ARTIFICIAL INTELLIGENCE DISCOVERS A NOVEL MACROPHAGE-MYOFIBROBLAST TRANSITION INHIBITOR FOR TARGETING THE MACROPHAGE-DRIVEN CHRONIC RENAL FAILURE

 

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https://storage.unitedwebnetwork.com/files/1099/f5173049c4d64ec29a4aee83d6f55448.pdf
ARTIFICIAL INTELLIGENCE DISCOVERS A NOVEL MACROPHAGE-MYOFIBROBLAST TRANSITION INHIBITOR FOR TARGETING THE MACROPHAGE-DRIVEN CHRONIC RENAL FAILURE

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Jeff Yat-Fai
CHUNG
Jeff Yat-Fai CHUNG jeffchung@link.cuhk.edu.hk The Chinese University of Hong Kong Department of Anatomical and Cellular Pathology,State Key Laboratory of Translational Oncology Shatin Hong Kong, China * The Chinese University of Hong Kong Li Ka Shing Institute of Health Sciences Shatin Hong Kong, China
Philip Chiu-Tsun TANG philtang@link.cuhk.edu.hk The Chinese University of Hong Kong Department of Anatomical and Cellular Pathology, State Key Laboratory of Translational Oncology Shatin Hong Kong, China - The Chinese University of Hong Kong Li Ka Shing Institute of Health Sciences Shatin Hong Kong, China
Max Kam-Kwan CHAN maxchankamkwan@link.cuhk.edu.hk The Chinese University of Hong Kong Department of Anatomical and Cellular Pathology Shatin Hong Kong, China - The Chinese University of Hong Kong Li Ka Shing Institute of Health Sciences Shatin Hong Kong, China
David J. Nikolic-Paterson david.nikolic-paterson@monash.edu Monash University Department of Nephrology Melbourne Australia -
Hui-Yao LAN hylan@cuhk.edu.hk The Chinese University of Hong Kong Li Ka Shing Institute of Health Sciences Shatin Hong Kong, China -
Sydney Chi-Wai TANG scwtang@hku.hk The University of Hong Kong Department of Medicine Pokfulam Hong Kong, China -
Patrick Ming-Kuen TANG patrick.tang@cuhk.edu.hk The Chinese University of Hong Kong Department of Anatomical and Cellular Pathology, State Key Laboratory of Translational Oncology Shatin Hong Kong, China - The Chinese University of Hong Kong Li Ka Shing Institute of Health Sciences Shatin Hong Kong, China The Chinese University of Hong Kong Peter Hung Pain Research Institute Shatin Hong Kong, China
 
 
 
 
 
 
 
 

Chronic kidney disease (CKD) is a leading global cause of mortality, often linked with non-communicable diseases such as diabetes, cardiovascular disease, and cancer. Effective treatments to halt kidney fibrosis remain lacking. Macrophage–Myofibroblast Transition (MMT) is a critical driver of CKD progression. This study aimed to develop a novel therapeutic strategy targeting MMT by inhibiting its key regulator, the neuronal transcription factor Pou4f1.

An artificial intelligence (AI)-based drug discovery platform was employed to identify a novel anti-fibrotic compound ISO. The inhibitory activity of ISO on Pou4f1 DNA binding and MMT formation was tested in vitro using TGF-β1–stimulated macrophages. In vivo efficacy was evaluated in unilateral ureteral obstruction (UUO) and renal ischemia–reperfusion injury (IRI) mouse models using a LyzM-tdTomato macrophage lineage-tracing system. Safety was assessed by histological examination of major organs following ISO administration.

 

ISO specifically blocked Pou4f1 and effectively inhibited MMT formation in vitro. In vivo, ISO treatment significantly reduced MMT-driven renal fibrosis in both UUO and IRI models. Importantly, no detectable toxicity or histological abnormalities were observed in vital organs of ISO-treated mice.

 

ISO represents a first-in-class Pou4f1 inhibitor that effectively prevents MMT formation and kidney fibrosis without adverse effects. ISO may serve as a safe and effective MMT-targeted therapy for the treatment of CKD progression.

This work was supported by the Research Grants Council of Hong Kong (C4013-24GF, 24102723, 14107624, ); RGC Postdoctoral Fellowship Scheme (PDFS2122-4S06, PDFS2425-4S07); Health and Medical Research Fund (10210726, 11220576); CU Medicine Passion for Perfection Scheme (PFP202210-004) and Faculty Innovation Award 2019 (4620528); Peter Hung Pain Research Institute Research Fund (8423011); CUHK Strategic Seed Funding for Collaborative Research Scheme (178896941, MK/WW/SSFCRS2425/0461/25jh); Direct Grant for Research (2025.142); and the Postdoctoral Fellowship Scheme (NL/LT/PDFS2022/0360/22lt, WW/PDFS2023/0640/23en, FPFS/23-24/046).

 

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