Chih-hsuan Hsin (1), Marc S. Stoffel (1), Malaz Gazzaz (1, 4), Elke Schäffeler (2,3), Matthias Schwab (2,5,6), Xia Li (1), Uwe Fuhr (1), Max Taubert (1)
(1) Department I of Pharmacology, University Hospital Cologne, Germany (2) Dr. Margarete-Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany, (3) University of Tuebingen, Tuebingen, Germany, (4) Department of Clinical Pharmacy, College of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia, (5) Department of Clinical Pharmacology, University Hospital Tuebingen, Germany, (6) Department of Pharmacy and Biochemistry, University of Tuebingen, Tuebingen, Germany
Introduction/Objectives:
Membrane transporters play an essential role in drug development due to potential transporter-based interactions. When the activity of membrane transporters is altered by inhibition, induction or in presence of certain genetic polymorphisms, the pharmacokinetics and -dynamics of substrate drugs might change relevantly.1 P-glycoprotein (P-gp, gene code ABCB1), the first well characterized membrane transporter, has been shown to affect the pharmacokinetics of numerous clinically relevant drugs2 , including digoxin. Therefore, the FDA recommends to evaluate potential drug-drug interactions based on P-gp using digoxin as a phenotyping drug, since the rate of digoxin transport is limited by P-gp in the intestine and kidney.3,4,5 However, results of phenotyping might be confounded by ABCB1 polymorphisms. Particularly, the single nucleotide polymorphisms (SNPs) c.1236C>T (rs1128503), c.2677 G>T/A (rs2032582) and c.3435 C>T (rs1045642) have been associated with changes in P-gp transporter kinetics.6 Studies on the effects of c.1236C>T, c.2677 G>T/A and c.3435 C>T on digoxin pharmacokinetics currently are inconclusive,7 which might be a consequence of considering only one of the 3 SNPs to define genotypes. Since SNPs are in linkage disequilibrium and form common haplotypes,8 considering the entire haplotype structure might be necessary to properly assess the role of common polymorphisms in digoxin pharmacokinetics. 8
To quantify differences in digoxin pharmacokinetics between subjects with common haplotypes using a population pharmacokinetic modeling approach.
Methods:
Data from 40 healthy Caucasian subjects participating in two clinical trials (mean age 40.4±16, mean body mass index 24.0 ±3.18kg/m2) who received single oral doses of 0.5 mg digoxin was evaluated. Blood and urine samples were collected by a dense sampling scheme and a validated high-pressure liquid chromatography–tandem mass spectrometry method was used to quantify digoxin concentrations. Genotyping of ABCB1 was carried out using the DMET™ Plus Array9 (Affymetrix, Santa Clara, California, United States). The following ABCB1 haplotypes were defined: *1/*1 (CC/GG/CC), *1/*2 (CC/GG/CT), *1/*13 (CT/GT/CT) and *13/*13 (TT/TT/TT) (base pairs of SNPs c.1236/2677/3435) with 12, 4, 15 and 9 subjects, respectively.8 A population pharmacokinetic model was developed using NONMEM 7.4.110 by starting with a one-compartment model and increasing the model complexity step-wise. After identification of a proper base model, the effect of haplotypes on pharmacokinetic parameters related to absorption and elimination of digoxin was evaluated via a step-wise procedure. For all modelling steps, changes in objective function value OFV, goodness of fit (GOF) and visual predictive checks (VPC) were considered.
Results:
A three compartmental model with zero order absorption and linear elimination appropriately described the observed pharmacokinetic data. The estimated median clearance CL/F was 15.8 L/h (19.7%), of which 53.8% (55.6%) were attributed to renal clearance (CV%). The median central (V1/F) and peripheral (V2/F, V3/F) volumes of distribution were 135 L (18.4%), 140 L (100.8%) and 452 L (35.4%), respectively. The estimated median duration of zero order absorption was 0.461 h (8.5%) with a lag-time of 0.086 h (58.8%). Homozygote carriers of ABCB1*13/*13 showed a lower CL/F (63.6% compared to other volunteers) while the renal clearance showed no difference between genotypes. Consequently, total exposure in terms of AUC was higher in ABCB1*13/*13 (compared to ABCB1*1/*1).
The identified difference in ABCB1*13/*13 is in line with a previous study by Xu et al., who reported a higher AUC in subjects with this haplotype.11 Other main pharmacokinetic parameters were also comparable to published data.12, 13 A previous study by Frankfort et al. did not show a significant correlation between CL/F and ABCB1*13 or ABCB1*1 haplotypes.14 However, their study was performed in medicated geriatric patients under steady state conditions, which prevents from a direct comparison to our results.
Conclusions:
The developed joint model described digoxin plasma concentrations and urinary excretion in two clinical trials well. ABCB1*13/*13 influenced only non-renal pharmacokinetic processes. The genotype effect supports the use of digoxin as a probe to assess intestinal P-gp activity. Considering haplotypes might be important when using digoxin as a P-gp phenotyping drug.
References:
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[12]Kirby, B. J. et al. Complex drug interactions of the HIV protease inhibitors 3: effect of simultaneous or staggered dosing of digoxin and ritonavir, nelfinavir, rifampin, or bupropion. Drug Metab. Dispos. 40, 610–616 (2012).
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[14] Frankfort, S. V., et al. Role of ABCB1 genotypes and haplotypes in digoxin steady state pharmacokinetics in geriatric patients. Pharmacotherapy, clinical pharmacology and biomarker research in geriatric patients (2006): 35
Reference: PAGE 28 (2019) Abstr 9124 [www.page-meeting.org/?abstract=9124]
Poster: Drug/Disease Modelling - Other Topics