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Background <p>Tetracyline, a widely used antibiotic, is associated with nephrotoxicity, though its underlying mechanisms require further investigation.</p> Methods <p>We investigated the toxicological process of tetracycline-induced renal injury using network toxicology, molecular docking, and molecular dynamics simulations. The potential targets of tetracycline were obtained by integrating target information from multiple databases, and a protein-protein interaction (PPI) network was established. The key targets were then screened based on Degree and MCODE scores. Meanwhile, enrichment analysis was conducted on the potential targets. The molecular docking of the core targets and the five target proteins with the lowest binding energies was then subjected to molecular dynamics simulations.</p> Results <p>Based on in silico analyses, tetracycline is predicted to potentially induce renal injury by interacting with key targets (TP53, ESR1, BCL2, TNF, HSP90AA1). Our results also suggest it might exacerbate renal injury via AGE-RAGE, Hepatitis C, MAPK, IL-17, cancer-related pathways, and PI3K-Akt, which are involved in regulating inflammation, apoptosis, and metabolism.</p> Conclusion <p>Collectively, our in silico analyses propose a potential molecular mechanism of tetracycline nephrotoxicity, providing a preliminary theoretical framework for understanding its renal hazards, which requires further experimental validation.</p> Clinical trial number <p>Not applicable.</p>

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Unraveling the mechanism of tetracycline-induced renal injury: an evaluation of drug safety based on network toxicology and molecular dynamics simulations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月3日

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  • Yimao Wu,
  • Yalun Liang,
  • Jintao Liang,
  • Zifeng Chen,
  • Bo Tang,
  • Junlong Zhu,
  • Yifeng Shen

摘要

Background

Tetracyline, a widely used antibiotic, is associated with nephrotoxicity, though its underlying mechanisms require further investigation.

Methods

We investigated the toxicological process of tetracycline-induced renal injury using network toxicology, molecular docking, and molecular dynamics simulations. The potential targets of tetracycline were obtained by integrating target information from multiple databases, and a protein-protein interaction (PPI) network was established. The key targets were then screened based on Degree and MCODE scores. Meanwhile, enrichment analysis was conducted on the potential targets. The molecular docking of the core targets and the five target proteins with the lowest binding energies was then subjected to molecular dynamics simulations.

Results

Based on in silico analyses, tetracycline is predicted to potentially induce renal injury by interacting with key targets (TP53, ESR1, BCL2, TNF, HSP90AA1). Our results also suggest it might exacerbate renal injury via AGE-RAGE, Hepatitis C, MAPK, IL-17, cancer-related pathways, and PI3K-Akt, which are involved in regulating inflammation, apoptosis, and metabolism.

Conclusion

Collectively, our in silico analyses propose a potential molecular mechanism of tetracycline nephrotoxicity, providing a preliminary theoretical framework for understanding its renal hazards, which requires further experimental validation.

Clinical trial number

Not applicable.