A framework for collating and synthesizing evidence from human and animal studies: the GALENOS approach

Abstract Background Evidence syntheses can support early-stage research by identifying and prioritising promising new directions. Combining human and animal evidence can provide insights that neither source alone can offer, particularly for emerging interventions, therapeutic targets and mechanisms of action. Involving people with lived experience (PWLE) can further improve the relevance and interpretability of evidence. However, integrating human and animal studies within a living, co-produced process raises methodological, translational, and epistemological challenges. Methods We describe a framework for co-producing research questions, conducting living systematic reviews of human and animal studies, synthesising and appraising each evidence thesis, and using structured triangulation to inform research prioritisation. The framework was developed within the Global Alliance for Living Evidence on aNxiety, depressiOn and pSychosis (GALENOS) by methodologists, statisticians, domain experts, and PWLE. The GALENOS framework builds on established best practices in systematic reviews, co-production methods, and emerging automation approaches, while introducing targeted methodological developments that integrate these components into structured Summary of Evidence Tables (SETs) designed for evidence triangulation. It further advances concepts of triangulation by providing the GALENOS Approach to Triangulating Evidence (GATE): a structured approach for integrating human and animal evidence within SETs to support research prioritisation and translational decision-making. Examples from GALENOS living systematic reviews, including those on TAAR1 agonists for psychosis and on pro-dopaminergic interventions for anhedonia, illustrate how the framework is applied in practice. Conclusions The GALENOS framework shows how living, co-produced, and triangulated evidence can support early-phase research and strengthen research prioritisation. By combining source-specific synthesis with transparent appraisal and structured triangulation, the approach can help identify which findings are robust, which uncertainties matter most and which future studies are most likely to advance mental health science.