Parasite and host kinases as targets for antimalarials
Bibliographic record
Abstract
Introduction:The deployment of Artemisinin-based combination therapies and transmission control measures led to a decrease in the global malaria burden over the recent decades.Unfortunately, this trend is now reversing, in part due to resistance against available treatments, calling for the development of new drugs against untapped targets to prevent cross-resistance.Areas covered: In view of their demonstrated druggability in noninfectious diseases, protein kinases represent attractive targets.Kinase-focussed antimalarial drug discovery is facilitated by the availability of kinase-targeting scaffolds and large libraries of inhibitors, as well as high-throughput phenotypic and biochemical assays.We present an overview of validated Plasmodium kinase targets and their inhibitors, and briefly discuss the potential of host cell kinases as targets for host-directed therapy. Expert opinion:We propose priority research areas, including (i) diversification of Plasmodium kinase targets (at present most efforts focus on a very small number of targets); (ii) polypharmacology as an avenue to limit resistance (kinase inhibitors are highly suitable in this respect); and (iii) preemptive limitation of resistance through host-directed therapy (targeting host cell kinases that are required for parasite survival) and transmission-blocking through targeting sexual stage-specific kinases as a strategy to protect curative drugs from the spread of resistance.
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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 | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".