The role of systematic reviews in identifying the limitations of preclinical animal research, 2000-2022: part 1

Assessing the application of barbed sutures in comparison to conventional sutures for surgical applications: a global systematic review and meta-analysis of preclinical animal studies

BACKGROUND

Following an initiative published by Lancet in 2002 and an IDEAL-D framework, the value of preclinical animal studies has garnered increasing attention in recent research. Numerous preclinical animal experiments tried to generate evidence to guide the development of barbed sutures. However, discernible drawbacks and incongruities in outcomes have emerged between clinical and preclinical animal studies. Therefore, this meta-analysis aimed to review the preclinical animal experiments comparing barbed sutures with conventional sutures. The authors hope to facilitate clinical translation of barbed sutures by evaluating effectiveness, safety, and physical properties/reliability.

MATERIALS AND METHODS

A systematic search of PubMed, EMBASE, Cochrane Library, and ClinicalTrials.gov was conducted to identify controlled preclinical animal experiments comparing barbed sutures with conventional sutures. The risk of bias was assessed using SYRCLE. GRADE approach was used to evaluate evidence quality. Revman was applied to analyze all the data. Subgroup, sensitivity, and meta-regression analyses were also performed.

RESULTS

A total of 62 articles were eligible with low to moderate quality, including 2158 samples from 10 different animal species across 27 surgical procedures. Barbed suture exhibited a significant reduction in suture time, limited change in Cross-Sectional Area (CSA), and decreased instances of tissue disruption (all P <0.05). Subgroup analyses, considering both clinical and research significance, indicated that barbed sutures might cause more specific adverse events and demonstrate suboptimal performance of physical properties/reliability. Meta-regression suggested that heterogeneity resulted from variations in studies and animal models.

CONCLUSION

Although barbed suture demonstrated superiority in numerous surgeries for time efficiency, its safety and physical properties/reliability might be influenced by diverse preclinical models, sutures' brand, surgeries, and anatomical sites. Further evaluation, based on standardized and well-designed animal experiments, is essential for medical device development and applications in human beings.

It's Time to Review the Three Rs, to Make them More Fit for Purpose in the 21st Century

The Three Rs have become widely accepted and pursued, and are now the go-to framework that encourages the humane use of animals in science, where no other option is believed to exist. However, many people, including scientists, harbour varying degrees of concern about the value and impact of the Three Rs. This ranges from a continued adherence to the Three Rs principles in the belief that they have performed well, through a belief that there should be more emphasis (or indeed a sole focus) on replacement, to a view that the principles have hindered, rather than helped, a critical approach to animal research that should have resulted in replacement to a much greater extent. This critical review asks questions of the Three Rs and their implementation, and provides an overview of the current situation surrounding animal use in biomedical science (chiefly in research). It makes a case that it is time to move away from the Three Rs and that, while this happens, the principles need to be made more robust and enforced more efficiently. To expedite a shift from animal use in science, toward a much greater and quicker adoption of human-specific New Approach Methodologies (NAMs), some argue for a straightforward focus on the best available science.

Mitigating animal methods bias to reduce animal use and improve biomedical translation

Nonanimal biomedical research methods have advanced rapidly over the last decade making them the first-choice model for many researchers due to improved translatability and avoidance of ethical concerns. Yet confidence in novel nonanimal methods is still being established and they remain a small portion of nonclinical biomedical research, which can lead peer reviewers to evaluate animal-free studies or grant proposals in a biased manner. This "animal methods bias" is the preference for animal-based research methods where they are not necessary or where nonanimal-based methods are suitable. It affects the fair consideration of animal-free biomedical research, hampering the uptake and dissemination of these approaches by putting pressure on researchers to conduct animal experiments and potentially perpetuating the use of poorly translatable model systems. An international team of researchers and advocates called the Coalition to Illuminate and Address Animal Methods Bias (COLAAB) aims to provide concrete evidence of the existence and consequences of this bias and to develop and implement solutions towards overcoming it. The COLAAB recently developed the first of several mitigation tools: the Author Guide for Addressing Animal Methods Bias in Publishing, which is described herein along with broader implications and future directions of this work.

Auto-STEED: A data mining tool for automated extraction of experimental parameters and risk of bias items from in vivo publications

Background

Systematic reviews, i.e., research summaries that address focused questions in a structured and reproducible manner, are a cornerstone of evidence-based medicine and research. However, certain systematic review steps such as data extraction are labour-intensive which hampers their applicability, not least with the rapidly expanding body of biomedical literature.

Objective

To bridge this gap, we aimed at developing a data mining tool in the R programming environment to automate data extraction from neuroscience in vivo publications. The function was trained on a literature corpus (n=45 publications) of animal motor neuron disease studies and tested in two validation corpora (motor neuron diseases, n=31 publications; multiple sclerosis, n=244 publications).

Results

Our data mining tool Auto-STEED (Automated and STructured Extraction of Experimental Data) was able to extract key experimental parameters such as animal models and species as well as risk of bias items such as randomization or blinding from in vivo studies. Sensitivity and specificity were over 85 and 80%, respectively, for most items in both validation corpora. Accuracy and F-scores were above 90% and 0.9 for most items in the validation corpora. Time savings were above 99%.

Conclusions

Our developed text mining tool Auto-STEED is able to extract key experimental parameters and risk of bias items from the neuroscience in vivo literature. With this, the tool can be deployed to probe a field in a research improvement context or to replace one human reader during data extraction resulting in substantial time-savings and contribute towards automation of systematic reviews. The function is available on Github.