Elucidation of DPP‐4 involvement in systemic distribution and renal reabsorption of linagliptin by PBPK modeling with a cluster Gauss–Newton method

Abstract

Abstract The dipeptidyl peptidase‐4 (DPP‐4) inhibitor linagliptin (LNG) exhibits target‐mediated drug disposition (TMDD) in clinical settings, characterized by saturable binding to plasma soluble DPP‐4 (sDPP‐4) and tissue transmembrane DPP‐4 (tDPP‐4). Previous studies have indicated that saturable renal reabsorption of LNG contributes to its nonlinear urinary excretion observed in humans and wild‐type mice, but not in Dpp‐4 knockout mice. To elucidate the mechanisms underlying these complex phenomena, including DPP‐4‐related renal reabsorption of LNG, we employed physiologically‐based pharmacokinetic (PBPK) modeling combined with a cluster Gauss–Newton method (CGNM). The CGNM facilitated the exploration of parameters in rat and human PBPK models for LNG and the determination of parameter identifiability. Through PBPK–CGNM analysis using reported autoradiography data ([14C]‐LNG) in wild‐type and Dpp‐4‐deficient rats, DPP‐4‐specific distributions of LNG in various tissues were clearly differentiated from nonspecific parts. By fitting to human plasma concentrations and urinary and fecal excretions of LNG after intravenous and oral administrations, multiple unknown PBPK parameters were simultaneously estimated by the CGNM. Notably, the amount of tDPP‐4 and the reabsorption clearance for LNG–DPP‐4 complexes were identifiable, indicating their critical role in explaining the complex nonlinear pharmacokinetics of LNG. Compared with previous PBPK analyses, the CGNM allowed us to incorporate greater model complexity (e.g., consideration of tDPP‐4 expressions and in vitro binding kinetics), ultimately resulting in a more accurate reproduction of LNG's TMDD. In conclusion, by considering LNG as a high‐affinity probe for DPP‐4, comprehensive PBPK–CGNM analyses suggested a dynamic whole‐body distribution of DPP‐4, including its involvement in the renal reabsorption of LNG.