PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Virtual Screening of a Series of Phytocompounds from Lagenaria Siceraria for the Identification of Potential Antidiabetic Drug Candidates, in Silico Study and Drug Design Approaches
Version 1
: Received: 30 May 2023 / Approved: 1 June 2023 / Online: 1 June 2023 (05:41:13 CEST)
How to cite:
Sultana, A.; Noushin, F.; Ali, M. L.; Sultan, M. Z.; Chowdhury, J. A.; Chowdhury, A. A.; Kabir, S.; Amran, M. S. Virtual Screening of a Series of Phytocompounds from Lagenaria Siceraria for the Identification of Potential Antidiabetic Drug Candidates, in Silico Study and Drug Design Approaches. Preprints2023, 2023060038. https://doi.org/10.20944/preprints202306.0038.v1
Sultana, A.; Noushin, F.; Ali, M. L.; Sultan, M. Z.; Chowdhury, J. A.; Chowdhury, A. A.; Kabir, S.; Amran, M. S. Virtual Screening of a Series of Phytocompounds from Lagenaria Siceraria for the Identification of Potential Antidiabetic Drug Candidates, in Silico Study and Drug Design Approaches. Preprints 2023, 2023060038. https://doi.org/10.20944/preprints202306.0038.v1
Sultana, A.; Noushin, F.; Ali, M. L.; Sultan, M. Z.; Chowdhury, J. A.; Chowdhury, A. A.; Kabir, S.; Amran, M. S. Virtual Screening of a Series of Phytocompounds from Lagenaria Siceraria for the Identification of Potential Antidiabetic Drug Candidates, in Silico Study and Drug Design Approaches. Preprints2023, 2023060038. https://doi.org/10.20944/preprints202306.0038.v1
APA Style
Sultana, A., Noushin, F., Ali, M. L., Sultan, M. Z., Chowdhury, J. A., Chowdhury, A. A., Kabir, S., & Amran, M. S. (2023). Virtual Screening of a Series of Phytocompounds from Lagenaria Siceraria for the Identification of Potential Antidiabetic Drug Candidates, in Silico Study and Drug Design Approaches. Preprints. https://doi.org/10.20944/preprints202306.0038.v1
Chicago/Turabian Style
Sultana, A., Shaila Kabir and Md. Shah Amran. 2023 "Virtual Screening of a Series of Phytocompounds from Lagenaria Siceraria for the Identification of Potential Antidiabetic Drug Candidates, in Silico Study and Drug Design Approaches" Preprints. https://doi.org/10.20944/preprints202306.0038.v1
Abstract
Background: Currently, limited number of therapeutic options are available to treat Diabetes mellitus, and to find a potential candidate, laboratory work takes time and also needs animal studies. So, the aim of this study was to determine the efficacy of some natural phytocompounds with the aid of molecular docking, bioinformatics and in silico drug design approaches. Method: Two proteins (Human CYP3A4 linked to metformin and Human dipeptidyl peptidase-IV) were selected and molecular docking studies were conducted using Pymol, AutoDock Vina, PyRx, and Discovery Studio. Different important pharmacokinetic parameters like ADME and toxicity data were obtained from online databases SwissADME and pkCSM program. Results: It was found that human dipeptidyl peptidase-IV (PDB ID: 4A5S) has exhibited a maximal affinity of -9.7 Kcal/mol for bryonolic acid and hesperidin, but only -6.7 kcal/mol for metformin hydrochloride. Similarly, the highest affinity of hesperidin for human CYP3A4 bound to metformin (PDB ID: 5G5J) is -10.7 Kcal/mol compared to metformin hydrochlor (-6.3 Kcal/mol). Besides, all the compounds have been documented outstanding ADMET profile, and accepted by drug-likeness or Lipinski rule. Conclusions: The present study suggested that these compounds can be further investigated in vitro and in vivo to establish them as lead compounds against Diabetes mellitus..
Keywords
Lagenaria siceraria; In silico study; Drug design; Diabetes mellitus; Molecular docking; ADMET
Subject
Medicine and Pharmacology, Pharmacy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.