Gas chromatography-mass spectrometry metabolic profiling, molecular simulation and dynamics of diverse phytochemicals of Punica granatum L. leaves against estrogen receptor
Bibliographic record
Abstract
Introduction: Breast cancer is the most common type of cancer globally and its treatment with many FDA-approved synthetic drugs manifests various side effects. Alternatively, phytochemicals are natural reserves of novel drugs for cancer therapy. Punica granatum commonly known as pomegranate is a rich source of phytopharmaceuticals. Methods: The phytoconstituents of Punica granatum leaves were profiled using GC-MS/MS in the present work. Cytoscape-assisted network pharmacology of principal and prognostic biomarkers, which are immunohistochemically tested in breast cancer tissue, was carried out for the identification of protein target. Followed by, rigorous virtual screening of 145 phytoconstituents against the three ER isoforms (α, β and γ) was performed using Discovery Studio. The docked complexes were further evaluated for their flexibility and stability using GROMACS2016 through 50 ns long molecular dynamic simulations. Results: In the current study, we report the precise and systematic GC-MS/MS profiling of phytoconstituents (19 novel metabolites out of 145) of hydromethanolic extract of Punica granatum L. (pomegranate) leaves. These phytocompounds are various types of fatty acids, terpenes, heterocyclic compounds and flavonoids. 4-coumaric acid methyl ester was identified as the best inhibitor of ER isoforms with drug-likeness and no toxicity from ADMET screening. γ-ligand binding domain complex showed the best interactions with minimum RMSD, constant Rg, and the maximum number of hydrogen bonds. Conclusion: We conclude that 4-coumaric acid methyl ester exhibits favourable drug-like properties comparable to tamoxifen, an FDA-approved breast cancer drug and can be tested further in preclinical studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".