Open AccessImportance of Systematic Reviews and Meta-analyses of Animal Studies: Challenges for Animal-to-Human Translation
Author(s) -
Zahra Bahadoran,
Parvin Mirmiran,
Khosrow Kashfi,
Asghar Ghasemi
Publication year - 2020
Publication title -
journal of the american association for laboratory animal science
Language(s) - English
Resource type - Journals
eISSN - 2769-6677
pISSN - 1559-6109
DOI - 10.30802/aalas-jaalas-19-000139
Subject(s) - generalizability theory , systematic review , meta analysis , animal testing , publication bias , clinical study design , psychological intervention , medline , modalities , strengths and weaknesses , clinical trial , medicine , psychology , biology , pathology , ecology , developmental psychology , biochemistry , social science , social psychology , psychiatry , sociology
Results of animal experiments are used for understanding the pathophysiology of diseases, assessing safety and efficacy of newly developed drugs, and monitoring environmental health hazards among others. Systematic reviews and meta-analyses of animal data are important tools to condense animal evidence and translate the data into practical clinical applications. Such studies are conducted to explore heterogeneity, to generate new hypotheses about pathophysiology and treatment, to design new clinical trial modalities, and to test the efficacy and the safety of the various interventions. Here, we provide an overview regarding the importance of systematic reviews and meta-analyses of animal data and discuss common challenges and their potential solutions. Current evidence highlights various problems and challenges that surround these issues, including lack of generalizability of data obtained from animal models, failure in translating data obtained from animals to humans, poor experimental design and the reporting of the animal studies, heterogeneity of the data collected, and methodologic weaknesses of animal systematic reviews and meta-analyses. Systematic reviews and meta-analyses of animal studies can catalyze translational processes more effectively if they focus on a well-defined hypothesis along with addressing clear inclusion and exclusion criteria, publication bias, heterogeneity of the data, and a coherent and well-balanced assessment of studies' quality.