Toward a Combinatorial Approach for the Prediction of IgG Half-Life and Clearance

The serum half-life and clearance of therapeutic monoclonal antibodies (mAbs) are critical factors that impact their efficacy and optimal dosing regimen. The pH-dependent binding of an mAb to the neonatal Fc receptor (FcRn) has long been recognized as an important determinant of its pharmacokinetics. However, FcRn affinity alone is not a reliable predictor of mAb half-life, suggesting that other biologic or biophysical mechanisms must be accounted for. mAb thermal stability, which reflects its unfolding and aggregation propensities, may also relate to its pharmacokinetic properties. However, no rigorous statistical regression methods have been used to identify combinations of physical parameters that best predict biologic properties. In this work, a panel of eight mAbs with published human pharmacokinetic data were selected for biophysical analyses of FcRn binding and thermal stability. Biolayer interferometry was used to characterize FcRn/mAb binding at acidic and neutral pH, while differential scanning calorimetry was used to determine thermodynamic unfolding parameters. Individual binding or stability parameters were generally weakly correlated with half-life and clearance values. Least absolute shrinkage and selection operator regression was used to identify the combination of two parameters with the best correlation to half-life and clearance as being the FcRn binding response at pH 7.0 and the change in heat capacity. Leave-one-out subsampling yielded a root mean square difference between observed and predicted half-life of just 2.7 days (16%). Thus, the incorporation of multiple biophysical parameters into a cohesive model may facilitate early-stage prediction of in vivo half-life and clearance based on simple in vitro experiments.