The current drug discovery landscape for trypanosomiasis and leishmaniasis: Challenges and strategies to identify drug targets
Bibliographic record
Abstract
Human trypanosomiasis and leishmaniasis are vector-borne neglected tropical diseases caused by infection with the protozoan parasites Trypanosoma spp. and Leishmania spp., respectively. Once restricted to endemic areas, these diseases are now distributed worldwide due to human migration, climate change, and anthropogenic disturbance, causing significant health and economic burden globally. The current chemotherapy used to treat these diseases has limited efficacy, and drug resistance is spreading. Hence, new drugs are urgently needed. Phenotypic compound screenings have prevailed as the leading method to discover new drug candidates against these diseases. However, the publication of the complete genome sequences of multiple strains, advances in the application of CRISPR/Cas9 technology, and in vivo bioluminescence-based imaging have set the stage for advancing target-based drug discovery. This review analyses the limitations of the narrow pool of available drugs presently used for treating these diseases. It describes the current drug-based clinical trials highlighting the most promising leads. Furthermore, the review presents a focused discussion on the most important biological and pharmacological challenges that target-based drug discovery programs must overcome to advance drug candidates. Finally, it examines the advantages and limitations of modern research tools designed to identify and validate essential genes as drug targets, including genomic editing applications and in vivo imaging.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".