Use of quercetin in animal feed : effects on the P-gp expression and pharmacokinetics of orally administrated enrofloxacin in chicken

Modulation of P-glycoprotein (P-gp, encoded by Mdr1) by xenobiotics plays central role in pharmacokinetics of various drugs. Quercetin has a potential to modulate P-gp in rodents, however, its effects on P-gp modulation in chicken are still unclear. Herein, study reports role of quercetin in modulation of P-gp expression and subsequent effects on the pharmacokinetics of enrofloxacin in broilers. Results show that P-gp expression was increased in a dose-dependent manner following exposure to quercetin in Caco-2 cells and tissues of chicken. Absorption rate constant and apparent permeability coefficient of rhodamine 123 were decreased, reflecting efflux function of P-gp in chicken intestine increased by quercetin. Quercetin altered pharmacokinetic of enrofloxacin by decreasing area under curve, peak concentration, and time to reach peak concentration and by increasing clearance rate. Molecular docking shows quercetin can form favorable interactions with binding pocket of chicken xenobiotic receptor (CXR). Results provide convincing evidence that quercetin induced P-gp expression in tissues by possible interaction with CXR, and consequently reducing bioavailability of orally administered enrofloxacin through restricting its intestinal absorption and liver/kidney clearance in broilers. The results can be further extended to guide reasonable use of quercetin to avoid drug-feed interaction occurred with co-administered enrofloxacin or other similar antimicrobials.Peer reviewedFinal Published versio

Simulation-based cheminformatic analysis of organelle-targeted molecules: lysosomotropic monobasic amines

Cell-based molecular transport simulations are being developed to facilitate exploratory cheminformatic analysis of virtual libraries of small drug-like molecules. For this purpose, mathematical models of single cells are built from equations capturing the transport of small molecules across membranes. In turn, physicochemical properties of small molecules can be used as input to simulate intracellular drug distribution, through time. Here, with mathematical equations and biological parameters adjusted so as to mimic a leukocyte in the blood, simulations were performed to analyze steady state, relative accumulation of small molecules in lysosomes, mitochondria, and cytosol of this target cell, in the presence of a homogenous extracellular drug concentration. Similarly, with equations and parameters set to mimic an intestinal epithelial cell, simulations were also performed to analyze steady state, relative distribution and transcellular permeability in this non-target cell, in the presence of an apical-to-basolateral concentration gradient. With a test set of ninety-nine monobasic amines gathered from the scientific literature, simulation results helped analyze relationships between the chemical diversity of these molecules and their intracellular distributions

P-gp Inhibition Potential in Cell-Based Models: Which “Calculation” Method is the Most Accurate?

The objective was to directly compare the four different “calculation” methods of assessing P-gp inhibition potential using experimental data obtained from ~60 structurally diverse internal research and marketed compounds. Bidirectional studies for digoxin (probe for P-gp substrate) were performed with and without test compounds (at 10 μM). Four different calculation methods were applied to the same dataset (raw bidirectional permeability values) to obtain the “percent inhibition of P-gp” for these compounds using the different methods. Significantly different inhibition potential was obtained with the “exact” same experimental dataset depending on the calculation method used. Subsequently, entirely different conclusions regarding the “inhibition potential” of test compound was reached due to the different calculation methods. Based on the direct comparison of these methods, method no. 3 (i.e., inhibition of B to A permeability of digoxin) is recommended as the calculation method ideal during screening stages due to its high throughput amenability. The methodology is capable of rapidly screening compounds with adequate reliability for early stage drug discovery. Method no. 3 provides an abridged version of a bidirectional study that is fully capable of identifying all non-inhibitors (0–20%), moderate inhibitors (20–60%), and potent inhibitors (>60%) and demonstrates high correlation with method no. 1 (inhibition based on both A to B and B to A permeability of digoxin). Nevertheless, method no. 1 might be appropriate for more detailed mechanistic studies required in late stage discovery and development