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A Trans-Agency Workshop on the Pathophysiology of Radiation-Induced Lung Injury.


ABSTRACT: Research and development of medical countermeasures (MCMs) for radiation-induced lung injury relies on the availability of animal models with well-characterized pathophysiology, allowing effective bridging to humans. To develop useful animal models, it is important to understand the clinical condition, advantages and limitations of individual models, and how to properly apply these models to demonstrate MCM efficacy. On March 20, 2019, a meeting sponsored by the Radiation and Nuclear Countermeasures Program (RNCP) within the National Institute of Allergy and Infectious Diseases (NIAID) brought together medical, scientific and regulatory communities, including academic and industry subject matter experts, and government stakeholders from the Food and Drug Administration (FDA) and the Biomedical Advanced Research and Development Authority (BARDA), to identify critical research gaps, discuss current clinical practices for various forms of pulmonary damage, and consider available animal models for radiation-induced lung injury.

SUBMITTER: Cassatt DR 

PROVIDER: S-EPMC9648426 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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A Trans-Agency Workshop on the Pathophysiology of Radiation-Induced Lung Injury.

Cassatt David R DR   Gorovets Alex A   Karimi-Shah Banu B   Roberts Rosemary R   Price Paul W PW   Satyamitra Merriline M MM   Todd Nushin N   Wang Sue-Jane SJ   Marzella Libero L  

Radiation research 20220401 4


Research and development of medical countermeasures (MCMs) for radiation-induced lung injury relies on the availability of animal models with well-characterized pathophysiology, allowing effective bridging to humans. To develop useful animal models, it is important to understand the clinical condition, advantages and limitations of individual models, and how to properly apply these models to demonstrate MCM efficacy. On March 20, 2019, a meeting sponsored by the Radiation and Nuclear Countermeas  ...[more]

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