Steroidal Saponins: Naturally Occurring Compounds as Inhibitors of the Hallmarks of Cancer
Bibliographic record
Abstract
Cancer is a global health burden responsible for an exponentially growing number of incidences and mortalities, regardless of the significant advances in its treatment. The identification of the hallmarks of cancer is a major milestone in understanding the mechanisms that drive cancer initiation, development, and progression. In the past, the hallmarks of cancer have been targeted to effectively treat various types of cancers. These conventional cancer drugs have shown significant therapeutic efficacy but continue to impose unfavorable side effects on patients. Naturally derived compounds are being tested in the search for alternative anti-cancer drugs. Steroidal saponins are a group of naturally occurring compounds that primarily exist as secondary metabolites in plant species. Recent studies have suggested that steroidal saponins possess significant anti-cancer capabilities. This review aims to summarize the recent findings on steroidal saponins as inhibitors of the hallmarks of cancer and covers key studies published between the years 2014 and 2024. It is reported that steroidal saponins effectively inhibit the hallmarks of cancer, but poor bioavailability and insufficient preclinical studies limit their utilization.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".