Evaluation of the Anticancer Activity of Medicinal Plants Predominantly Accumulating Ellagic Acid Compounds
Bibliographic record
Abstract
Cancer remains a major global health challenge, prompting the search for natural therapeutic agents with selective anticancer activity. This study investigated extracts from 12 medicinal plant species (a total of 21 samples) rich in phenolic compounds, particularly ellagic acid and its derivatives, to evaluate their antioxidant properties and ability to inhibit cancer cell viability. Spectrometric analysis and high-performance liquid chromatography (HPLC) with electrochemical detection (ED) and ultraviolet–visible (UV-VIS) detection were used for compound identification. The anticancer activity of plant extracts was tested using the MTS cell proliferation assay to determine anticancer activity on 4T1, A549, Caki-1, HCT116, and MCF7 cancer cell lines. The HEK-293 healthy cell line was used to determine extracts cytotoxicity. Study results indicate that black walnut (Juglans nigra L.), fireweed (Chamaenerion angustifolium L.), and pedunculate oak (Quercus robur L.) have the highest contents of bioactive compounds. Among tested extracts, fireweed showed the lowest IC50 values, thus the strongest anticancer activity against 4T1 cells (IC50 = 0.28 ± 0.01 RE (rutin equivalents) mg/g), while black walnut was most effective against Caki-1 and HCT116 (IC50 = 1.56 ± 0.01; 2.56 ± 0.02 RE mg/g). IC50 values are reported in rutin equivalents (RE) to maintain consistency with antioxidant normalization parameters used throughout the study. Extended incubation increased anticancer activity across most medicinal plant extracts, with fireweed and Canadian goldenrod demonstrating rapid and sustained potency already at 24 h. After 72 h, the most active fireweed extract inhibited approximately 104–190 million cancer cells per gram of dried plant material, demonstrating substantial antiproliferative activity consistent with the IC50 findings. Importantly, none of the extracts showed cytotoxicity to healthy HEK-293 cells. Overall, the findings highlight several plant species with significant anticancer potential, underscoring their promise as sources of natural bioactive compounds for future cancer prevention and treatment research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".