Structure, heterogeneity and developability assessment of therapeutic antibodies

Druggability properties of a L309K mutation in the antibody CH2 domain

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

A High Threshold of Biotherapeutic Aggregate Numbers is Needed to Induce an Immunogenic Response In Vitro, In Vivo, and in the Clinic

BACKGROUND AND PURPOSE

There is concern that subvisible aggregates in biotherapeutic drug products pose a risk to patient safety. We investigated the threshold of biotherapeutic aggregates needed to induce immunogenic responses.

METHODS AND RESULTS

Highly aggregated samples were tested in cell-based assays and induced cellular responses in a manner that depended on the number of particles. The threshold of immune activation varied by disease state (cancer, rheumatoid arthritis, allergy), concomitant therapies, and particle number. Compared to healthy donors, disease state patients showed an equal or lower response at the late phase (7 days), suggesting they may not have a higher risk of responding to aggregates. Xeno-het mice were used to assess the threshold of immune activation in vivo. Although highly aggregated samples (~ 1,600,000 particles/mL) induced a weak and transient immunogenic response in mice, a 100-fold dilution of this sample (~ 16,000 particles/mL) did not induce immunogenicity. To confirm this result, subvisible particles (up to ~ 18,000 particles/mL, containing aggregates and silicone oil droplets) produced under representative administration practices (created upon infusion of a drug product through an IV catheter) did not induce a response in cell-based assays or appear to increase the rate of adverse events or immunogenicity during phase 3 clinical trials.

CONCLUSION

The ability of biotherapeutic aggregates to elicit an immune response in vitro, in vivo, and in the clinic depends on high numbers of particles. This suggests that there is a high threshold for aggregates to induce an immunogenic response which is well beyond that seen in standard biotherapeutic drug products.

Innovations and trends in antibody repertoire analysis

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

Leveraging bioanalytical characterization of fractionated monoclonal antibody pools to identify aggregation-prone and less filterable proteoforms during virus filtration

Monoclonal antibodies (mAbs) are an essential class of biotherapeutics. A platform process is used for mAb development to ensure clinically safe and stable molecules. Regulatory authorities ensure that mAb production processes include sufficient viral clearance steps to achieve less than one virus particle per million doses of product. Virus filtration is used for size-based removal of enveloped and nonenveloped viruses during downstream processing of mAbs. Process development in mAb purification relies on empirical approaches and often includes adsorptive prefiltration to mitigate virus filter fouling. Opportunities for molecular-level prediction of mAb filterability are needed to plug the existing knowledge gap in downstream processing. A molecular-level approach to understanding the factors influencing mAb filterability may reduce process development time, material loss, and processing costs due to oversized virus filters. In this work, pH step gradient fractionation was applied on polished bulk mAb feed to obtain concentrated pools of fractionated mAb variants. Biophysical properties and quality attributes of fractionated pools, including oligomeric state (size), isoelectric point profile, diffusion interaction parameters, and glycoform profile, were determined using bioanalytical methods. Filterability (loading and throughput) of fractionated pools were evaluated. Statistical methods were used to obtain correlations between quality attributes of mAb fractions and filterability on the Viresolve Pro virus filter.

Recent advances in generative biology for biotherapeutic discovery

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

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.

i-shaped antibody engineering enables conformational tuning of biotherapeutic receptor agonists

The ability to leverage antibodies to agonize disease relevant biological pathways has tremendous potential for clinical investigation. Yet while antibodies have been successful as antagonists, immune mediators, and targeting agents, they are not readily effective at recapitulating the biology of natural ligands. Among the important determinants of antibody agonist activity is the geometry of target receptor engagement. Here, we describe an engineering approach inspired by a naturally occurring Fab-Fab homotypic interaction that constrains IgG in a unique i-shaped conformation. i-shaped antibody (iAb) engineering enables potent intrinsic agonism of five tumor necrosis factor receptor superfamily (TNFRSF) targets. When applied to bispecific antibodies against the heterodimeric IL-2 receptor pair, constrained bispecific IgG formats recapitulate IL-2 agonist activity. iAb engineering provides a tool to tune agonist antibody function and this work provides a framework for the development of intrinsic antibody agonists with the potential for generalization across broad receptor classes.

Quantitatively Differentiating Antibodies Using Charge-State Manipulation, Collisional Activation, and Ion Mobility-Mass Spectrometry

Antibody-based therapeutics continue to expand both in the number of products and in their use in patients. These heterogeneous proteins challenge traditional drug characterization strategies, but ion mobility (IM) and mass spectrometry (MS) approaches have eased the challenge of higher-order structural characterization. Energy-dependent IM-MS, e.g., collision-induced unfolding (CIU), has been demonstrated to be sensitive to subtle differences in structure. In this study, we combine a charge-reduction method, cation-to-anion proton-transfer reactions (CAPTR), with energy-dependent IM-MS and varied solution conditions to probe their combined effects on the gas-phase structures of IgG1κ and IgG4κ from human myeloma. CAPTR paired with MS-only analysis improves the confidence of charge-state assignments and the resolution of the interfering protein species. Collision cross-section distributions were determined for each of the charge-reduced products. Similarity scoring was used to quantitatively compare distributions determined from matched experiments analyzing samples of the two antibodies. Relative to workflows using energy-dependent IM-MS without charge-state manipulation, combining CAPTR and energy-dependent IM-MS enhanced the differentiation of these antibodies. Combined, these results indicate that CAPTR can benefit many aspects of antibody characterization and differentiation.

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.

A candidate antibody drug for prevention of malaria

Over 75% of malaria-attributable deaths occur in children under the age of 5 years. However, the first malaria vaccine recommended by the World Health Organization (WHO) for pediatric use, RTS,S/AS01 (Mosquirix), has modest efficacy. Complementary strategies, including monoclonal antibodies, will be important in efforts to eradicate malaria. Here we characterize the circulating B cell repertoires of 45 RTS,S/AS01 vaccinees and discover monoclonal antibodies for development as potential therapeutics. We generated >28,000 antibody sequences and tested 481 antibodies for binding activity and 125 antibodies for antimalaria activity in vivo. Through these analyses we identified correlations suggesting that sequences in Plasmodium falciparum circumsporozoite protein, the target antigen in RTS,S/AS01, may induce immunodominant antibody responses that limit more protective, but subdominant, responses. Using binding studies, mouse malaria models, biomanufacturing assessments and protein stability assays, we selected AB-000224 and AB-007088 for advancement as a clinical lead and backup. We engineered the variable domains (Fv) of both antibodies to enable low-cost manufacturing at scale for distribution to pediatric populations, in alignment with WHO's preferred product guidelines. The engineered clone with the optimal manufacturing and drug property profile, MAM01, was advanced into clinical development.

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

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

Improved characterization of trastuzumab deruxtecan with PTCR and internal fragments implemented in middle-down MS workflows

Antibody-drug conjugates (ADCs) are highly complex proteins mainly due to the structural microvariability of the mAb, along with the additional heterogeneity afforded by the bioconjugation process. Top-down (TD) and middle-down (MD) strategies allow the straightforward fragmentation of proteins to elucidate the conjugated amino acid residues. Nevertheless, these spectra are very crowded with multiple overlapping and unassigned ion fragments. Here we report on the use of dedicated software (ClipsMS) and application of proton transfer charge reduction (PTCR), to respectively expand the fragment ion search space to internal fragments and improve the separation of overlapping fragment ions for a more comprehensive characterization of a recently approved ADC, trastuzumab deruxtecan (T-DXd). Subunit fragmentation allowed between 70 and 90% of sequence coverage to be obtained. Upon addition of internal fragment assignment, the three subunits were fully sequenced, although internal fragments did not contribute significantly to the localization of the payloads. Finally, the use of PTCR after subunit fragmentation provided a moderate sequence coverage increase between 2 and 13%. The reaction efficiently decluttered the fragmentation spectra allowing increasing the number of fragment ions characteristic of the conjugation site by 1.5- to 2.5-fold. Altogether, these results show the interest in the implementation of internal fragment ion searches and more particularly the use of PTCR reactions to increase the number of signature ions to elucidate the conjugation sites and enhance the overall sequence coverage of ADCs, making this approach particularly appealing for its implementation in R&D laboratories.

Determinants of Developability and Evolvability of Synthetic Miniproteins as Ligand Scaffolds

Binding ligands empower molecular therapeutics and diagnostics. Despite an array of protein scaffolds engineered for binding, the biophysical elements that drive developability and evolvability are not fully understood. In particular, engineering novel function while maintaining biophysical integrity within the context of small, single-domain proteins is challenged by integration of the structural framework and the evolved binding site. Miniproteins present a challenge to our limits of protein engineering capability and provide advantages in physiological targeting, modularity for multi-functional constructs, and unique binding modes. Herein, we evaluate the ability of hyperstable synthetic miniproteins, originally designed for foldedness, to function as binding scaffolds. We synthesized 45 combinatorial libraries, with 109 variants, systematically varied across two topologies, each with five starting frameworks and four or five diverse, structurally distinct paratopes, to elucidate their impact on evolvability and developability. We evaluated evolvability with yeast display binding selections against four targets. High-throughput assays -stability via yeast display and soluble expression via split-GFP in E. coli - measured developability. The comprehensive, robust dataset demonstrates how protein topology, parental framework, and paratope structure and location all impact scaffold performance. A hyperstable framework and localized diversity are not sufficient for an effective scaffold, but several designs of these elements within synthetic miniproteins designed solely for stability result in scaffold libraries with effective evolvability and developability. Engineered variants were well-folded, thermally stable, and bound target with single-digit nanomolar affinity. Thus, hyperstable synthetic miniproteins can serve as precursors to developable, evolvable mini-scaffolds with unique potential for physiological transport, modularity, and binding modes.

Comparative Analysis of Protein Surface Hydrophobicity Maps Determined by Sparse Sampling INDUS and Spatial Aggregation Propensity

Protein surface hydrophobicity plays a central role in various biological processes such as protein folding and aggregation, as well as in the design and manufacturing of biotherapeutics. While the hydrophobicity of protein surface patches has been linked to their constituent residue hydropathies, recent research has shown that protein surface hydrophobicity is more complex and characterized by the response of water to these surfaces. In this work, we employ water density perturbations to map the surface hydrophobicity of a set of model proteins using sparse indirect umbrella sampling simulations (SSI). This technique is used to identify hydrophobic surface patches for the set of model proteins, and the results are compared to those obtained from the widely adopted spatial aggregation propensity (SAP) technique. While SAP-based calculations show agreement with SSI in some cases, there are several examples of disagreement. We identify four general classes of difference in behavior and study factors that contribute to these differences. We find that the SAP method can sometimes mask the effect of weakly nonpolar or isolated nonpolar residues that can lead to strong hydrophobic patches on the protein surface. In addition, hydrophobic patches identified by SAP can exhibit shifts in both position and strength on the SSI map. Our results demonstrate that the combination of topography and chemical context controls the hydrophobicity of a given patch above and beyond the intrinsic polarity of the residues present on the patch surface. The availability of more accurate protein hydrophobicity maps in concert with new classes of hydrophobic molecular descriptors may create significant opportunities for in silico prediction of protein behavior for a range of applications, such as protein design, biomanufacturability, and downstream bioprocessing.

An Online Two-Dimensional Approach to Characterizing the Charge-Based Heterogeneity of Recombinant Monoclonal Antibodies Using a 2D-CEX-AEX-MS Workflow

Assessment of product quality attributes such as charge heterogeneity is an upmost requisite for the release of a monoclonal antibody (mAb). Analytical techniques, such as cation-exchange chromatography (CEX), accomplish this, causing the mAb to separate into acidic, main species, and basic variants. Here, an online volatile-salt-containing two-dimensional liquid chromatography (2D-LC) method coupled with mass spectrometry (MS) was performed to characterize the charge heterogeneity of mAbs using CEX chromatography in the first dimension (D1) and anion-exchange chromatography (AEX) in the second dimension (D2). The main peak of the CEX profile of D1 was transferred through a 2D heart-cut method to D2 for further analysis by the AEX-MS method. In the CEX method, mAb A showed 10 distinct variants, while the AEX method resulted in eight variants. However, a total of 13 variants were successfully resolved for mAb A in the 2D method. Similarly, mAb B exhibited seven variants in the CEX method and four variants in the AEX method, but the 2D-LC method revealed a total of nine variants for mAb B. Likewise, mAb C displayed seven variants in CEX and seven variants in AEX, whereas the 2D-LC method unveiled a total of 11 variants for mAb C. Additionally, native MS analysis revealed that the resolved charge variants were identified as amidation, oxidation, and isomerization of Asp variants in the main peak, which were not resolved in stand-alone methods. The present study demonstrates how 2D-LC can assist in identifying minor variations in charge distribution or conformation of mAb variants that would otherwise not be picked up by a single analytical method alone.

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

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

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.

Recent advances in structural mass spectrometry methods in the context of biosimilarity assessment: from sequence heterogeneities to higher order structures

Biotherapeutics and their biosimilar versions have been flourishing in the biopharmaceutical market for several years. Structural and functional characterization is needed to achieve analytical biosimilarity through the assessment of critical quality attributes as required by regulatory authorities. The role of analytical strategies, particularly mass spectrometry-based methods, is pivotal to gathering valuable information for the in-depth characterization of biotherapeutics and biosimilarity assessment. Structural mass spectrometry methods (native MS, HDX-MS, top-down MS, etc.) provide information ranging from primary sequence assessment to higher order structure evaluation. This review focuses on recent developments and applications in structural mass spectrometry for biotherapeutic and biosimilar characterization.

Enhancing Selectivity of Protein Biopharmaceuticals in Ion Exchange Chromatography through Addition of Organic Modifiers

Charge heterogeneity among therapeutic monoclonal antibodies (mAbs) is considered an important critical quality attribute and requires careful characterization to ensure safe and efficacious drug products. The charge heterogeneity among mAbs is the result of chemical and enzymatic post-translational modifications and leads to the formation of acidic and basic variants that can be characterized using cation exchange chromatography (CEX). Recently, the use of mass spectrometry-compatible salt-mediated pH gradients has gained increased attention to elute the proteins from the charged stationary phase material. However, with the increasing antibody product complexity, more and more selectivity is required. Therefore, in this study, we set out to improve the selectivity by using a solvent-enriched mobile phase composition for the analysis of a variety of mAbs and bispecific antibody products. It was found that the addition of the solvents to the mobile phase appeared to modify the hydrate shell surrounding the protein and alter the retention behavior of the studied proteins. Therefore, this work demonstrates that the use of solvent-enriched mobile phase composition could be an attractive additional method parameter during method development in CEX.

Characterization of Higher Order Structural Changes of a Thermally Stressed Monoclonal Antibody via Mass Spectrometry Footprinting and Other Biophysical Approaches

Characterizing changes in the higher order structure (HOS) of monoclonal antibodies upon stressed conditions is critical to gaining a better understanding of the product and process. One single biophysical approach may not be best suited to assess HOS comprehensively; thus, the synergy from multiple, complementary approaches improves characterization accuracy and resolution. In this study, we employed two mass spectrometry (MS )-based footprinting techniques, namely, fast photochemical oxidation of proteins (FPOP)-MS and hydrogen-deuterium exchange (HDX)-MS, supported by dynamic light scattering (DLS), differential scanning calorimetry (DSC), circular dichroism (CD), and nuclear magnetic resonance (NMR) to study changes to the HOS of a mAb upon thermal stress. The biophysical techniques report a nuanced characterization of the HOS in which CD detects no changes to the secondary or tertiary structure, yet DLS measurements show an increase in the hydrodynamic radius. DSC indicates that the stability decreases, and chemical or conformational changes accumulate with incubation time according to NMR. Furthermore, whereas HDX-MS does not indicate HOS changes, FPOP-MS footprinting reveals conformational changes at residue resolution for some amino acids. The local phenomena observed with FPOP-MS indicate that several residues show various patterns of degradation during thermal stress: no change, an increase in solvent exposure, and a biphasic response to solvent exposure. All evidences show that FPOP-MS efficiently resolves subtle structural changes and novel degradation pathways upon thermal stress treatment at residue-level resolution.

Predicting Colloidal Stability of High-Concentration Monoclonal Antibody Formulations in Common Pharmaceutical Buffers Using Improved Polyethylene Glycol Induced Protein Precipitation Assay

Colloidal stability is an important consideration when developing high concentration mAb formulations. PEG-induced protein precipitation is a commonly used assay to assess the colloidal stability of protein solutions. However, the practical usefulness and the current theoretical model for this assay have yet to be verified over a large formulation space across multiple mAbs and mAb-based modalities. In the present study, we used PEG-induced protein precipitation assays to evaluate colloidal stability of 3 mAbs in 24 common formulation buffers at 20 and 5 °C. These prediction assays were conducted at low protein concentration (1 mg/mL). We also directly characterized high concentration (100 mg/mL) formulations for cold-induced phase separation, turbidity, and concentratibility by ultrafiltration. This systematic study allowed analysis of the correlation between the results of low concentration assays and the high concentration attributes. The key findings of this study include the following: (1) verification of the usefulness of three different parameters (Cmid, μB, and Tcloud) from PEG-induced protein precipitation assays for ranking colloidal stability of high concentration mAb formulations; (2) a new method to implement PEG-induced protein precipitation assay suitable for high throughput screening with low sample consumption; (3) improvement in the theoretical model for calculating robust thermodynamic parameters of colloidal stability (μB and εB) that are independent of specific experimental settings; (4) systematic evaluation of the effects of pH and buffer salts on colloidal stability of mAbs in common formulation buffers. These findings provide improved theoretical and practical tools for assessing the colloidal stability of mAbs and mAb-based modalities during formulation development.

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.

Expanding the Analytical Toolbox: Developing New Lys-C Peptide Mapping Methods with Minimized Assay-Induced Artifacts to Fully Characterize Antibodies

Peptide mapping is an important tool used to confirm that the correct sequence has been expressed for a protein and to evaluate protein post-translational modifications (PTMs) that may arise during the production, processing, or storage of protein drugs. Our new orally administered drug (Ab-1), a single-domain antibody, is highly stable and resistant to proteolysis. Analysis via the commonly used tryptic mapping method did not generate sufficient sequence coverage. Alternative methods were needed to study the Ab-1 drug substance (75 mg/mL) and drug product (3 mg/mL). To meet these analytical needs, we developed two new peptide mapping methods using lysyl endopeptidase (Lys-C) digestion. These newly developed protein digestion protocols do not require desalting/buffer-exchange steps, thereby reducing sample preparation time and improving method robustness. Additionally, the protein digestion is performed under neutral pH with methionine acting as a scavenger to minimize artifacts, such as deamidation and oxidation, which are induced during sample preparation. Further, the method for low-concentration samples performs comparably to the method for high-concentration samples. Both methods provide 100% sequence coverage for Ab-1, and, therefore, enable comprehensive characterization for its product quality attribute (PQA) assessment. Both methods can be used to study other antibody formats.

Predicting and Interpreting Protein Developability Via Transfer of Convolutional Sequence Representation

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

An assessment of the impact of Raman based glucose feedback control on CHO cell bioreactor process development

Process analytical technology (PAT) tools such as Raman Spectroscopy have become established tools for real time measurement of CHO cell bioreactor process variables and are aligned with the QbD approach to manufacturing. These tools can have a significant impact on process development if adopted early, creating an end-to-end PAT/QbD focused process. This study assessed the impact of Raman based feedback control on early and late phase development bioreactors by using a Raman based PLS model and PAT management system to control glucose in two CHO cell line bioreactor processes. The impact was then compared to bioreactor processes which used manual bolus fed methods for glucose feed delivery. Process improvements were observed in terms of overall bioreactor health, product output and product quality. Raman controlled batches for Cell Line 1 showed a reduction in glycation of 43.4% and 57.9%, respectively. Cell Line 2 batches with Raman based feedback control showed an improved growth profile with higher VCD and viability and a resulting 25% increase in overall product titer with an improved glycation profile. The results presented here demonstrate that Raman spectroscopy can be used in both early and late-stage process development and design for consistent and controlled glucose feed delivery.

Protein engineering via sequence-performance mapping

Discovery and evolution of new and improved proteins has empowered molecular therapeutics, diagnostics, and industrial biotechnology. Discovery and evolution both require efficient screens and effective libraries, although they differ in their challenges because of the absence or presence, respectively, of an initial protein variant with the desired function. A host of high-throughput technologies-experimental and computational-enable efficient screens to identify performant protein variants. In partnership, an informed search of sequence space is needed to overcome the immensity, sparsity, and complexity of the sequence-performance landscape. Early in the historical trajectory of protein engineering, these elements aligned with distinct approaches to identify the most performant sequence: selection from large, randomized combinatorial libraries versus rational computational design. Substantial advances have now emerged from the synergy of these perspectives. Rational design of combinatorial libraries aids the experimental search of sequence space, and high-throughput, high-integrity experimental data inform computational design. At the core of the collaborative interface, efficient protein characterization (rather than mere selection of optimal variants) maps sequence-performance landscapes. Such quantitative maps elucidate the complex relationships between protein sequence and performance-e.g., binding, catalytic efficiency, biological activity, and developability-thereby advancing fundamental protein science and facilitating protein discovery and evolution.

How can we discover developable antibody-based biotherapeutics?

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

Development of a highly sensitive imaged cIEF immunoassay for studying AAV capsid protein charge heterogeneity

Post-translational modifications (PTMs) of adeno-associated virus (AAV) capsid proteins tune and regulate the AAV infective life cycle, which can impact the safety and efficacy of AAV gene therapy products. Many of these PTMs induce changes in protein charge heterogeneity, including deamidation, oxidation, glycation, and glycosylation. To characterize the charge heterogeneity of a protein, imaged capillary isoelectric focusing (icIEF) has become the gold standard method. We have previously reported an icIEF method with native fluorescence detection for denatured AAV capsid protein charge heterogeneity analysis. Although well suited for final products, the method does not have sufficient sensitivity for upstream, low-concentration AAV samples, and lacks the specificity for capsid protein detection in complex samples like cell culture supernatants and cell lysates. In contrast, the combination of icIEF, protein capture, and immunodetection affords significantly higher sensitivity and specificity, addressing the challenges of the icIEF method. By leveraging different primary antibodies, the icIEF immunoassay provides additional selectivity and affords a detailed characterization of individual AAV capsid proteins. In this study, we describe an icIEF immunoassay method for AAV analysis that is 90 times more sensitive than native fluorescence icIEF. This icIEF immunoassay provides AAV stability monitoring, where changes in individual capsid protein charge heterogeneity can be observed in response to heat stress. When applied to different AAV serotypes, this method also provides serotype identity with reproducible quantification of VP protein peak areas and apparent isoelectric point (pI). Overall, the described icIEF immunoassay is a sensitive, reproducible, quantitative, specific, and selective tool that can be used across the AAV biomanufacturing process, especially in upstream process development where complex sample types are often encountered.

AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains

Over 120 FDA-approved antibody-based therapeutics are used to treat a variety of diseases.However, many candidates could fail because of unfavorable physicochemical properties. Light-chain amyloidosis is one form of aggregation that can lead to severe safety risks in clinical development. Therefore, screening candidates with a less amyloidosis risk at the early stage can not only save the time and cost of antibody development but also improve the safety of antibody drugs. In this study, based on the dipeptide composition of 742 amyloidogenic and 712 non-amyloidogenic antibody light chains, a support vector machine-based model, AB-Amy, was trained to predict the light-chain amyloidogenic risk. The AUC of AB-Amy reaches 0.9651. The excellent performance of AB-Amy indicates that it can be a useful tool for the in silico evaluation of the light-chain amyloidogenic risk to ensure the safety of antibody therapeutics under clinical development. A web server is freely available at http://i.uestc.edu.cn/AB-Amy/.

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.

An optimized thermodynamics integration protocol for identifying beneficial mutations in antibody design

Accurate identification of beneficial mutations is central to antibody design. Many knowledge-based (KB) computational approaches have been developed to predict beneficial mutations, but their accuracy leaves room for improvement. Thermodynamic integration (TI) is an alchemical free energy algorithm that offers an alternative technique for identifying beneficial mutations, but its performance has not been evaluated. In this study, we developed an efficient TI protocol with high accuracy for predicting binding free energy changes of antibody mutations. The improved TI method outperforms KB methods at identifying both beneficial and deleterious mutations. We observed that KB methods have higher accuracies in predicting deleterious mutations than beneficial mutations. A pipeline using KB methods to efficiently exclude deleterious mutations and TI to accurately identify beneficial mutations was developed for high-throughput mutation scanning. The pipeline was applied to optimize the binding affinity of a broadly sarbecovirus neutralizing antibody 10-40 against the circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant. Three identified beneficial mutations show strong synergy and improve both binding affinity and neutralization potency of antibody 10-40. Molecular dynamics simulation revealed that the three mutations improve the binding affinity of antibody 10-40 through the stabilization of an altered binding mode with increased polar and hydrophobic interactions. Above all, this study presents an accurate and efficient TI-based approach for optimizing antibodies and other biomolecules.

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.

Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction

Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.

A multiparameter optimization in middle-down analysis of monoclonal antibodies by LC-MS/MS

In antibody-based drug research, a complete characterization of antibody proteoforms covering both the amino acid sequence and all posttranslational modifications remains a major concern. The usual mass spectrometry-based approach to achieve this goal is bottom-up proteomics, which relies on the digestion of antibodies but does not allow the diversity of proteoforms to be assessed. Middle-down and top-down approaches have recently emerged as attractive alternatives but are not yet mastered and thus used in routine by many analytical chemistry laboratories. The work described here aims at providing guidelines to achieve the best sequence coverage for the fragmentation of intact light and heavy chains generated from a simple reduction of intact antibodies using Orbitrap mass spectrometry. Three parameters were found crucial to this aim: the use of an electron-based activation technique, the multiplex selection of precursor ions of different charge states, and the combination of replicates.

Trastuzumab Charge Variants: a Study on Physicochemical and Pharmacokinetic Properties

Background

Post-translational modifications in bioprocessing and storage of recombinant mAbs are the main sources of charge variants. While the profile of these kinds of variants is considered an important attribute for the therapeutic mAbs, there is controversy about their direct role in safety and efficacy. In this study, the physicochemical and pharmacokinetic (PK) properties of the separated charge variants belonging to a trastuzumab potential biosimilar, were examined.

Methods

The acidic peaks, basic peaks, and main variants of trastuzumab were separated and enriched by semi-preparative weak cation exchange. A panel of analytical techniques was utilized to characterize the physicochemical properties of these variants. The binding affinity to HER2 and FcγRs and the PK parameters were evaluated for each variant.

Results

Based on the results, the charge variants of the proposed biosimilar had no significant influence on the examined efficacy and PK parameters.

Conclusion

During the development and production of biosimilar monoclonal antibodies, evaluating the effect of their charge variants on efficacy and PK parameters is needed.

Benefits and Limitations of High-Resolution Cyclic IM-MS for Conformational Characterization of Native Therapeutic Monoclonal Antibodies

Monoclonal antibodies (mAbs) currently represent the main class of therapeutic proteins. mAbs approved by regulatory agencies are selected from IgG1, IgG2, and IgG4 subclasses, which possess different interchain disulfide connectivities. Ion mobility coupled to native mass spectrometry (IM-MS) has emerged as a valuable approach to tackle the challenging characterization of mAbs' higher order structures. However, due to the limited resolution of first-generation IM-MS instruments, subtle conformational differences on large proteins have long been hard to capture. Recent technological developments have aimed at increasing available IM resolving powers and acquisition mode capabilities, namely, through the release of high-resolution IM-MS (HR-IM-MS) instruments, like cyclic IM-MS (cIM-MS). Here, we outline the advantages and drawbacks of cIM-MS for better conformational characterization of intact mAbs (∼150 kDa) in native conditions compared to first-generation instruments. We first assessed the extent to which multipass cIM-MS experiments could improve the separation of mAbs' conformers. These initial results evidenced some limitations of HR-IM-MS for large native biomolecules which possess rich conformational landscapes that remain challenging to decipher even with higher IM resolving powers. Conversely, for collision-induced unfolding (CIU) approaches, higher resolution proved to be particularly useful (i) to reveal new unfolding states and (ii) to enhance the separation of coexisting activated states, thus allowing one to apprehend gas-phase CIU behaviors of mAbs directly at the intact level. Altogether, this study offers a first panoramic overview of the capabilities of cIM-MS for therapeutic mAbs, paving the way for more widespread HR-IM-MS/CIU characterization of mAb-derived formats.

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.

Analytical Techniques for the Characterization and Quantification of Monoclonal Antibodies

Monoclonal antibodies (mAbs) are a fast-growing class of biopharmaceuticals. They are widely used in the identification and detection of cell makers, serum analytes, and pathogenic agents, and are remarkably used for the cure of autoimmune diseases, infectious diseases, or malignancies. The successful application of therapeutic mAbs is based on their ability to precisely interact with their appropriate target sites. The precision of mAbs rely on the isolation techniques delivering pure, consistent, stable, and safe lots that can be used for analytical, diagnostic, or therapeutic applications. During the creation of a biologic, the key quality features of a particular mAb, such as structure, post-translational modifications, and activities at the biomolecular and cellular levels, must be characterized and profiled in great detail. This implies the requirement of powerful state of the art analytical techniques for quality control and characterization of mAbs. Until now, various analytical techniques have been developed to characterize and quantify the mAbs according to the regulatory guidelines. The present review summarizes the major techniques used for the analyses of mAbs which include chromatographic, electrophoretic, spectroscopic, and electrochemical methods in addition to the modifications in these methods for improving the quality of mAbs. This compilation of major analytical techniques will help students and researchers to have an overview of the methodologies employed by the biopharmaceutical industry for structural characterization of mAbs for eventual release of therapeutics in the drug market.

Protein Stability After Administration: A Physiologic Consideration

Regulatory authorities and the scientific community have identified the need to monitor the in vivo stability of therapeutic proteins (TPs). Due to the unique physiologic conditions in patients, the stability of TPs after administration can deviate largely from their stability under drug product (DP) conditions. TPs can degrade at substantial rates once immersed in the in vivo milieu. Changes in protein stability upon administration to patients are critical as they can have implications on patient safety and clinical effectiveness of DPs. Physiologic conditions are challenging to simulate and require dedicated in vitro models for specific routes of administration. Advancements of in vitro models enable to simulate the exposure to physiologic conditions prior to resource demanding pre-clinical and clinical studies. This enables to evaluate the in vivo stability and thus may allow to improve the safety/efficacy profile of DPs. While in vitro-in vivo correlations are challenging, benchmarking DP candidates enables to identify liabilities and optimize molecules. The in vivo stability should be an integral part of holistic stability assessments during early development. Such assessments can accelerate development timelines and lead to more stable DPs for patients.

Toward real-world automated antibody design with combinatorial Bayesian optimization

Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.

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.

Assessing developability early in the discovery process for novel biologics

Beyond potency, a good developability profile is a key attribute of a biological drug. Selecting and screening for such attributes early in the drug development process can save resources and avoid costly late-stage failures. Here, we review some of the most important developability properties that can be assessed early on for biologics. These include the influence of the source of the biologic, its biophysical and pharmacokinetic properties, and how well it can be expressed recombinantly. We furthermore present in silico, in vitro, and in vivo methods and techniques that can be exploited at different stages of the discovery process to identify molecules with liabilities and thereby facilitate the selection of the most optimal drug leads. Finally, we reflect on the most relevant developability parameters for injectable versus orally delivered biologics and provide an outlook toward what general trends are expected to rise in the development of biologics.

A systematic review of commercial high concentration antibody drug products approved in the US: formulation composition, dosage form design and primary packaging considerations

Three critical aspects that define high concentration antibody products (HCAPs) are as follows: 1) formulation composition, 2) dosage form, and 3) primary packaging configuration. HCAPs have become successful in the therapeutic sector due to their unique advantage of allowing subcutaneous self-administration. Technical challenges, such as physical and chemical instability, viscosity, delivery volume limitations, and product immunogenicity, can hinder successful development and commercialization of HCAPs. Such challenges can be overcome by robust formulation and process development strategies, as well as rational selection of excipients and packaging components. We compiled and analyzed data from US Food and Drug Administration-approved and marketed HCAPs that are ≥100 mg/mL to identify trends in formulation composition and quality target product profile. This review presents our findings and discusses novel formulation and processing technologies that enable the development of improved HCAPs at ≥200 mg/mL. The observed trends can be used as a guide for further advancements in the development of HCAPs as more complex antibody-based modalities enter biologics product development.

Analytical Workflows to Unlock Predictive Power in Biotherapeutic Developability

PURPOSE

Forming accurate data models that assist the design of developability assays is one area that requires a deep and practical understanding of the problem domain. We aim to incorporate expert knowledge into the model building process by creating new metrics from instrument data and by guiding the choice of input parameters and Machine Learning (ML) techniques.

METHODS

We generated datasets from the biophysical characterisation of 5 monoclonal antibodies (mAbs). We explored combinations of techniques and parameters to uncover the ones that better describe specific molecular liabilities, such as conformational and colloidal instability. We also employed ML algorithms to predict metrics from the dataset.

RESULTS

We found that the combination of Differential Scanning Calorimetry (DSC) and Light Scattering thermal ramps enabled us to identify domain-specific aggregation in mAbs that would be otherwise overlooked by common developability workflows. We also found that the response to different salt concentrations provided information about colloidal stability in agreement with charge distribution models. Finally, we predicted DSC transition temperatures from the dataset, and used the order of importance of different metrics to increase the explainability of the model.

CONCLUSIONS

The new analytical workflows enabled a better description of molecular behaviour and uncovered links between structural properties and molecular liabilities. In the future this new understanding will be coupled with ML algorithms to unlock their predictive power during developability assessment.

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

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

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.

High concentration formulation developability approaches and considerations

The growing need for biologics to be administered subcutaneously and ocularly, coupled with certain indications requiring high doses, has resulted in an increase in drug substance (DS) and drug product (DP) protein concentrations. With this increase, more emphasis must be placed on identifying critical physico-chemical liabilities during drug development, including protein aggregation, precipitation, opalescence, particle formation, and high viscosity. Depending on the molecule, liabilities, and administration route, different formulation strategies can be used to overcome these challenges. However, due to the high material requirements, identifying optimal conditions can be slow, costly, and often prevent therapeutics from moving rapidly into the clinic/market. In order to accelerate and derisk development, new experimental and in-silico methods have emerged that can predict high concentration liabilities. Here, we review the challenges in developing high concentration formulations, the advances that have been made in establishing low mass and high-throughput predictive analytics, and advances in in-silico tools and algorithms aimed at identifying risks and understanding high concentration protein behavior.

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.

Embedding Dynamics in Intrinsic Physicochemical Profiles of Market-Stage Antibody-Based Biotherapeutics

Adequate stability, manufacturability, and safety are crucial to bringing an antibody-based biotherapeutic to the market. Following the concept of holistic in silico developability, we introduce a physicochemical description of 91 market-stage antibody-based biotherapeutics based on orthogonal molecular properties of variable regions (Fvs) embedded in different simulation environments, mimicking conditions experienced by antibodies during manufacturing, formulation, and in vivo. In this work, the evaluation of molecular properties includes conformational flexibility of the Fvs using molecular dynamics (MD) simulations. The comparison between static homology models and simulations shows that MD significantly affects certain molecular descriptors like surface molecular patches. Moreover, the structural stability of a subset of Fv regions is linked to changes in their specific molecular interactions with ions in different experimental conditions. This is supported by the observation of differences in protein melting temperatures upon addition of NaCl. A DEvelopability Navigator In Silico (DENIS) is proposed to compare mAb candidates for their similarity with market-stage biotherapeutics in terms of physicochemical properties and conformational stability. Expanding on our previous developability guidelines (Ahmed et al. Proc. Natl. Acad. Sci. 2021, 118 (37), e2020577118), the hydrodynamic radius and the protein strand ratio are introduced as two additional descriptors that enable a more comprehensive in silico characterization of biotherapeutic drug candidates. Test cases show how this approach can facilitate identification and optimization of intrinsically developable lead candidates. DENIS represents an advanced computational tool to progress biotherapeutic drug candidates from discovery into early development by predicting drug properties in different aqueous environments.