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2000
Volume 33, Issue 5
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

Abstract

Introduction

The disability and mortality related to Parkinson's disease (PD), a neurodegenerative disease, are increasing globally at a faster rate than other neurological disorders. With no permanent cure for PD, there is an urgent need to develop novel and effective anti-PD drugs.

Methods

Targeting monoamine oxidases (MAO), which catalyze the breakdown of neurotransmitters, is one way to treat neurodegenerative diseases. In this context, an initial molecular docking of twenty designed sulfonyl derivatives of benzimidazole against monoamine oxidase B (MAO-B) associated with PD was conducted using AutoDock Vina.

Results

The results were compared with those of the conventional inhibitors, selegiline and rasagiline. Based on the docking score, the pharmacokinetic properties (ADME), drug-likeness, and toxicity profiles of the newly synthesized molecules were examined using SwissADME, PreADMET, ProTox-3.0, vNN, and ADMETlab web tools. Then, twelve potential derivatives were synthesized and characterized by IR, 1H-NMR, 13C-NMR, 19F-NMR (for some compounds), and mass spectrometry. Derivatives and were the two molecules having the best binding affinity of -11.9 and -11.8 kcal/mol, respectively, against MAO-B, exhibiting a higher binding affinity compared to that of some commercially available drugs. A 50 ns MD simulation run was performed to observe the stability of the top two docked complexes, and , in order to further validate the efficacy of those two substances. Moreover, the MM-PBSA method was used to calculate the final, binding free energy of the simulated () complex.

Conclusion

This study indicates that the binding affinity of most of the hits was superior to that of known MAO inhibitors; therefore, these newly synthesized benzimidazole derivatives may be developed into essential drug candidates for the treatment of PD.

This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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