Translational Approach for Predicting Human Pharmacokinetics of Engineered Therapeutic Monoclonal Antibodies with Increased FcRn-Binding Mutations

INTRODUCTION: Recently, increasing FcRn binding by Fc engineering has become a promising approach for prolonging the half-life of therapeutic monoclonal antibodies (mAbs). This study is the first to investigate the optimization of an allometric scaling approach for engineered mAbs based on cynomolgus monkey data to predict human pharmacokinetics. METHODS: Linear two-compartmental model parameters (clearance [CL]; volume of distribution in the central compartment [V(c)]; inter-compartmental clearance [Q]; volume of distribution in the peripheral compartment [V(p)]) after the intravenous (IV) injection of engineered mAbs (M252Y/S254T/T256E or M428L/N434S mutations) in cynomolgus monkeys and humans were collected from published data. We explored the optimal exponent for allometric scaling to predict parameters in humans based on cynomolgus monkey data. Moreover, the plasma concentration–time profile of engineered mAbs after IV injection in humans was predicted using parameters estimated based on an optimized exponent. RESULTS: For engineered mAbs, a significant positive correlation between cynomolgus monkeys and humans was observed for CL, but not for other parameters. Whereas conventional exponents (CL: 0.8, Q: 0.75, V(c): 1.0, V(p): 0.95) previously established for normal mAbs showed poor prediction accuracy for CL and Q of engineered mAbs, the newly optimized exponents (CL: 0.55, Q: 0.6, V(c): 0.95, V(p): 0.95) achieved superior predictability for engineered mAbs. Moreover, the optimized exponents accurately predicted plasma mAb concentration–time profiles after IV injection of engineered mAbs in humans. CONCLUSIONS: We found that engineered mAbs require specially optimized exponents to accurately predict pharmacokinetic parameters and plasma concentration–time profiles after IV injections in humans based on cynomolgus monkey data. This optimized approach can contribute to a more accurate prediction of human pharmacokinetics in the development of engineered mAbs.