Laura Maria Fuhr (1), Fatima Zahra Marok (1), Thorsten Lehr (1)
(1) Department of Clinical Pharmacy, Saarland University, Saarbrücken, Germany
Introduction: The tricyclics amitriptyline and nortriptyline are both indicated for the treatment of several conditions, including depressive disorders or neuropathic pain [1]. While nortriptyline is a metabolite of amitriptyline, it is also applied as a drug itself [1]. Here, the demethylation of amitriptyline to form nortriptyline is primarily mediated through cytochrome P450 (CYP) 2C19 [2]. Additionally, CYP2D6 is responsible for the hydroxylation of amitriptyline and nortriptyline [1,2]. Due to the extensive metabolism via the highly polymorphic enzymes CYP2D6 and CYP2C19, the pharmacokinetics of both drugs are highly susceptible to drug-gene interactions (DGIs) [1], as well as drug-drug interactions (DDIs) [3]. Here, physiologically based pharmacokinetic (PBPK) modeling can provide valuable insight into the impact of DGIs and DDIs on the pharmacokinetics of amitriptyline and nortriptyline.
Objectives:
- To establish a PBPK model of nortriptyline and a parent-metabolite PBPK model of amitriptyline and nortriptyline
- To describe CYP2D6 and CYP2C19 mediated DGIs for amitriptyline and nortriptyline
- To describe DDIs with nortriptyline and amitriptyline as victim drugs
Methods: The PBPK models were developed with PK-Sim® (Version 11.2, Open Systems Pharmacology [4]). Information on the physicochemical properties as well as the absorption, distribution, metabolism, and elimination (ADME) of amitriptyline and nortriptyline were gathered from the literature, and plasma concentration-time profiles of both compounds were digitized from published clinical studies. Respective profiles were divided into a training and a test dataset, used for model building and evaluation, respectively. Parameters that could not be informed from the literature were optimized using the parameter identification tool of PK-Sim®. The models were evaluated by (1) comparing predicted plasma concentration-time profiles to the observed profiles, (2) comparing predicted to the observed area under the plasma concentration-time curve (AUC) and maximum plasma concentration (Cmax) values in goodness-of-fit plots and (3) calculating mean relative deviations (MRDs) of all predicted plasma concentrations and geometric mean fold errors (GMFEs) of all AUC and Cmax ratios.
Results: A PBPK model of nortriptyline was built first and extended to include the parent drug amitriptyline. Seven plasma concentration-time profiles from five clinical studies were used for the development of the nortriptyline PBPK model. The model describes the metabolism of nortriptyline via CYP3A4 and CYP2D6. To describe CYP2D6 mediated DGIs, the enzyme activity for different CYP2D6 activity scores was determined according to Rüdesheim et al. [5]. With mean MRD value of 1.29 (range: 1.11 – 1.78), the nortriptyline PBPK model performed adequately and was subsequently utilized for the development of an amitriptyline-nortriptyline parent-metabolite PBPK model. Here, 20 clinical studies, providing 38 and 31 plasma concentration-time profiles of amitriptyline and nortriptyline, were used for model development. The parent-metabolite model describes the metabolism of amitriptyline to nortriptyline via CYP2C19 and CYP3A4 as well as the metabolism of amitriptyline by CYP2D6. Similar to CYP2D6 mediated DGIs of nortriptyline, CYP2D6 activity was determined for different activity score groups according to Rüdesheim et al. [5], while the activity of CYP2C19 was optimized for different activity score groups during a parameter identification. The adequate performance of the parent-metabolite PBPK model is indicated by mean MRD values of 1.74 (range: 1.04 – 3.57) for amitriptyline and 1.67 (range: 1.08 – 4.19) for nortriptyline. Subsequently, the developed PBPK models were applied to predict DDIs with paroxetine and ketoconazole, using published PBPK models of respective compounds [5,6]. The DDIs are sufficiently described with mean GMFEs of 1.15 and 1.19 for predicted DDI Cmax and AUC ratios calculated for the paroxetine-nortriptyline DDI and GMFEs of 1.19 and 1.13 for predicted DDI Cmax and AUC ratios calculated for the ketoconazole-amitriptyline DDI.
Conclusion: A whole-body PBPK model of nortriptyline and a parent-metabolite PBPK model of amitriptyline and nortriptyline has been successfully developed and applied to describe DGIs and DDIs involving CYP2C19 and CYP2D6. Overall, the models can be applied to investigate the impact of DDIs, DGIs as well as complex drug-drug gene interactions.
Funding: The project has received support from the project “Improve Safety in Polymedication by Managing Drug-Drug-Gene Interactions” (SafePolyMed). The SafePolyMed project receives funding from the European Union’s Horizon Europe Research and Innovation Programme under Grant Agreement No. 101057639. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the Health and Digital Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.
References:
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[3] Laine K, Tybring G, Härtter S, et al. Inhibition of cytochrome P4502D6 activity with paroxetine normalizes the ultrarapid metabolizer phenotype as measured by nortriptyline pharmacokinetics and the debrisoquin test. Clin Pharmacol Ther. 2001;70(4):327–335.
[4] Open Systems Pharmacology. Open Systems Pharmacology Community [Internet]. 2023 [cited 2023 Sep 27]. Available from: www.open-systems-pharmacology.org.
[5] Rüdesheim S, Selzer D, Mürdter T, et al. Physiologically Based Pharmacokinetic Modeling to Describe the CYP2D6 Activity Score-Dependent Metabolism of Paroxetine, Atomoxetine and Risperidone. Pharmaceutics. 2022;14(8):1734.
[6] Marok FZ, Wojtyniak J, Fuhr LM, et al. A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug–Drug Interaction Perpetrators. Pharmaceutics. 2023;15(2):679.
Reference: PAGE 32 (2024) Abstr 11211 [www.page-meeting.org/?abstract=11211]
Poster: Drug/Disease Modelling - Absorption & PBPK