Chalcone Derivatives as Potential Inhibitors of P-Glycoprotein and NorA: An In Silico and In Vitro Study.

The human P-glycoprotein (P-gp) and the NorA transporter are the major culprits of multidrug resistance observed in various bacterial strains and cancer cell lines, by extruding drug molecules out of the targeted cells, leading to treatment failures in clinical settings. Inhibiting the activity of these efflux pumps has been a well-known strategy of drug design studies in this regard. In this manuscript, our earlier published machine learning models and homology structures of P-gp and NorA were utilized to screen a chemolibrary of 95 in-house chalcone derivatives, identifying two hit compounds, namely, F88 and F90, as potential modulators of both transporters, whose activity on Staphylococcus aureus strains overexpressing NorA and resistant to ciprofloxacin was subsequently confirmed. The findings of this study are expected to guide future research towards developing novel potent chalconic inhibitors of P-gp and/or NorA.

Computational predictive models for P-glycoprotein inhibition of in-house chalcone derivatives and drug-bank compounds.

The human P-glycoprotein (P-gp) efflux pump is of great interest for medicinal chemists because of its important role in multidrug resistance (MDR). Because of the high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of this transmembrane protein, ligand-based, and structure-based approaches which were machine learning, homology modeling, and molecular docking were combined for this study. In ligand-based approach, individual two-dimensional quantitative structure-activity relationship models were developed using different machine learning algorithms and subsequently combined into the Ensemble model which showed good performance on both the diverse training set and the validation sets. The applicability domain and the prediction quality of the developed models were also judged using the state-of-the-art methods and tools. In our structure-based approach, the P-gp structure and its binding region were predicted for a docking study to determine possible interactions between the ligands and the receptor. Based on these in silico tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening using prediction models and molecular docking in an attempt to restore cancer cell sensitivity to cytotoxic drugs.

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.

Pharmacophore modeling for antitargets.

The pharmacophore modeling in modern drug research has been applied for both bioactivity profiling and early stage of risk assessment of potential side effects and toxicity due to interactions of drug candidates with antitargets namely P-glycoprotein, hERG, cytochrome P450 and pregnane X-receptor. In this article, an existing state concerning with pharmacophore modeling applied for promiscuous proteins in drug research were updated and reviewed. In an attempt to create new safe medicines faster, the partial overlap of substrate properties of hERG, P-glycoprotein, pregnane X-receptor and cytochrome P450 has to be considered and drug safety has to be dealt on a system level on the off-targets.

Synthesis and anti Methicillin resistant Staphylococcus aureus activity of substituted chalcones alone and in combination with non-beta-lactam antibiotics.

A total of 30 chalcone analogues was synthesized via a base catalyzed Claisen Schmidt condensation and screened for their in vitro antibacterial activity against Methicillin-sensitive Staphylococcus aureus (MSSA) and Methicillin-resistant Staphylococcus aureus (MRSA) alone or in combination with non beta-lactam antibiotics namely ciprofloxacin, chloramphenicol, erythromycin, vancomycin, doxycycline and gentamicin. In the checkerboard technique, fractional inhibitory concentration indices (FICI) show that the following combinations like ciprofloxacin with 25 (4'-bromo-2-hydroxychalcone); doxycycline with 21 (4-hydroxychalcone); doxycycline with 25; and doxycycline with 4 (2',2-dihydroxychalcone) were synergistic against MRSA. In term SAR study, the relationship between chalcone structure and their antibacterial activity against S. aureus and synergy with tested antibiotics were discussed. Possible mechanisms for antibacterial activity of chalcones alone as well as the synergistic effect in combinations were proposed by molecular modeling studies, respectively. Combinations of chalcones with conventional antibiotics could be an effective alternative in the treatment of infection caused by MRSA.

A support vector machine classification model for benzo[c]phenathridine analogues with toposiomerase-I inhibitory activity.

Benzo[c]phenanthridine (BCP) derivatives were identified as topoisomerase I (TOP-I) targeting agents with pronounced antitumor activity. In this study, a support vector machine model was performed on a series of 73 analogues to classify BCP derivatives according to TOP-I inhibitory activity. The best SVM model with total accuracy of 93% for training set was achieved using a set of 7 descriptors identified from a large set via a random forest algorithm. Overall accuracy of up to 87% and a Matthews coefficient correlation (MCC) of 0.71 were obtained after this SVM classifier was validated internally by a test set of 15 compounds. For two external test sets, 89% and 80% BCP compounds, respectively, were correctly predicted. The results indicated that our SVM model could be used as the filter for designing new BCP compounds with higher TOP-I inhibitory activity.