In-silico prediction of concentration-dependent viscosity curves for monoclonal antibody solutions

Abstract

Early stage developability assessments of monoclonal antibody (mAb) candidates can help reduce risks and costs associated with their product development. Forecasting viscosity of highly concentrated mAb solutions is an important aspect of such developability assessments. Reliable predictions of concentration-dependent viscosity behaviors for mAb solutions in platform formulations can help screen or optimize drug candidates for flexible manufacturing and drug delivery options. Here, we present a computational method to predict concentration-dependent viscosity curves for mAbs solely from their sequence—structural attributes. This method was developed using experimental data on 16 different mAbs whose concentration-dependent viscosity curves were experimentally obtained under standardized conditions. Each concentration-dependent viscosity curve was fitted with a straight line, via logarithmic manipulations, and the values for intercept and slope were obtained. Intercept, which relates to antibody diffusivity, was found to be nearly constant. In contrast, slope, the rate of increase in solution viscosity with solute concentration, varied significantly across different mAbs, demonstrating the importance of intermolecular interactions toward viscosity. Next, several molecular descriptors for electrostatic and hydrophobic properties of the 16 mAbs derived using their full-length homology models were examined for potential correlations with the slope. An equation consisting of hydrophobic surface area of full-length antibody and charges on VH, VL, and hinge regions was found to be capable of predicting the concentration-dependent viscosity curves of the antibody solutions. Availability of this computational tool may facilitate material-free high-throughput screening of antibody candidates during early stages of drug discovery and development.