Galantamine Based Novel Acetylcholinesterase Enzyme Inhibitors: A Molecular Modeling Design Approach.

Acetylcholinesterase (AChE) enzymes play an essential role in the development of Alzheimer's disease (AD). Its excessive activity causes several neuronal problems, particularly psychopathies and neuronal cell death. A bioactive pose on the hAChE B site of the human acetylcholinesterase (hAChE) enzyme employed in this investigation, which was obtained from the Protein Data Bank (PDB ID 4EY6), allowed for the prediction of the binding affinity and free binding energy between the protein and the ligand. Virtual screening was performed to obtain structures similar to Galantamine (GNT) with potential hAChE activity. The top 200 hit compounds were prioritized through the use of filters in ZincPharmer, with special features related to the pharmacophore. Critical analyses were carried out, such as hierarchical clustering analysis (HCA), ADME/Tox predictions, molecular docking, molecular simulation studies, synthetic accessibility (SA), lipophilicity, water solubility, and hot spots to confirm the stable binding of the two promising molecules (ZINC16951574-LMQC2, and ZINC08342556-LMQC5). The metabolism prediction, with metabolites M3-2, which is formed by Glutathionation reaction (Phase II), M1-2, and M2-2 formed from the reaction of S-oxidation and Aliphatic hydroxylation (Phase I), were both reactive but with no side effects. Theoretical synthetic routes and prediction of synthetic accessibility for the most promising compounds are also proposed. In conclusion, this study shows that in silico modeling can be used to create new drug candidate inhibitors for hAChE. The compounds ZINC16951574-LMQC2, and ZINC08342556-LMQC5 are particularly promising for oral administration because they have a favorable drug-likeness profile, excellent lipid solubility, high bioavailability, and adequate pharmacokinetics.

Potential inhibitors of the enzyme acetylcholinesterase and juvenile hormone with insecticidal activity: study of the binding mode via docking and molecular dynamics simulations.

Models validation in QSAR, pharmacophore, docking and others can ensure the accuracy and reliability of future predictions in design and selection of molecules with biological activity. In this study, pyriproxyfen was used as a pivot/template to search the database of the Maybridge Database for potential inhibitors of the enzymes acetylcholinesterase and juvenile hormone as well. The initial virtual screening based on the 3D shape resulted in 2000 molecules with Tanimoto index ranging from 0.58 to 0.88. A new reclassification was performed on the overlapping of positive and negative charges, which resulted in 100 molecules with Tanimoto's electrostatic score ranging from 0.627 to 0.87. Using parameters related to absorption, distribution, metabolism and excretion and the pivot molecule, the molecules selected in the previous stage were evaluated regarding these criteria, and 21 were then selected. The pharmacokinetic and toxicological properties were considered and for 12 molecules, the DEREK software not fired any alert of toxicity, which were thus considered satisfactory for prediction of biological activity using the Web server PASS. In the molecular docking with insect acetylcholinesterase, the Maybridge3_002654 molecule had binding affinity of -11.1 kcal/mol, whereas in human acetylcholinesterase, the Maybridge4_001571molecule show in silico affinity of -10.2 kcal/mol, and in the juvenile hormone, the molecule MCULE-8839595892 show in silico affinity value of -11.6 kcal/mol. Subsequent long-trajectory molecular dynamics studies indicated considerable stability of the novel molecules compared to the controls.AbbreviationsQSARquantitative structure-activity relationshipsPASSprediction of activity spectra for substancesCommunicated by Ramaswamy H. Sarma.

Identification of New Inhibitors with Potential Antitumor Activity from Polypeptide Structures via Hierarchical Virtual Screening.

Leukemias are neoplasms that affect hematopoietic cells, which are developed by genetic alterations (mutations) that lead to the loss of proliferation control mechanisms (maturation and/or cell death). The α4ß1 integrin receptor is a therapeutic target for inflammation, autoimmune diseases and lymphoid tumors. This study was carried out to search through the antagonists-based virtual screening for α4ß1 receptor. Initially, seventeen (17) structures were selected (based on the inhibitory activity values, IC50) and the structure with the best value was chosen as the pivot. The pharmacophoric pattern was determined from the online PharmaGist server and resulted in a model of score value equal to 97.940 with 15 pharmacophoric characteristics that were statistically evaluated via Pearson correlations, principal component analysis (PCA) and hierarchical clustering analysis (HCA). A refined model generated four pharmacophoric hypotheses totaling 1.478 structures set of Zinc_database. After, the pharmacokinetic, toxicological and biological activity predictions were realized comparing with pivot structure that resulted in five (ZINC72088291, ZINC68842860, ZINC14365931, ZINC09588345 and ZINC91247798) structures with optimal in silico predictions. Therefore, future studies are needed to confirm antitumor potential activity of molecules selected this work with in vitro and in vivo assays.

A QSAR, pharmacokinetic and toxicological study of new artemisinin compounds with anticancer activity.

The Density Functional Theory (DFT) method and the 6-31G** basis set were employed to calculate the molecular properties of artemisinin and 20 derivatives with different degrees of cytotoxicity against the human hepatocellular carcinoma HepG2 line. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to select the most important descriptors related to anticancer activity. The significant molecular descriptors related to the compounds with anticancer activity were the ALOGPS_log, Mor29m, IC5 and GAP energy. The Pearson correlation between activity and most important descriptors were used for the regression partial least squares (PLS) and principal component regression (PCR) models built. The regression PLS and PCR were very close, with variation between PLS and PCR of R(2) = ± 0.0106, R(2)(ajust) = ± 0.0125, s = ± 0.0234, F(4,11) = ± 12.7802, Q(2) = ± 0.0088, SEV = ± 0.0132, PRESS = ± 0.4808 and SPRESS = ± 0.0057. These models were used to predict the anticancer activity of eight new artemisinin compounds (test set) with unknown activity, and for these new compounds were predicted pharmacokinetic properties: human intestinal absorption (HIA), cellular permeability (PCaCO2), cell permeability Maden Darby Canine Kidney (PMDCK), skin permeability (P(Skin)), plasma protein binding (PPB) and penetration of the blood-brain barrier (C(Brain/Blood)), and toxicological: mutagenicity and carcinogenicity. The test set showed for two new artemisinin compounds satisfactory results for anticancer activity and pharmacokinetic and toxicological properties. Consequently, further studies need be done to evaluate the different proposals as well as their actions, toxicity, and potential use for treatment of cancers.