Physiologically Based Pharmacokinetic Modeling of Dasatinib to Describe Enzyme-Mediated and pH-Dependent Drug–Drug Interaction Scenarios
Christina Kovar (1,2), Helena Leonie Hanae Loer (1), Matthias Schwab (2,3) and Thorsten Lehr (1)
(1) Clinical Pharmacy, Saarland University, Saarbrücken, Germany (2) Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology and University of Tübingen, Stuttgart, Germany (3) Departments of Clinical Pharmacology, Pharmacy and Biochemistry, University of Tübingen, Tübingen, Germany
Introduction: The clinical application of dasatinib - a second-generation tyrosine kinase inhibitor - in the treatment of chronic myeloid leukemia is associated with various challenges [1]. Firstly, since approximately 80% of the absorbed dasatinib dose is metabolized via cytochrome P450 (CYP) 3A4 [2], systemic exposure can be affected by enzyme-mediated drug–drug interactions (DDIs) involving CYP3A4 inhibitors and inducers [3,4]. Secondly, as a weak base and a Biopharmaceutical Classification System (BCS) class II compound demonstrating low solubility and high permeability, dasatinib is additionally susceptible to pH-dependent DDIs resulting from acid-reducing agents (ARAs) like proton pump inhibitors [5,6]. Lastly, as patients have a mean age of 64 years at diagnosis [7], DDI liability is further increased due to multiple co-morbidities and polypharmacy [8]. To tackle these difficulties in dasatinib therapy, physiologically based pharmacokinetic (PBPK) modeling can be a useful tool to quantitatively describe and predict DDI scenarios.
Objectives:
- Development of a whole-body PBPK model of dasatinib
- Prediction of enzyme-mediated and pH-dependent DDI scenarios involving dasatinib as victim drug
Methods: A PBPK model of dasatinib was developed in PK-Sim® (version 11.0) as part of the Open Systems Pharmacology Suite [9]. In a comprehensive literature search, among others, information on physicochemical properties as well as plasma profiles of healthy volunteers and cancer patients after orally administered dasatinib in single- and multiple-dose studies were collected. Plasma profiles were divided into a training and a test dataset for model building (n=6 profiles) and evaluation (n=54 profiles), respectively. Furthermore, published PBPK models of the strong CYP3A4 inducer rifampicin and the strong CYP3A4 inhibitor ketoconazole were used to evaluate the impact of CYP3A4 on the exposure of dasatinib [10,11]. The impaired solubility of dasatinib due to intake of the ARAs rabeprazole, famotidine and the antacid containing aluminium and magnesium hydroxides was captured by increasing the gastric pH according to the literature. Model performance was assessed by graphically comparing observed to predicted plasma profiles as well as their respective area under the plasma concentration–time curve (AUC) and maximum plasma concentration (Cmax) values. For the DDIs, predicted and observed AUC and Cmax ratios were calculated. Moreover, model evaluation was complemented with the computation of geometric mean fold errors (GMFEs) of predicted and observed AUC and Cmax values/ratios.
Results: A dasatinib PBPK model was developed based on a total of 60 plasma profiles from orally administered dasatinib in healthy volunteers and cancer patients with a dose range from 15 to 200 mg. Dasatinib’s metabolism via CYP3A4 was implemented as Michaelis-Menten kinetic process, while non-CYP3A4 mediated metabolism was captured by an unspecific hepatic clearance. The Michaelis-Menten constant (Km) for the metabolism via CYP3A4 was extracted from the literature, while the respective catalytic rate constant (kcat) and the unspecific hepatic clearance parameter were estimated. The established model is able to precisely describe and predict the plasma profiles of dasatinib with GMFE values for AUC and Cmax of 1.39 and 1.45, respectively. Moreover, a good performance in DDI predictions is demonstrated by GMFEs of 1.39 and 1.53 for predicted DDI AUC and Cmax ratios.
Conclusions: A whole-body PBPK model for dasatinib was successfully developed. The model is capable to describe and predict dasatinib plasma profiles in both healthy volunteers and cancer patients for different DDI scenarios including enzyme-mediated and pH-dependent DDIs with dasatinib as victim drug. Moreover, the established model can be applied to simulate further DDI scenarios with perpetrator drugs that have not been investigated yet in clinical trials and subsequently support model-informed precision dosing of dasatinib to enhance treatment safety and efficacy.
Funding: This work was funded by the Robert Bosch Stiftung (Stuttgart, Germany) and a grant from the German Federal Ministry of Education and Research (BMBF 031L0188D, “GUIDE-IBD”). Thorsten Lehr was supported by the German Federal Ministry of Education and Research (BMBF, Horizon 2020 INSPIRATION grant 643271), under the frame of ERACoSysMed.
References:
[1] European Medicines Agency (EMA) Annex I: Summary of product characteristics (Sprycel). Available online: https://www.ema.europa.eu/en/documents/product-information/sprycel-epar-product-information_en.pdf.
[2] Christopher, L.J. et al. Metabolism and disposition of dasatinib after oral administration to humans. Drug Metab. Dispos. 2008, 36, 1357–64.
[3] Johnson, F.M. et al. Phase 1 pharmacokinetic and drug-interaction study of dasatinib in patients with advanced solid tumors. Cancer 2010, 116, 1582–91.
[4] Center for Drug Evaluation and Research Clinical Pharmacology and Biopharmaceutics Review(s): NDA Review - Dasatinib. Available online: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2006/021986s000_Sprycel__ClinPharmR.pdf.
[5] Yago, M.R. et al. The use of betaine HCl to enhance dasatinib absorption in healthy volunteers with rabeprazole-induced hypochlorhydria. AAPS J. 2014, 16, 1358–65.
[6] Eley, T. et al. Phase I study of the effect of gastric acid pH modulators on the bioavailability of oral dasatinib in healthy subjects. J. Clin. Pharmacol. 2009, 49, 700–9.
[7] Amercian Cancer Society Cancer Facts and Figures. Available online: https://www.cancer.org/cancer/chronic-myeloid-leukemia/about/statistics.html.
[8] Al-Ameri, M.N. et al. Prevalence of Poly-pharmacy in the Elderly: Implications of Age, Gender, Co-morbidities and Drug Interactions. SOJ Pharm. Pharm. Sci. 2014, 1(3), 1–7.
[9] Lippert, J. et al. Open Systems Pharmacology Community-An Open Access, Open Source, Open Science Approach to Modeling and Simulation in Pharmaceutical Sciences. CPT pharmacometrics Syst. Pharmacol. 2019, 8, 878–882.
[10] Marok, F.Z. et al. Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug–Drug Interaction Perpetrators. Pharmaceutics 2023, 15.
[11] Hanke, N. et al. PBPK Models for CYP3A4 and P-gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin. CPT pharmacometrics Syst. Pharmacol. 2018, 7, 647–659.