Reduction of monoclonal antibody viscosity using interpretable machine learning

ABSTRACTEarly 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

Part 1. High-Throughput Mass Spectrometry for Biopharma: A Universal Modality and Target Independent Analytical Method for Accurate Biomolecule Characterization

Reversed-phase liquid chromatographic mass spectrometry (rpLC-MS) is a universal, platformed, and essential analytical technique within pharmaceutical and biopharmaceutical research. Typical rpLC method gradient times can range from 5 to 20 min. As monoclonal antibody (mAb) therapies continue to evolve and bispecific antibodies (BsAbs) become more established, research stage engineering panels will clearly evolve in size. Therefore, high-throughput (HT) MS and automated deconvolution methods are key for success. Additionally, newer therapeutics such as bispecific T-cell engagers and nucleic acid-based modalities will also require MS characterization. Herein, we present a modality and target agnostic HT solid-phase extraction (SPE) MS method that affords the analysis of a 96-well plate in 41.4 min, compared to the traditional rpLC-MS method that would typically take 14.4 h. The described method can accurately determine the molecular weights for monodispersed and highly polydispersed biotherapeutic species and membrane proteins; determine levels of glycosylation, glycation, and formylation; detect levels of chain mispairing; and determine accurate drug-to-antibody ratio values

Degradable PEGylated protein conjugates utilizing RAFT polymerization

Poly(ethylene glycol) (PEG)-protein therapeutics exhibit enhanced pharmacokinetics, but have drawbacks including decreased protein activities and polymer accumulation in the body. Therefore a major aim for second-generation polymer therapeutics is to introduce degradability into the backbone. Herein we describe the synthesis of poly(poly(ethylene glycol methyl ether methacrylate)) (pPEGMA) degradable polymers with protein-reactive end-groups via reversible addition-fragmentation chain transfer (RAFT) polymerization, and the subsequent covalent attachment to lysozyme through a reducible disulfide linkage. RAFT copolymerization of cyclic ketene acetal (CKA) monomer 5,6-benzo-2-methylene-1,3-dioxepane (BMDO) with PEGMA yielded two polymers with number-average molecular weight (M(n)) (GPC) of 10.9 and 20.9 kDa and molecular weight dispersities (Ð) of 1.34 and 1.71, respectively. Hydrolytic degradation of the polymers was analyzed by (1)H-NMR and GPC under basic and acidic conditions. The reversible covalent attachment of these polymers to lysozyme, as well as the hydrolytic and reductive cleavage of the polymer from the protein, was analyzed by gel electrophoresis and mass spectrometry. Following reductive cleavage of the polymer, an increase in activity was observed for both conjugates, with the released protein having full activity. This represents a method to prepare PEGylated proteins, where the polymer is readily cleaved from the protein and the main chain of the polymer is degradable