Atomic 3D-QSAR study based on pharmacophore modeling of resveratrol derivatives as selective COX-2 inhibitors

Molecular Docking Study of Phytosterols in Lygodium microphyllum Towards SIRT1 and AMPK, the in vitro Brine Shrimp Toxicity Test, and the Phenols and Sterols Levels in the Extract

Background

Lygodium microphyllum is a fern plant with various pharmacological activities, and phytosterols were reported contained in the n-hexane and ethyl acetate extract of this plant. Phytosterols are known to inhibit steatosis, oxidative stress, and inflammation. Sirtuin 1 (SIRT1) and adenosine monophosphate-activated protein kinase (AMPK) are the key proteins that control lipogenesis. However, information about L. microphyllum on SIRT1 and AMPK is still lacking.

Purpose

This study aims to investigate the binding mode of phytosterols in L. microphyllum extract towards AMPK and SIRT1, and the toxicity of the extract against brine shrimp (Artemia salina) larvae, and to determine the phenols and sterols levels in the extract.

Methods

The molecular docking was performed towards SIRT1 and AMPK using AutoDock v4.2.6, the toxicity of the extract was assayed against brine shrimp (Artemia salina) larvae, and the phytosterols were analyzed by employing a thin layer chromatography densitometry, and the total phenols were by spectrophotometry.

Results

The molecular docking study revealed that β-sitosterol and stigmasterol could occupy the active allosteric-binding site of SIRT1 and AMPK by binding to important residues similar to the protein's activators. The cold extraction of the plant yields 15.86% w/w. Phytochemical screening revealed the presence of phenols, steroids, flavonoids, alkaloids, and saponins. The total phenols are equivalent to 126 mg gallic acid (GAE)/g dry extract, the total sterols are 954.04 µg/g, and the β-sitosterol level is 283.55 µg/g. The LC50 value of the extract towards A. salina larvae is 203.704 ppm.

Conclusion

Lygodium microphyllum extract may have the potential to be further explored for its pharmacology activities, particularly in the discovery of plant-based anti-dyslipidemic drug candidates. However, further studies are needed to confirm their roles in alleviating lipid disorders.

Classification of Cyclooxygenase-2 Inhibitors Using Support Vector Machine and Random Forest Methods

This work reports the classification study conducted on the biggest COX-2 inhibitor data set so far. Using 2925 diverse COX-2 inhibitors collected from 168 pieces of literature, we applied machine learning methods, support vector machine (SVM) and random forest (RF), to develop 12 classification models. The best SVM and RF models resulted in MCC values of 0.73 and 0.72, respectively. The 2925 COX-2 inhibitors were reduced to a data set of 1630 molecules by removing intermediately active inhibitors, and 12 new classification models were constructed, yielding MCC values above 0.72. The best MCC value of the external test set was predicted to be 0.68 by the RF model using ECFP_4 fingerprints. Moreover, the 2925 COX-2 inhibitors were clustered into eight subsets, and the structural features of each subset were investigated. We identified substructures important for activity including halogen, carboxyl, sulfonamide, and methanesulfonyl groups, as well as the aromatic nitrogen atoms. The models developed in this study could serve as useful tools for compound screening prior to lab tests.