A novel approach to the discovery of anti-tumor pharmaceuticals: searching for activators of liponecrosis
Bibliographic record
Abstract
// Anthony Arlia-Ciommo 1 , Veronika Svistkova 1 , Sadaf Mohtashami 1 and Vladimir I. Titorenko 1 1 Department of Biology, Concordia University, Montreal, Quebec, Canada Correspondence to: Vladimir I. Titorenko, email: // Keywords : cancer, anti-cancer therapeutics, lipid metabolism, programmed cell death, liponecrosis, yeast Received : August 20, 2015 Accepted : November 21, 2015 Published : November 30, 2015 Abstract A recently conducted chemical genetic screen for pharmaceuticals that can extend longevity of the yeast Saccharomyces cerevisiae has identified lithocholic acid as a potent anti-aging molecule. It was found that this hydrophobic bile acid is also a selective anti-tumor chemical compound; it kills different types of cultured cancer cells if used at concentrations that do not compromise the viability of non-cancerous cells. These studies have revealed that yeast can be successfully used as a model organism for high-throughput screens aimed at the discovery of selectively acting anti-tumor small molecules. Two metabolic traits of rapidly proliferating fermenting yeast, namely aerobic glycolysis and lipogenesis, are known to be similar to those of cancer cells. The mechanisms underlying these key metabolic features of cancer cells and fermenting yeast have been established; such mechanisms are discussed in this review. We also suggest how a yeast-based chemical genetic screen can be used for the high-throughput development of selective anti-tumor pharmaceuticals that kill only cancer cells. This screen consists of searching for chemical compounds capable of increasing the abundance of membrane lipids enriched in unsaturated fatty acids that would therefore be toxic only to rapidly proliferating cells, such as cancer cells and fermenting yeast.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".