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Exploring potential therapeutic combinations for castration-sensitive prostate cancer using supercomputers: a proof of concept study

Authors :
Draško Tomić
Jure Murgić
Ana Fröbe
Karolj Skala
Antonela Vrljičak
Branka Medved Rogina
Branimir Kolarek
Viktor Bojović
Source :
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Publication Year :
2024
Publisher :
Nature Portfolio, 2024.

Abstract

Abstract To address the challenge of finding new combination therapies against castration-sensitive prostate cancer, we introduce Vini, a computational tool that predicts the efficacy of drug combinations at the intracellular level by integrating data from the KEGG, DrugBank, Pubchem, Protein Data Bank, Uniprot, NCI-60 and COSMIC databases. Vini is a computational tool that predicts the efficacy of drugs and their combinations at the intracellular level. It addresses the problem comprehensively by considering all known target genes, proteins and small molecules and their mutual interactions involved in the onset and development of cancer. The results obtained point to new, previously unexplored combination therapies that could theoretically be promising candidates for the treatment of castration-sensitive prostate cancer and could prevent the inevitable progression of the cancer to the incurable castration-resistant stage. Furthermore, after analyzing the obtained triple combinations of drugs and their targets, the most common targets became clear: ALK, BCL-2, mTOR, DNA and androgen axis. These results may help to define future therapies against castration-sensitive prostate cancer. The use of the Vini computer model to explore therapeutic combinations represents an innovative approach in the search for effective treatments for castration-sensitive prostate cancer, which, if clinically validated, could potentially lead to new breakthrough therapies.

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
20452322
Volume :
14
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Reports
Publication Type :
Academic Journal
Accession number :
edsdoj.65230bb41df4ffab900166261910519
Document Type :
article
Full Text :
https://doi.org/10.1038/s41598-024-69880-9