No significant influence of OCT1 genotypes on the pharmacokinetics of morphine in adult surgical patients.

We investigated the impact of genetic variants in OCT1 (SLC22A1) on morphine, morphine-3-glucuronide (M3G) and morphine-6-glucuronide (M6G) pharmacokinetics in adult patients scheduled for major surgery. Blood samples were taken before and 5, 10, 15, 30, 45, 60 and 90 min after a bolus of morphine (0.15 mg/kg). Patients were genotyped for the genetic variants (rs12208357, rs34059508, rs72552763 and rs34130495) in OCT1. Eighty-six patients completed the trial. The mean difference (95% confidence interval) for dose adjusted morphine, M3G and M6G AUC was 0.9 (-0.7-2.4), -5.9 (-11.8 to -0.03) and -1.1 (-2.5-0.4) h/L*10-6 , respectively, in patients with two reduced function alleles compared to patients with no reduced function alleles in OCT1. Accordingly, the (AUCM3G/Dose )/(AUCmorphine/Dose ) and (AUCM6G/Dose )/(AUCmorphine/Dose ) ratio was reduced, -1.8 (-3.2 to -0.4) and -0.4 (-0.7 to -0.03), respectively, when comparing the same groups. OCT1 variants had no influence on the experience of pain, adverse events or the number of PCA doses used. In conclusion, genetic variants in OCT1 had a small and clinically unimportant impact on the exposure of morphine after intravenous administration. Our results do not support pre-emptive genotyping for OCT1 prior to morphine administration in patients scheduled for major surgery.

Oral and intravenous pharmacokinetics of metformin with and without oral codeine intake in healthy subjects: A cross-over study.

The aim of the study was to investigate if there is a clinically relevant drug interaction between metformin and codeine. Volunteers were randomized to receive on four separate occasions: (A) orally administered metformin (1 g), (B) intravenously administered metformin (0.5 g), (C) five doses of tablet codeine 25 mg; the last dose was administered together with oral metformin (1 g), and (D) five doses of tablet codeine 25 mg; the last dose was administered together with metformin (0.5 g) intravenously. Blood samples were drawn for 24 h after administration of metformin, and for 6 h after administration of codeine and analyzed using liquid chromatography and tandem mass spectrometry. Healthy volunteers genotyped as CYP2D6 normal metabolizers (*1/*1) without known reduced function variants in the OCT1 gene (rs12208357, rs34130495, rs34059508, and rs72552763) were invited. The median absorption fraction of metformin was 0.31 and was not influenced by codeine intake. The median time to maximum concentration ( T max ) after oral intake of metformin was 2 h without, and 3 h with codeine (p = 0.06). The geometric mean ratios of the areas under the plasma concentration time-curve (AUCs) for morphine and its metabolites M3G and M6G for oral intake of metformin-to-no metformin were 1.21, 1.31, and 1.27, respectively, and for i.v. metformin-to-no metformin 1.28, 1.34, and 1.30, respectively. Concomitant oral and i.v. metformin increased the plasma levels of morphine, M3G and M6G. These small pharmacokinetic changes may well contribute to an increased risk of early discontinuation of metformin. Hence, a clinically relevant drug-drug interaction between metformin and codeine seems plausible.

Quantification of In Vivo Metabolic Activity of CYP2D6 Genotypes and Alleles Through Population Pharmacokinetic Analysis of Vortioxetine.

Assignment of CYP2D6 phenotype from genotype data can be challenging and despite efforts to standardize translation, there is currently no universally accepted method. To facilitate standardization, there remains a need to precisely quantify the in vivo function of different CYP2D6 genotypes. Vortioxetine is metabolized to its major metabolite, Lu AA34443, primarily via CYP2D6. The aim of this study was to quantify the in vivo CYP2D6 activity of different CYP2D6 alleles and genotypes through population pharmacokinetic (PopPK) modeling of vortioxetine and Lu AA34443. Plasma concentration data of vortioxetine and Lu AA34443 from 1,140 subjects originating from 29 clinical pharmacology studies were pooled for the analysis. A joint PopPK model described the pharmacokinetics of vortioxetine and Lu AA34443 simultaneously and provided estimates of the CYP2D6-mediated metabolism for each subject. Subjects normally classified as CYP2D6 intermediate metabolizers (IMs) showed different levels of CYP2D6 activity with carriers of one fully functional allele and one null function allele having 77% higher CYP2D6 activity compared with carriers of two decreased function alleles (P < 0.0001). The decreased function alleles were associated with different levels of reduction of CYP2D6 activity. Fixing the activity of fully functional alleles to 1.0, the relative activities of CYP2D6*9, CYP2D6*10, CYP2D6*17, and CYP2D6*41 were 0.22, 0.37, 0.17, and 0.21, respectively. The activity of CYP2D6*10 was shown to be significantly greater than that of CYP2D6*17 (P = 0.01) and CYP2D6*41 (P = 0.02). These results warrant further discussion of current CYP2D6 genotype-phenotype classification systems particularly regarding decreased function alleles and the IM phenotype.

Using a limited sampling strategy to investigate the interindividual pharmacokinetic variability in metformin: A large prospective trial.

AIMS: Recently a limited sampling strategy (LSS) for determination of metformin' pharmacokinetics was developed. The LSS utilizes the plasma concentration of metformin 3 and 10 hours after oral intake of a single dose to estimate the area under the concentration-time curve up to 24 hours (AUC0-24h ). The main purpose of this study was to support the feasibility of this strategy in a large prospective trial. METHODS: Volunteers orally ingested two 500-mg tablets of metformin hydrochloride. A blood sample was drawn three and ten hours after the ingestion. Urine was collected for 0-10 and 10-24 hours and urine volumes recorded. The AUC0-24h was calculated using the equation AUC0-24h = 4.779 * C3 + 13.174 * C10 . Additionally, all participants were genotyped for the single-nucleotide polymorphism A270S in OCT2, g.-66 T > C in MATE1, R61C, G465R, G401S and the deletion M420del in OCT1. RESULTS: In total, 212 healthy volunteers participated. The median (25th - 75th interquartile range) AUC0 - 24h , CLrenal , C3 and C10 , were 10 600 (8470-12 500) ng* hr* mL-1 , 29 (24-34) L* hour-1 , 1460 (1180-1770) and 260 (200-330) ng* mL-1 , respectively, which is in agreement with our previous results. GFRi was correlated with metformin AUC and CLrenal (P < .001). As expected, we found a great pharmacokinetic interindividual variability among the volunteers and no effect of the OCT1 genotype on the AUC0 - 24h . We were unable to reproduce our previous finding of a gene-gene interaction (OCT2 and MATE1) effect on CLrenal in this cohort. CONCLUSION: This study further supports the use of the 2-point LSS algorithm in large pharmacokinetic trials.

Interaction potential between clarithromycin and individual statins-A systematic review.

The high prevalence of statin and clarithromycin utilization creates potential for overlapping use. The objectives of this MiniReview were to investigate the evidence base for drug-drug interactions between clarithromycin and currently marketed statins and to present management strategies for these drug combinations. We conducted a systematic literature review following PRISMA guidelines with English language studies retrieved from PubMed and EMBASE (from inception through March 2019). We included 29 articles (16 case reports, 5 observational, 5 clinical pharmacokinetic and 3 in vitro studies). Based on mechanistic/clinical studies involving clarithromycin or the related macrolide erythromycin (both strong inhibitors of CYP3A4 and of hepatic statin uptake transporters OATP1B1 and OATP1B3), clarithromycin is expected to substantially increase systemic exposure to simvastatin and lovastatin (>5-fold increase in area under the plasma concentration-time curve (AUC)), moderately increase AUCs of atorvastatin and pitavastatin (2- to 4-fold AUC increase) and slightly increase pravastatin exposure (≈2-fold AUC increase) while having little effect on fluvastatin or rosuvastatin. The 16 cases of statin-clarithromycin adverse drug reactions (rhabdomyolysis (n = 14) or less severe clinical myopathy) involved a CYP3A4-metabolized statin (simvastatin, lovastatin or atorvastatin). In line, a cohort study found concurrent use of clarithromycin and CYP3A4-metabolized statins to be associated with a doubled risk of hospitalization with rhabdomyolysis or other statin-related adverse events as compared with azithromycin-statin co-administration. If clarithromycin is necessary, we recommend (a) avoiding co-administration with simvastatin, lovastatin or atorvastatin; (b) withholding or dose-reducing pitavastatin; (c) continuing pravastatin therapy with caution, limiting pravastatin dose to 40 mg daily; and (d) continuing fluvastatin or rosuvastatin with caution.