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

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

ABSTRACTBiologic 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