AI-based prediction and validation in zebrafish model elucidates potential bioactivity of novel thiazolone derivative
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Le résumé fourni par la source
The multifactorial etiology of age-related complex diseases, including Alzheimer's disease (AD), cancer, and type 2 diabetes mellitus (T2DM), challenges the efficacy of single-target therapeutics. The development of multi-target-directed ligands (MTDLs) represents a promising strategy to address these overlapping pathological networks. In this study, we employed a high-throughput phenotypic screening platform integrated with artificial intelligence (AI) to evaluate the pharmacological potential of a novel thiazolone derivative, Compound M. Consensus clustering analysis of zebrafish behavioral and physiological phenotypes against 103 clinical drugs predicted that Compound M possesses anti-AD activity at low concentrations and antitumor/antihyperglycemic effects at higher concentrations. Subsequent in vivo and in vitro validations confirmed these pleiotropic activities. In a zebrafish xenograft model, Compound M significantly inhibited Jurkat tumor cell proliferation (P < 0.001), exhibiting an in vitro cell viability IC50 of 10.7 μM. Furthermore, the compound demonstrated potent hypoglycemic effects in a high-fat diet-induced zebrafish model, comparable to the standard drug metformin. In an AlCl 3 -induced AD zebrafish model, Compound M significantly reduced acetylcholinesterase (AChE) activity by 38% (P < 0.001). In silico ADMET profiling predicted favorable intestinal absorption but limited blood-brain barrier (BBB) permeability and a potential risk for cardiac toxicity (hERG). Molecular docking studies revealed that Compound M engages the active sites of AChE and BACE1 primarily through hydrophobic interactions, acting as a partial-site binder. These findings establish Compound M as a promising lead scaffold for MTDL development, while highlighting specific structural optimization strategies—such as scaffold extension and physicochemical tuning—to enhance its CNS penetration and safety profile.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- AI-based prediction and validation in zebrafish model elucidates potential bioactivity of novel thiazolone derivative
- Date Crossref
- 01/04/2026
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Shanghai University of Sport Research Institute for Doping Control pays non établi dans la noticeUniversité ou école supérieure
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Huaibei Normal University pays non établi dans la noticeUniversité ou école supérieure
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China Guangzhou Analysis and Testing Center pays non établi dans la noticeInstitution
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Soochow University pays non établi dans la noticeUniversité ou école supérieure
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Zhangjiagang First People's Hospital pays non établi dans la noticeÉtablissement de santé
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China Anti-Doping Agency pays non établi dans la noticeStructure de recherche
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School of Chemistry and Materials Science Key Laboratory of Green and Precise Synthetic Chemistry and Applications pays non établi dans la noticeUniversité ou école supérieure
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Guang Dong Longseek Testing Co. Research and Development Centre pays non établi dans la noticeOrganisation à but non lucratif
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Zhangjiagang Hospital of Sochow University pays non établi dans la noticeUniversité ou école supérieure
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Drug and Food Anti‐doping Laboratory pays non établi dans la noticeStructure de recherche
Research Institute for Doping Control — Shanghai University of Sport, Huaibei Normal University et China Guangzhou Analysis and Testing Center, avec 7 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.