The current drug discovery landscape for trypanosomiasis and leishmaniasis: Challenges and strategies to identify drug targets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it