Design and Evaluation of NSAID Derivatives as AKR1C3 Inhibitors for Breast Cancer Treatment through Computer-Aided Drug Design and In Vitro Analysis.
Autores organización
Autores
- Fonseca-Benítez V
- Acosta-Guzmán P
- Sánchez JE
- Alarcón Z
- Jiménez RA
Unidades de investigación
Resumen
Breast cancer is a major global health issue, causing high incidence and mortality rates as well as psychological stress for patients. Chemotherapy resistance is a common challenge, and the Aldo-keto reductase family one-member C3 enzyme is associated with resistance to anthracyclines like doxorubicin. Recent studies have identified celecoxib as a potential treatment for breast cancer. Virtual screening was conducted using a quantitative structure-activity relationship model to develop similar drugs; this involved backpropagation of artificial neural networks and structure-based virtual screening. The screening revealed that the C-6 molecule had a higher affinity for the enzyme (-11.4 kcal/mol), a lower half-maximal inhibitory concentration value (1.7 µM), and a safer toxicological profile than celecoxib. The compound C-6 was synthesized with an 82% yield, and its biological activity was evaluated. The results showed that C-6 had a more substantial cytotoxic effect on MCF-7 cells (62%) compared to DOX (63%) and celecoxib (79.5%). Additionally, C-6 had a less harmful impact on healthy L929 cells than DOX and celecoxib. These findings suggest that C-6 has promising potential as a breast cancer treatment.
Datos de la publicación
- ISSN/ISSNe:
- 1420-3049, 1420-3049
- Tipo:
- Article
- Páginas:
- -
- PubMed:
- 38675620
Molecules MDPI
Citas Recibidas en Scopus: 2
Documentos
- No hay documentos
Filiaciones
Filiaciones no disponibles
Keywords
- CADD, artificial neural networks (ANNs), breast cancer, organic synthesis
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Citar la publicación
Fonseca V,Acosta P,Sánchez JE,Alarcón Z,Jiménez RA,Guevara J. Design and Evaluation of NSAID Derivatives as AKR1C3 Inhibitors for Breast Cancer Treatment through Computer-Aided Drug Design and In Vitro Analysis. Molecules. 2024. 29. (8):IF:4,200. (2).
Portal de investigación