Quantitative prediction of the extent of drug-drug interaction using a physiologically based pharmacokinetic model that includes inhibition of drug metabolism determined in cryopreserved hepatocytes

Transporter-mediated Natural Product-Drug Interactions

The increasing use of natural products in clinical practice has raised great concerns about the potential natural product-drug interactions (NDIs). Drug transporters mediate the transmembrane passage of a broad range of drugs, and thus are important determinants for drug pharmacokinetics and pharmacodynamics. Generally, transporters can be divided into ATP binding cassette (ABC) family and solute carrier (SLC) family. Numerous natural products have been identified as inhibitors, substrates, inducers, and/or activators of drug transporters. This review article aims to provide a comprehensive summary of the recent progress on the research of NDIs, focusing on the main drug transporters, such as P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), organic anion transporter 1 and 3 (OAT1/OAT3), organic anion-transporting polypeptide 1B1 and 1B3 (OATP1B1/OATP1B3), organic cation transporter 2 (OCT2), multidrug and toxin extrusion protein 1 and 2-K (MATE1/MATE2-K). Additionally, the challenges and strategies of studying NDIs are also discussed.

Assessing the Mechanism of Fluoxetine-Mediated CYP2D6 Inhibition

Fluoxetine is still one of the most widely used antidepressants in the world. The drug is extensively metabolized by several cytochrome P450 (CYP450) enzymes and subjected to a myriad of CYP450-mediated drug interactions. In a multidrug regimen, preemptive mitigation of drug-drug interactions requires knowledge of fluoxetine actions on these CYP450 enzymes. The major metabolic pathway of fluoxetine leading to the formation of its active metabolite, norfluoxetine, is mediated by CYP2D6. Fluoxetine and norfluoxetine are strong affinity substrates of CYP2D6 and can inhibit, potentially through various mechanisms, the metabolism of other sensitive CYP2D6 substrates. Remarkably, fluoxetine-mediated CYP2D6 inhibition subsides long after fluoxetine first passes through the liver and even remains long after the discontinuation of the drug. Herein, we review pharmacokinetic and pharmacogenetic information to help us understand the mechanisms underlying the prolonged inhibition of CYP2D6 following fluoxetine administration. We propose that long-term inhibition of CYP2D6 is likely a result of competitive inhibition. This is due to strong affinity binding of fluoxetine and norfluoxetine to the enzyme and unbound fluoxetine and norfluoxetine levels circulating in the blood for a long period of time because of their long elimination half-life. Additionally, we describe that fluoxetine is a CYP2C9 substrate and a mechanism-based inhibitor of CYP2C19.

Application of unbound liver-to-plasma concentration ratio to quantitative projection of cytochrome P450-mediated drug-drug interactions using physiologically based pharmacokinetic modelling approach

1. This study evaluated the prediction accuracy of cytochrome P450 (CYP)-mediated drug-drug interaction (DDI) using minimal physiologically-based pharmacokinetic (PBPK) modelling incorporating the hepatic accumulation factor of an inhibitor (i.e. unbound liver/unbound plasma concentration ratio [K_p,uu,liver]) based on 22 clinical DDI studies. 2. K_p,uu,liver values were estimated using three methods: (1) ratio of cell-to-medium ratio in human cryopreserved hepatocytes (C/M_u) at 37 °C to that on ice (K_p,uu,C/M), (2) multiplication of total liver/unbound plasma concentration ratio (K_p,u,liver) estimated from C/M_u at 37 °C with unbound fraction in human liver homogenate (K_p,uu,cell) and (3) observed K_p,uu,liver in rats after intravenous infusion (K_p,uu,rat). 3. PBPK model using each K_p,uu,liver projected the area under the curve (AUC) increase of substrates more accurately than the model assuming a K_p,uu,liver of 1 for the average fold error and root mean square error did. Particularly, the model with a K_p,uu,liver of 1 underestimated the AUC increase of triazolam following co-administration with CYP3A4 inhibitor itraconazole by five-fold, whereas the AUC increase projected using the model incorporating the K_p,uu,C/M, K_p,uu,cell, or K_p,uu,rat of itraconazole and hydroxyitraconazole was within approximately two-fold of the actual value. 4. The results indicated that incorporating K_p,uu,liver into the PBPK model improved the accuracy of DDI projection.