Machine learning-guided synthesis of nanomaterials for breast cancer therapy.
Breast cancer
Hydrogel
Machine learning
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
28 10 2024
28 10 2024
Historique:
received:
23
07
2024
accepted:
17
10
2024
medline:
29
10
2024
pubmed:
29
10
2024
entrez:
29
10
2024
Statut:
epublish
Résumé
Breast cancer is a common malignant tumor, which mostly occurs in female population and is caused by excessive proliferation of breast epithelial cells. Breast cancer can cause nipple discharge, breast lumps and other symptoms, but these symptoms lack certain specificity and are easily confused with other diseases, thus affecting the early treatment of the disease. Once the tumor progresses to the advanced stage, distant metastasis can occur, leading to dysfunction of the affected organs, and even threatening the patients' lives. In this study, we synthesized high drug-loading gel particles and applied them to control the release of insoluble drugs. This method is simple to prepare, cost-effective, and validates their potential in breast cancer therapy. We first characterized the morphology and physicochemical properties of gel loaded with newly synthesized compound 1 by scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FT-IR), and thermal gravimetric analysis (TGA). Using newly synthesized insoluble compound 1 as a model drug, its efficacy in treating breast cancer was investigated. The results showed that hydrogel@compound 1 was able to significantly inhibit the proliferation, migration and invasion of breast cancer cells. Additionally, we utilized machine learning to screen three structurally similar compounds, which showed promising therapeutic effects, providing a new approach for the development of novel small-molecule drugs.
Identifiants
pubmed: 39468211
doi: 10.1038/s41598-024-76924-7
pii: 10.1038/s41598-024-76924-7
doi:
Substances chimiques
Antineoplastic Agents
0
Hydrogels
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
25795Informations de copyright
© 2024. The Author(s).
Références
Roy, M., Fowler, A. M., Ulaner, G. A. & Mahajan, A. Molecular classification of breast cancer. PET Clin. 18, 441–458 (2023).
doi: 10.1016/j.cpet.2023.04.002
pubmed: 37268505
Fahad Ullah, M. Breast cancer: Current perspectives on the disease status. Adv. Exp. Med. Biol. 1152, 51–64 (2019).
doi: 10.1007/978-3-030-20301-6_4
pubmed: 31456179
Bandyopadhyay, S., Bluth, M. H. & Ali-Fehmi, R. Breast carcinoma: Updates in molecular profiling 2018. Clin. Lab. Med. 38, 401–420 (2018).
doi: 10.1016/j.cll.2018.02.006
pubmed: 29776638
Watkins, E. J. Overview of breast cancer. JAAPA 32, 13–17 (2019).
doi: 10.1097/01.JAA.0000580524.95733.3d
pubmed: 31513033
Khoury, T. Metaplastic breast carcinoma revisited; subtypes determine outcomes: Comprehensive pathologic, clinical, and molecular review. Clin. Lab. Med. 43, 221–243 (2023).
doi: 10.1016/j.cll.2023.03.002
pubmed: 37169444
De Vincentiis, L., Mariani, M. P., Cesinaro, A. M., Dalena, A. M. & Ferrara, G. Sebaceous carcinoma of the breast: Fact or fiction? A case report and a review of the literature. Int. J. Surg. Pathol. 29, 211–215 (2021).
doi: 10.1177/1066896920937784
pubmed: 32608286
Badr, N. M., Berditchevski, F. & Shaaban, A. M. The immune microenvironment in breast carcinoma: Predictive and prognostic role in the neoadjuvant setting. Pathobiology 87, 61–74 (2020).
doi: 10.1159/000504055
pubmed: 31715606
Cooper, C. L. et al. Molecular alterations in metaplastic breast carcinoma. J. Clin. Pathol. 66, 522–528 (2013).
doi: 10.1136/jclinpath-2012-201086
pubmed: 23372178
Suzuki, T. et al. Androgens in human breast carcinoma. Med. Mol. Morphol. 43, 75–81 (2010).
doi: 10.1007/s00795-010-0494-3
pubmed: 20683693
Rani, E., Nibhoria, S. & Shilpa,. Metaplastic breast carcinoma with mesenchymal differentiation: A case series. J. Cancer Res. Ther. 19, 2052–2055 (2023).
doi: 10.4103/jcrt.jcrt_1517_21
pubmed: 38376317
Roy, M., Roy, A., Rustagi, S. & Pandey, N. An overview of nanomaterial applications in pharmacology. BioMed. Res. Int. 2023, 4838043 (2023).
doi: 10.1155/2023/4838043
pubmed: 37388336
pmcid: 10307208
Ovais, M., Guo, M. & Chen, C. Tailoring nanomaterials for targeting tumor-associated macrophages. Adv. Mater. 31, 1808303 (2019).
doi: 10.1002/adma.201808303
Cheng, Y. et al. Wetting transition in nanochannels for biomimetic free-blocking on-demand drug transport. J. Mater. Chem. B 6, 6269–6277 (2018).
doi: 10.1039/C8TB01838C
pubmed: 32254617
Zhang, X. et al. A smart O2-generating nanocarrier optimizes drug transportation comprehensively for chemotherapy improving. APSB 11, 3608–3621 (2021).
Li, X. et al. Nano carriers for drug transport across the blood–brain barrier. J. Drug Target. 25, 17–28 (2017).
doi: 10.1080/1061186X.2016.1184272
pubmed: 27126681
Heller, D. A. et al. Development of single-walled carbon nanotube-based optical sensors via data analytics. ECS Meet. Abstr. MA2021-01(10), 523–523. https://doi.org/10.1149/MA2021-0110523mtgabs (2021).
doi: 10.1149/MA2021-0110523mtgabs
Schaefer, J., Lehne, M., Schepers, J., Prasser, F. & Thun, S. The use of machine learning in rare diseases: A scoping review. Orphanet. J. Rare Dis. 15, 145 (2020).
doi: 10.1186/s13023-020-01424-6
pubmed: 32517778
pmcid: 7285453
Ahsan, M. M., Luna, S. A. & Siddique, Z. Machine-learning-based disease diagnosis: A comprehensive review. Healthcare 10, 541 (2022).
doi: 10.3390/healthcare10030541
pubmed: 35327018
pmcid: 8950225
Le, D. H. Machine learning-based approaches for disease gene prediction. Brief. Funct. Genomics 19, 350–363 (2020).
doi: 10.1093/bfgp/elaa013
pubmed: 32567652
Zhang, S., Su, Q. & Chen, Q. Application of machine learning in animal disease analysis and prediction. Curr. Bioinform. 16, 972–982 (2021).
doi: 10.2174/1574893615999200728195613
Singh Kumar, A., Ling, J. & Malviya, R. Prediction of cancer treatment using advancements in machine learning. Recent Pat. Anticancer Drug Discov. 18, 364–378 (2023).
doi: 10.2174/1574892818666221018091415
Li, S., Yi, H., Leng, Q., Wu, Y. & Mao, Y. New perspectives on cancer clinical research in the era of big data and machine learning. Surg. Oncol. 52, 102009 (2024).
doi: 10.1016/j.suronc.2023.102009
pubmed: 38215544
Jeong, Y. et al. Application of transcriptome-based gene set featurization for machine learning model to predict the origin of metastatic cancer. Curr. Issues Mol. Biol. 46, 7291–7302 (2024).
doi: 10.3390/cimb46070432
pubmed: 39057073
pmcid: 11276602
Ghanat Bari, M., Ung, C. Y., Zhang, C., Zhu, S. & Li, H. Machine learning-assisted network inference approach to identify a new class of genes that coordinate the functionality of cancer networks. Sci. Rep. 7, 6993 (2017).
doi: 10.1038/s41598-017-07481-5
pubmed: 28765560
pmcid: 5539301
Arslan, E., Schulz, J. & Rai, K. Machine learning in epigenomics: Insights into cancer biology and medicine. Biochim. Biophys. Acta 1876, 188588 (2021).
Osama, S., Shaban, H. & Ali, A. A. Gene reduction and machine learning algorithms for cancer classification based on microarray gene expression data: A comprehensive review. Expert Syst. Appl. 213, 118946 (2023).
doi: 10.1016/j.eswa.2022.118946