Refining humane endpoints in mouse models of disease by systematic review and machine learning-based endpoint definition

Ideally, humane endpoints allow early termination of experiments by minimizing an animal's discomfort, distress and pain while ensuring that scientific objectives are reached. Yet, lack of commonly agreed methodology and heterogeneity of cut-off values published in the literature remain a chall...

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Bibliographic Details
Published in:ALTEX, alternatives to animal experimentation alternatives to animal experimentation, 2019-01, Vol.36 (4), p.555
Main Authors: Mei, Jie, Banneke, Stefanie, Lips, Janet, Kuffner, Melanie T.C, Hoffmann, Christian J, Dirnagl, Ulrich, Endres, Matthias, Harms, Christoph, Emmrich, Julius V
Format: Article
Language:English
Subjects:
Animal experimentation
Body weight
Chronic diseases
Diseases
Machine learning
Medical research
Physiological aspects
Time
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Summary:Ideally, humane endpoints allow early termination of experiments by minimizing an animal's discomfort, distress and pain while ensuring that scientific objectives are reached. Yet, lack of commonly agreed methodology and heterogeneity of cut-off values published in the literature remain a challenge to the accurate determination and application of humane endpoints. With the aim to synthesize and appraise existing humane endpoint definitions for commonly used physiological parameters, we conducted a systematic review of mouse studies of acute and chronic disease models that used body weight, temperature and/or sickness scores for endpoint definition. We searched for studies in two electronic databases (MEDLINE/Pubmed and Embase). Out of 110 retrieved full -text manuscripts, 34 studies were included. We found large intra- and inter-model variance in humane endpoint determination and application due to varying animal models, lack of standardized experimental protocols, and heterogeneity of performance metrics (part 1). We then used previously published and unpublished data on weight, temperature, and sickness scores from mouse models of sepsis and stroke and applied machine learning models to assess the usefulness of this method for parameter selection and endpoint definition across models. Machine learning models trained with physiological data and sickness severity score or modified DeSimoni neuroscore identified animals with a high risk of death at an early time point in both mouse models of stroke (male: 93.2% at 72 h post-treatment; female: 93.0% at 48 h post-treatment) and sepsis (96.2% at 24 h post-treatment), thus demonstrating generalizability of endpoint determination across models (part 2).
ISSN:1868-596X
1868-596X
DOI:10.14573/altex.1812231