Dataset Information

Prediction of Metabolic Interactions With Oxycodone via CYP2D6 and CYP3A Inhibition Using a Physiologically Based Pharmacokinetic Model.

ABSTRACT: Evaluation of a potential risk of metabolic drug-drug interactions (DDI) is of high importance in the clinical setting. In this study, a physiologically based pharmacokinetic (PBPK) model was developed for oxycodone and its two primary metabolites, oxymorphone and noroxycodone, in order to assess different DDI scenarios using published in vitro and in vivo data. Once developed and refined, the model was able to simulate pharmacokinetics of the three compounds and the DDI extent in case of coadministration with an inhibitor, as well as the oxymorphone concentration variation between CYP2D6 extensive metabolizers (EM) and poor metabolizers (PM). The reliability of the model was tested against published clinical studies monitoring different inhibitors and dose regimens, and all predicted area under the concentration-time curve (AUC) ratios were within the twofold acceptance range. This approach represents a strategy to evaluate the impact of coadministration of different CYP inhibitors using mechanistic incorporation of drug-dependent and system-dependent available in vitro and in vivo data.

SUBMITTER: Marsousi N

PROVIDER: S-EPMC4288002 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Prediction of Metabolic Interactions With Oxycodone via CYP2D6 and CYP3A Inhibition Using a Physiologically Based Pharmacokinetic Model.

Marsousi N N Daali Y Y Rudaz S S Almond L L Humphries H H Desmeules J J Samer C F CF

CPT: pharmacometrics & systems pharmacology 20141217

Evaluation of a potential risk of metabolic drug-drug interactions (DDI) is of high importance in the clinical setting. In this study, a physiologically based pharmacokinetic (PBPK) model was developed for oxycodone and its two primary metabolites, oxymorphone and noroxycodone, in order to assess different DDI scenarios using published in vitro and in vivo data. Once developed and refined, the model was able to simulate pharmacokinetics of the three compounds and the DDI extent in case of coadmi ...[more]

PMID: 25518025

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Project description:The potential of inhibitory metabolites of perpetrator drugs to contribute to drug-drug interactions (DDIs) is uncommon and underestimated. However, the occurrence of unexpected DDI suggests the potential contribution of metabolites to the observed DDI. The aim of this study was to develop a physiologically-based pharmacokinetic (PBPK) model for bupropion and its three primary metabolites-hydroxybupropion, threohydrobupropion and erythrohydrobupropion-based on a mixed "bottom-up" and "top-down" approach and to contribute to the understanding of the involvement and impact of inhibitory metabolites for DDIs observed in the clinic. PK profiles from clinical researches of different dosages were used to verify the bupropion model. Reasonable PK profiles of bupropion and its metabolites were captured in the PBPK model. Confidence in the DDI prediction involving bupropion and co-administered CYP2D6 substrates could be maximized. The predicted maximum concentration (Cmax) area under the concentration-time curve (AUC) values and Cmax and AUC ratios were consistent with clinically observed data. The addition of the inhibitory metabolites into the PBPK model resulted in a more accurate prediction of DDIs (AUC and Cmax ratio) than that which only considered parent drug (bupropion) P450 inhibition. The simulation suggests that bupropion and its metabolites contribute to the DDI between bupropion and CYP2D6 substrates. The inhibitory potency from strong to weak is hydroxybupropion, threohydrobupropion, erythrohydrobupropion, and bupropion, respectively. The present bupropion PBPK model can be useful for predicting inhibition from bupropion in other clinical studies. This study highlights the need for caution and dosage adjustment when combining bupropion with medications metabolized by CYP2D6. It also demonstrates the feasibility of applying the PBPK approach to predict the DDI potential of drugs undergoing complex metabolism, especially in the DDI involving inhibitory metabolites.

Project description:The cytochrome P450 2D6 (CYP2D6) is a key xenobiotic-metabolizing enzyme involved in the clearance of many drugs. Genetic polymorphisms in CYP2D6 contribute to the large inter-individual variability in drug metabolism and could affect metabolic phenotyping of CYP2D6 probe substances such as dextromethorphan (DXM). To study this question, we (i) established an extensive pharmacokinetics dataset for DXM; and (ii) developed and validated a physiologically based pharmacokinetic (PBPK) model of DXM and its metabolites dextrorphan (DXO) and dextrorphan O-glucuronide (DXO-Glu) based on the data. Drug-gene interactions (DGI) were introduced by accounting for changes in CYP2D6 enzyme kinetics depending on activity score (AS), which in combination with AS for individual polymorphisms allowed us to model CYP2D6 gene variants. Variability in CYP3A4 and CYP2D6 activity was modeled based on in vitro data from human liver microsomes. Model predictions are in very good agreement with pharmacokinetics data for CYP2D6 polymorphisms, CYP2D6 activity as described by the AS system, and CYP2D6 metabolic phenotypes (UM, EM, IM, PM). The model was applied to investigate the genotype-phenotype association and the role of CYP2D6 polymorphisms for metabolic phenotyping using the urinary cumulative metabolic ratio (UCMR), DXM/(DXO + DXO-Glu). The effect of parameters on UCMR was studied via sensitivity analysis. Model predictions indicate very good robustness against the intervention protocol (i.e. application form, dosing amount, dissolution rate, and sampling time) and good robustness against physiological variation. The model is capable of estimating the UCMR dispersion within and across populations depending on activity scores. Moreover, the distribution of UCMR and the risk of genotype-phenotype mismatch could be estimated for populations with known CYP2D6 genotype frequencies. The model can be applied for individual prediction of UCMR and metabolic phenotype based on CYP2D6 genotype. Both, model and database are freely available for reuse.

Project description:What is already known about this subjectOxycodone is an opioid analgesic that is metabolized mainly in the liver by cytochrome P450 (CYP) 2D6 and 3A4 enzymes. So far, the effects of CYP2D6 or CYP3A4 inhibitors on the pharmacokinetics of oxycodone in humans have not been systematically studied.What this study addsDrug interactions arising from CYP2D6 inhibition most likely have minor clinical importance for oral oxycodone. When both of CYP2D6 and CYP3A4 pathways are inhibited, the exposure to oral oxycodone is increased substantially.AimThe aim of this study was to find out whether the inhibition of cytochrome P450 2D6 (CYP2D6) with paroxetine or concomitant inhibition of CYP2D6 and CYP3A4 with paroxetine and itraconazole, altered the pharmacokinetics and pharmacological response of orally administered oxycodone.MethodsA randomized placebo-controlled cross-over study design with three phases was used. Eleven healthy subjects ingested 10 mg of oral immediate release oxycodone on the fourth day of pre-treatment with either placebo, paroxetine (20 mg once daily) or paroxetine (20 mg once daily) and itraconazole (200 mg once daily) for 5 days. The plasma concentrations of oxycodone and its oxidative metabolites were measured for 48 h, and pharmacological (analgesic and behavioural) effects were evaluated.ResultsParoxetine alone reduced the area under concentration-time curve (AUC(0,0-48 h)) of the CYP2D6 dependent metabolite oxymorphone by 44% (P < 0.05), but had no significant effects on the plasma concentrations of oxycodone or its pharmacological effects when compared with the placebo phase. When both oxidative pathways of the metabolism of oxycodone were inhibited with paroxetine and itraconazole, the mean AUC(0,infinity) of oxycodone increased by 2.9-fold (P < 0.001), and its C(max) by 1.8-fold (P < 0.001). Visual analogue scores for subjective drug effects, drowsiness and deterioration of performance were slightly increased (P < 0.05) after paroxetine + itraconazole pre-treatment when compared with placebo.ConclusionsDrug interactions arising from CYP2D6 inhibition most likely have minor clinical importance for oral oxycodone if the function of the CYP3A4 pathway is normal. When both CYP2D6 and CYP3A4 pathways are inhibited, the exposure to oral oxycodone is increased substantially.

Dataset Information

Prediction of Metabolic Interactions With Oxycodone via CYP2D6 and CYP3A Inhibition Using a Physiologically Based Pharmacokinetic Model.

Publications

Prediction of Metabolic Interactions With Oxycodone via CYP2D6 and CYP3A Inhibition Using a Physiologically Based Pharmacokinetic Model.

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