SSC-ILD mouse model induced by osmotic minipump delivered bleomycin: effect of Nintedanib.

Sci Rep

Pharmacology & Toxicology Department, Chiesi Farmaceutici S.p.A., Corporate Pre-Clinical R&D, Largo Belloli, 11/A, 43122, Parma, Italy.

Published: September 2021

AI Article Synopsis

  • * This study validated a mouse model of SSc-ILD using bleomycin infusion to screen anti-fibrotic drugs, with Nintedanib (NINT) acting as a reference for its potential effectiveness on fibrotic activity.
  • * Results showed that NINT slowed lung fibrosis progression but had limited effects on other measures, indicating that the model could aid in developing better clinical drug candidates by integrating ongoing and endpoint assessments.

Article Abstract

Systemic sclerosis (SSc) is an autoimmune disease characterized by an excessive production and accumulation of collagen in the skin and internal organs often associated with interstitial lung disease (ILD). Its pathogenetic mechanisms are unknown and the lack of animal models mimicking the features of the human disease is creating a gap between the selection of anti-fibrotic drug candidates and effective therapies. In this work, we intended to pharmacologically validate a SSc-ILD model based on 1 week infusion of bleomycin (BLM) by osmotic minipumps in C57/BL6 mice, since it will serve as a tool for secondary drug screening. Nintedanib (NINT) has been used as a reference compound to investigate antifibrotic activity either for lung or skin fibrosis. Longitudinal Micro-CT analysis highlighted a significant slowdown in lung fibrosis progression after NINT treatment, which was confirmed by histology. However, no significant effect was observed on lung hydroxyproline content, inflammatory infiltrate and skin lipoatrophy. The modest pharmacological effect reported here could reflect the clinical outcome, highlighting the reliability of this model to better profile potential clinical drug candidates. The integrative approach presented herein, which combines longitudinal assessments with endpoint analyses, could be harnessed in drug discovery to generate more reliable, reproducible and robust readouts.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8445948PMC
http://dx.doi.org/10.1038/s41598-021-97728-zDOI Listing

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