Facilitating healthcare decisions by assessing the certainty in the evidence from preclinical animal studies

Abstract

Laboratory animal studies are used in a wide range of human health related research areas, such as basic biomedical research, drug research, experimental surgery and environmental health. The results of these studies can be used to inform decisions regarding clinical research in humans, for example the decision to proceed to clinical trials. If the research question relates to potential harms with no expectation of benefit (e.g., toxicology), studies in experimental animals may provide the only relevant or controlled data and directly inform clinical management decisions.Systematic reviews and meta-analyses are important tools to provide robust and informative evidence summaries of these animal studies. Rating how certain we are about the evidence could provide important information about the translational probability of findings in experimental animal studies to clinical practice and probably improve it. Evidence summaries and certainty in the evidence ratings could also be used (1) to support selection of interventions with best therapeutic potential to be tested in clinical trials, (2) to justify a regulatory decision limiting human exposure (to drug or toxin), or to (3) support decisions on the utility of further animal experiments. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach is the most widely used framework to rate the certainty in the evidence and strength of health care recommendations. Here we present how the GRADE approach could be used to rate the certainty in the evidence of preclinical animal studies in the context of therapeutic interventions. We also discuss the methodological challenges that we identified, and for which further work is needed. Examples are defining the importance of consistency within and across animal species and using GRADE’s indirectness domain as a tool to predict translation from animal models to humans.

The protective effect of anterior cruciate ligament reconstruction on articular cartilage: a systematic review of animal studies

“…Reasons for synthesizing animal evidence include the intervention still being in development (e.g., never tested on humans or still in the preclinical phase), or that clinical experiments are considered unethical (e.g., gross macroscopic assessment of cartilage damage after ACL reconstruction in humans). Considering evidence from animal studies might change the assessment of the likely magnitude of the effect or might potentially increase our certainty in the evidence 65 . When the results of a review like this will be used to inform the clinical field, the indirectness of the results need to be taken into account.…”

Animal models of chemotherapy-induced peripheral neuropathy: a machine-assisted systematic review and meta-analysis

“…We anticipate that there will be a similar improvement in studies reporting the use of animal models of CIPN. We propose that once studies achieve sufficient quality, it will be possible to use a GRADE type analysis or process to rate the certainty of the evidence of animal studies [21]. The measures to reduce the risk of bias that we have assessed for the reporting of are largely derived from what is known to be important in clinical trials, and the extent to these measures impact upon the findings of animal studies has yet to be fully elucidated.…”

Learning from Principles of Evidence-Based Medicine to Optimize Nonclinical Research Practices

“…The British Medical Journal has developed a suite of online tools (https://bestpractice.bmj.com/ info/us/toolkit) with a section on how to use GRADE, and various electronic databases and journals that summarize evidence are also available to clinicians. In a recent development, the GRADE Working Group has begun to explore how to rate evidence from nonclinical animal studies, and the first attempt to implement GRADE in the nonclinical space has successfully been performed on a sample of systematic reviews and examples, with further efforts planned (Hooijmans et al 2018). In contrast, with the exception of those who also have medical training or clinical research experience, most scientists are unaware of the guiding principles of EBM and are unfamiliar with formal decision-enabling algorithms.…”

“…In contrast, with the exception of those who also have medical training or clinical research experience, most scientists are unaware of the guiding principles of EBM and are unfamiliar with formal decision-enabling algorithms. At least in part due to the diversity of nonclinical experiments, systematic reviews and metaanalyses are far less common in nonclinical phases than in clinical ones, and there are very few broadly accepted tools with which to assess nonclinical data quality (Hooijmans et al 2018;Sena et al 2014). Pioneering work in this area came from the stroke field, with nonclinical research guidelines and an assessment tool elaborated by STAIR, the Stroke Therapy Academic Industry Roundtable (Hooijmans et al 2014) (https://www.thestair.org).…”