Generation of mAb Variants with Less Attractive Self-Interaction but Preserved Target Binding by Well-Directed Mutation

Reduction of monoclonal antibody viscosity using interpretable machine learning

Early identification of antibody candidates with drug-like properties is essential for simplifying the development of safe and effective antibody therapeutics. For subcutaneous administration, it is important to identify candidates with low self-association to enable their formulation at high concentration while maintaining low viscosity, opalescence, and aggregation. Here, we report an interpretable machine learning model for predicting antibody (IgG1) variants with low viscosity using only the sequences of their variable (Fv) regions. Our model was trained on antibody viscosity data (>100 mg/mL mAb concentration) obtained at a common formulation pH (pH 5.2), and it identifies three key Fv features of antibodies linked to viscosity, namely their isoelectric points, hydrophobic patch sizes, and numbers of negatively charged patches. Of the three features, most predicted antibodies at risk for high viscosity, including antibodies with diverse antibody germlines in our study (79 mAbs) as well as clinical-stage IgG1s (94 mAbs), are those with low Fv isoelectric points (Fv pIs < 6.3). Our model identifies viscous antibodies with relatively high accuracy not only in our training and test sets, but also for previously reported data. Importantly, we show that the interpretable nature of the model enables the design of mutations that significantly reduce antibody viscosity, which we confirmed experimentally. We expect that this approach can be readily integrated into the drug development process to reduce the need for experimental viscosity screening and improve the identification of antibody candidates with drug-like properties.

Variable domain mutational analysis to probe the molecular mechanisms of high viscosity of an IgG1 antibody

Subcutaneous injection is the preferred route of administration for many antibody therapeutics for reasons that include its speed and convenience. However, the small volume limit (typically ≤ 2 mL) for subcutaneous delivery often necessitates antibody formulations at high concentrations (commonly ≥100 mg/mL), which may lead to physicochemical problems. For example, antibodies with large hydrophobic or charged patches can be prone to self-interaction giving rise to high viscosity. Here, we combined X-ray crystallography with computational modeling to predict regions of an anti-glucagon receptor (GCGR) IgG1 antibody prone to self-interaction. An extensive mutational analysis was undertaken of the complementarity-determining region residues residing in hydrophobic surface patches predicted by spatial aggregation propensity, in conjunction with residue-level solvent accessibility, averaged over conformational ensembles from molecular dynamics simulations. Dynamic light scattering (DLS) was used as a medium throughput screen for self-interaction of ~ 200 anti-GCGR IgG1 variants. A negative correlation was found between the viscosity determined at high concentration (180 mg/mL) and the DLS interaction parameter measured at low concentration (2-10 mg/mL). Additionally, anti-GCGR variants were readily identified with reduced viscosity and antigen-binding affinity within a few fold of the parent antibody, with no identified impact on overall developability. The methods described here may be useful in the optimization of other antibodies to facilitate their therapeutic administration at high concentration.

Hydrogen-Deuterium Exchange Epitope Mapping of Glycosylated Epitopes Enabled by Online Immobilized Glycosidase

Hydrogen-deuterium exchange coupled with mass spectrometry (HDX-MS) is widely used for monoclonal antibody (mAb) epitope mapping, which aids in the development of therapeutic mAbs and vaccines, as well as enables the understanding of viral immune evasion. Numerous mAbs are known to recognize N-glycosylated epitopes and to bind in close proximity to an N-glycan site; however, glycosylated protein sites are typically obscured from HDX detection as a result of the inherent heterogeneity of glycans. To overcome this limitation, we covalently immobilized the glycosidase PNGase Dj on a solid resin and incorporated it into an online HDX-MS workflow for post-HDX deglycosylation. The resin-immobilized PNGase Dj exhibited robust tolerance to various buffer conditions and was employed in a column format that can be readily adapted into a typical HDX-MS platform. Using this system, we were able to obtain full sequence coverage of the SARS-CoV-2 receptor-binding domain (RBD) and map the glycosylated epitope of the glycan-binding mAb S309 to the RBD.

Recent advancements in mass spectrometry for higher order structure characterization of protein therapeutics

Molecular characterization of higher order structure (HOS) in protein therapeutics is crucial to the selection of candidate molecules, understanding of structure-function relationships, formulation development, stability assessment, and comparability studies. Recent advances in mass spectrometry (MS), including native MS, hydrogen/deuterium exchange (HDX)-MS, and fast photochemical oxidation of proteins (FPOP) coupled with MS, have provided orthogonal ways to characterize HOS of protein therapeutics. In this review, we present the utility of native MS, HDX-MS and FPOP-MS in protein therapeutics discovery and development, with a focus on epitope mapping, aggregation assessment, and comparability studies. We also discuss future trends in the application of these MS methods to HOS characterization.