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Inhalation Toxicology
International Forum for Respiratory Research
Volume 23, 2011 - Issue 14
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Research Article

Simvastatin pretreatment prevents ambient particle-induced lung injury in mice

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Pages 889-896 | Received 12 May 2011, Accepted 08 Sep 2011, Published online: 29 Nov 2011
 

Abstract

Air particulate pollution negatively affects the health of the population exposed, being the lung the main target organ. Simvastatin (SV) is widely used for the prevention and risk reduction of coronary disease. Its pleiotropic effects may provide benefit for lung diseases. Here, we investigated the preventive effect of simvastatin pretreatment on acute intranasal exposure to ROFA (Residual Oil Fly Ash), and UAP (Urban Air Particle from Buenos Aires). Male BALB/c mice were randomized in two groups to receive either saline (control, C) solution or SV (1 mg/kg bw /day; ip) for 14 days. After SV treatment, ROFA or UAP (1 mg/kg bw) or saline were intranasally delivered for 24 hours generating 4 subgroups for the ROFA experiment (C, SV, ROFA and SV+ROFA) and 3 subgroups for the UAP experiment (C, SV, UAP and SV+UAP). Biomarkers of lung injury were examined in BAL cells evaluating total cell number (TCN), cell differential (CD) and superoxide anion generation (O2-), in lung homogenates assessing superoxide dismutase activity (SOD) and tumor necrosis factor α (TNFα); and in blood samples determining interleukin 6 (IL-6) production. ROFA and UAP produced an acute pulmonary injury, characterized by an increase in BAL, TCN and neutrophilic inflammatory influx, a rise in O2- generation, and production of the proinflammatory TNFα cytokine. SV pretreatment had no significant effect per se on any of these biomarkers but prevented the pulmonary cytotoxicity and inflammation induced by ROFA and UAP. Our results encourage further studies to determine the preventive effects on lung injury induced by air pollutants.

Acknowledgments

We thank Dr. Paulo H Saldiva for kindly providing the collector sampler and A Perez de la Hoz for assistance and technical expertize.

Declaration of interest

This work was partially supported by the National University of San Martín (Grants #A053 and A097).

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