Methods: Six different polymers were used to prepare FLU nanopolymeric particles: hydroxyl propyl methylcellulose (HPMC), poly (vinylpyrrolidone) (PVP), poly (vinyl alcohol) (PVA), ethyl cellulose (EC), Eudragit (EUD), and Pluronics®. A low-energy method, nanoprecipitation, was used to prepare the polymeric nanoparticles.
Results and conclusion: The combination of HPMC-PVP and EUD-PVP was found most effective to produce stable FLU nanoparticles, with particle sizes of 250 nm ±2.0 and 280 nm ±4.2 and polydispersity indices of 0.15 nm ±0.01 and 0.25 nm ±0.03, respectively. The molecular modeling studies endorsed the same results, showing highest polymer drug binding free energies for HPMC-PVP-FLU (-35.22 kcal/mol ±0.79) and EUD-PVP-FLU (-25.17 kcal/mol ±1.12). In addition, it was observed that Ethocel® favored a wrapping mechanism around the drug molecules rather than a linear conformation that was witnessed for other individual polymers. The stability studies conducted for 90 days demonstrated that HPMC-PVP-FLU nanoparticles stored at 2°C-8°C and 25°C were more stable. Crystallinity of the processed FLU nanoparticles was confirmed using differential scanning calorimetry, powder X-ray diffraction analysis and TEM. The Fourier transform infrared spectroscopy (FTIR) studies showed that there was no chemical interaction between the drug and chosen polymer system. The HPMC-PVP-FLU nanoparticles also showed enhanced dissolution rate (P<0.05) compared to the unprocessed counterpart. The in vitro antibacterial studies showed that HPMC-PVP-FLU nanoparticles displayed superior effect against gram-positive bacteria compared to the unprocessed FLU and positive control.
METHODS: Kinetic studies were used to investigate the interactions between the three GSTs and each of glutathione, 1-chloro-2,4-dinitrobenzene, cibacron blue, ethacrynic acid, S-hexyl glutathione, hemin and protoporphyrin IX. Since hemin displacement is intended for PfGST inhibitors, the interactions between hemin and other ligands at PfGST binding sites were studied kinetically. Computationally determined binding modes and energies were interlinked with the kinetic results to resolve enzymes-ligands interaction models at atomic level.
RESULTS: The results showed that hemin and cibacron blue have different binding modes in the three GSTs. Hemin has two binding sites (A and B) with two binding modes at site-A depending on presence of GSH. None of the ligands were able to compete hemin binding to PfGST except ethacrynic acid. Besides bind differently in GSTs, the isolated anthraquinone moiety of cibacron blue is not maintaining sufficient interactions with GSTs to be used as a lead. Similarly, the ethacrynic acid uses water bridges to mediate interactions with GSTs and at least the conjugated form of EA is the true hemin inhibitor, thus EA may not be a suitable lead.
CONCLUSIONS: Glutathione analogues with bulky substitution at thiol of cysteine moiety or at γ-amino group of γ-glutamine moiety may be the most suitable to provide GST inhibitors with hemin competition.
METHODS: The involved approaches build molecules from fragments that are either isosteric to GSH sub-moieties (ligand-based) or successfully docked to GSH binding sub-pockets (structure-based). Compared to reference GST inhibitor of S-hexyl GSH, ligands with improved rigidity, synthetic accessibility, and affinity to receptor were successfully designed. The method involves joining fragments to create ligands. The ligands were then explored using molecular docking, Cartesian coordinate's optimization, and simplified free energy determination as well as MD simulation and MMPBSA calculations. Several tools were used which include OPENEYE toolkit, Open Babel, Autodock Vina, Gromacs, and SwissParam server, and molecular mechanics force field of MMFF94 for optimization and CHARMM27 for MD simulation. In addition, in-house scripts written in Matlab were used to control fragments connection and automation of the tools.
RESULTS: Compounds 2, 4, 8, 12 and 20 exhibited the highest activity (IC50 = 69.20, 59.60, 49.40, 50.20 and 83.20 μM, respectively) compared with the standard acarbose (IC50 = 143.54 μM).
CONCLUSION: A new class of potent α-glucosidase inhibitors was identified, and the molecular docking predicted plausible binding interaction of the targets in the binding pocket of α-glucosidase and rationalized the structure-activity relationship (SARs) of the target compounds.