In vitro and in silico evaluation of synergistic antioxidant potential in a polyherbal formulation from Abelmoschus esculentus and Telfairia occidentalis
Bibliographic record
Abstract
Polyherbal formulations are increasingly investigated for their synergistic antioxidant potential against oxidative stress-related disorders. This study evaluated a polyherbal ethanol extract derived from Abelmoschus esculentus pods and Telfairia occidentalis leaves (AETO-PHF) through integrated in vitro and in silico approaches. GC-MS analysis identified 37 compounds, with dodecanoic acid (16.24%) and 9-octadecenoic acid (Z)-,2,3-dihydroxypropyl ester (16.41%) as predominant constituents. Antioxidant assays revealed potent dose-dependent radical scavenging, with IC₅₀ values of 61.39 ± 0.17 µg/mL (DPPH), 11.15 ± 0.15 µg/mL (H₂O₂ scavenging), 61.75 ± 0.00 µg/mL (FRAP), and 38.97 ± 2.66 µg/mL (NO inhibition). These results were statistically comparable (p > 0.05) to ascorbic acid (61.38 ± 0.58, DPPH; 61.71 ± 0.20, FRAP; and 38.94 ± 0.00, NO inhibition µg/mL) and gallic acid (11.30 ± 0.84 µg/mL, H₂O₂ scavenging). Molecular docking against cytochrome c peroxidase showed strong interactions of dodecanoic acid (- 5.7 kcal/mol) and 9-octadecenoic acid ester (- 6.2 kcal/mol), both surpassing the binding affinity of the reference antioxidant ascorbic acid (- 5.5 kcal/mol). Molecular dynamics simulations confirmed stable protein-ligand complexes with favorable RMSD, RMSF, and hydrogen-bond interaction profiles. These findings validate the traditional use of A. esculentus and T. occidentalis, demonstrate synergistic antioxidant efficacy of their polyherbal blend, and provide molecular-level insights into their mechanism of action. AETO-PHF represents a promising candidate for nutraceutical and therapeutic applications against oxidative stress-related diseases, meriting further in vivo and clinical studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".