From the Bench to the Bedside: How Natural Products Can Find Their Way
Bibliographic record
Abstract
Secondary metabolites from plants serve as defense against herbivores, microbes, viruses, or competing plants. Many medicinal plants have pharmacological activities and may, thus, be a source for novel treatment strategies. We have systematically analyzed medicinal plants used in traditional Chinese medicine during the past decade and focused our interest on Artemisia annua L. (qinhao, sweet wormwood). We found that the active principle of Artemisia annua L., artemisinin, exerts not only anti-malarial activity but also profound cytotoxicity against tumor cells. The inhibitory activity of artemisinin and its derivatives towards cancer cells is in the nano- to micromolar range. Candidate genes that may contribute to the sensitivity and resistance of tumor cells to artemisinins were identified by pharmacogenomic and molecular pharmacological approaches. Target validation was performed using cell lines transfected with candidate genes or corresponding knockout cells. These genes are from classes with different biological functions; for example, regulation of proliferation (BUB3, cyclins, CDC25A), angiogenesis (vascular endothelial growth factor and its receptor, matrix metalloproteinase-9, angiostatin, thrombospondin-1) or apoptosis (BCL-2, BAX). Artesunate triggers apoptosis both by p53-dependent and -independent pathways. Anti-oxidant stress genes (thioredoxin, catalase, ƒ×-glutamyl-cysteine synthetase, glutathione S-transferases) as well as the epidermal growth factor receptor confer resistance to artesunate. Cell lines over-expressing genes that confer resistance to established anti-tumor drugs (MDR1, MRP1, BCRP, dihydrofolate reductase, ribonucleotide reductase) were not cross-resistant to artesunate, indicating that artesunate is not involved in multidrug resistance. The anticancer activity of artesunate has also been shown in human xenograft tumors in mice. First encouraging experiences were obtained in the clinical treatment of patients suffering from uveal melanoma. A phase I with artesunate in metastasized breast cancer has been launched in 2008. References: [1] Efferth T, et al. (2001) Int J Oncol 18: 767–773. [2] Efferth T, et al. (2002) J Mol Med 80: 233–242. [3] Efferth T, et al. (2002) Biochem Pharmacol 64: 617–623. [4] Efferth T, et al. (2003) Int J Oncol 23: 1231–1235. [5] Efferth T, et al. (2003) Mol Pharmacol 64: 382–394. [6] Efferth T, et al. (2004) Free Rad Biol Med 37: 998–1009. [7] Efferth T, et al. (2004) Biochem Pharmacol 67: 1689–1799. [8] Dell'Eva R, et al. (2004) Biochem Pharmacol 68: 2359–2366. [9] Efferth T, (2005) Drug Res Updates 8: 85–97. [10] Efferth T, (2006) Curr Drug Targets 7: 407–421. [11] Efferth T, (2007) Planta Med 73: 299–309. [12] Kelter G, et al. (2007) PLoS One 2: e798. [13] Efferth T, et al. (2007) Trends Mol Med 13: 353–361. [14] Efferth T, et al. (2007) Curr Med Chem. 14: 2024–2032. [15] Efferth T, et al. (2008) Mol Cancer Ther 7: 152–61. [16] Li PCH, et al (2008) Cancer Res 68: 4347–51.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".