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Engineering Intravenously Administered Nanoparticles to Reduce Infusion Reaction and Stop Bleeding in a Large Animal Model of Trauma.

Chimdiya OnwukweNuzhat MaishaMark HollandMatt VarleyRebecca GroynomDaShawn HickmanNishant UppalAndrew ShoffstallJeffrey UstinErin B Lavik
Published in: Bioconjugate chemistry (2018)
Bleeding from traumatic injury is the leading cause of death for young people across the world, but interventions are lacking. While many agents have shown promise in small animal models, translating the work to large animal models has been exceptionally difficult in great part because of infusion-associated complement activation to nanomaterials that leads to cardiopulmonary complications. Unfortunately, this reaction is seen in at least 10% of the population. We developed intravenously infusible hemostatic nanoparticles that were effective in stopping bleeding and improving survival in rodent models of trauma. To translate this work, we developed a porcine liver injury model. Infusion of the first generation of hemostatic nanoparticles and controls 5 min after injury led to massive vasodilation and exsanguination even at extremely low doses. In naïve animals, the physiological changes were consistent with a complement-associated infusion reaction. By tailoring the zeta potential, we were able to engineer a second generation of hemostatic nanoparticles and controls that did not exhibit the complement response at low and moderate doses but did at the highest doses. These second-generation nanoparticles led to cessation of bleeding within 10 min of administration even though some signs of vasodilation were still seen. While the complement response is still a challenge, this work is extremely encouraging in that it demonstrates that when the infusion-associated complement response is managed, hemostatic nanoparticles are capable of rapidly stopping bleeding in a large animal model of trauma.
Keyphrases
  • low dose
  • atrial fibrillation
  • liver injury
  • physical activity
  • spinal cord injury
  • machine learning
  • risk assessment
  • high intensity
  • artificial intelligence
  • human health