Dataset Information

Computational Study on Selective PDE9 Inhibitors on PDE9-Mg/Mg, PDE9-Zn/Mg, and PDE9-Zn/Zn Systems.

ABSTRACT: PDE9 inhibitors have been studied to validate their potential to treat diabetes, neurodegenerative disorders, cardiovascular diseases, and erectile dysfunction. In this report, we have selected highly potent previously reported selective PDE9 inhibitors BAY73-6691R, BAY73-6691S, 28r, 28s, 3r, 3s, PF-0447943, PF-4181366, and 4r to elucidate the differences in their interaction patterns in the presence of different metal systems such as Zn/Mg, Mg/Mg, and Zn/Zn. The initial complexes were generated by molecular docking followed by molecular dynamics simulation for 100 ns in triplicate for each system to understand the interactions' stability. The results were carefully analyzed, focusing on the ligands' non-bonded interactions with PDE9 in different metal systems.

SUBMITTER: Sivakumar D

PROVIDER: S-EPMC8151263 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundThe present study compares antiepileptic drugs and aromatase (CYP19) inhibitors for chemical and structural similarity. Human aromatase is well known as an important pharmacological target in anti-breast cancer therapy, but recent research demonstrates its role in epileptic seizures, as well. The current antiepileptic treatment methods cause severe side effects that endanger patient health and often preclude continued use. As a result, less toxic and more tolerable antiepileptic drugs (AEDs) are needed, especially since every individual responds differently to given treatment options.MethodsThrough a pharmacophore search, this study shows that a model previously designed to search for new classes of aromatase inhibitors is able to identify antiepileptic drugs from the set of drugs approved by the Food and Drug Administration. Chemical and structural similarity analyses were performed using five potent AIs, and these studies returned a set of AEDs that the model identifies as hits.ResultsThe pharmacophore model returned 73% (19 out of 26) of the drugs used specifically to treat epilepsy and approximately 82% (51 out of 62) of the compounds with anticonvulsant properties. Therefore, this study supports the possibility of identifying AEDs with a pharmacophore model that had originally been designed to identify new classes of aromatase inhibitors. Potential candidates for anticonvulsant therapy identified in this manner are also reported. Additionally, the chemical and structural similarity between antiepileptic compounds and aromatase inhibitors is proved using similarity analyses.ConclusionsThis study demonstrates that a pharmacophore search using a model based on aromatase inhibition and the enzyme's structural features can be used to screen for new candidates for antiepileptic therapy. In fact, potent aromatase inhibitors and current antiepileptic compounds display significant - over 70% - chemical and structural similarity, and the similarity analyses performed propose a number of antiepileptic compounds with high potential for aromatase inhibition.

Dataset Information

Computational Study on Selective PDE9 Inhibitors on PDE9-Mg/Mg, PDE9-Zn/Mg, and PDE9-Zn/Zn Systems.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets