Editorial: Drug discovery for emerging and neglected tropical diseases: advances, challenges and perspectives
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».