Identification of efflux pump inhibitors for Pseudomonas aeruginosa MexAB-OprM via ligand-based pharmacophores, 2D-QSAR, molecular docking, and molecular dynamics approaches.

Efflux pumps have been reported as one of the significant mechanisms by which bacteria evade the effects of multiple antibiotics. The tripartite efflux pump MexAB-OprM in Pseudomonas aeruginosa is one of the most significant multidrug efflux systems due to its broad resistance to antibiotics such as chloramphenicol, fluoroquinolones, lipophilic ß-lactam antibiotics, nalidixic acid, novobiocin, rifampicin, and tetracycline. A promising strategy to overcome this resistance mechanism is to combine antibiotics with efflux pump inhibitors (EPIs), which can increase their intracellular concentration to enhance their biological activities. Based on 143 EPIs with chemically diverse skeletons, the 3D pharmacophore and 2D-QSAR modelings were developed and used for the virtual screening on 9.2 million compounds including ZINC15, DrugBank, and Traditional Chinese Medicine databases to identify new EPIs. The molecular docking was also performed to evaluate the binding affinity of potential EPIs to the distal-binding pocket of MexB and resulted in 611 potential EPIs. The structure-activity relationship analyses suggested that nitrogen heterocyclic compounds, piperazine and pyridine scaffolds, and amide derivatives are the most favorable chemically features for MexAB inhibitory activities. The results from molecular dynamics analysis in 100 ns indicated that ZINC009296881 and ZINC009200074 were the most potential MexB inhibitors with strong binding affinity to the distal pocket and MM/GBSA ∆Gbind values of - 38.97 and - 30.19 kcal mol-1, respectively. The predicted pharmacokinetic properties and toxicity of these compounds indicated their potential oral drugs. Multistep virtual screening of EPIs for MexAB-OprM, efflux pump multidrug resistant of P. aeruginosa.

Prediction model of human ABCC2/MRP2 efflux pump inhibitors: a QSAR study.

The overexpression of ABCC2/MRP2, an ATP-binding cassette transporter, contributes to multidrug resistance in cancer cells. In this study, a quantitative structure-activity relationship (QSAR) analysis on ABCC2 inhibitors has been carried out, aiming to establish a computational prediction model for ABCC2 modulators. Seven classification models and two regression models were built by SONNIA 4.2, and two other regression models were built by MOE 2008.10 based on a data set comprising 372 compounds collected from 16 relevant publications. The CPG-C iABCC2 model for classifying ABCC2 inhibitors has total accuracy of 0.88 and Matthews correlation coefficient MCC = 0.75. The CPG-C iEG model for classifying ABCC2 inhibitors (substrate EG: ß-estradiol 17-ß-D-glucuronide) has total accuracy of 0.91 and MCC = 0.82. The regression model PLS EG-IC50 for predicting ABCC2 inhibitors (substrate EG) gave root-mean-square error RMSE = 0.26, Q2 = 0.73 and [Formula: see text]. The regression model PLS CDCF-IC50 for predicting ABCC2 inhibitors [substrate CDCF: 5(6)-carboxy-2',7'-dichlorofluorescein] gave RMSE = 0.31, Q2 = 0.74 and [Formula: see text]. Four 2D-QSAR models were applied to 1661 compounds, with results indicating 369 compounds having the ability to reverse the efflux of both EG and CDCF by ABCC2, 152 among them having IC50 < 100 µM.

Structure-Based Discovery of ABCG2 Inhibitors: A Homology Protein-Based Pharmacophore Modeling and Molecular Docking Approach.

ABCG2 is an ABC membrane protein reverse transport pump, which removes toxic substances such as medicines out of cells. As a result, drug bioavailability is an unexpected change and negatively influences the ADMET (absorption, distribution, metabolism, excretion, and toxicity), leading to multi-drug resistance (MDR). Currently, in spite of promising studies, screening for ABCG2 inhibitors showed modest results. The aim of this study was to search for small molecules that could inhibit the ABCG2 pump. We first used the WISS MODEL automatic server to build up ABCG2 homology protein from 655 amino acids. Pharmacophore models, which were con-structed based on strong ABCG2 inhibitors (IC50 < 1 µM), consist of two hydrophobic (Hyd) groups, two hydrogen bonding acceptors (Acc2), and an aromatic or conjugated ring (Aro|PiR). Using molecular docking method, 714 substances from the DrugBank and 837 substances from the TCM with potential to inhibit the ABCG2 were obtained. These chemicals maybe favor synthesized or extracted and bioactivity testing.

Discovery of AcrAB-TolC pump inhibitors: Virtual screening and molecular dynamics simulation approach.

AcrAB-TolC tripartite efflux pump, which belongs to the RND superfamily, is a main multi-drug efflux system of Escherichia coli (E. coli) because of the broad resistance on various antibiotics. With the discovering of efflux pump inhibitors (EPIs), a combination between these and antibiotics is one of the most promising therapies. Therefore, building a virtual screening model with prediction capacities for the efflux pump inhibitory activities of candidates from DrugBank and ZINC15 dataset, is one of the key goals of this project. Based on the database of 170 diverse chemical structures collected from 28 research journals, two 2D-QSAR models and a 3D-pharmacophore model have been performed. On the AcrB protein (PDB 4DX7), two binding sites have been discovered that match to the hydrophobic trap in the distal pocket and the switch loop in the proximal pocket. After virtual screening processes, twenty candidate AcrAB-TolC inhibitors have been subjected to molecular dynamics simulations, binding free energy calculations and ADMET predictions. The results indicate that three compounds namely DB09233, DB02581, and DB15224 are potential inhibitors with ΔGbind of -42.30 ± 4.58, -40.76 ± 7.30 and -31.06 ± 7.63 kcal.mol-1, respectively.Communicated by Ramaswamy H. Sarma.

Computational assessment and in vitro test of phytochemicals of Usnea aciculifera as potential inhibitors of Escherichia coli efflux pump AcrB.

Lichens produce secondary metabolites that have many pharmaceutical activities such as antimicrobial, antioxidant, antiviral, anticancer, antigenotoxic, anti-inflammatory, analgesic and antipyretic activities. However, there is limited research on their efflux pump inhibitory activities. Twelve phytochemicals were isolated from Usnea aciculifera, and their activity of AcrAB-TolC efflux pump inhibition was evaluated. Four potential compounds, which are diffractaic acid (2), 8' -O- methylstictic acid (5), 3-hydroxy-4-(methoxycarbonyl)-2,5-dimethylphenyl 2,4-dimethoxy-3,6-dimethylbenzoate (8) and 3-hydroxy-4-(methoxycarbonyl)-2,5-dimethylphenyl 2-hydroxy-4-methoxy-3,6-dimethylbenzoate (9), were found by virtual screening using pharmacophore and 2D-QSAR model. Compound 8 exhibited AcrB inhibition activity in vitro with an accumulation H33342 percentage compared with untreated control of 202% at a concentration of 50 µM and increased the antibacterial activity of levofloxacin by four-fold at a concentration of 200 µM. By molecular docking and molecular dynamics (MD) simulation, the binding affinity of depside and depsidone derivatives to AcrB was also clarified. Despite the poor docking score to the AcrB binding site, compound 8 was the most stable among the four complexes at 20 ns of MD simulation. The analysis of long MD at 100 ns indicated that compound 8 interacts strongly with the residues in the distal pocket, creating a stable complex with ΔGbind of -31.51 kcal.mol-1. According to the ADMETlab 2.0 web server's predictions of pharmacokinetics and toxicities, compound 8 has the potential for drug development.Communicated by Ramaswamy H. Sarma.

Virtual screening for novel Staphylococcus Aureus NorA efflux pump inhibitors from natural products.

NorA is a member of the Major Facilitator Superfamily (MFS) drug efflux pumps that have been shown to mediate antibiotic resistance in Staphylococcus aureus (SA). In this study, QSAR analysis, virtual screening and molecular docking were implemented in an effort to discover novel SA NorA efflux pump inhibitors. Originally, a set of 47 structurally diverse compounds compiled from the literature was used to develop linear QSAR models and another set of 15 different compounds were chosen for extra validation. The final model which was estimated by statistical values for the full data set (n = 45, Q(2) = 0.80, RMSE = 0.20) and for the external test set (n = 15, R(2) = 0.60, |res|max = 0.75, |res|min = 0.02) was applied on the collection of 182 flavonoides and the traditional Chinese medicine (TCM) database to screen for novel NorA inhibitors. Finally, 33 lead compounds that met the Lipinski's rules of five/three and had good predicted pIC50 values from in silico screening process were employed to analyze the binding ability by docking studies on NorA homology model in place of its unavailable crystal structures at two active sites, the central channel and the Walker B.