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.

Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics.

Machine learning has been recently used to predict therapeutic antibody aggregation rates and viscosity at high concentrations (150 mg/ml). These works focused on commercially available antibodies, which may have been optimized for stability. In this study, we measured accelerated aggregation rates at 45°C and viscosity at 150 mg/ml for 20 preclinical and clinical-stage antibodies.

Critical reagents for ligand-binding assays: process development methodologies to enable high-quality reagents.

Development of biotherapeutics require pharmacokinetic/pharmacodynamic (PK/PD) and immunogenicity assays that are frequently in a ligand-binding assay (LBA) format. Conjugated critical reagents for LBAs are generated conjugation of the biotherapeutic drug or anti-drug molecule with a label. Since conjugated critical reagent quality impacts LBA performance, control of the generation process is essential.

Molecular computations of preferential interactions of proline, arginine.HCl, and NaCl with IgG1 antibodies and their impact on aggregation and viscosity.

Preferential interactions of excipients with the antibody surface govern their effect on the stability of antibodies in solution. We probed the preferential interactions of proline, arginine.HCl (Arg.

Machine Learning Models of Antibody-Excipient Preferential Interactions for Use in Computational Formulation Design.

Preferential interactions of formulation excipients govern their impact on the stability properties of proteins in solution. The ability to predict these interactions without the need to perform experiments would enable formulation design to begin early in the development of a new antibody therapeutic. With that in mind, we developed a feature set to numerically describe local regions of an antibody's surface for use in machine learning applications.

Highland games: A benchmarking exercise in predicting biophysical and drug properties of monoclonal antibodies from amino acid sequences.

Biopharmaceutical product and process development do not yet take advantage of predictive computational modeling to nearly the degree seen in industries based on smaller molecules. To assess and advance progress in this area, spirited coopetition (mutually beneficial collaboration between competitors) was successfully used to motivate industrial scientists to develop, share, and compare data and methods which would normally have remained confidential. The first "Highland Games" competition was held in conjunction with the October 2018 Recovery of Biological Products Conference in Ashville, NC, with the goal of benchmarking and assessment of the ability to predict development-related properties of six antibodies from their amino acid sequences alone.

Molecular Computations of Preferential Interaction Coefficients of IgG1 Monoclonal Antibodies with Sorbitol, Sucrose, and Trehalose and the Impact of These Excipients on Aggregation and Viscosity.

Preferential interactions of formulation excipients govern their overall interactions with protein molecules, and molecular dynamics simulations allow for the examination of the interactions at the molecular level. We used molecular dynamics simulations to examine the interactions of sorbitol, sucrose, and trehalose with three different IgG1 antibodies to gain insight into how these excipients impact aggregation and viscosity. We found that sucrose and trehalose reduce aggregation more than sorbitol because of their larger size and their stronger interactions with high-spatial aggregation propensity residues compared to sorbitol.

Understanding the Role of Preferential Exclusion of Sugars and Polyols from Native State IgG1 Monoclonal Antibodies and its Effect on Aggregation and Reversible Self-Association.

Purpose: To investigate differences in the preferential exclusion of trehalose, sucrose, sorbitol and mannitol from the surface of three IgG1 monoclonal antibodies (mAbs) and understand its effect on the aggregation and reversible self-association of mAbs at high-concentrations.

Methods: Preferential exclusion was measured using vapor pressure osmometry. Effect of excipient addition on accelerated aggregation kinetics was quantified using size exclusion chromatography and on reversible self-association was quantified using dynamic light scattering.

Developability Assessment of Engineered Monoclonal Antibody Variants with a Complex Self-Association Behavior Using Complementary Analytical and in Silico Tools.

Monoclonal antibodies (mAbs) are complex molecular structures. They are often prone to development challenges particularly at high concentrations due to undesired solution properties such as reversible self-association, high viscosity, and liquid-liquid phase separation. In addition to formulation optimization, applying protein engineering can provide an alternative mitigation strategy.

Utility of High Throughput Screening Techniques to Predict Stability of Monoclonal Antibody Formulations During Early Stage Development.

Selecting optimal formulation conditions for monoclonal antibodies for first time in human clinical trials is challenging due to short timelines and reliance on predictive assays to ensure product quality and adequate long-term stability. Accelerated stability studies are considered to be the gold standard for excipient screening, but they are relatively low throughput and time consuming. High throughput screening (HTS) techniques allow for large amounts of data to be collected quickly and easily, and can be used to screen solution conditions for early formulation development.

An "Fc-Silenced" IgG1 Format With Extended Half-Life Designed for Improved Stability.

Multiple mutation combinations in the IgG Fc have been characterized to tailor immune effector function or IgG serum persistence to fit desired biological outcomes for monoclonal antibody therapeutics. An unintended consequence of introducing mutations in the Fc (particularly the C2 domain) can be a reduction in biophysical stability which can correlate with increased aggregation propensity, poor manufacturability, and lower solubility. Herein, we characterize the changes in IgG conformational and colloidal stability when 2 sets of C2 mutations "TM" (L234F/L235E/P331S) and "YTE" (M252Y/S254T/T256E) are combined to generate an antibody format lacking immune receptor binding and exhibiting extended half-life.

Computational tool for the early screening of monoclonal antibodies for their viscosities.

Highly concentrated antibody solutions often exhibit high viscosities, which present a number of challenges for antibody-drug development, manufacturing and administration. The antibody sequence is a key determinant for high viscosity of highly concentrated solutions; therefore, a sequence- or structure-based tool that can identify highly viscous antibodies from their sequence would be effective in ensuring that only antibodies with low viscosity progress to the development phase. Here, we present a spatial charge map (SCM) tool that can accurately identify highly viscous antibodies from their sequence alone (using homology modeling to determine the 3-dimensional structures).