LAUSR

Background and objective The subcutaneous injection of therapeutic monoclonal antibodies is increasingly used in the treatment of several diseases because of its convenience. Thus, a simple and accurate method of predicting the pharmacokinetics of monoclonal antibodies after a subcutaneous injection in humans would be a valuable tool for preclinical/clinical development. In this study, we investigated whether the pharmacokinetics of monoclonal antibodies after a subcutaneous injection in humans can be predicted using only pharmacokinetic data after a subcutaneous injection in cynomolgus monkeys. Methods First, we compared the accuracy of three approaches to predict the apparent clearance (CL/ F ) and apparent volume of distribution ( V d / F ) for 15 monoclonal antibodies in humans (1) allometric scaling from cynomolgus monkeys; (2) geometric mean of reported values in humans; (3) estimation from a regression line based on CL/ F in humans [only V d / F ]). Then, using the predicted CL/ F and V d / F , and the geometric mean of reported absorption rate constant of mAbs the plasma concentration–time profiles of 13 monoclonal antibodies after subcutaneous injections in humans were simulated. Results In a comparison of approaches, the first approach showed the best prediction accuracy for CL/ F with an exponent of 0.9 (100% and 73% prediction accuracy within 2- and 1.5-fold of the observed value),and the third approach was the best for V d / F (100% prediction accuracy within 1.5-fold of the observed value). Next, using the first approach for CL/ F and the third approach for V d / F , we accurately predicted the plasma concentration–time profiles of 13 monoclonal antibodies after subcutaneous injections in humans. Conclusion This simple approach can be applied in preclinical and clinical settings to predict the pharmacokinetics of monoclonal antibodies after subcutaneous injections in humans. Further, this approach requires only CL/ F after a subcutaneous injection in cynomolgus monkeys, contributing to animal welfare and reducing costs.