Use of Pharmacophore Modeling, 3D-atom-based QSAR, ADMET,
Docking, and Molecular Dynamics Studies for the Development of
Psoralen-based Derivatives as Antifungal Agents

Kalyani   D.   Asgaonkar; Shital   M.   Patil; Trupti   S.   Chitre; Arati      Prabhu; Krishna   S.   Shevate; Ashwini   K.   Sagar; Akshata   P.   Naik

Abstract

Background: The mortality and morbidity rates in patients caused by fungi are extremely high. 3-4 % of species of fungi like Candida and Aspergillus are responsible for >99% of invasive fungal infections.

Aim: The goal of the current work was to use several In-silico methods, such as Pharmacophore modeling and 3D-QSAR, to design New chemical entities (NCEs) that have antifungal activity.

Materials & Methods: A dataset of 40 Psoralen derivatives was taken from available literature, and then, the pharmacophore hypothesis and 3D-QSAR model development were generated using Schrodinger 2023-1 software. After designing a library of 36 compounds, they were subjected to ADMET prediction. Screened compounds from the ADMET study were docked with 14 alpha demethylase CYP51 (PDB ID: 3LD6) using Schrödinger software. Molecular dynamics (MD) simulation studies were performed on PDB-3LD6 using Desmond-v7.2.

Results & Discussion: The top-ranked hypothesis, AHRRR_1, was taken into consideration when designing the library of potential NCEs.In order to check the drug likeliness of the compounds, all 36 designed NCEs were subjected to ADMET prediction using the QikProp tool. The majority of compounds have a good partition coefficient index (less than five). Qplog HERG value was found to be less, making them safer and less toxic. C- 4, 6, 9, 13, 15, 22, 24, 27, 31, and 33 have shown compliance with Lipinski’s rule with zero violations. Compounds C-9, C-13, C-22, C-24, and C-27 have shown better docking scores than the standard Ketoconazole. Compounds C-9, 24, and 27 have shown a greater number of hydrophobic and hydrogen bond interactions in comparison with the other compounds. Compounds 9, 24, and 27 showed good stability after 100ns molecular simulation simulations.

Conclusion: In the current work, the application of insilico methods such as pharmacophore hypothesis, 3D QSAR, ADMET study, docking, and simulation studies have helped to optimize Psoralen pharmacophore for potential antifungal activity. Therefore, the outcomes of the present study could provide insights into the discovery of new potential alpha demethylase inhibitors with improved selectivity and activity against fungal infections.

Graphical Abstract

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Cite As

Anti-Infective Agents

Use of Pharmacophore Modeling, 3D-atom-based QSAR, ADMET, Docking, and Molecular Dynamics Studies for the Development of Psoralen-based Derivatives as Antifungal Agents

Abstract

Graphical Abstract