Machine Learning Applied to Determine the Molecular Descriptors Responsible for the Viscosity Behavior of Concentrated Therapeutic Antibodies

Predicting the solution viscosity
of monoclonal antibody (mAb)
drug products remains as one of the main challenges in antibody drug
design, manufacturing, and delivery. In this work, the concentration-dependent
solution viscosity of 27 FDA-approved mAbs was measured at pH 6.0
in 10 mM histidine-HCl. Six mAbs exhibited high viscosity (>30
cP)
in solutions at 150 mg/mL mAb concentration. Combining molecular modeling
and machine learning feature selection, we found that the net charge
in the mAbs and the amino acid composition in the Fv region are key
features which govern the viscosity behavior. For mAbs whose behavior
was not dominated by charge effects, we observed that high viscosity
is correlated with more hydrophilic and fewer hydrophobic residues
in the Fv region. A predictive model based on the net charges of mAbs
and a high viscosity index is presented as a fast screening tool for
classifying low- and high-viscosity mAbs.