Full Text

Turn on search term navigation

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Background: The current study utilizes in silico molecular docking/molecular dynamics to evaluate the binding affinity of apigenin and safranal with 5HT1AR/5HT2AR, followed by assessment of in vivo effects of these compounds on depressive and anxious behavior. Methods: The docking between apigenin and safranal and the 5HT1A and 5HT2A receptors was performed utilizing AutoDock Vina software, while MD and protein-lipid molecular dynamics simulations were executed by AMBER16 software. For in vivo analysis, healthy control (HC), disease control (DC), fluoxetine-, and apigenin-safranal-treated rats were tested for changes in depression and anxiety using the forced swim test (FST) and the elevated plus-maze test (EPMT), respectively. Results: The binding affinity estimations identified the superior interacting capacity of apigenin over safranal for 5HT1A/5HT2A receptors over 200 ns MD simulations. Both compounds exhibit oral bioavailability and absorbance. In the rodent model, there was a significant increase in the overall mobility time in the FST, while in the EPMT, there was a decrease in latency and an increase in the number of entries for the treated and HC rats compared with the DC rats, suggesting a reduction in depressive/anxiety symptoms after treatment. Conclusions: Our analyses suggest apigenin and safranal as prospective medication options to treat depression and anxiety.

Details

Title
Interactions of Apigenin and Safranal with the 5HT1A and 5HT2A Receptors and Behavioral Effects in Depression and Anxiety: A Molecular Docking, Lipid-Mediated Molecular Dynamics, and In Vivo Analysis
Author
Amin, Faiq 1 ; Ibrahim, Mahmoud A A 2   VIAFID ORCID Logo  ; Rizwan-ul-Hasan, Syed 3 ; Khaliq, Saima 4 ; Gabr, Gamal A 5   VIAFID ORCID Logo  ; Muhammad 4 ; Khan, Asra 1 ; Sidhom, Peter A 6   VIAFID ORCID Logo  ; Tikmani, Prashant 1 ; Shawky, Ahmed M 7   VIAFID ORCID Logo  ; Ahmad, Saara 1 ; Syed Hani Abidi 8   VIAFID ORCID Logo 

 Department of Biological and Biomedical Sciences, Aga Khan University, Karachi 74800, Pakistan 
 Computational Chemistry Laboratory, Chemistry Department, Faculty of Science, Minia University, Minia 61519, Egypt; School of Health Sciences, University of KwaZulu-Natal, Westville, Durban 4000, South Africa 
 Department of Computer Science, DHA Suffa University, Karachi 75500, Pakistan 
 Department of Biochemistry, Federal Urdu University of Arts, Science and Technology, Karachi 75300, Pakistan 
 Department of Pharmacology and Toxicology, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia 
 Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tanta University, Tanta 31527, Egypt 
 Science and Technology Unit (STU), Umm Al-Qura University, Makkah 21955, Saudi Arabia 
 Department of Biological and Biomedical Sciences, Aga Khan University, Karachi 74800, Pakistan; Department of Biomedical Sciences, Nazarbayev University School of Medicine, Nur-Sultan 010000, Kazakhstan 
First page
8658
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
14203049
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2756773443
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.