Tramadol, an analgesic classified as an “atypical opioid”, exhibits both opioid and non-opioid mechanisms of action. This study aimed to explore these mechanisms, specifically the opioid-, cannabinoid-, nitric oxide-, and potassium channel-based mechanisms, which contribute to the peripheral antinociception effect of tramadol, in an experimental rat model. The nociceptive threshold was determined using paw pressure withdrawal. To examine the mechanisms of action, several substances were administered intraplantarly: naloxone, a non-selective opioid antagonist (50 µg/paw); AM251 (80 µg/paw) and AM630 (100 µg/paw) as the selective antagonists for types 1 and 2 cannabinoid receptors, respectively; nitric oxide synthase inhibitors L-NOArg, L-NIO, L-NPA, and L-NIL (24 µg/paw); and the enzyme inhibitors of guanylatocyclase and phosphodiesterase of cGMP, ODQ, and zaprinast. Additionally, potassium channel blockers glibenclamide, tetraethylammonium, dequalinium, and paxillin were used. The results showed that opioid and cannabinoid receptor antagonists did not reverse tramadol's effects. L-NOarg, L-NIO, and L-NPA partially reversed antinociception, while ODQ completely reversed, and zaprinast enhanced tramadol's antinociception effect. Notably, glibenclamide blocked tramadol's antinociception in a dose-dependent manner. These findings suggest that tramadol's peripheral antinociception effect is likely mediated by the nitrergic pathway and sensitive ATP potassium channels, rather than the opioid and cannabinoid pathways.

Background: Cytochrome P450 (CYP450) enzymes play a central role in the metabolism of xenobiotics, including plant-derived compounds such as terpenoids. Objectives: This study aimed to predict the molecular interactions of linalool (LIN) and linalyl acetate (LINAct), major constituents of lavender essential oil, with the canine CYP2B11, CYP2C21, and CYP2D15 isoforms, using in silico approaches. Methods: Three-dimensional (3D) models of the target enzymes were generated through homology modeling using SWISS-MODEL and validated based on global model quality estimate (GMQE) and QMEAN Z-score metrics. Ligand structures were optimized in the Molecular Operating Environment (MOE), and pharmacophoric features were analyzed. Molecular docking simulations were performed using AutoDock Vina, followed by visualization of interactions in MOE. Results: LIN and LINAct exhibit favorable binding affinities with all three isoforms, suggesting their potential as substrates or modulators. Hydrogen bonding and hydrophobic interactions were the predominant forces stabilizing the ligand–enzyme complexes. Conclusions: These findings provide a computational basis for understanding the hepatic metabolism of LIN and LINAct in dogs, offering preliminary insights into the role of specific CYP isoforms in their biotransformation.