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Ventilation following established ARDS: a preclinical model framework to improve predictive power

Abstract

Background Despite advances in understanding the pathophysiology of acute respiratory distress syndrome, effective pharmacological interventions have proven elusive. We believe this is a consequence of existing preclinical models being designed primarily to explore biological pathways, rather than predict treatment effects. Here, we describe a mouse model in which both therapeutic intervention and ventilation were superimposed onto existing injury and explored the impact of β-agonist treatment, which is effective in simple models but not clinically.

Methods Mice had lung injury induced by intranasal lipopolysaccharide (LPS), which peaked at 48 hours post-LPS based on clinically relevant parameters including hypoxaemia and impaired mechanics. At this peak of injury, mice were treated intratracheally with either terbutaline or tumour necrosis factor (TNF) receptor 1-targeting domain antibody, and ventilated with moderate tidal volume (20 mL/kg) to induce secondary ventilator-induced lung injury (VILI).

Results Ventilation of LPS-injured mice at 20 mL/kg exacerbated injury compared with low tidal volume (8 mL/kg). While terbutaline attenuated VILI within non-LPS-treated animals, it was ineffective to reduce VILI in pre-injured mice, mimicking its lack of clinical efficacy. In contrast, anti-TNF receptor 1 antibody attenuated secondary VILI within pre-injured lungs, indicating that the model was treatable.

Conclusions We propose adoption of a practical framework like that described here to reduce the number of ultimately ineffective drugs reaching clinical trials. Novel targets should be evaluated alongside interventions which have been previously tested clinically, using models that recapitulate the (lack of) clinical efficacy. Within such a framework, outperforming a failed pharmacologic should be a prerequisite for drugs entering trials.

  • ARDS
  • critical care
  • pulmonary oedema
  • innate immunity
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