Damanpreet Singh1, Dinesh Y Gawande1, Tanveer Singh1, Vladimir Poroikov2, Rajesh Kumar Goel3. 1. Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala 147002, Punjab, India. 2. Institute of Biomedical Chemistry of Russian Academy of Medical Sciences, 10, Pogodinskaya Street, Moscow 119121, Russia. 3. Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala 147002, Punjab, India. Electronic address: goelrkpup@gmail.com.
Abstract
BACKGROUND: Exploration of therapeutic mechanism is an integral part of medicinal plant based drug discovery for better understanding of pharmacological behavior of these agents. But, its study using conventional hit and trial wet laboratory experiments proves to be very tedious, time consuming and expensive, thus encouraging development of in silico techniques. Hence, an in silico technique has been devised using a computer software Prediction of Activity Spectra for Substances (PASS) to study pharmacodynamics of medicinal plants. The technique has been presented with a case study using Ficus religiosa L. (Moraceae) in which its anticonvulsant mechanism has been elucidated with PASS and further validated experimentally. METHODS: Pentylenetetrazol (PTZ)-induced convulsion test was used to study the anticonvulsant effect of standardized bark extract of F. religiosa. Thereafter, structure of all the reported bioactive metabolites in the bark was subjected to PASS software to obtain biological activity spectrum of each compound. The mechanism signifying anticonvulsant effect was selected from the spectrum and was further validated using in vitro test. RESULTS AND DISCUSSION: The extract showed significant anticonvulsant effect in PTZ test. PASS analysis showed a high activity score for GABA aminotransferase (GABA-AT) inhibitory effect of the bioactive metabolites present in the bark. In vitro GABA-AT enzyme assay results were in concordance with the predicted mechanism by PASS for the observed anticonvulsant effect, as the extract showed potent inhibition of the enzyme. The results of present study showed the in silico technique to be useful for elucidation of unknown therapeutic mechanisms of medicinal plants.
BACKGROUND: Exploration of therapeutic mechanism is an integral part of medicinal plant based drug discovery for better understanding of pharmacological behavior of these agents. But, its study using conventional hit and trial wet laboratory experiments proves to be very tedious, time consuming and expensive, thus encouraging development of in silico techniques. Hence, an in silico technique has been devised using a computer software Prediction of Activity Spectra for Substances (PASS) to study pharmacodynamics of medicinal plants. The technique has been presented with a case study using Ficus religiosa L. (Moraceae) in which its anticonvulsant mechanism has been elucidated with PASS and further validated experimentally. METHODS:Pentylenetetrazol (PTZ)-induced convulsion test was used to study the anticonvulsant effect of standardized bark extract of F. religiosa. Thereafter, structure of all the reported bioactive metabolites in the bark was subjected to PASS software to obtain biological activity spectrum of each compound. The mechanism signifying anticonvulsant effect was selected from the spectrum and was further validated using in vitro test. RESULTS AND DISCUSSION: The extract showed significant anticonvulsant effect in PTZ test. PASS analysis showed a high activity score for GABA aminotransferase (GABA-AT) inhibitory effect of the bioactive metabolites present in the bark. In vitro GABA-AT enzyme assay results were in concordance with the predicted mechanism by PASS for the observed anticonvulsant effect, as the extract showed potent inhibition of the enzyme. The results of present study showed the in silico technique to be useful for elucidation of unknown therapeutic mechanisms of medicinal plants.
Keywords:
Anticonvulsant; Ficus religiosa L. (Moraceae); GABA aminotransferase; In silico predictions; Medicinal plants; Prediction of Activity Spectra for Substances (PASS)
Authors: Rajib Hossain; Chandan Sarkar; Shardar Mohammad Hafiz Hassan; Rasel Ahmed Khan; Mohammad Arman; Pranta Ray; Muhammad Torequl Islam; Sevgi Durna Daştan; Javad Sharifi-Rad; Zainab M Almarhoon; Miquel Martorell; William N Setzer; Daniela Calina Journal: Chin J Integr Med Date: 2021-12-15 Impact factor: 2.626