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A Structured Approach to Optimizing Animal Model Selection for Human Translation: The Animal Model Quality Assessment.


ABSTRACT: Animal studies in pharmaceutical drug discovery are common in preclinical research for compound evaluation before progression into human clinical trials. However, high rates of drug development attrition have prompted concerns regarding animal models and their predictive translatability to the clinic. To improve the characterization and evaluation of animal models for their translational relevance, the authors developed a tool to transparently reflect key features of a model that may be considered in both the application of the model but also the likelihood of successful translation of the outcomes to human patients. In this publication, we describe the rationale for the development of the Animal Model Quality Assessment tool, the questions used for the animal model assessment, and a high-level scoring system for the purpose of defining predictive translatability. Finally, we provide an example of a completed Animal Model Quality Assessment for the adoptive T-cell transfer model of colitis as a mouse model to mimic inflammatory bowel disease in humans.

SUBMITTER: Storey J 

PROVIDER: S-EPMC9291347 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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A Structured Approach to Optimizing Animal Model Selection for Human Translation: The Animal Model Quality Assessment.

Storey Joanne J   Gobbetti Thomas T   Olzinski Alan A   Berridge Brian R BR  

ILAR journal 20211201 1-2


Animal studies in pharmaceutical drug discovery are common in preclinical research for compound evaluation before progression into human clinical trials. However, high rates of drug development attrition have prompted concerns regarding animal models and their predictive translatability to the clinic. To improve the characterization and evaluation of animal models for their translational relevance, the authors developed a tool to transparently reflect key features of a model that may be consider  ...[more]

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