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AI-powered omics-based drug pair discovery for pyroptosis therapy targeting triple-negative breast cancer.
- Source :
-
Nature communications [Nat Commun] 2024 Aug 30; Vol. 15 (1), pp. 7560. Date of Electronic Publication: 2024 Aug 30. - Publication Year :
- 2024
-
Abstract
- Due to low success rates and long cycles of traditional drug development, the clinical tendency is to apply omics techniques to reveal patient-level disease characteristics and individualized responses to treatment. However, the heterogeneous form of data and uneven distribution of targets make drug discovery and precision medicine a non-trivial task. This study takes pyroptosis therapy for triple-negative breast cancer (TNBC) as a paradigm and uses data mining of a large TNBC cohort and drug databases to establish a biofactor-regulated neural network for rapidly screening and optimizing compound pyroptosis drug pairs. Subsequently, biomimetic nanococrystals are prepared using the preferred combination of mitoxantrone and gambogic acid for rational drug delivery. The unique mechanism of obtained nanococrystals regulating pyroptosis genes through ribosomal stress and triggering pyroptosis cascade immune effects are revealed in TNBC models. In this work, a target omics-based intelligent compound drug discovery framework explores an innovative drug development paradigm, which repurposes existing drugs and enables precise treatment of refractory diseases.<br /> (© 2024. The Author(s).)
- Subjects :
- Humans
Female
Animals
Mitoxantrone pharmacology
Mitoxantrone therapeutic use
Xanthones pharmacology
Cell Line, Tumor
Antineoplastic Agents pharmacology
Antineoplastic Agents therapeutic use
Mice
Artificial Intelligence
Data Mining
Neural Networks, Computer
Triple Negative Breast Neoplasms drug therapy
Triple Negative Breast Neoplasms metabolism
Triple Negative Breast Neoplasms genetics
Triple Negative Breast Neoplasms pathology
Pyroptosis drug effects
Drug Discovery methods
Subjects
Details
- Language :
- English
- ISSN :
- 2041-1723
- Volume :
- 15
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Nature communications
- Publication Type :
- Academic Journal
- Accession number :
- 39215014
- Full Text :
- https://doi.org/10.1038/s41467-024-51980-9