Physiologically based pharmacokinetic modeling of tramadol to inform dose adjustment and drug‐drug interactions according to CYP2D6 phenotypes

Abstract

Objectives The objective of this study was to establish physiologically based pharmacokinetic (PBPK) models of tramadol and its active metabolite O‐desmethyltramadol (M1) and to explore the influence of CYP2D6 gene polymorphism on the pharmacokinetics of tramadol and M1. Furthermore, we used PBPK modeling to prospectively predict the extent of drug‐drug interactions (DDIs) in the presence of genetic polymorphisms when tramadol was co‐administered with the CYP2D6 inhibitors duloxetine and paroxetine. Methods Plasma concentrations of tramadol and M1 were used to adjust the turnover frequency (Kcat) of CYP2D6 for phenotype populations with different CYP2D6 genotypes. PBPK models were developed to capture the pharmacokinetics between CYP2D6 extensive metabolizers (EMs), intermediate metabolizers (IMs), poor metabolizers (PMs), and ultra‐rapid metabolizers (UMs). The validated models were then used to support dose adjustment in different CYP2D6 phenotypes and to predict the extent of CYP2D6‐mediated DDIs when tramadol was co‐administered with paroxetine or duloxetine. Results The PBPK models we built accurately describe tramadol and M1 exposure in the population with different CYP2D6 phenotypes. In our prediction, the area under the concentration‐time curve (AUCinf‐tDlast ) of M1 is 70% lower in PMs than in EMs, 27% lower in IMs, and 15% higher in UMs. Based on the models we built, we suggest that the oral dose of tramadol should be 50% higher for IMs and 25% lower for UMs to achieve an approximately equivalent plasma exposure of M1 as in EMs. When tramadol was co‐administered with paroxetine or duloxetine, the magnitude of the inhibitor‐substrate interaction was lowest in EMs (0.45), secondary in IMs (0.39), and highest in PMs (0.18) in terms of M1. Conclusion The current example uses the PBPK model to guide dose adjustment of tramadol and to predict the effect of CYP2D6 genetic polymorphisms on DDIs for rational clinical use of tramadol in the future.

Physiologically Based Pharmacokinetic Modeling to Assess the Impact of CYP2D6‐Mediated Drug‐Drug Interactions on Tramadol and O‐Desmethyltramadol Exposures via Allosteric and Competitive Inhibition

“…Furthermore, we reproduced the individual contribution of different metabolic enzymes to the biotransformation of tramadol into O-desmethyltramadol. The results of this analysis suggest that CYP2D6 accounts for approximately 51% of tramadol's hepatic clearance, which is in line with values reported in literature (43%-48%)[53][54][55][56] . The results further suggest that approximately 20% of tramadol's hepatic biotransformation to O-desmethyltramadol is mediated by an unidentified CYP450 isoform, which could explain noticeable amounts of O-desmethyltramadol reported for CYP2D6 PMs[15][16][17][18] .…”

“…The results of this analysis suggest that CYP2D6 accounts for ≈51% of tramadol's hepatic clearance, which is in line with values reported in literature (43%‐48%). 53 , 54 , 55 , 56 The results further suggest that ≈20% of tramadol's hepatic biotransformation to O‐desmethyltramadol is mediated by an unidentified CYP450 isoform, which could explain noticeable amounts of O‐desmethyltramadol reported for CYP2D6 PMs. 15 , 16 , 17 , 18 The combined PBPK model for tramadol and O‐desmethyltramadol was developed and externally verified.…”

PBPK-based dose finding for sildenafil in pregnant women for antenatal treatment of congenital diaphragmatic hernia

“…1 ). The model was accepted when the C max and AUC GMFE was within a two-fold error margin (0.5–2.00) ( Xu et al, 2021 ). …”

An Overview of Physiologically-Based Pharmacokinetic Models for Forensic Science

“…Other oral, IV, or subcutaneous opioid PBPK models exist for morphine [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ], alfentanil [ 49 , 50 , 51 , 52 ], remifentanil [ 53 , 54 , 55 ], sufentanil [ 56 ], tramadol [ 56 , 57 , 58 , 59 , 60 , 61 ], fentanyl [ 62 , 63 , 64 , 65 , 66 , 67 ], oxycodone [ 40 , 64 , 68 , 69 ], buprenorphine [ 64 , 70 , 71 , 72 , 73 , 74 , 75 ], codeine [ 76 , 77 ], methadone [ 78 , 79 , 80 , 81 , 82 , 83 , 84 ], and carfentanil to predict drug-drug interactions and drug disposition in the plasma and various ...…”