Fatima Zahra Marok (1), Elizaveta Burgakova (1), Laura Fuhr (1), Thorsten Lehr (1)
(1) Clinical Pharmacy, Saarland University, Saarbruecken, Germany
Introduction: Bupropion is a noradrenaline and dopamine reuptake inhibitor used for treatment of major depressive disorder and smoking cessation [1]. In therapy, the compound is either administered alone or in combination with further anti-depressant agents. Bupropion itself is pharmacologically active, but also transformed to three active metabolites by cytochrome P450 (CYP) 2B6 as well as several carbonyl reductases. Furthermore, it is reported to be substrate of CYP2C19 and CYP3A4 [2]. As CYP2B6 is the main metabolizing enzyme, bupropion pharmacokinetics (PK) is altered during co-administration of CYP2B6 perpetrator drugs like efavirenz or rifampicin [3, 4]. Additionally, bupropion as well as its three active metabolites bear a relevant drug-drug-interaction (DDI) potential by inhibiting CYP2D6, which is responsible for the metabolism of several anti-depressant drugs such as desipramine [5].
Objectives:
- To build a physiologically based pharmacokinetic (PBPK) model for bupropion and its metabolites hydroxybupropion, erythrohydrobupropion and threohydrobupropion
- To predict the application of bupropion as immediate (IR), sustained (SR) and extended release (ER) tablet formulation
- To predict DDIs of bupropion and its metabolites as CYP2D6 perpetrator drugs regarding the CYP2D6 victim drug desipramine
- To predict DDIs of bupropion as a CYP2B6 victim drug regarding the CYP2B6 perpetrator drug rifampicin
Methods: The PBPK models were developed with PK-Sim® and MoBi® (version 8.0) as part of the Open Systems Pharmacology Suite [6]. Data for model development were extracted from literature, including physicochemical parameters and plasma concentration-time profiles for all compounds. Data were separated into a training and test dataset for model development and evaluation, respectively. Additionally, an already published rifampicin model [7] was used for evaluation of the CYP2B6 DDI. An unpublished in-house desipramine model was used for prediction of CYP2D6 DDI. For model evaluation, predicted and observed plasma concentration-time profiles were compared as well as was their respective area under concentration-time curve (AUC) and maximum concentrations (Cmax).
Results: Four whole-body PBPK models for bupropion and its metabolites hydroxybupropion, erythrohydrobupropion and threohydrobupropion were developed. The compiled data consists of 23 studies (dose range of 50-300 mg, applied as IR, SR or ER tablets in 215 healthy volunteers). Metabolism of bupropion is described by (i) transformation via CYP2B6 to hydroxybupropion, (ii) transformation via 11b-hydroxysteroid dehydrogenase-1 to erythro- and threohydrobupropion and (iii) transformation by CYP2C19 summarizing metabolism to unknown non-active metabolites. Hydroxy-, erythrohydro and threohydrobupropion are furthermore metabolized via the uridine 5′-diphospho-glucuronosyltransferase (UGT) 2B7. The models precisely predict the PK of all four compounds for single and multiple dose application for immediate, sustained and extended release formulations. More than 80 % of the predicted AUC and Cmax values lie within the two-fold acceptance range of the observed data. Furthermore, the mean predicted versus observed AUC ratio is 1.07 for bupropion, hydroxybupropion, erythrohydrobupropion and threohydrobupropion, whereas the mean predicted versus observed Cmax ratio is 1.22. Additionally, mean predicted to observed DDI AUC ratios for the rifampicin DDIs show ratios of 1.55 and 1.56 for bupropion and hydroxybupropion, respectively.
Conclusions: A comprehensive set of PBPK models for bupropion, hydroxybupropion and the two enantiomers erythrohydrobupropion and threohydrobupropion was successfully developed. The models capture the important impact of DDIs regarding bupropion application. Furthermore, the models introduce a newly established DDI network regarding CYP2B6.
Funding: The project has received support from the project “Open-source modelling 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] M. Fava et al., “15 Years of Clinical Experience With Bupropion HCl,” Prim. Care Companion J. Clin. Psychiatry, vol. 07, no. 03, pp. 106–113, Jun. 2005.
[2] J. E. Sager, L. S. L. Price, and N. Isoherranen, “Stereoselective Metabolism of Bupropion to OH-bupropion, Threohydrobupropion, Erythrohydrobupropion, and 4’-OH-bupropion in vitro,” Drug Metab. Dispos., vol. 44, no. 10, pp. 1709–1719, Sep. 2016.
[3] S. M. Robertson, F. Maldarelli, V. Natarajan, E. Formentini, R. M. Alfaro, and S. R. Penzak, “Efavirenz induces CYP2B6-mediated hydroxylation of bupropion in healthy subjects,” J. Acquir. Immune Defic. Syndr., vol. 49, no. 5, pp. 513–519, 2008.
[4] K. K. Loboz et al., “Cytochrome P450 2B6 activity as measured by bupropion hydroxylation: Effect of induction by rifampin and ethnicity,” Clin. Pharmacol. Ther., vol. 80, no. 1, pp. 75–84, 2006.
[5] M. J. Reese, R. M. Wurm, K. T. Mur, G. T. Generaux, L. St. John-Williams, and D. J. McCohn, “An in Vitro Mechanistic Study to Elucidate the Desipramine/Bupropion Clinical Drug-Drug Interaction,” Pharmacology, vol. 36, no. 7, pp. 1198–1201, 2008.
[6] T. Eissing et al., “A computational systems biology software platform for multiscale modeling and simulation: integrating whole-body physiology, disease biology, and molecular reaction networks.,” Front. Physiol., vol. 2, no. February, pp. 1–10, 2011.
[7] N. Hanke 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., p. Supplementary document, Aug. 2018.
Reference: PAGE () Abstr 9333 [www.page-meeting.org/?abstract=9333]
Poster: Drug/Disease Modelling - Absorption & PBPK