Antibacterial, anthelmintic, and analgesic activities of Piper sylvaticum (Roxb.) leaves and in silico molecular docking and PASS prediction studies of its isolated compounds.

Abstract

Background In the present study, we investigated the antibacterial, anthelmintic, and analgesic activities of methanol extract of P. sylvaticum leaves (MEPSL) in experimental models. Then, computational analysis (in silico molecular docking and PASS prediction) was performed to determine the potent phytoconstituents of total six isolated compounds of this plant for antibacterial and anthelmintic activities. Methods Qualitative and quantitative phytochemical studies were carried out by established methods. In vitro antibacterial activity was determined by disc diffusion technique and anthelmintic activity was tested against Tubifex tubifex worm whereas analgesic activity was determined by the acetic acid-induced writhing test in mice. Molecular docking study was performed using Schrödinger Maestro 10.1 and an online tool used for PASS prediction. Results Our phytochemical study revealed the presence of alkaloids, flavonoids, saponins, and also indicated a substantial amount of phenols (65.83 mg), flavonoids (102.56 mg), and condensed tannins (89.32 mg). MEPSL showed good antibacterial activity against both gram-positive and gram-negative bacteria. Our result exhibited that MEPSL has strong anthelmintic action compared to standard levamisole. In addition, the extract also showed a dose-dependent and statistically significant analgesic activity at the doses of 200 and 400 mg/kg, body weight. Docking studies showed that piperine and piperlonguminine have the best scores for the tested enzymes. PASS predicted the antibacterial and anthelmintic activity of both phytoconstituents. Conclusions This study suggests that MEPSL possess significant antibacterial, anthelmintic, and analgesic activities which could be related to the presence of several phytochemicals. The phytoconstituents, i.e. piperine and piperlonguminine were found to be most effective in computational studies.