Network Pharmacology, Machine Learning, and In Vivo Validation of Danzhi Jiangtang Capsule in Diabetic Nephropathy

Ziqiang Li; Ying Yuan; Yueyue Zhao; Yifang Deng; Gang Yin; Anlu Shen

doi:10.3791/71328

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Research Article

Network Pharmacology, Machine Learning, and In Vivo Validation of Danzhi Jiangtang Capsule in Diabetic Nephropathy

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DOI:

10.3791/71328

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July 28th, 2026

Ziqiang Li¹ , Ying Yuan¹ , Yueyue Zhao¹ , Yifang Deng¹ , Gang Yin¹ , Anlu Shen¹

¹Anhui University of Chinese Medicine

Summary

Danzhi Jiangtang Capsule (DJC) may attenuate diabetic nephropathy (DN), as evidenced by changes in chemokine signaling and pyroptosis markers, supported by bioinformatics and in vivo validation.

Abstract

Although Danzhi Jiangtang Capsule (DJC) is a traditional Chinese herbal preparation used clinically for diabetes, how it may protect the kidney during diabetic nephropathy (DN) has not been fully clarified. This investigation was designed to explore potential mechanisms by which DJC affects DN, with a focus on the NLR family pyrin domain-containing 3 (NLRP3)/Caspase-1/Gasdermin D (GSDMD) pyroptosis-related signaling cascade.

An integrated strategy combining network pharmacology and machine learning was employed. The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM) were used to screen bioactive ingredients and their corresponding protein targets of DJC. DN-associated genes were retrieved from Gene Expression Omnibus (GEO), GeneCards, and Online Mendelian Inheritance in Man (OMIM). Key candidate targets were screened and ranked using multiple machine learning algorithms. The binding affinity between DJC’s active ingredients and core targets was assessed via molecular docking. Finally, the therapeutic efficacy and predicted mechanisms were evaluated in db/db diabetic mice.

Network pharmacology analysis identified 599 DJC targets and 68 overlapping genes shared with DN. Using machine learning algorithms, C-C motif chemokine ligand 2 (CCL2) and CASP1 were identified as prioritized candidate targets. Molecular docking predicted possible strong binding affinities between DJC active ingredients and these core proteins. Functional enrichment analyses (GO/KEGG) suggested that DJC modulation is associated with inflammatory responses and the MAPK pathway. In vivo validation showed that DJC treatment attenuated renal injury and fibrosis markers. These findings suggest that DJC may attenuate DN in part through CCL2/C-C motif chemokine receptor 2 (CCR2)-related pyroptosis signaling changes.

Introduction

The global burden of diabetes mellitus (DM) continues to escalate, presenting a formidable public health challenge. An estimated 5.2 million deaths annually stem from DM and its related complications¹. DN counts as a severe microvascular complication and a major contributor to end-stage renal disease (ESRD) development, affecting approximately 30-40% of the diabetic population²^,³. Multiple intertwined factors contribute to DN pathological progression, including metabolic dysfunction, hemodynamic remodeling, and persistent inflammatory responses. Pathological insults, including high gluc....