Drug-drug interactions are a serious clinical issue. An important mechanism underlying drug-drug interactions is induction or inhibition of drug transporters that mediate the cellular uptake and efflux of xenobiotics. Especially drug transporters of the small intestine, liver and kidney are major determinants of the pharmacokinetic profile of drugs. Transporter-mediated drug-drug interactions in these three organs can considerably influence the pharmacokinetics and clinical effects of drugs. In this article, we focus on probe drugs lacking significant metabolism to highlight mechanisms of interactions of selected intestinal, hepatic and renal drug transporters (e.g., organic anion transporting polypeptide [OATP] 1A2, OATP2B1, OATP1B1, OATP1B3, P-gp, organic anion transporter [OAT] 1, OAT3, breast cancer resistance protein [BCRP], organic cation transporter [OCT] 2 and multidrug and toxin extrusion protein [MATE] 1). Genotype-dependent drug-drug interactions are also discussed.
"In vitro drug-drug interaction (DDI) studies performed as part of the development program indicated that pradigastat has the potential to inhibit the activity of the breast cancer resistance protein (BCRP), organic anion transporting polypeptides OATP1B1 and OATP1B3, and organic anion transporter-3 (OAT3). BCRP is an efflux transporter highly expressed on the apical membranes of intestinal enterocytes and hepatocytes, whereas OATP1B1/OATP1B3 and OAT3 are uptake transporters that facilitate the uptake of substrates from blood into hepatocytes and renal proximal tubule cells, respectively    . "
[Show abstract][Hide abstract] ABSTRACT: Objective:
An in vitro drugdrug interaction (DDI) study was performed to assess the potential for pradigastat to inhibit breast cancer resistance protein (BCRP), organic anion-transporting polypeptide (OATP), and organic anion transporter 3 (OAT3) transport activities. To understand the relevance of these in vitro findings, a clinical pharmacokinetic DDI study using rosuvastatin as a BCRP, OATP, and OAT3 probe substrate was conducted.
The study used cell lines that stably expressed or over-expressed the respective transporters. The clinical study was an open-label, single sequence study where subjects (n = 36) received pradigastat (100 mg once daily x 3 days thereafter 40 mg once daily) and rosuvastatin (10 mg once daily), alone and in combination.
Pradigastat inhibited BCRP-mediated efflux activity in a dose-dependent fashion in a BCRP over-expressing human ovarian cancer cell line with an IC(50) value of 5 μM. Similarly, pradigastat inhibited OATP1B1, OATP1B3 (estradiol 17β glucuronide transport), and OAT3 (estrone 3 sulfate transport) activity in a concentrationdependent manner with estimated IC(50) values of 1.66 ± 0.95 μM, 3.34 ± 0.64 μM, and 0.973 ± 0.11 μM, respectively. In the presence of steady state pradigastat concentrations, AUC(τ, ss) of rosuvastatin was unchanged and its Cmax,ss decreased by 14% (5.30 and 4.61 ng/mL when administered alone and coadministered with pradigastat, respectively). Pradigastat AUC(τ, ss) and C(max, ss) were unchanged when coadministered with rosuvastatin at steady state. Both rosuvastatin and pradigastat were well tolerated.
These data indicate no clinically relevant pharmacokinetic interaction between pradigastat and rosuvastatin.
International journal of clinical pharmacology and therapeutics 03/2015; 53(05). DOI:10.5414/CP202275 · 1.22 Impact Factor
"Induction of P-gp can provoke elimination of P-gp substrates and decrease their bioavailability. In contrast, inhibition can increase bioavailability and amplify therapeutic efficacy, but also toxicity  . An example of inhibition is the interaction of loperamide and P-gp inhibitors at the blood–brain barrier. "
[Show abstract][Hide abstract] ABSTRACT: In contrast to drugs for therapeutic use, there are only few data available concerning interactions between P-glycoprotein (P-gp) and drugs of abuse (DOA). In this work, interactions between structurally diverse DOA and P-gp were investigated using different strategies. First, the effect on the P-gp ATPase activity was studied by monitoring of ATP consumption after addition to recombinant, human P-gp. Second, DOA showing an increased ATP consumption were further characterized regarding their transport across filter grown Caco-2- monolayers. Analyses were performed by luminescence and liquid chromatography-mass spectrometry, respectively. Among the nine DOA initially screened, benzedrone, diclofensine, glaucine, JWH-200, MDBC, WIN-55,212-2 showed an increase of ATP consumption in the ATPase stimulation assay. In Caco-2 transport studies, Glaucine, JWH-200, mitragynine, WIN-55,212-2 could moreover be identified as non-transported substrates, but inhibitors of P-gp activity. Thus, drug-drug or drug-food interactions should be very likely for these compounds
"Drug accumulation in target tissues is often associated with tissue-specific toxicities, and it is important to account for it. However, we did not observe a direct modulation of CsA PK by its PD in our in vitro system, even though CsA interactions with transporters are known . In particular, CYP 3A5 levels were not affected by CsA levels, so CsA metabolism was not disturbed by induction or repression. "
[Show abstract][Hide abstract] ABSTRACT: Background
Incorporation of omic data streams for building improved systems biology models has great potential for improving their predictions of biological outcomes. We have recently shown that cyclosporine A (CsA) strongly activates the nuclear factor (erythroid-derived 2)-like 2 pathway (Nrf2) in renal proximal tubular epithelial cells (RPTECs) exposed in vitro. We present here a quantitative calibration of a differential equation model of the Nrf2 pathway with a subset of the omics data we collected.
In vitro pharmacokinetic data on CsA exchange between cells, culture medium and vial walls, and data on the time course of omics markers in response to CsA exposure were reasonably well fitted with a coupled PK-systems biology model. Posterior statistical distributions of the model parameter values were obtained by Markov chain Monte Carlo sampling in a Bayesian framework. A complex cyclic pattern of ROS production and control emerged at 5 μM CsA repeated exposure. Plateau responses were found at 15 μM exposures. Shortly above those exposure levels, the model predicts a disproportionate increase in cellular ROS quantity which is consistent with an in vitro EC50 of about 40 μM for CsA in RPTECs.
The model proposed can be used to analyze and predict cellular response to oxidative stress, provided sufficient data to set its parameters to cell-specific values. Omics data can be used to that effect in a Bayesian statistical framework which retains prior information about the likely parameter values.
BMC Systems Biology 06/2014; 8(1):76. DOI:10.1186/1752-0509-8-76 · 2.44 Impact Factor
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