Abstract
Systematic reviews provide a structured framework for summarizing the available evidence in a comprehensive, objective, and transparent manner. They inform evidence-based guidelines in medicine, public policy, and more recently, in environmental health and toxicology. Many regulatory agencies have extended and adapted the well-established systematic review methods, initially developed for clinical studies, for their assessment needs. The use of systematic reviews to summarize evidence from existing human, animal, and mechanistic studies can reduce reliance on animal test data in risk assessment and can help avoid unnecessary duplication of animal experiments that have already been conducted. As alternative test methods can be expected to play an increasing role in human health risk assessment in the future, systematic reviews can be particularly helpful in validating these alternatives. The field of evidence-based toxicology has undergone extensive development since its first meeting in 2007 as a result of collaborative efforts among international experts and public health agencies, particularly with respect to the use of mechanistic data and evidence integration. The continued development and wider adoption of systematic review methodology can lead to better 3R implementation. As undertaking a systematic review can be a complex and lengthy process, it is important to understand the main steps involved. Key steps, along with current best practices, are described with references to guidance from organizations with expertise in evidence synthesis. Applications of systematic reviews in clinical, observational, and experimental studies are presented. Finally, software tools available to facilitate and increase the efficiency of completing a systematic review are described.
Highlights
Systematic reviews are comprehensive summaries of existing literature where the available evidence on a clearly formulated research question is synthesized using a transparent and objective stepwise process
This was followed by a collaborative workshop the same year, organized by Evidence-Based Toxicology Collaboration (EBTC) and EFSA, to further explore the integration of mechanistic data and examine evidence-based methods to construct mechanistic frameworks (such as Adverse Outcome Pathways (AOPs)) that can be used in the development of non-animal toxicity tests
Systematic reviews are becoming increasingly common in healthcare, public policy, and more recently, in evidence-based toxicology
Summary
Systematic reviews are comprehensive summaries of existing literature where the available evidence on a clearly formulated research question is synthesized using a transparent and objective stepwise process. Systematic reviews can serve as important tools to summarize findings from alternative test methods, including new approach methodologies (NAMs), providing validation for these non-animal test methods This can lead to replacement of animal testing in some cases, in instances where animal data does translate well to human health risks (Ritskes-Hoitinga and van Luijk, 2019). A workshop by EBTC and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group, in 2019, explored how mechanistic data can be better integrated into systematic reviews of chemical risk assessments4 This was followed by a collaborative workshop the same year, organized by EBTC and EFSA, to further explore the integration of mechanistic data and examine evidence-based methods to construct mechanistic frameworks (such as Adverse Outcome Pathways (AOPs)) that can be used in the development of non-animal toxicity tests (report in preparation). Systematic review software that has the potential to improve the efficiency at every stage of systematic reviews are described and key applications are listed for reviewers to consult
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
