Exhaustive Sampling of Docking Poses Reveals Binding Hypotheses for Propafenone Type Inhibitors of P-Glycoprotein

Overexpression of the xenotoxin transporter P-glycoprotein (P-gp) represents one major reason for the development of multidrug resistance (MDR), leading to the failure of antibiotic and cancer therapies. Inhibitors of P-gp have thus been advocated as promising candidates for overcoming the problem of MDR. However, due to lack of a high-resolution structure the concrete mode of interaction of both substrates and inhibitors is still not known. Therefore, structure-based design studies have to rely on protein homology models. In order to identify binding hypotheses for propafenone-type P-gp inhibitors, five different propafenone derivatives with known structure-activity relationship (SAR) pattern were docked into homology models of the apo and the nucleotide-bound conformation of the transporter. To circumvent the uncertainty of scoring functions, we exhaustively sampled the pose space and analyzed the poses by combining information retrieved from SAR studies with common scaffold clustering. The results suggest propafenone binding at the transmembrane helices 5, 6, 7 and 8 in both models, with the amino acid residue Y307 playing a crucial role. The identified binding site in the non-energized state is overlapping with, but not identical to, known binding areas of cyclic P-gp inhibitors and verapamil. These findings support the idea of several small binding sites forming one large binding cavity. Furthermore, the binding hypotheses for both catalytic states were analyzed and showed only small differences in their protein-ligand interaction fingerprints, which indicates only small movements of the ligand during the catalytic cycle

Prediction of Promiscuous P-Glycoprotein Inhibition Using a Novel Machine Learning Scheme

BACKGROUND: P-glycoprotein (P-gp) is an ATP-dependent membrane transporter that plays a pivotal role in eliminating xenobiotics by active extrusion of xenobiotics from the cell. Multidrug resistance (MDR) is highly associated with the over-expression of P-gp by cells, resulting in increased efflux of chemotherapeutical agents and reduction of intracellular drug accumulation. It is of clinical importance to develop a P-gp inhibition predictive model in the process of drug discovery and development. METHODOLOGY/PRINCIPAL FINDINGS: An in silico model was derived to predict the inhibition of P-gp using the newly invented pharmacophore ensemble/support vector machine (PhE/SVM) scheme based on the data compiled from the literature. The predictions by the PhE/SVM model were found to be in good agreement with the observed values for those structurally diverse molecules in the training set (n = 31, r(2) = 0.89, q(2) = 0.86, RMSE = 0.40, s = 0.28), the test set (n = 88, r(2) = 0.87, RMSE = 0.39, s = 0.25) and the outlier set (n = 11, r(2) = 0.96, RMSE = 0.10, s = 0.05). The generated PhE/SVM model also showed high accuracy when subjected to those validation criteria generally adopted to gauge the predictivity of a theoretical model. CONCLUSIONS/SIGNIFICANCE: This accurate, fast and robust PhE/SVM model that can take into account the promiscuous nature of P-gp can be applied to predict the P-gp inhibition of structurally diverse compounds that otherwise cannot be done by any other methods in a high-throughput fashion to facilitate drug discovery and development by designing drug candidates with better metabolism profile

2D- and 3D-QSAR studies of a series of benzopyranes and benzopyrano[3,4b][1,4]-oxazines as inhibitors of the multidrug transporter P-glycoprotein

The ATP-binding cassette efflux transporter P-glycoprotein (P-gp) is notorious for contributing to multidrug resistance in antitumor therapy. Due to its expression in many blood-organ barriers, it also influences the pharmacokinetics of drugs and drug candidates and is involved in drug/drug- and drug/nutrient interactions. However, due to lack of structural information the molecular basis of ligand/transporter interaction still needs to be elucidated. Towards this goal, a series of Benzopyranes and Benzopyrano[3,4b][1,4]oxazines have been synthesized and pharmacologically tested for their ability to inhibit P-gp mediated daunomycin efflux. Both quantitative structure-activity relationship (QSAR) models using simple physicochemical and novel GRID-independent molecular descriptors (GRIND) were established to shed light on the structural requirements for high P-gp inhibitory activity. The results from 2D-QSAR showed a linear correlation of vdW surface area (Å(2)) of hydrophobic atoms with the pharmacological activity. GRIND (3D-QSAR) studies allowed to identify important mutual distances between pharmacophoric features, which include one H-bond donor, two H-bond acceptors and two hydrophobic groups as well as their distances from different steric hot spots of the molecules. Activity of the compounds particularly increases with increase of the distance of an H-bond donor or a hydrophobic feature from a particular steric hot spot of the benzopyrane analogs

Positron emission tomography studies on binding of central nervous system drugs and P-glycoprotein function in the rodent brain

The permeability of the blood-brain barrier (BBB) is one of the factors determining the bioavailability of drugs in the brain. The BBB only allows passage of lipophilic drugs by passive diffusion. However, some lipophilic drugs hardly enter the brain. The transmembrane protein P-glycoprotein (P-gp) is one of the carrier systems that is responsible for transportation of drugs out of the brain. P-Glycoprotein affects the pharmacokinetics of many drugs and can be inhibited by administration of modulators or competitive substrates. Identification and classification of central nervous system (CNS) drugs as P-gp substrates or inhibitors are of crucial importance in drug development. Positron emission tomography (PET) studies can play an important role in the screening process as a follow-up of high-throughput in vitro assays. Several rodent studies have shown the potential value of PET to measure the effect of P-gp on the pharmacokinetics and brain uptake of radiolabeled compounds. P-Glycoprotein-mediated effects were observed for two 5-HT1a receptor ligands, [F-18]MPPF vs. [carbonyl-C-11] WAY100635. Under control conditions, the specific brain uptake of [F-18]MPPF is five- to eightfold lower than that of [C-11]WAY100635. After cyclosporin A (CsA) modulation, [F-18]MPPF uptake in the rat brain increased five- to tenfold. Cerebral uptake of [carbonyl-C-11]WAY100635 was also increased by modulation, but in general the increase was lower than that observed for [F-18]MPPF (two- to threefold). Brain uptake of the P-adrenergic receptor ligands [C-11]carazolol and [F-18]fluorocarazolol was increased in P-gp knockout mice and CsA-treated rats. Both the specific and nonspecific binding of [F-18]fluorocarazolol were doubled by CsA. Cerebral uptake of [C-11]carazolol in rats was much lower than that of [18F]fluorocarazolol and no specific binding was measured. After CsA modulation, the uptake of [C-11]carazolol increased five- to sixfold, but this uptake was not receptor-mediated. Quantitative PET studies in rodents on P-gp functionality demonstrated a dose-dependent increase of radioligands after administration of CsA. Studies with [C-11]verapamil and [C-11]carvedilol showed that complete modulation was achieved at 50 mg/kg CsA. The distribution volume of [C-11]carvedilol increased from 0.25 in the control study to 1.0 after full modulation with CsA. By quantitative PET measurement of P-gp function, the dose of modulators required to increase the concentration of CNS drugs may be determined, which may result in improved drug therapy

Recent progress in the computational prediction of aqueous solubility and absorption

The computational prediction of aqueous solubility and/or human absorption has been the goal of many researchers in recent years. Such anin silico counterpart to the biopharmaceutical classification system (BCS) would have great utility. This review focuses on recent developments in the computational prediction of aqueous solubility, P-glycoprotein transport, and passive absorption. We find that, while great progress has been achieved, models that can reliably affect chemistry and development are still lacking. We briefly discuss aspects of emerging scientific understanding that may lead to breakthroughs in the computational modeling of these properties