Investigation of Hydantoin‐Based Drugs Used in the Treatment of Epilepsy Using Quantum Chemical Calculations, Molecular Docking, Molecular Dynamics, <scp>ADMET</scp>, In Vitro, and Spectroscopic Methods

ABSTRACT Present investigation deals with the structural and pharmacokinetic properties of hydantoin‐based drug molecules such as phenytoin, mephenytoin, and ethotoin. Hydantoin‐based drugs are widely used as anticonvulsants in the treatment of epilepsy. In this study, optimized structures, and theoretical vibrational wavenumbers of phenytoin, mephenytoin, and ethotoin molecules were determined using Gaussian 09 program with density functional theory (DFT) and B3LYP/6‐311++G(d,p) basis set, vibration mode assignments were performed with the GAR2PED program, and the theoretical results were supported by FTIR and Raman spectroscopy. In addition, thermodynamic parameters, Mulliken charge values, HOMO‐LUMO, natural bond orbital, MEP, hyperpolarizability analyzes of hydantoin‐based molecules were performed. Docking analysis of all molecules with the GABA‐AT receptor, which has an important place in epilepsy studies, were also carried out. Then, the molecular dynamic (MD) simulations of the hydantoin‐based drugs‐GABA‐AT complexes were realized for 50 ns. ADMET profiles of all molecules were determined and presented by parameters of toxicity and drug‐likeness. Additionally, to determine the effects of hydantoin‐based drugs on glioblastoma cells, cytotoxic effects of phenytoin, mephenytoin, and ethotoin were evaluated on U‐87 Human glioblastoma cell line.

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Publication Details

Journal
International Journal of Quantum Chemistry
Published
2024-09-26
DOI
https://doi.org/10.1002/qua.27485
Citations
4
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
1.45

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article

Investigation of Hydantoin‐Based Drugs Used in the Treatment of Epilepsy Using Quantum Chemical Calculations, Molecular Docking, Molecular Dynamics, ADMET, In Vitro, and Spectroscopic Methods

Gizem Akman, Serda Kecel‐Gunduz, Bilge Bıçak, Minnet Can Kandemir
4 citations
International Journal of Quantum Chemistry
Computational Drug Discovery Methods
1.45
article

Investigation of Hydantoin‐Based Drugs Used in the Treatment of Epilepsy Using Quantum Chemical Calculations, Molecular Docking, Molecular Dynamics, ADMET, In Vitro, and Spectroscopic Methods

Gizem Akman, Serda Kecel‐Gunduz, Bilge Bıçak, Minnet Can Kandemir
article en
4 citations

Abstract

ABSTRACT Present investigation deals with the structural and pharmacokinetic properties of hydantoin‐based drug molecules such as phenytoin, mephenytoin, and ethotoin. Hydantoin‐based drugs are widely used as anticonvulsants in the treatment of epilepsy. In this study, optimized structures, and theoretical vibrational wavenumbers of phenytoin, mephenytoin, and ethotoin molecules were determined using Gaussian 09 program with density functional theory (DFT) and B3LYP/6‐311++G(d,p) basis set, vibration mode assignments were performed with the GAR2PED program, and the theoretical results were supported by FTIR and Raman spectroscopy. In addition, thermodynamic parameters, Mulliken charge values, HOMO‐LUMO, natural bond orbital, MEP, hyperpolarizability analyzes of hydantoin‐based molecules were performed. Docking analysis of all molecules with the GABA‐AT receptor, which has an important place in epilepsy studies, were also carried out. Then, the molecular dynamic (MD) simulations of the hydantoin‐based drugs‐GABA‐AT complexes were realized for 50 ns. ADMET profiles of all molecules were determined and presented by parameters of toxicity and drug‐likeness. Additionally, to determine the effects of hydantoin‐based drugs on glioblastoma cells, cytotoxic effects of phenytoin, mephenytoin, and ethotoin were evaluated on U‐87 Human glioblastoma cell line.

International Journal of Quantum ChemistryVol. 124(19)
Istanbul University (TR), Freie Universität Berlin (DE)
Bilimsel Araştirma Projeleri Birimi, Istanbul Üniversitesi, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu
Openalex Percentile: Top 16%
Computational Drug Discovery Methods
1.45
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