Editorial: Drug discovery for emerging and neglected tropical diseases: advances, challenges and perspectives
Bibliographic record
Abstract
experimental strategies to uncover targets and modes of action of known and new antimicrobials. Lastly, it also includes a review article focused on the valuable lessons gained during the COVID-19 pandemic that could help to face and prevent future arbovirus epidemics.Chirawurah and colleagues (Chirawurah et al., 2023) present follow-up studies to evaluate the potency of three Malaria Box compounds -previously selected as potent antimalarials (Chirawurah et al., 2017) -against a panel of twenty clinical isolates of Plasmodium falciparum. Using in vitro growth inhibitory assays, the authors validated the activity of these compounds and compared their activity with those observed for reference compounds. Most importantly, the article underscored the need to incorporate clinical isolates (including drug-resistant isolates) in antimalarial compound screening activities.Fairlamb and Wyllie (Fairlamb and Wyllie, 2023) systematically discuss the crucial role of investigating the mode of action in kinetoplastid drug discovery, highlighting the tools currently available for target identification and validation, including genomics, proteomics, and metabolomics, as well as informatic approaches. The authors also provide examples of how to apply these tools to identify and exclude undesirable molecular pathways, detect potential toxic properties, and manage a balanced portfolio of target-based campaigns. Finally, the authors review the primary drug targets (e.g. proteasome and protein kinases) currently in clinical development against Leishmania and trypanosomes.Related to the same topic, Hauser and Maser (Hauser and Mäser, 2023) described an interesting in silico work of an integrative bioinformatic approach that was applied to discover potential targets of suramin, one of the pharmacopoeia's most promiscuous drugs. A list of 44 diverse proteins was identified as potential targets of suramin, presenting common functional motifs. These findings are crucial to understanding the nature of suramin's mechanism of action and, ultimately, to design new and more selective inhibitors.Rosa-Nunes et al. (Rosa-Nunes et al., 2023) comprehensively reviewed the scientific and technical advances achieved after the COVID-19 pandemic regarding prophylaxis, antiviral drug development, and immunization strategies. The article exemplifies relevant approaches currently used for disease management, including the use of mRNA-based vaccines and the administration of replication inhibitors (e.g. nucleoside analogues and protease inhibitors). Lastly, the authors discuss how we could explore the lessons learned from the COVID-19 pandemic and how to apply them to control neglected viral infections caused by arboviruses.As a final remark, this Research Topic shows recent progress in the efforts for the discovery and development of new drugs for NTDs, with emphasis on parasitic and virus infections. Studies such as those collected herein contribute to reducing the existing research gaps that hinder the achievement of more effective and affordable treatments for vulnerable populations worldwide. We thank all the contributors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".