Pharmaceutical research · 2026
Binding Kinetics Can Explain Linear and Nonlinear Pharmacokinetics of Dipeptidyl Peptidase-IV Inhibitors Within a Single Target-Mediated Framework.
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Elalem Adam, Ette Mfonabasi E, Mager Donald E
Аннотация
INTRODUCTION: Dipeptidyl peptidase-IV inhibitors (DPP4i) are a class of small-molecule drugs for the treatment of patients with type 2 diabetes mellitus. Despite their common mechanism of action, the pharmacokinetics (PK) of DPP4i are varied, with some exhibiting nonlinear target-mediated drug disposition (TMDD). The purpose of this analysis is to test the hypothesis that differences in binding kinetics can explain the relative contribution of TMDD to the variable PK across six drugs in this class.
METHODS: Mean PK profiles of six drugs at various dose levels were digitized from the literature, and noncompartmental analysis was performed to determine linearity. All data were analyzed simultaneously with a TMDD model using nonlinear mixed-effects modeling in Monolix. The percentage contribution of target-mediated clearance for each drug was calculated and compared with binding rate constants.
RESULTS: A TMDD model with a peripheral compartment effectively captured the pharmacokinetics of all six drugs. The final model supported the hypothesis that the slower rate of dissociation (koff) is responsible for the TMDD behavior in select drugs in this class and generated a shared set of system parameters to describe the PK of DPP4i. Population parameters were estimated with adequate precision. A single system-specific total receptor concentration was estimated (0.9 nM).
CONCLUSION: A single TMDD model was established for all six DPP4i. The final model explains the behavior of the selected drugs despite their class binding specificity, and a nonlinear structural model was able to adequately capture the behavior of linear drugs when properly constrained.
METHODS: Mean PK profiles of six drugs at various dose levels were digitized from the literature, and noncompartmental analysis was performed to determine linearity. All data were analyzed simultaneously with a TMDD model using nonlinear mixed-effects modeling in Monolix. The percentage contribution of target-mediated clearance for each drug was calculated and compared with binding rate constants.
RESULTS: A TMDD model with a peripheral compartment effectively captured the pharmacokinetics of all six drugs. The final model supported the hypothesis that the slower rate of dissociation (koff) is responsible for the TMDD behavior in select drugs in this class and generated a shared set of system parameters to describe the PK of DPP4i. Population parameters were estimated with adequate precision. A single system-specific total receptor concentration was estimated (0.9 nM).
CONCLUSION: A single TMDD model was established for all six DPP4i. The final model explains the behavior of the selected drugs despite their class binding specificity, and a nonlinear structural model was able to adequately capture the behavior of linear drugs when properly constrained.