Optimising experimental research in respiratory diseases: an ERS statement

Animal Experimentation 0301 basic medicine Biomedical Research 170 [SDV]Life Sciences [q-bio] Advisory Committees 610 [SDV.MHEP.PSR]Life Sciences [q-bio]/Human health and pathology/Pulmonology and respiratory tract HIGHLAND WHITE TERRIERS ACUTE LUNG INJURY 03 medical and health sciences INBRED MOUSE STRAINS Animals Humans Societies, Medical ALLERGIC AIRWAY INFLAMMATION COMMUNITY-ACQUIRED PNEUMONIA CIGARETTE-SMOKE EXPOSURE Respiration Disorders DISTRESS-SYNDROME 3. Good health Europe Disease Models, Animal ANIMAL-MODELS ARTERIAL-HYPERTENSION IDIOPATHIC PULMONARY-FIBROSIS
DOI: 10.1183/13993003.02133-2017 Publication Date: 2018-05-17T07:40:11Z
ABSTRACT
Experimental models are critical for the understanding of lung health and disease and are indispensable for drug development. However, the pathogenetic and clinical relevance of the models is often unclear. Further, the use of animals in biomedical research is controversial from an ethical perspective.The objective of this task force was to issue a statement with research recommendations about lung disease models by facilitating in-depth discussions between respiratory scientists, and to provide an overview of the literature on the available models. Focus was put on their specific benefits and limitations. This will result in more efficient use of resources and greater reduction in the numbers of animals employed, thereby enhancing the ethical standards and translational capacity of experimental research.The task force statement addresses general issues of experimental research (ethics, species, sex, age,ex vivoandin vitromodels, gene editing). The statement also includes research recommendations on modelling asthma, chronic obstructive pulmonary disease, pulmonary fibrosis, lung infections, acute lung injury and pulmonary hypertension.The task force stressed the importance of using multiple models to strengthen validity of results, the need to increase the availability of human tissues and the importance of standard operating procedures and data quality.
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