Laura Maria Fuhr, Thorsten Lehr
Clinical Pharmacy, Saarland University, 66123 Saarbruecken
Introduction: Cytochrome P450 (CYP) 3A4 is highly expressed in liver and intestine and metabolizes up to 50% of marketed drugs, independent of the drug class [1]. Therefore, the evaluation of the drug-drug interaction (DDI) potential of a compound through CYP3A4 metabolism, inhibition or induction is very important, not only during drug development. The anticonvulsant carbamazepine is a substrate of CYP3A4 [2] and furthermore classified by the FDA as a strong index inducer of the respective enzyme [3]. DDIs are clinically relevant for patients under long-term carbamazepine treatment, as they often receive concomitant drugs, e.g. for their anticonvulsant therapy or for treatment of other diseases [4]. Physiologically based pharmacokinetic (PBPK) modeling is a helpful tool to quantitatively describe and predict the pharmacokinetics (PK) of carbamazepine as well as the impact of DDIs regarding CYP3A4 metabolism, where carbamazepine can act as a victim or a perpetrator drug.
Objectives:
- To develop a whole-body PBPK model of carbamazepine
Methods: The PBPK model was developed with PK-Sim® and MoBi® (Version 8.0, Open Systems Pharmacology [5]). Drug-dependent parameters as well as plasma concentration-time profiles of carbamazepine and of its main metabolite carbamazepine-10,11-epoxide from clinical studies were gathered during an extensive literature research. The digitized concentration-time profiles were divided into a training and a test dataset, used for model building and model evaluation, respectively. Parameters that could not be informed from literature were optimized by fitting the model to the entire training dataset. Predicted plasma concentration-time profiles of carbamazepine were compared to the observed profiles of the test dataset and further evaluated in goodness-of-fit plots. The performance of the final PBPK model was additionally evaluated by comparison of predicted to observed area under the plasma concentration-time curve (AUC) and maximum plasma concentration (Cmax) values. As quantitative measures of the descriptive and predictive model performance, mean relative deviations (MRDs) of all predicted compared to observed plasma concentrations and geometric mean fold errors (GMFEs) of all AUC and Cmax ratios (predicted compared to observed PK parameter) were calculated. Model prediction was deemed successful when the predicted PK parameters lay within the two-fold acceptance range of observed values.
Results: A total of 30 clinical studies, providing 38 carbamazepine and 16 carbamazepine-10,11-epoxide plasma profiles, was used for the development of the carbamazepine parent-metabolite PBPK model. The model applies metabolism of carbamazepine by CYP2C8 and CYP3A4 to the carbamazepine-10,11-epoxide and by UDP Glucuronosyltransferase 2B7. All metabolic pathways were described by Michaelis-Menten kinetics. The autoinductive effect of carbamazepine on CYP3A4 was implemented, describing the synthesis rate of CYP3A4 by an Emax model. Carbamazepine-10,11-epoxide is metabolized by epoxide hydrolase 1, described by first order kinetics. The studies of the training and test datasets are well described and predicted. 92% and 68% of all simulated plasma concentrations deviate less than two-fold from the observed values for carbamazepine and carbamazepine-10,11-epoxide, respectively. The mean MRD for carbamazepine and carbamazepine-10,11-epoxide plasma concentrations over all studies is 1.44 and 1.89, respectively. AUC and Cmax ratios of carbamazepine show GMFEs of 1.25 (range 1.00-1.93, n=38) and 1.19 (range 1.01-1.68, n=38), while GMFEs for AUC and Cmax ratios for carbamazepine-10,11-epoxide are 1.61 (1.01-2.72, n=16) and 1.51 (1.11-2.56, n=16), respectively.
Conclusions: A whole-body PBPK model of carbamazepine has been successfully developed. The model precisely describes and predicts plasma concentration-time profiles of carbamazepine and its metabolite carbamazepine-10,11-epoxide over a wide dosing range. As a future application, the model will be coupled to CYP3A4 victim or perpetrator drugs, to investigate the DDI potential of carbamazepine. Furthermore, the drug is classified by the FDA as strong inducer of CYP2B6 and weak inducer of CYP2C9 [3], which will also be evaluated by coupling the model with sensitive substrates of respective enzymes.
Funding: The project has received support from the project “Open-source modeling framework for automated quality control and management of complex life science systems models” (OSMOSES), which is funded by the German Federal Ministry of Education and Research (BMBF, grant ID: 031L0161C).
References:
[1] Tirona RG, Kim RB. Introduction to clinical pharmacology, in Robertson G and Williams GH (ed.) Clinical and translational science. Academic Press (2017) 365-388.
[2] Kerr BM, Thummel KE, Wurden CJ. Human liver carbamazepine metabolism. Role of CYP3A4 and CYP2C8 in 10,11-epoxide formation. Biochemical Pharmacology. (1994) 47(11):1969-1979.
[3] U.S. Food and Drug Administration. Drug development and drug interactions: table of substrates, inhibitors and inducers. https://www.fda.gov/drugs/drug-interactions-labeling/drug-development-and-drug-interactions-table-substrates-inhibitors-and-inducers. Accessed: 10 Feb 2020.
[4] Spina E, Pisani F, Perucca E. Clinically significant pharmacokinetic drug interactions with carbamazepine. Clin Pharmacokinet. (1996) 31(3):198-214.
[5] Open Systems Pharmacology. http://www.open-systems-pharmacology.org/. Accessed: 10 Feb 2020
Reference: PAGE () Abstr 9349 [www.page-meeting.org/?abstract=9349]
Poster: Drug/Disease Modelling - Absorption & PBPK