Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/90759
Título: | Machine learning-assisted optimization of drug combinations in zeolite-based delivery systems for melanoma therapy |
Autor(es): | Bertão, Ana Raquel Teixeira, Filipe Ivasiv, Viktoriya Parpot, Pier Almeida Aguiar, Cristina Fonseca, A. M. Bañobre-López, Manuel Baltazar, Fátima Neves, Isabel C. |
Palavras-chave: | ANN models machine learning melanoma therapy microbial infections ZDS formulations zeolite |
Data: | 2024 |
Editora: | American Chemical Society |
Revista: | ACS Applied Materials and Interfaces |
Citação: | Bertão, A. R., Teixeira, F., Ivasiv, V., Parpot, P., Almeida-Aguiar, C., Fonseca, A. M., … Neves, I. C. (2024, January 25). Machine Learning-Assisted Optimization of Drug Combinations in Zeolite-Based Delivery Systems for Melanoma Therapy. ACS Applied Materials & Interfaces. American Chemical Society (ACS). http://doi.org/10.1021/acsami.3c18224 |
Resumo(s): | Two independent artificial neural network (ANN) models were used to determine the optimal drug combination of zeolite-based delivery systems (ZDS) for cancer therapy. The systems were based on the NaY zeolite using silver (Ag+) and 5-fluorouracil (5-FU) as antimicrobial and antineoplastic agents. Different ZDS samples were prepared, and their characterization indicates the successful incorporation of both pharmacologically active species without any relevant changes to the zeolite structure. Silver acts as a counterion of the negative framework, and 5-FU retains its molecular integrity. The data from the A375 cell viability assays, involving ZDS samples (solid phase), 5-FU, and Ag+ aqueous solutions (liquid phase), were used to train two independent machine learning (ML) models. Both models exhibited a high level of accuracy in predicting the experimental cell viability results, allowing the development of a novel protocol for virtual cell viability assays. The findings suggest that the incorporation of both Ag and 5-FU into the zeolite structure significantly potentiates their anticancer activity when compared to that of the liquid phase. Additionally, two optimal AgY/5-FU@Y ratios were proposed to achieve the best cell viability outcomes. The ZDS also exhibited significant efficacy against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus); the predicted combination ratio is also effective against S. aureus, underscoring the potential of this approach as a therapeutic option for cancer-associated bacterial infections. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/90759 |
DOI: | 10.1021/acsami.3c18224 |
ISSN: | 1944-8244 |
Versão da editora: | https://pubs.acs.org/doi/10.1021/acsami.3c18224 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series ICVS - Artigos em revistas internacionais / Papers in international journals CBMA - Artigos/Papers CDQuim - Artigos (Papers) DBio - Artigos/Papers |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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berta~o-et-al-2024-machine-learning-assisted-optimization-of-drug-combinations-in-zeolite-based-delivery-systems-for.pdf | 7,14 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons