Evaluation of the Effect of CYP2D6 Genotypes on Tramadol and O-Desmethyltramadol Pharmacokinetic Profiles in a Korean Population Using Physiologically-Based Pharmacokinetic Modeling

Abstract

Tramadol is a μ-opioid receptor agonist and a monoamine reuptake inhibitor. O-desmethyltramadol (M1), the major active metabolite of tramadol, is produced by CYP2D6. A physiologically-based pharmacokinetic model was developed to predict changes in time-concentration profiles for tramadol and M1 according to dosage and CYP2D6 genotypes in the Korean population. Parallel artificial membrane permeation assay was performed to determine tramadol permeability, and the metabolic clearance of M1 was determined using human liver microsomes. Clinical study data were used to develop the model. Other physicochemical and pharmacokinetic parameters were obtained from the literature. Simulations for plasma concentrations of tramadol and M1 (after 100 mg tramadol was administered five times at 12-h intervals) were based on a total of 1000 virtual healthy Koreans using SimCYP® simulator. Geometric mean ratios (90% confidence intervals) (predicted/observed) for maximum plasma concentration at steady-state (Cmax,ss) and area under the curve at steady-state (AUClast,ss) were 0.79 (0.69–0.91) and 1.04 (0.85–1.28) for tramadol, and 0.63 (0.51–0.79) and 0.67 (0.54–0.84) for M1, respectively. The predicted time–concentration profiles of tramadol fitted well to observed profiles and those of M1 showed under-prediction. The developed model could be applied to predict concentration-dependent toxicities according to CYP2D6 genotypes and also, CYP2D6-related drug interactions.

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

“…The steady‐state concentration of M1 in IMs and PMs is obviously lower than that in EMs in our prediction, which is consistent with the observed data used in the published PBPK model. In our prediction, the AUC inf‐tDlast of M1 is 70% lower in EMs than for PMs, 27% lower for IMs, and 15% higher for UMs, whereas the observed data obtained in the Korean population stated that the AUC inf‐tDlast of M1 is 77% lower for PMs than in EMs and 14% lower for IMs 20 . The slightly lower concentration of M1 in IMs in our prediction compared with observed data in the Korean population might be due to the discrepancy of demographic information, as the wide PK variability of many drugs in populations with different CYP2D6 phenotypes is largely explained by the CYP2D6 genetic polymorphism but can also be influenced by age, renal and hepatic function, and disease status.…”

Physiologically-based pharmacokinetic model for clozapine in Korean patients with schizophrenia

“…The mean of each predicted PK parameters was compared with the published mean in the corresponding clinical study, and the ratio of the predicted/observed values was obtained. The model was considered to fit well if the ratio of the predicted/observed values were within 30% (0.7–1.3) [ 16 ]. Data for healthy Korean and Caucasian populations were obtained from the SimCYP ® equipped population library.…”

The potential of CYP2D6 phenotyping to improve opioid dosing strategies

“…Two of the studies used healthy volunteers and the remaining three studies used patient samples (post-surgical laparoscopy, primary care, opioid dependent). Nationality and ethnicity of the participants were explicitly reported in two of the studies: Korean nationals (Jeong et al, 2019), Caucasian (Fonseca et al, 2011). Sample size can be described as small ( N < 30) in Bastami et al, 2014 and Jeong et al, 2019 medium ( N = 59) in Wang et al, 2019 and large ( N > 100) in Takahasi et al, 2017 and Fonseca et al, 2011.…”

“…Bastami et al, 2014 and Wang et al, 2019 used an RCT design, considered the ‘gold standard’ for study design (Hariton & Locascio, 2018). Other designs included clinical trial (Fonseca et al, 2011) and (Jeong at al., 2019), naturalistic cohort observation (Takahasi et al, 2017), and primary data modelling (Jeong et al, 2019). Whilst the inclusion of modelling studies in systematic reviews has been questioned, there is stronger support for those based on primary data, as is the case with the one modelling study included here.…”