A novel alpha-amylase inhibitor-based spirooxindole-pyrrolidine-clubbed thiochromene-pyrzaole pharmacophores: Unveiling the [3+2] cycloaddition reaction by molecular electron density theory

A novel spirooxindole-pyrrolidine clubbed thiochromene and pyrazole motifs were synthesized by [3+2] cycloaddition (32CA) reactions in one step process starting from the ethylene-based thiochromene and pyrazole scaffolds with the secondary amino-acids and substituted isatins in high yield. The 32CA reaction of AY 10 with ethylene derivative 6 has also been studied with Molecular Electron Density Theory. The high nucleophilic character of AY 10, N = 4.39 eV, allows explaining that the most favorable TS-on is 13.9 kcal mol(-1) below the separated reagent. This 32CA, which takes place through a non-concerted one-step mechanism, presents a total ortho regio- and endo stereoselectivity, which is controlled by the formation of two intramolecular (HO)-O-center dot center dot center dot hydrogen bonds. The design of spirooxindole-pyrrolidines engrafted thiochromene and pyrazole was tested for alpha-amylase inhibition and show a high efficacy in nanoscale range of reactivity. The key interaction between the most active hybrids and the receptor was studied by molecular docking. The physiochemical properties of the designed spirooxindole-pyrrolidines were carried out by in silico ADMET prediction. The newly synthesized most potent hybrid could be considered as a lead compound for drug discovery development for type 2 diabetes mellitus (T2DM).

Automated summary

A novel spirooxindole-pyrrolidine compound was synthesized through a one-step process using [3+2] cycloaddition reactions. The compound showed high efficacy as an alpha-amylase inhibitor and could potentially be used for drug discovery in treating type 2 diabetes mellitus (T2DM). The reaction mechanism and interaction with the receptor were studied using molecular electron density theory and molecular docking, respectively, while the physiochemical properties were predicted using in silico ADMET prediction.