A Combined Virtual Screening 2D and 3D QSAR Methodology for the Selection of New Anticonvulsant Candidates from a Natural Product Library

Abstract

A virtual screening methodology combining a discriminant function (df) based on Dragon 2D-descriptors, general ADME filters, and a pharmacophore identified through superposition of rigid analogs was applied in order to identify new anticonvulsant agents among 10 903 natural products. Fifty-six compounds were selected from the application of this df, Lipinskis rule-of-five and other criteria for prediction of oral bioavailability, and the optimal value of log P for a compound to diffuse passively through the blood -brain barrier. Seven of these compounds were removed because they did not belong to descriptor space defined by the training set. The remaining 49 compounds were fitted to the pharmacophore structural constrains, selecting 7 structures with RMS distance value below 0.2. Systematic conformational analysis was performed to find the global minimum conformation, using the PM3 base included in Hyperchem 6.03. The global minimum conformations were further refined at a 6-31G** level with Gaussian 03. Restricted optimization was then performed to determine the energy difference between the energy minimum conformation and the active conformation defined by the pharmacophore, retaining those structures whose energy differences were below 7 kcal/mol. Four new potential anticonvulsants which fulfill the df and the pharmacophore requisites were selected. One of these structures, 2-(4,6-dimethyl-1-benzofuran-3-yl)acetic acid, was acquired and assayed in the MES and Rotorod tests, confirming anticonvulsant activity in mice at 30 and 100 mg/kg (0.5 and 4 h, i.p.) and absence of neurotoxicity at the tested doses.

Combined Virtual Screening Strategies

“…In the absence of the 3D structure of sodium channels linked to epilepsy, Gavernet et al performed a combined 2D/3D VS approach that includes the subsequent use of a discriminant function based on topological descriptors from the program Dragon [60], general ADME filters such as Lipinski's rule of five and Veber's rules for estimating drug bioavailability and the optimal log P value for a compound to diffuse passively through the blood-brain-barrier, and, finally, fitting to a Sybyl pharmacophore [61]. From more than 10,000 structures from InterBioScreen database, the 2D/ADME filters yielded a total of 56 structures, which were fitted to the pharmacophore.…”

Plant-Based Phytochemicals as Possible Alternative to Antibiotics in Combating Bacterial Drug Resistance

“…In addition, to predict characteristics of molecular structures like protein-ligand biding interactions, in silico simulations can be used. Various computational approaches have been reported, such as the QSAR model (quantitative structure activity relationship); [71,72], which is used to predict compounds with high specificity for targets. Bioactivity databases, such as CHEMBL [73] or PubChem [74] are used to evaluate datasets for compatibility among naturally obtained products and synthetically obtained pharmacologically active molecules.…”

Computer-Aided Identification of Anticonvulsant Effect of Natural Nonnutritive Sweeteners Stevioside and Rebaudioside A

“…It is worth highlighting that these two models have previously been jointly applied to screen NP libraries, with good results. 15…”