Gabriella Ribeiro 1,4, Beatriz Paranhos 1, Fabiane Dörr 1, Maurício Yonamine 1, Bianca Villanova 2, Lorena Guerra 2, Adrieli Raminelli 2, Jose Reis 2, Caio de Paula 2, Anna Zacharias 2, Jaime Hallak 2,3, Rafael dos Santos 2,3, Frederico Martins 1, Tania Marcourakis 1
1 University of Sao Paulo (Sao Paulo, Brazil), 2 Ribeirao Preto Medical School, University of Sao Paulo (Ribeirao Preto, Brazil), 3 National Institute of Translational Science and Technology in Medicine (Ribeirao Preto, Brazil), 4 KU Leuven (Leuven, Belgium)
Introduction: Ayahuasca is a psychedelic preparation containing N,N-dimethyltryptamine (DMT) and β-carbolines, such as harmine (HRM), a reversible monoamine oxidase A (MAO-A) inhibitor responsible for enabling the oral bioavailability of DMT [1]. The concomitant use of ayahuasca with selective serotonin reuptake inhibitors (SSRIs), such as fluoxetine (FL) and paroxetine (PR), which are recognized CYP2D6 inhibitors, has raised concerns regarding potential pharmacokinetic interactions, since both DMT and HRM undergo cytochrome P450-mediated metabolism [2]. CYP2D6 is a highly polymorphic enzyme associated with marked interindividual variability in drug exposure [3]. However, its influence on the pharmacokinetics of ayahuasca alkaloids remains poorly understood. In this context, physiologically based pharmacokinetic (PBPK) modeling emerges as a promising tool for predicting drug–drug interactions and simulating genetic variability scenarios, including different metabolizer phenotypes.
Objectives: This study aimed to develop and qualify PBPK models to predict interactions between ayahuasca alkaloids and SSRIs. Additionally, scenarios of poor, normal, and ultra-rapid metabolizers were simulated to evaluate the isolated impact of CYP2D6 genetic variability on the systemic exposure of DMT and HRM.
Methods: PBPK models for DMT and HRM were developed and qualified using PK-Sim version 11.3, based on plasma concentration–time data from a controlled clinical study in which six volunteers received oral ayahuasca (Ribeiro et al., 2026). The models for FL, norfluoxetine (NFL), and PR were developed using published clinical data and incorporated enzyme inhibition parameters to represent their inhibitory potential [5,6]. Drug–drug interaction simulations were performed under both acute and chronic SSRI dosing conditions. Additionally, simulations were conducted to evaluate the impact of CYP2D6 genetic polymorphisms on the pharmacokinetics of DMT and HRM using the same previously developed and validated PBPK model. For these simulations, CYP2D6 expression was adjusted to represent different phenotypes: 0 for PMs, 1 for NMs, and 2 for UMs.
Results: Both FL and PR increased HRM exposure and caused moderate increases in systemic DMT concentrations. HRM exposure increased approximately 1.2–1.3-fold, while DMT exposure increased approximately 2.1-fold, indicating a moderate pharmacokinetic interaction between these compounds. These changes suggest that coadministration may alter metabolic clearance processes, leading to higher systemic availability of both analytes. Simulations incorporating different CYP2D6 phenotypes demonstrated a clear influence of genetic variability on exposure levels. Poor metabolizers exhibited increased systemic exposure to DMT, with AUC rising by 53.34% and Cmax by 40.49%, and similarly increased HRM exposure, with AUC increasing by 30.6% and Cmax by 22.8% compared to normal metabolizers. In contrast, ultrarapid metabolizers showed reduced exposure to both compounds, with DMT AUC decreasing by 25.1% and Cmax by 21.23%, and HRM AUC decreasing by 25.27% and Cmax by 16.7% relative to NMs. Overall, these findings indicate that both metabolic inhibition and CYP2D6 polymorphisms substantially modulate the magnitude and direction of systemic exposure changes.
Conclusion: The PBPK models enabled the simulation of DMT and HRM pharmacokinetics, as well as the evaluation of drug–drug interaction scenarios and the impact of CYP2D6 polymorphisms. The simulations indicated that SSRIs alter exposure to ayahuasca alkaloids, producing interactions of moderate magnitude, and that CYP2D6 genetic variability can significantly influence the systemic exposure of DMT and HRM.
References:
[1] McKenna D, Riba J. New world tryptamine hallucinogens and the neuroscience of ayahuasca. 2015.
[2] Maldonado EN, Lemasters JJ. Warburg revisited: regulation of mitochondrial metabolism by voltage-dependent anion channels in cancer cells. J Pharmacol Exp Ther. 2012;342:637–641.
[3] Ribeiro GSG et al. Physiologically-based pharmacokinetic modeling of enantioselective hydroxychloroquine kinetics and impact of genetic polymorphisms. Braz J Pharm Sci. 2025;61:e24302.
[4] Ribeiro GSG et al. Predicting drug–drug interactions between ayahuasca alkaloids and SSRIs using physiologically based pharmacokinetic modeling. Front Mol Biosci. 2026.
[5] Cho CK et al. PBPK modeling to predict the pharmacokinetics of venlafaxine and its active metabolite in different CYP2D6 genotypes and drug–drug interactions with clarithromycin and paroxetine. Arch Pharm Res. 2024;47:481–504.
[6] Jeong H et al. Prediction of fluoxetine and norfluoxetine pharmacokinetic profiles using physiologically based pharmacokinetic modeling. J Clin Pharm. 2021;61:1505–1513.
Reference: PAGE 34 (2026) Abstr 11915 [www.page-meeting.org/?abstract=11915]
Poster: Drug/Disease Modelling - Safety