Abstract

Bosentan is a high-affinity antagonist of endothelin receptors and one of the earliest examples for target-mediated drug disposition (a type of nonlinear pharmacokinetics (PKs) caused by saturable target binding). The previous physiologically-based PK (PBPK) modeling indicated that the nonlinear PKs of bosentan was explainable by considering saturable hepatic uptake. However, it remained unexamined to what extent the saturable target binding contributes to the nonlinear PKs of bosentan. Here, we developed a PBPK model incorporating saturable target binding and hepatic uptake, and analyzed the clinical bosentan PK data using the Cluster Gauss-Newton method (CGNM). The PBPK model without target binding fell short in capturing the bosentan concentrations below 100 nM, based on the PK profiles and the Goodness-of-Fit plot. Both global and local identifiability analyses (using the CGNM and Fisher Information Matrix, respectively) informed that the target binding parameters were identifiable only if the observations from the lowest dose (10 mg) were included. By analyzing blood PK profiles alone, the PBPK model with target binding yielded practically identifiable target binding parameters and predicted the maximum target occupancies of 0.6-0.8 at clinical bosentan doses. Our results indicate that target binding, albeit not a major contributor to the nonlinear bosentan PKs, may offer a prediction of target occupancy from blood PK profiles alone and potential guidance on achieving optimal efficacy outcomes, under the condition when the high-affinity drug target is responsible for the efficacy of interest, and when the dose ranges cover varying degrees of target binding.

Significance Statement By incorporating saturable target binding, our PBPK model predicted in vivo target occupancy of bosentan based only on the blood concentration–time profiles obtained from a wide range of doses. Our analysis highlights the potential utility of PBPK models that incorporate target binding in predicting target occupancy in vivo.