RESUMEN
Of the 450 cell membrane transporters responsible for shuttling substrates, nutrients, hormones, neurotransmitters, antioxidants, and signaling molecules, approximately nine are associated with clinically relevant drug-drug interactions (DDIs) due to their role in drug and metabolite transport. Therefore, a clinical study evaluating potential transporter DDIs is recommended if an investigational product is intestinally absorbed, undergoes renal or hepatic elimination, or is suspected to either be a transporter substrate or perpetrator. However, many of the transporter substrates and inhibitors administered during a DDI study also affect cytochrome P450 (CYP) activity, which can complicate data interpretation. To overcome these challenges, the assessment of endogenous biomarkers can help elucidate the mechanism of complex DDIs when multiple transporters or CYPs may be involved. This perspective article will highlight how creative study designs are currently being utilized to address complex transporter DDIs and the role of physiology-based -pharmacokinetic (PBPK) models can play.
RESUMEN
Trimethoprim is a frequently-prescribed antibiotic and therefore likely to be co-administered with other medications, but it is also a potent inhibitor of multidrug and toxin extrusion protein (MATE) and a weak inhibitor of cytochrome P450 (CYP) 2C8. The aim of this work was to develop a physiologically-based pharmacokinetic (PBPK) model of trimethoprim to investigate and predict its drug-drug interactions (DDIs). The model was developed in PK-Sim®, using a large number of clinical studies (66 plasma concentration-time profiles with 36 corresponding fractions excreted in urine) to describe the trimethoprim pharmacokinetics over the entire published dosing range (40 to 960 mg). The key features of the model include intestinal efflux via P-glycoprotein (P-gp), metabolism by CYP3A4, an unspecific hepatic clearance process, and a renal clearance consisting of glomerular filtration and tubular secretion. The DDI performance of this new model was demonstrated by prediction of DDIs and drug-drug-gene interactions (DDGIs) of trimethoprim with metformin, repaglinide, pioglitazone, and rifampicin, with all predicted DDI and DDGI AUClast and Cmax ratios within 1.5-fold of the clinically-observed values. The model will be freely available in the Open Systems Pharmacology model repository, to support DDI studies during drug development.
RESUMEN
Nadolol is a nonmetabolized ß-adrenoceptor antagonist and is a substrate of OATP1A2, but not of OATP2B1. However, other drug transporters involved in translocation of nadolol have not been characterized in detail. We therefore investigated nadolol as a potential substrate of the hepatic uptake transporters OATP1B1, OATP1B3, and OCT1 and of the renal transporters OCT2, MATE1, and MATE2-K expressed in HEK cells. Moreover, the importance of P-glycoprotein (P-gp) for nadolol transport was studied using double transfected MDCK-OCT1-P-gp cells. Nadolol was not transported by OATP1B1 and OATP1B3. In contrast, a significantly higher nadolol accumulation (at 1 and 10 µM) was found in OCT1, OCT2, MATE1, and MATE2-K cells compared to control cells (P < 0.01). Km values for OCT2-, MATE1-, and MATE2-K-mediated nadolol uptake were 122, 531, and 372 µM, respectively. Cimetidine (100 µM, P < 0.01) and trimethoprim (100 µM, P < 0.001) significantly inhibited OCT1-, OCT2-, MATE1-, and MATE2-K-mediated nadolol transport. The P-gp inhibitor zosuquidar significantly reduced basal to apical nadolol transport in monolayers of MDCK-OCT1-P-gp cells. In summary, nadolol is a substrate of the cation transporters OCT1, OCT2, MATE1, MATE2-K, and of P-gp. These data will aid future in vivo studies on potential transporter-mediated drug-drug or drug-food interactions with involvement of nadolol.