A whole‐genome scan for Artemisinin cytotoxicity reveals a novel therapy for human brain tumors
Bibliographic record
Abstract
The natural compound Artemisinin is the most widely used antimalarial drug worldwide. Based on its cytotoxicity, it is also used for anticancer therapy. Artemisinin and its derivates are endoperoxides that damage proteins in eukaryotic cells; their definite mechanism of action and host cell targets, however, have remained largely elusive. Using yeast and haploid stem cell screening, we demonstrate that a single cellular pathway, namely porphyrin (heme) biosynthesis, is required for the cytotoxicity of Artemisinins. Genetic or pharmacological modulation of porphyrin production is sufficient to alter its cytotoxicity in eukaryotic cells. Using multiple model systems of human brain tumor development, such as cerebral glioblastoma organoids, and patient-derived tumor spheroids, we sensitize cancer cells to dihydroartemisinin using the clinically approved porphyrin enhancer and surgical fluorescence marker 5-aminolevulinic acid, 5-ALA. A combination treatment of Artemisinins and 5-ALA markedly and specifically killed brain tumor cells in all model systems tested, including orthotopic patient-derived xenografts in vivo. These data uncover the critical molecular pathway for Artemisinin cytotoxicity and a sensitization strategy to treat different brain tumors, including drug-resistant human glioblastomas.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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 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".