Inhibitors of <i>Leishmania</i> GDP-Mannose Pyrophosphorylase Identified by High-Throughput Screening of Small-Molecule Chemical Library
Bibliographic record
Abstract
The current treatment for leishmaniasis is based on chemotherapy, which relies on a handful of drugs with serious limitations, such as high cost, toxicity, and a lack of efficacy in regions of endemicity. Therefore, the development of new, effective, and affordable antileishmanial drugs is a global health priority. Leishmania synthesizes a range of mannose-rich glycoconjugates that are essential for parasite virulence and survival. A prerequisite for glycoconjugate biosynthesis is the conversion of monosaccharides to the activated mannose donor, GDP-mannose, the product of a reaction catalyzed by GDP-mannose pyrophosphorylase (GDP-MP). The deletion of the gene encoding GDP-MP in Leishmania led to a total loss of virulence, indicating that the enzyme is an ideal drug target. We developed a phosphate sensor-based high-throughput screening assay to quantify the activity of GDP-MP and screened a library containing approximately 80,000 lead-like compounds for GDP-MP inhibitors. On the basis of their GDP-MP inhibitory properties and chemical structures, the activities of 20 compounds which were not toxic to mammalian cells were tested against ex vivo amastigotes and in macrophage amastigote assays. The most potent compound identified in the primary screen (compound 3), a quinoline derivative, demonstrated dose-dependent activity in both assays (50% inhibitory concentration = 21.9 microM in the macrophage assay) and was shown to be nontoxic to human fibroblasts. In order to elucidate signs of an early structure-activity relationship (SAR) for this class of compounds, we obtained and tested analogues of compound 3 and undertook limited medicinal chemistry optimization, which included the use of a number of SAR probes of the piperazinyl aryl substituent of compound 3. We have identified novel candidate compounds for the design and synthesis of antileishmanial therapeutics.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".