Biogenic trypanocidal sesquiterpenes: lead compounds to design future trypanocidal drugs - a mini review
Bibliographic record
Abstract
Human trypanosomiasis is a parasitic disease among poor people in Africa and Latin America. Therapy against African and American trypanosomiasis is based on a few drugs that often cause severe side-effects. Therefore, it is essential to develop drug discovery especially from natural origins. Sesquiterpenes, a diverse group of natural terpenoids, are found in essential oils of many plants and show a broad range of bioactivities. They act through multiple mechanisms in the chemotherapy of trypanosomiasis. Some of these active compounds contain hydroperoxides, aldehydes, alcohols, α,β-unsaturated γ-lactone and even halogenated moieties. Among the compounds reported, sesquiterpene lactones showed a potent anti-trypanosoma effect comparable with commercial trypanocidal drugs. Trypanocidal activity of sesquiterpene lactones mostly depends on the reaction between γ-lactone moieties and nucleophile groups of trypanithione, which is essential for Trypanosoma defense against the oxidative stresses. Elatol is a sesquiterpenoid from marine algae, with a different structure and considerable trypanocidal activity which could be an interesting candidate for further antiprotozoal investigations. To develop novel drugs with higher efficacy and lower toxicity from natural products, this review summarizes the more recent information on trypanocidal activities of various sesquiterpenes.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".