The Identification of Potential Drugs for Dengue Hemorrhagic Fever: Network-Based Drug Reprofiling Study
Notice bibliographique
Résumé
Background Dengue fever can progress to dengue hemorrhagic fever (DHF), a more serious and occasionally fatal form of the disease. Indicators of serious disease arise about the time the fever begins to reduce (typically 3 to 7 days following symptom onset). There are currently no effective antivirals available. Drug repurposing is an emerging drug discovery process for rapidly developing effective DHF therapies. Through network pharmacology modeling, several US Food and Drug Administration (FDA)-approved medications have already been researched for various viral outbreaks. Objective We aimed to identify potentially repurposable drugs for DHF among existing FDA-approved drugs for viral attacks, symptoms of viral fevers, and DHF. Methods Using target identification databases (GeneCards and DrugBank), we identified human–DHF virus interacting genes and drug targets against these genes. We determined hub genes and potential drugs with a network-based analysis. We performed functional enrichment and network analyses to identify pathways, protein-protein interactions, tissues where the gene expression was high, and disease-gene associations. Results Analyzing virus-host interactions and therapeutic targets in the human genome network revealed 45 repurposable medicines. Hub network analysis of host-virus-drug associations suggested that aspirin, captopril, and rilonacept might efficiently treat DHF. Gene enrichment analysis supported these findings. According to a Mayo Clinic report, using aspirin in the treatment of dengue fever may increase the risk of bleeding complications, but several studies from around the world suggest that thrombosis is associated with DHF. The human interactome contains the genes prostaglandin-endoperoxide synthase 2 (PTGS2), angiotensin converting enzyme (ACE), and coagulation factor II, thrombin (F2), which have been documented to have a role in the pathogenesis of disease progression in DHF, and our analysis of most of the drugs targeting these genes showed that the hub gene module (human-virus-drug) was highly enriched in tissues associated with the immune system (P=7.29 × 10–24) and human umbilical vein endothelial cells (P=1.83 × 10–20); this group of tissues acts as an anticoagulant barrier between the vessel walls and blood. Kegg analysis showed an association with genes linked to cancer (P=1.13 × 10–14) and the advanced glycation end products–receptor for advanced glycation end products signaling pathway in diabetic complications (P=3.52 × 10–14), which indicates that DHF patients with diabetes and cancer are at risk of higher pathogenicity. Thus, gene-targeting medications may play a significant part in limiting or worsening the condition of DHF patients. Conclusions Aspirin is not usually prescribed for dengue fever because of bleeding complications, but it has been reported that using aspirin in lower doses is beneficial in the management of diseases with thrombosis. Drug repurposing is an emerging field in which clinical validation and dosage identification are required before the drug is prescribed. Further retrospective and collaborative international trials are essential for understanding the pathogenesis of this condition.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».