Abstract
BACKGROUND AND OBJECTIVES: Cytochrome P450 2C9 (CYP2C9) is involved in the biotransformation of many commonly used drugs, and significant drug interactions have been reported for CYP2C9 substrates. Previously published physiologically based pharmacokinetic (PBPK) models of tolbutamide are based on an assumption that its metabolic clearance is exclusively through CYP2C9; however, many studies indicate that CYP2C9 metabolism is only responsible for 80-90% of the total clearance. Therefore, these models are not useful for predicting the magnitude of CYP2C9 drug-drug interactions (DDIs). This paper describes the development and verification of SimCYP(®)-based PBPK models that accurately describe the human pharmacokinetics of tolbutamide when dosed alone or in combination with the CYP2C9 inhibitors sulfaphenazole and tasisulam. METHODS: A PBPK model was optimized in SimCYP(®) for tolbutamide as a CYP2C9 substrate, based on published in vitro and clinical data. This model was verified to replicate the magnitude of DDI reported with sulfaphenazole and was further applied to simulate the DDI with tasisulam, a small molecule investigated for the treatment of cancer. A clinical study (CT registration # NCT01185548) was conducted in patients with cancer to assess the pharmacokinetic interaction of tasisulum with tolbutamide. A PBPK model was built for tasisulam, and the clinical study design was replicated using the optimized tolbutamide model. RESULTS: The optimized tolbutamide model accurately predicted the magnitude of tolbutamide AUC increase (5.3-6.2-fold) reported for sulfaphenazole. Furthermore, the PBPK simulations in a healthy volunteer population adequately predicted the increase in plasma exposure of tolbutamide in patients with cancer (predicted AUC ratio = 4.7-5.4; measured mean AUC ratio = 5.7). CONCLUSIONS: This optimized tolbutamide PBPK model was verified with two strong CYP2C9 inhibitors and can be applied to the prediction of CYP2C9 interactions for novel inhibitors. Furthermore, this work highlights the utility of mechanistic models in navigating the challenges in conducting clinical pharmacology studies in cancer patients.