Cysteamine, the Molecule Used To Treat Cystinosis, Potentiates the Antimalarial Efficacy of Artemisinin
Bibliographic record
Abstract
Malaria continues to be a major threat to global health. Artemisinin combination therapy (ACT) is the recommended treatment for clinical malaria; however, recent reports of parasite resistance to artemisinin in certain areas where malaria is endemic have stressed the need for developing more efficacious ACT. We report that cysteamine (Cys), the aminothiol used to treat nephropathic cystinosis in humans, strongly potentiates the efficacy of artemisinin against the Plasmodium parasite in vivo. Using a mouse model of infection with Plasmodium chabaudi AS, we observe that Cys dosing used to treat cystinosis in humans can strongly potentiate (by 3- to 4-fold) the antimalarial properties of the artemisinin derivatives artesunate and dihydroartemisinin. Addition of Cys to suboptimal doses of artemisinin delays the appearance of blood parasitemia, strongly reduces the extent of parasite replication, and significantly improves survival in a model of lethal P. chabaudi infection. Cys, the natural product of the enzyme pantetheinase, has a history of safe use for the clinical management of cystinosis. Our findings suggest that Cys could be included in novel ACTs to improve efficacy against Plasmodium parasite replication, including artemisinin-resistant isolates. Future work will include clinical evaluation of novel Cys-containing ACTs and elucidation of the mechanism underlying the potentiation effect of Cys.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".