparative analysis. Ultra-Performance Liquid Chromatography (UPLC) ESI-QToF was used to analyze the modifications leading to charge heterogeneities at intact protein and peptide levels. The C-terminal lysine clipping and glycosylation profiles of the samples were monitored by intact mAb analysis. The post-translational modifications, including oxidation, deamidation, and N-terminal pyroglutamic acid formation, were determined by peptide mapping analysis in the selected signal peptides. The relative binding affinities of the fractionated charge isoforms against the antigen, VEGF-A, and the neonatal receptor, FcRn, were revealed by Surface Plasmon Resonance (SPR) studies. The results show that all CEX fractions from the innovator product and the SIMAB054 shared the same structural variants, albeit in different ratios. Common glycoforms and post-translational modifications were the same, but at different percentages for some samples. The dissimilarities were mostly originating from the presence of extra C-term Lysin residues, which are prone to enzymatic degradation in the body, and thus they were previously assessed as clinically irrelevant. Another critical finding was the presence of different glyco proteoforms in different charge species, such as increased galactosylation in the acidic and afucosylation in the basic species. SPR characterization of the isolated charge variants further confirmed that basic species found in the CEX analyses of the biosimilar candidate were also present in the innovator product, although at lower amounts. The charge variants’ in vitro antigen- and neonatal receptor-binding activities varied amongst the samples, which could be further investigated in vivo with a larger sample set to reveal the impact on the pharmacokinetics of drug candidates. Minor structural differences may explain antigen-binding differences in the isolated charge variants, which is a key parameter in a comparability exercise. Consequently, such a biosimilar candidate may not comply with high regulatory standards unless the binding differences observed are justified and demonstrated not to have any clinical impact.

Reducing Interferences in Glycosylation Site Mapping

A current method to locate sites of N-linked glycosylation on a protein involves the identification of deamidated sites after releasing the glycans with peptide-N-glycosidase F (PNGase F). PNGase F deglycosylation converts glycosylated Asn residues into Asp. The 1-Da mass tag created by this process is observable by liquid chromatography-tandem mass spectrometry analysis. A potential interference to this method of N-glycosylation site mapping is the chemical deamidation of Asn residues, which occurs spontaneously and can result in false positives. Deamidation is a pH-dependent process that results in the formation of iso-Asp (i-Asp) and native Asp (n-Asp) by a succinimide intermediate, whereas PNGase F deglycosylation results in the conversion of the glycosylation Asn residue into n-Asp. N-linked glycosylation sites can thus be identified by the presence of a single chromatographic peak corresponding to an n-Asp residue within the consensus sequence Asn-X-Ser/Thr, whereas sites of deamidation led to 2 chromatographic peaks resulting from the presence of n-Asp and i-Asp. The intent of this study is to alert investigators in the field to the potential and unexpected errors resulting from this phenomenon and to suggest a strategy to overcome this pitfall and limit the number of false-positive identifications.

A systematic review of recent trends in research on therapeutically significant L-asparaginase and acute lymphoblastic leukemia

L-asparaginases are mostly obtained from bacterial sources for their application in the therapy and food industry. Bacterial L-asparaginases are employed in the treatment of Acute Lymphoblastic Leukemia (ALL) and its subtypes, a type of blood and bone marrow cancer that results in the overproduction of immature blood cells. It also plays a role in the food industry in reducing the acrylamide formed during baking, roasting, and frying starchy foods. This importance of the enzyme makes it to be of constant interest to the researchers to isolate novel sources. Presently L-asparaginases from E. coli native and PEGylated form, Dickeya chrysanthemi (Erwinia chrysanthemi) are in the treatment regime. In therapy, the intrinsic glutaminase activity of the enzyme is a major drawback as the patients in treatment experience side effects like fever, skin rashes, anaphylaxis, pancreatitis, steatosis in the liver, and many complications. Its significance in the food industry in mitigating acrylamide is also a major reason. Acrylamide, a potent carcinogen was formed when treating starchy foods at higher temperatures. Acrylamide content in food was analyzed and pre-treatment was considered a valuable option. Immobilization of the enzyme is an advancing and promising technique in the effective delivery of the enzyme than in free form. The concept of machine learning by employing the Artificial Network and Genetic Algorithm has paved the way to optimize the production of L-asparaginase from its sources. Gene-editing tools are gaining momentum in the study of several diseases and this review focuses on the CRISPR-Cas9 gene-editing tool in ALL.

Investigation of Tryptic Protein Digestion in Microdroplets and in Bulk Solution

Recent studies have shown that ultrafast enzymatic digestion of proteins can be achieved in microdroplet within 250 μs. Further investigation of peptides resulting from microdroplet digestion (MD) would be necessary to evaluate it as an alternative to the conventional bulk digestion for bottom-up and biotherapeutic protein characterization. Herein we examined and compared protein tryptic digestion in both MD and bulk solution. In the case of MD of β-lactoglobulin B, the preservation of long peptides was observed due to the short digestion time. In addition, MD is applicable to digest both high- and low-abundance proteins in mixture. In the case of digesting NIST 8671 mAb antibody containing a low level of commonly encountered host cell protein (HCP) PLBL2 (mAb:PLBL2 = 100:1 by weight), MD produced lower levels of digestion-induced chemical modifications of asparagine/glutamine deamidation, compared with overnight digestion. No significant difference between MD and bulk digestion was observed in terms of trypsin digestion specificity based on examination of semi- and unspecific-cleaved peptides. Our study suggests that MD, a fast digestion approach, could be adopted for bottom-up proteomics research and for peptide mapping of mAbs to characterize site-specific deamidation and glycosylation, for the purpose of development of biopharmaceuticals.

High-Resolution Demultiplexing (HRdm) Ion Mobility Spectrometry-Mass Spectrometry for Aspartic and Isoaspartic Acid Determination and Screening

Isomeric peptide analyses are an analytical challenge of great importance to therapeutic monoclonal antibody and other biotherapeutic product development workflows. Aspartic acid (Asp, D) to isoaspartic acid (isoAsp, isoD) isomerization is a critical quality attribute (CQA) that requires careful control, monitoring, and quantitation during the drug discovery and production processes. While the formation of isoAsp has been implicated in a variety of disease states such as autoimmune diseases and several types of cancer, it is also understood that the formation of isoAsp results in a structural change impacting efficacy, potency, and immunogenic properties, all of which are undesirable. Currently, lengthy ultrahigh-performance liquid chromatography (UPLC) separations are coupled with MS for CQA analyses; however, these measurements often take over an hour and drastically limit analysis throughput. In this manuscript, drift tube ion mobility spectrometry-mass spectrometry (DTIMS-MS) and both a standard and high-resolution demultiplexing approach were utilized to study eight isomeric Asp and isoAsp peptide pairs. While the limited resolving power associated with the standard DTIMS analysis only separated three of the eight pairs, the application of HRdm distinguished seven of the eight and was only unable to separate DL and isoDL. The rapid high-throughput HRdm DTIMS-MS method was also interfaced with both flow injection and an automated solid phase extraction system to present the first application of HRdm for isoAsp and Asp assessment and demonstrate screening capabilities for isomeric peptides in complex samples, resulting in a workflow highly suitable for biopharmaceutical research needs.

A Descriptor set for Quantitative Structure-Property Relationship Prediction in Biologics

In addition to attaining the desired binding to their targets, a crucial aspect in the development of biotherapeutics is 'developability', which includes several desirable properties such as high solubility, low viscosity and aggregation, physico-chemical stability and low immunogenicity. The lack of any of these properties can lead to significant obstacles in advancing them to clinic; thus in silico methods capable of raising warning flags in earlier stages of development are highly beneficial. We have developed a computational framework based on a large and diverse set of protein specific descriptors ideal for making liability predictions using a machine-learning approach. This set offers a high degree of feature diversity classifiable by sequence, structure and surface patches. We assess the sensitivity and applicability of these descriptors in four dedicated case studies that are believed to be representative of biophysical characterizations commonly employed during the development process. In addition to data sets obtained from public sources, we have validated the descriptors on novel experimental data sets in order to address antibody developability and to generate prospective predictions on Adnectins. The results demonstrate that the descriptors are well suited to assist in the improvement of properties of systems that exhibit poor solubility or aggregation.

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

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

In silico prediction of post-translational modifications in therapeutic antibodies

Monoclonal antibodies are susceptible to chemical and enzymatic modifications during manufacturing, storage, and shipping. Deamidation, isomerization, and oxidation can compromise the potency, efficacy, and safety of therapeutic antibodies. Recently, in silico tools have been used to identify liable residues and engineer antibodies with better chemical stability. Computational approaches for predicting deamidation, isomerization, oxidation, glycation, carbonylation, sulfation, and hydroxylation are reviewed here. Although liable motifs have been used to improve the chemical stability of antibodies, the accuracy of in silico predictions can be improved using machine learning and molecular dynamic simulations. In addition, there are opportunities to improve predictions for specific stress conditions, develop in silico prediction of novel modifications in antibodies, and predict the impact of modifications on physical stability and antigen-binding.

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

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

Deamidation reaction network mapping of pharmacologic and related proteins: impact of solvation dielectric on the degradation energetics of asparagine dipeptides

Asn-X deamidation pathways in the FV region of the monoclonal antibody (mAb).

Deciphering deamidation and isomerization in therapeutic proteins: Effect of neighboring residue

Deamidation of asparagine (Asn) and isomerization of aspartic acid (Asp) residues are among the most commonly observed spontaneous post-translational modifications (PTMs) in proteins. Understanding and predicting a protein sequence's propensity for such PTMs can help expedite protein therapeutic discovery and development. In this study, we used proton-affinity calculations with semi-empirical quantum mechanics and microsecond long equilibrium molecular dynamics simulations to investigate mechanistic roles of structural conformation and chemical environment in dictating spontaneous degradation of Asn and Asp residues in 131 clinical-stage therapeutic antibodies. Backbone secondary structure, side-chain rotamer conformation and solvent accessibility were found to be key molecular indicators of Asp isomerization and Asn deamidation. Comparative analysis of backbone dihedral angles along with N-H proton affinity calculations provides a mechanistic explanation for the strong influence of the identity of the n + 1 residue on the rate of Asn/Asp degradation. With these findings, we propose a minimalistic physics-based classification model that can be leveraged to predict deamidation and isomerization propensity of proteins.

Simultaneous affinity maturation and developability enhancement using natural liability-free CDRs

Affinity maturation is often a necessary step for the development of potent therapeutic molecules. Many different diversification strategies have been used for antibody affinity maturation, including error-prone PCR, chain shuffling, and targeted complementary-determining region (CDR) mutation. Although effective, they can negatively impact antibody stability or alter epitope recognition. Moreover, they do not address the presence of sequence liabilities, such as glycosylation, asparagine deamidation, aspartate isomerization, aggregation motifs, and others. Such liabilities, if present or inadvertently introduced, can potentially create the need for new rounds of engineering, or even abolish the value of the antibody as a therapeutic molecule. Here, we demonstrate a sequence agnostic method to improve antibody affinities, while simultaneously eliminating sequence liabilities and retaining the same epitope binding as the parental antibody. This was carried out using a defined collection of natural CDRs as the diversity source, purged of sequence liabilities, and matched to the antibody germline gene family. These CDRs were inserted into the lead molecule in one or two sites at a time (LCDR1-2, LCDR3, HCDR1-2) while retaining the HCDR3 and framework regions unchanged. The final analysis of 92 clones revealed 81 unique variants, with each of 24 tested variants having the same epitope specificity as the parental molecule. Of these, the average affinity improved by over 100 times (to 96 pM), and the best affinity improvement was 231-fold (to 32 pM).

Oxidation and Deamidation of Monoclonal Antibody Products: Potential Impact on Stability, Biological Activity, and Efficacy

The role in human health of therapeutic proteins in general, and monoclonal antibodies (mAbs) in particular, has been significant and is continuously evolving. A considerable amount of time and resources are invested first in mAb product development and then in clinical examination of the product. Physical and chemical degradation can occur during manufacturing, processing, storage, handling, and administration. Therapeutic proteins may undergo various chemical degradation processes, including oxidation, deamidation, isomerization, hydrolysis, deglycosylation, racemization, disulfide bond breakage and formation, Maillard reaction, and β-elimination. Oxidation and deamidation are the most common chemical degradation processes of mAbs, which may result in changes in physical properties, such as hydrophobicity, charge, secondary or/and tertiary structure, and may lower the thermodynamic or kinetic barrier to unfold. This may predispose the product to aggregation and other chemical modifications, which can alter the binding affinity, half-life, and efficacy of the product. This review summarizes major findings from the past decade on the impact of oxidation and deamidation on the stability, biological activity, and efficacy of mAb products. Mechanisms of action, influencing factors, characterization tools, clinical impact, and risk mitigation strategies have been addressed.

Introducing protein deamidation: Landmark discoveries, societal outreach, and tentative priming workflow to address deamidation

Our current knowledge on protein deamidation results from a journey that started almost 100 years ago, when a handful of researchers first described the non-enzymatic "desamidation" of glutamine, and the effect of different anions on the catalytic rate of the reaction. Since then, the field has tremendously expended and now finds outreach in very diverse areas. In light of all the recent articles published in these areas, it seemed timely to propose an integrated review on the subject, including a short historical overview of the landmark discoveries in the field, highlighting the current global positioning of protein deamidation in biology and non-biology fields, and concluding with a workflow for those asking if a protein can deamidate, and identify the residues involved. This review is essentially intended to provide newcomers in the field with an overview of how deamidation has penetrated our society and what tools are currently at hand to identify and quantify protein deamidation.

Fully human recombinant antibodies against EphA2 from a multi-tumor patient immune library suitable for tumor-targeted therapy

Enhanced EphA2 expression is observed in a variety of epithelial-derived malignancies and is an important target for anti-tumor therapy. Currently, Therapeutic monoclonal antibodies against immune checkpoints have shown good efficacy for tumor treatment. In this study, we constructed an immune single-chain fragment variable (scFv) library using peripheral blood mononuclear cells (PBMCs) from 200 patients with a variety of malignant tumors. High affinity scFvs against EphA2 can be easily screened from the immune library using phage display technology. Anti-EphA2 scFvs can be modified into any form of recombinant antibody, including scFv-Fc and full-length IgG1 antibodies, and the recombinant antibody affinity was improved following modification. Among the modified anti-EphA2 antibodies the affinity of 77-IgG1 was significantly increased, reaching a pmol affinity level (10⁻¹²). We further demonstrated the binding activity of recombinant antibodies to the EphA2 protein, tumor cells, and tumor tissues using macromolecular interaction techniques, flow cytometry and immunohistochemistry. Most importantly, both the constructed scFvs-Fc, as well as the IgG1 antibodies against EphA2 were able to inhibit the growth of tumor cells to some extent. These results suggest that the immune libraries from patients with malignant tumors are more likely to screen for antibodies with high affinity and therapeutic effect. The constructed fully human scFv immune library has broad application prospects for specific antibody screening. The screened scFv-Fc and IgG1 antibodies against EphA2 can be used for the further study of tumor immunotherapy.

Application of an integrated computational antibody engineering platform to design SARS-CoV-2 neutralizers

As the COVID-19 pandemic continues to spread, hundreds of new initiatives including studies on existing medicines are running to fight the disease. To deliver a potentially immediate and lasting treatment to current and emerging SARS-CoV-2 variants, new collaborations and ways of sharing are required to create as many paths forward as possible. Here, we leverage our expertise in computational antibody engineering to rationally design/engineer three previously reported SARS-CoV neutralizing antibodies and share our proposal towards anti-SARS-CoV-2 biologics therapeutics. SARS-CoV neutralizing antibodies, m396, 80R and CR-3022 were chosen as templates due to their diversified epitopes and confirmed neutralization potency against SARS-CoV (but not SARS-CoV-2 except for CR3022). Structures of variable fragment (Fv) in complex with receptor binding domain (RBD) from SARS-CoV or SARS-CoV-2 were subjected to our established in silico antibody engineering platform to improve their binding affinity to SARS-CoV-2 and developability profiles. The selected top mutations were ensembled into a focused library for each antibody for further screening. In addition, we convert the selected binders with different epitopes into the trispecific format, aiming to increase potency and to prevent mutational escape. Lastly, to avoid antibody-induced virus activation or enhancement, we suggest application of NNAS and DQ mutations to the Fc region to eliminate effector functions and extend half-life.

Pragmatic mAb lead molecule engineering from a developability perspective

As the number of antibody drugs being approved and marketed increases, our knowledge of what makes potential drug candidates a successful product has increased tremendously. One of the critical parameters that have become clear in the field is the importance of mAb "developability." Efforts are being increasingly focused on simultaneously selecting molecules that exhibit both desirable biological potencies and manufacturability attributes. In the current study mutations to improve the developability profile of a problematic antibody that inconsistently precipitates in a batch scale-dependent fashion using a standard platform purification process are described. Initial bioinformatic analysis showed the molecule has no obvious sequence or structural liabilities that might lead it to precipitate. Subsequent analysis of the molecule revealed the presence of two unusual positively charged mutations on the light chain at the interface of VH and VL domains, which were hypothesized to be the primary contributor to molecule precipitation during process development. To investigate this hypothesis, straightforward reversion to the germline of these residues was carried out. The resulting mutants have improved expression titers and recovered stability within a forced precipitation assay, without any change to biological activity. Given the time pressures of drug development in industry, process optimization of the lead molecule was carried out in parallel to the "retrospective" mutagenesis approach. Bespoke process optimization for large-scale manufacturing was successful. However, we propose that such context-dependent sequence liabilities should be included in the arsenal of in silico developability screening early in development; particularly since this specific issue can be efficiently mitigated without the requirement for extensive screening of lead molecule variants.

Understanding the pathway and kinetics of aspartic acid isomerization in peptide mapping methods for monoclonal antibodies

Isomerization of aspartic acid (Asp) in therapeutic proteins could lead to safety and efficacy concerns. Thus, accurate quantitation of various Asp isomerization along with kinetic understanding of the variant formations is needed to ensure optimal process development and sufficient product quality control. In this study, we first observed Asp-succinimide conversion in complementarity-determining regions (CDRs) Asp-Gly motif of a recombinant mAb through ion exchange chromatography, intact protein analysis by mass spectrometry, and LC-MS/MS. Then, we developed a specific peptide mapping method, with optimized sample digestion conditions, to accurately quantitate Asp-succinimide-isoAsp variants at peptide level without method-induced isomerization. Various kinetics of Asp-succinimide-isoAsp isomerization pathways were elucidated using ¹⁸O labeling followed by LC-MS analysis. Molecular modeling and molecular dynamic simulation provide additional insight on the kinetics of Asp-succinimide formation and stability of succinimide intermediate. Findings of this work shed light on the molecular construct and the kinetics of the formation of isoAsp and succinimide in peptides and proteins, which facilitates analytical method development, protein engineering, and late phase development for commercialization of therapeutic proteins.

Micro-Heterogeneity of Antibody Molecules

Therapeutic monoclonal antibodies (mAbs) are mostly of the IgG class and constitute highly efficacious biopharmaceuticals for a wide range of clinical indications. Full-length IgG mAbs are large proteins that are subject to multiple posttranslational modifications (PTMs) during biosynthesis, purification, or storage, resulting in micro-heterogeneity. The production of recombinant mAbs in nonhuman cell lines may result in loss of structural fidelity and the generation of variants having altered stability, biological activities, and/or immunogenic potential. Additionally, even fully human therapeutic mAbs are of unique specificity, by design, and, consequently, of unique structure; therefore, structural elements may be recognized as non-self by individuals within an outbred human population to provoke an anti-therapeutic/anti-drug antibody (ATA/ADA) response. Consequently, regulatory authorities require that the structure of a potential mAb drug product is comprehensively characterized employing state-of-the-art orthogonal analytical technologies; the PTM profile may define a set of critical quality attributes (CQAs) for the drug product that must be maintained, employing quality by design parameters, throughout the lifetime of the drug. Glycosylation of IgG-Fc, at Asn297 on each heavy chain, is an established CQA since its presence and fine structure can have a profound impact on efficacy and safety. The glycoform profile of serum-derived IgG is highly heterogeneous while mAbs produced in mammalian cells in vitro is less heterogeneous and can be "orchestrated" depending on the cell line employed and the culture conditions adopted. Thus, the gross structure and PTM profile of a given mAb, established for the drug substance gaining regulatory approval, have to be maintained for the lifespan of the drug. This review outlines our current understanding of common PTMs detected in mAbs and endogenous IgG and the relationship between a variant's structural attribute and its impact on clinical performance.

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

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

Challenges of non-clinical safety testing for biologics: A Report of the 9th BioSafe European Annual General Membership Meeting

New challenges and other topics in non-clinical safety testing of biotherapeutics were presented and discussed at the nineth European BioSafe Annual General Membership meeting in November 2019. The session topics were selected by European BioSafe organization committee members based on recent company achievements, agency interactions and new data obtained in the non-clinical safety testing of biotherapeutics, for which data sharing would be of interest and considered as valuable information. The presented session topics ranged from strategies of in vitro testing, immunogenicity prediction, bioimaging, and developmental and reproductive toxicology (DART) assessments to first-in-human (FIH) dose prediction and bioanalytical challenges, reflecting the entire space of different areas of expertise and different molecular modalities. During the 9th meeting of the European BioSafe members, the following topics were presented and discussed in 6 main sessions (with 3 or 4 presentations per session) and in three small group breakout sessions: 1) DART assessment with biotherapeutics: what did we learn and where to go?; 2) Non-animal testing strategies; 3) Seeing is believing: new frontiers in imaging; 4) Predicting immunogenicity during early drug development: hope or despair?; 5) Challenges in FIH dose projections; and 6) Non-canonical biologics formats: challenges in bioanalytics, PKPD and biotransformation for complex biologics formats. Small group breakout sessions were organized for team discussion about 3 specific topics: 1) Testing of cellular immune function in vitro and in vivo; 2) MABEL approach (toxicology and pharmacokinetic perspective); and 3) mRNA treatments. This workshop report presents the sessions and discussions at the meeting.

Machine Learning Feature Selection for Predicting High Concentration Therapeutic Antibody Aggregation

Protein aggregation can hinder the development, safety and efficacy of therapeutic antibody-based drugs. Developing a predictive model that evaluates aggregation behaviors during early stage development is therefore desirable. Machine learning is a widely used tool to train models that predict data with different attributes. However, most machine learning techniques require more data than is typically available in antibody development. In this work, we describe a rational feature selection framework to develop accurate models with a small number of features. We applied this framework to predict aggregation behaviors of 21 approved monospecific monoclonal antibodies at high concentration (150 mg/mL), yielding a correlation coefficient of 0.71 on validation tests with only two features using a linear model. The nearest neighbors and support vector regression models further improved the performance, which have correlation coefficients of 0.86 and 0.80, respectively. This framework can be extended to train other models that predict different physical properties.

Prediction Machines: Applied Machine Learning for Therapeutic Protein Design and Development

The rapid growth in technological advances and quantity of scientific data over the past decade has led to several challenges including data storage and analysis. Accurate models of complex datasets were previously difficult to develop and interpret. However, improvements in machine learning algorithms have since enabled unparalleled classification and prediction capabilities. The application of machine learning can be seen throughout diverse industries due to their ease of use and interpretability. In this review, we describe popular machine learning algorithms and highlight their application in pharmaceutical protein development. Machine learning models have now been applied to better understand the nonlinear concentration dependent viscosity of protein solutions, predict protein oxidation and deamidation rates, classify sub-visible particles and compare the physical stability of proteins. We also applied several machine learning algorithms using previously published data and describe models with improved predictions and classification. The authors hope that this review can be used as a resource to others and encourage continued application of machine learning algorithms to problems in pharmaceutical protein development.

NGOME-Lite: Proteome-wide prediction of spontaneous protein deamidation highlights differences between taxa

Asparagines in proteins deamidate spontaneously, which changes the chemical structure of a protein and often affects its function. Current prediction algorithms for asparagine deamidation require a structure as an input or are too slow to be applied at a proteomic scale. We present NGOME-Lite, a new version of our sequence-based predictor for spontaneous asparagine deamidation that is faster by over two orders of magnitude at a similar degree of accuracy. The algorithm takes into account intrinsic sequence propensities and slowing down of deamidation by local structure. NGOME-Lite can run in a proteomic analysis mode that provides the half-time of the intact form of each protein, predicted by taking into account sequence propensities and structural protection or sequence propensities only, and a structure protection factor. The detailed analysis mode also provides graphical output for all Asn residues in the query sequence. We applied NGOME-Lite to over 257,000 sequences in 38 proteomes and found that different taxa differ in their predicted deamidation dynamics. Spontaneous protein deamidation is faster in Eukarya than in Bacteria because of a higher degree of structural protection in the latter. Predicted protein deamidation half-lifes correlate with protein turnover in human, mouse, rat, C. elegans and budding yeast but not in two plants and two bacteria. NGOME-Lite is implemented in a docker container available at https://ngome.proteinphysiologylab.org.

Sense and Nonsense of Elevated Column Temperature in Proteomic Bottom-up LC-MS Analyses

Elevated column temperature represents a simple means for improving chromatographic separation of peptides. Here, we demonstrated the advantages of the column temperature in peptide separation using state-of-the-art columns. More importantly, we also determined how temperature can impair proteomic bottom-up analyses. We found that an elevated temperature in combination with the acidic pH of the mobile phase induced in-column peptide hydrolysis with high specificity to Asp and accelerated five modification reactions of amino acids. The positive effects of temperature dominated in the 30 min long gradients since the column operated at 90 °C provided the largest number of identified peptides and proteins. However, the adverse effects of temperature on peptide integrity in longer liquid chromatography-mass spectrometry (LC-MS) analyses required its reduction to obtain optimum results. The largest number of peptides was identified using the column maintained at 75 °C in 60 min long gradients, at 60 °C in 120 min long gradients, and at 45 °C in 240 min long gradients. Our results indicate that no universal column temperature exists for bottom-up LC-MS analyses. Quite the contrary, the temperature setting must be selected rationally to exploit the full capabilities of the state-of-the-art mass spectrometers in proteomic LC-MS analyses, with the gradient time being a critical factor.

A synthetic mimic of phosphodiesterase type 5 based on corona phase molecular recognition of single-walled carbon nanotubes

Molecular recognition binding sites that specifically identify a target molecule are essential for life science research, clinical diagnoses, and therapeutic development. Corona phase molecular recognition is a technique introduced to generate synthetic recognition at the surface of a nanoparticle corona, but it remains an important question whether such entities can achieve the specificity of natural enzymes and receptors. In this work, we generate and screen a library of 24 amphiphilic polymers, preselected for molecular recognition and based on functional monomers including methacrylic acid, acrylic acid, and styrene, iterating upon a poly(methacrylic acid-co-styrene) motif. When complexed to a single-walled carbon nanotube, some of the resulting corona phases demonstrate binding specificity remarkably similar to that of phosphodiesterase type 5 (PDE5), an enzyme that catalyzes the hydrolysis of secondary messenger. The corona phase binds selectively to a PDE5 inhibitor, Vardenafil, as well as its molecular variant, but not to other potential off-target inhibitors. Our work herein examines the specificity and sensitivity of polymer "mutations" to the corona phase, as well as direct competitions with the native binding PDE5. Using structure perturbation, corona surface characterization, and molecular dynamics simulations, we show that the molecular recognition is associated with the unique three-dimensional configuration of the corona phase formed at the nanotube surface. This work conclusively shows that corona phase molecular recognition can mimic key aspects of biological recognition sites and drug targets, opening up possibilities for pharmaceutical and biological applications.

Mechanisms of Deamidation of Asparagine Residues and Effects of Main-Chain Conformation on Activation Energy

Deamidation of asparagine (Asn) residues is a nonenzymatic post-translational modification of proteins. Asn deamidation is associated with pathogenesis of age-related diseases and hypofunction of monoclonal antibodies. Deamidation rate is known to be affected by the residue following Asn on the carboxyl side and by secondary structure. Information about main-chain conformation of Asn residues is necessary to accurately predict deamidation rate. In this study, the effect of main-chain conformation of Asn residues on deamidation rate was computationally investigated using molecular dynamics (MD) simulations and quantum chemical calculations. The results of MD simulations for γS-crystallin suggested that frequently deamidated Asn residues have common main-chain conformations on the N-terminal side. Based on the simulated structure, initial structures for the quantum chemical calculations were constructed and optimized geometries were obtained using the B3LYP density functional method. Structures that were frequently deamidated had a lower activation energy barrier than that of the little deamidated structure. We also showed that dihydrogen phosphate and bicarbonate ions are important catalysts for deamidation of Asn residues.

How repertoire data are changing antibody science

Antibodies are vital proteins of the immune system that recognize potentially harmful molecules and initiate their removal. Mammals can efficiently create vast numbers of antibodies with different sequences capable of binding to any antigen with high affinity and specificity. Because they can be developed to bind to many disease agents, antibodies can be used as therapeutics. In an organism, after antigen exposure, antibodies specific to that antigen are enriched through clonal selection, expansion, and somatic hypermutation. The antibodies present in an organism therefore report on its immune status, describe its innate ability to deal with harmful substances, and reveal how it has previously responded. Next-generation sequencing technologies are being increasingly used to query the antibody, or B-cell receptor (BCR), sequence repertoire, and the amount of BCR data in public repositories is growing. The Observed Antibody Space database, for example, currently contains over a billion sequences from 68 different studies. Repertoires are available that represent both the naive state (i.e. antigen-inexperienced) and that after immunization. This wealth of data has created opportunities to learn more about our immune system. In this review, we discuss the many ways in which BCR repertoire data have been or could be exploited. We highlight its utility for providing insights into how the naive immune repertoire is generated and how it responds to antigens. We also consider how structural information can be used to enhance these data and may lead to more accurate depictions of the sequence space and to applications in the discovery of new therapeutics.