Artificial intelligence-assisted repurposing of lubiprostone alleviates tubulointerstitial fibrosis.
Transl Res
; 262: 75-88, 2023 12.
Article
em En
| MEDLINE
| ID: mdl-37541485
ABSTRACT
Tubulointerstitial fibrosis (TIF) is the most prominent cause which leads to chronic kidney disease (CKD) and end-stage renal failure. Despite extensive research, there have been many clinical trial failures, and there is currently no effective treatment to cure renal fibrosis. This demonstrates the necessity of more effective therapies and better preclinical models to screen potential drugs for TIF. In this study, we investigated the antifibrotic effect of the machine learning-based repurposed drug, lubiprostone, validated through an advanced proximal tubule on a chip system and in vivo UUO mice model. Lubiprostone significantly downregulated TIF biomarkers including connective tissue growth factor (CTGF), extracellular matrix deposition (Fibronectin and collagen), transforming growth factor (TGF-ß) downstream signaling markers especially, Smad-2/3, matrix metalloproteinase (MMP2/9), plasminogen activator inhibitor-1 (PAI-1), EMT and JAK/STAT-3 pathway expression in the proximal tubule on a chip model and UUO model compared to the conventional 2D culture. These findings suggest that the proximal tubule on a chip model is a more physiologically relevant model for studying and identifying potential biomarkers for fibrosis compared to conventional in vitro 2D culture and alternative of an animal model. In conclusion, the high throughput Proximal tubule-on-chip system shows improved in vivo-like function and indicates the potential utility for renal fibrosis drug screening. Additionally, repurposed Lubiprostone shows an effective potency to treat TIF via inhibiting 3 major profibrotic signaling pathways such as TGFß/Smad, JAK/STAT, and epithelial-mesenchymal transition (EMT), and restores kidney function.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Inteligência Artificial
/
Nefropatias
Tipo de estudo:
Prognostic_studies
Limite:
Animals
Idioma:
En
Revista:
Transl Res
Ano de publicação:
2023
Tipo de documento:
Article