The dose-corrected steady-state Selleckchem IWP-2 trough dabigatran concentration of the single

individual treated with phenytoin and phenobarbitone (0.04 µg/L per mg/day, in the individual with a trough concentration of 9 µg/L on dabigatran etexilate 110 mg twice daily) was notable as it was more than 3 SD below the mean dose-corrected trough concentration of our study population (0.32 µg/L per mg/day, which is equivalent to 70 µg/L on 110 mg twice daily). Further, it is well below target trough dabigatran concentrations that have been suggested in the literature; for example, Chin et al. [54] have proposed 30–130 µg/L. While phenytoin and phenobarbitone are known P-gp inducers, the impact of concomitant use on the pharmacokinetics of dabigatran has not previously been reported [55]. Rifampicin, another P-gp inducer, has been demonstrated to reduce dabigatran concentrations by around 67 % [10]. To our knowledge, these are the first data to support the notion that phenytoin and/or phenobarbitone have a significant effect on dabigatran concentrations. 4.1 Limitations Our study has several limitations. Firstly, the primary

aim, to assess and compare the correlations of the renal function equations with trough plasma dabigatran concentrations, may have been better addressed by gathering data from individuals given intravenous dabigatran. From such data, true dabigatran clearance could have been calculated, without the need to consider oral availability, which is affected by many covariates (see Table 1). The bias and imprecision of the renal function equations against dabigatran clearance Amino acid could then have been check details compared. However, this approach would also have been more challenging logistically. By comparison, trough concentrations are a convenient and useful representation of apparent oral clearance with which to compare the equations, as these have been correlated with the risk of thromboembolic and haemorrhagic outcomes in the setting of AF [4]. Secondly, there could be a statistical power problem since we had a dataset of only 52 individuals. By comparing

the equations with the lowest and highest R 2 for the multiple linear regression model for trough plasma dabigatran concentrations (CG and CKD-EPI_CrCys, respectively), we calculate that, for future studies, around 680 subjects are needed to have 80 % power (α = 0.05) to detect a difference between these two equations. This is valuable data to inform the conduct of future studies. Thirdly, we did not measure the active precursor of dabigatran, BIBR 951, or the active metabolites of dabigatran, its glucuronides [15]. While BIBR 951 is thought to have concentrations <0.4 % of those of dabigatran [15], the dabigatran glucuronides have been reported to make up 10–35 % of the total active drug concentrations following ingestion of dabigatran etexilate [7, 12, 15, 16, 56, 57].