Pregnant women are usually excluded from clinical trials. Physiologically based pharmacokinetic (PBPK) modelling may provide a method to predict pharmacokinetics in pregnant women, without the need to perform extensive in vivo clinical trials. Here, we used mechanistic modelling to delineate the potential impact of drug transporters on darunavir pharmacokinetics and to identify current knowledge gaps that limit accurate PBPK modelling of darunavir/ritonavir (darunavir/r) exposure in pregnancy. Simcyp (version 13.2) was used for PBPK modelling, using physicochemical and in vitro pharmacokinetic parameters of darunavir and ritonavir from the literature. The Michaelis–Menten constant (Km) and the maximum rate of metabolite formation (Vmax) for cytochrome P450 3A4-mediated darunavir biotransformation and inhibition by ritonavir were determined experimentally, while the contributions of hepatocyte influx and efflux transporters were assessed by sensitivity analysis. The simulations were compared with previously published clinical pharmacokinetic data. We found that use of a well-stirred liver model overestimated darunavir exposure substantially. A permeability-limited liver model, including hepatic uptake and efflux transporters and an efficient enterohepatic circulation step, resulted in an acceptable description of darunavir/r exposure. For the 600/100 mg darunavir/r twice-daily dose and the 800/100 mg once-daily dose, the estimated pharmacokinetic parameters were within a 2-fold range of the reported data. The predicted decreases in the area under the concentration–time curve (AUC) values during pregnancy for the twice- and once-daily doses were 27 and 41 %, respectively, which were in line with the observed decreases of 17–22 and 33 %. In conclusion, our data support a clinically relevant role of hepatic transporters in darunavir pharmacokinetics. By including them in our model, we successfully approximated the increase in darunavir exposure mediated by ritonavir co-administration and the decrease in darunavir exposure observed during pregnancy.Electronic supplementary materialThe online version of this article (doi:10.1007/s40262-015-0325-8) contains supplementary material, which is available to authorized users.

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Morphine is a widely used opioid analgesic, which shows large differences in clinical response in children, even when aiming for equivalent plasma drug concentrations. Age-dependent brain disposition of morphine could contribute to this variability, as developmental increase in blood-brain barrier (BBB) P-glycoprotein (Pgp) expression has been reported. In addition, age-related pharmacodynamics might also explain the variability in effect. To assess the influence of these processes on morphine effectiveness, a multi-compartment brain physiologically based pharmacokinetic/pharmacodynamic (PB-PK/PD) model was developed in R (Version 3.6.2). Active Pgp-mediated morphine transport was measured in MDCKII-Pgp cells grown on transwell filters and translated by an in vitro-in vivo extrapolation approach, which included developmental Pgp expression. Passive BBB permeability of morphine and its active metabolite morphine-6-glucuronide (M6G) and their pharmacodynamic parameters were derived from experiments reported in literature. Model simulations after single dose morphine were compared with measured and published concentrations of morphine and M6G in plasma, brain extracellular fluid (ECF) and cerebrospinal fluid (CSF), as well as published drug responses in children (1 day– 16 years) and adults. Visual predictive checks indicated acceptable overlays between simulated and measured morphine and M6G concentration-time profiles and prediction errors were between 1 and -1. Incorporation of active Pgp-mediated BBB transport into the PB-PK/PD model resulted in a 1.3-fold reduced brain exposure in adults, indicating only a modest contribution on brain disposition. Analgesic effect-time profiles could be described reasonably well for older children and adults, but were largely underpredicted for neonates. In summary, an age-appropriate morphine PB-PK/PD model was developed for the prediction of brain pharmacokinetics and analgesic effects. In the neonatal population, pharmacodynamic characteristics, but not brain drug disposition, appear to be altered compared to adults and older children, which may explain the reported differences in analgesic effect.

Background: Enalapril is often used in the treatment of cardiovascular diseases. Clinical data suggest that the urinary excretion of enalaprilat, the active metabolite of enalapril, is mediated by renal transporters. We aimed to identify enalaprilat specificity for renal proximal tubular transporters. Methods: Baculovirus-transduced HEK293 cells overexpressing proximal tubular transporters were used to study enalaprilat cellular uptake. Uptake into cells overexpressing the basolateral transporters OCT2, OAT1, OAT2, or OAT3 and apical transporters OAT4, PEPT1, PEPT2, OCTN1, OCTN2, MATE1, MATE2k, and URAT1 was compared with mock-transduced control cells. Transport by renal efflux transporters MRP2, MPR4, P-gp, and BCRP was tested using a vesicular assay. Enalaprilat concentrations were measured using LC-MS/MS. Results: Uptake of enalaprilat into cells expressing OAT3 as well as OAT4 was significantly higher compared to control cells. The enalaprilat affinity for OAT3 was 640 (95% CI: 520–770) µM. For OAT4, no reliable affinity constant could be determined using concentrations up to 3 mM. No transport was observed for other transporters. Conclusion: The affinity of enalaprilat for OAT3 and OAT4 was notably low compared to other substrates. Taking this affinity and clinically relevant plasma concentrations of enalaprilat and other OAT3 substrates into account, we believe that drug–drug interactions on a transporter level do not have a therapeutic consequence and will not require dose adjustments of enalaprilat itself or other OAT3 substrates.

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Moxifloxacin has an important role in the treatment of tuberculosis (TB). Unfortunately, coadministration with the cornerstone TB drug rifampicin results in suboptimal plasma exposure. We aimed to gain insight into the moxifloxacin pharmacokinetics and the interaction with rifampicin. Moreover, we provided a mechanistic framework to understand moxifloxacin pharmacokinetics. We developed a physiologically based pharmacokinetic model in Simcyp version 19, with available and newly generated in vitro and in vivo data, to estimate pharmacokinetic parameters of moxifloxacin alone and when administered with rifampicin. By combining these strategies, we illustrate that the role of P-glycoprotein in moxifloxacin transport is limited and implicate MRP2 as transporter of moxifloxacin-glucuronide followed by rapid hydrolysis in the gut. Simulations of multiple dose area under the plasma concentration-time curve (AUC) of moxifloxacin (400 mg once daily) with and without rifampicin (600 mg once daily) were in accordance with clinically observed data (predicted/observed [P/O] ratio of 0.87 and 0.80, respectively). Importantly, increasing the moxifloxacin dose to 600 mg restored the plasma exposure both in actual patients with TB as well as in our simulations. Furthermore, we extrapolated the single dose model to pediatric populations (P/O AUC ratios, 1.04-1.52) and the multiple dose model to children with TB (P/O AUC ratio, 1.51). In conclusion, our combined approach resulted in new insights into moxifloxacin pharmacokinetics and accurate simulations of moxifloxacin exposure with and without rifampicin. Finally, various knowledge gaps were identified, which may be considered as avenues for further physiologically based pharmacokinetic refinement.