Anticancer Mechanisms of Bioactive Compounds from Solanaceae: An Update
Bibliographic record
Abstract
Plants continue to provide unlimited pharmacologically active compounds that can treat various illnesses, including cancer. The Solanaceae family, besides providing economically important food plants, such as potatoes and tomatoes, has been exploited extensively in folk medicine, as it provides an array of bioactive compounds. Many studies have demonstrated the anticancer potency of some of the compounds, but the corresponding molecular targets are not well defined. However, advances in molecular cell biology and in silico modelling have made it possible to dissect some of the underlying mechanisms. By reviewing the literature over the last five years, we provide an update on anticancer mechanisms associated with phytochemicals isolated from species in the Solanaceae plant family. These mechanisms are conveniently grouped into cell cycle arrest, transcription regulation, modulation of autophagy, inhibition of signalling pathways, suppression of metabolic enzymes, and membrane disruption. The majority of the bioactive compounds exert their antiproliferative effects by inhibiting diverse signalling pathways, as well as arresting the cell cycle. Furthermore, some of the phytochemicals are effective against more than one cancer type. Therefore, understanding these mechanisms provides paths for future formulation of novel anticancer drugs, as well as highlighting potential areas of research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".