The framework addresses a problem that has plagued translational medicine for decades: most drugs that look promising in animal models fail in human trials, and many surprises in human research could have been anticipated if animal evidence had been examined more carefully and systematically. Traditional systematic reviews usually focus on a single evidence stream, applying methods honed for clinical trials to human studies and, less often, adapted checklists to laboratory animal experiments. Rarely are the two streams synthesized together with explicit attention to how they agree, conflict or leave gaps. The GALENOS team argues that this separation is particularly costly in early-stage research, where the goal is not to establish definitive treatment effects but to identify and prioritise promising new directions, therapeutic targets and mechanisms of action. Combining human and animal evidence, they contend, can yield insights neither source alone can provide.

At the heart of the framework is a commitment to living evidence. Rather than producing a static review that is outdated before it is published, GALENOS reviews are designed to be continually updated as new studies appear. This is made feasible through automation: the team employs tools such as the Automated Systematic Search Deduplicator to manage the flood of candidate records, and the Systematic Review Facility, or SyRF, to coordinate screening and data extraction, including crowdsourced contributions. Large language models are also being incorporated to support semi-automated screening and data handling, although the authors are careful to position automation as an aid to, not a replacement for, human judgment. The living approach is coupled with co-production: people with lived experience of anxiety, depression and psychosis help shape research questions, interpret findings and judge which uncertainties matter most to patients, ensuring that prioritisation reflects relevance as well as statistical strength.

Methodologically, the framework builds on established best practices in systematic review methodology while introducing novel components. Research questions are framed using PICO elements, defining population, intervention, comparator and outcome, but with careful translation across species and study designs, since an intervention in a mouse model of psychosis and an antipsychotic trial in humans must be mapped onto a common conceptual structure before their results can be compared. For animal studies, the team applies risk-of-bias tools derived from the SYstematic Review Centre for Laboratory animal Experimentation and reporting standards aligned with the ARRIVE guidelines, addressing the well-documented problems of small sample sizes, publication bias and methodological laxity in preclinical research. Human studies are appraised with conventional Cochrane-style tools, and both streams are assessed for the risk of bias arising from missing evidence using the ROB-ME approach.

Quantitative synthesis within the framework handles the awkward fact that human and animal studies measure outcomes on different scales and with different statistics. The authors describe how effect measures such as mean differences, standardised mean differences, risk ratios and odds ratios can be computed within each evidence stream, and how the normalised mean difference can bridge human and animal outcomes onto a comparable metric. Random-effects meta-analysis, using restricted maximum likelihood estimation, pools studies within each species and design category. Crucially, synthesis is not limited to pairwise comparisons; the framework accommodates networks of interventions and mechanisms, allowing reviewers to ask not only whether a target works but how it performs relative to alternatives across both human and animal data.

The most distinctive innovation is what the team calls the Summary of Evidence Table, or SET. A SET is a structured, transparent container that assembles the synthesized findings from human and animal studies on a given question, alongside appraisals of their credibility, consistency and biological plausibility. On top of the SETs sits the GALENOS Approach to Triangulating Evidence, or GATE, a formal procedure for integrating the streams. Rather than simply averaging across species, GATE asks structured questions: do human and animal findings agree in direction and magnitude, does one stream fill gaps the other cannot, and where they conflict, what biases or translational limitations best explain the discrepancy? The output is a graded judgement about the maturity of the evidence, which in turn feeds directly into research prioritisation decisions about which targets warrant clinical testing, which need better animal work, and which should be deprioritised.

The framework is not merely theoretical. The GALENOS consortium maintains living systematic reviews on concrete therapeutic questions, and the paper illustrates its application with two examples. One concerns trace amine-associated receptor 1, or TAAR1, agonists for psychosis, a target class that has recently progressed to clinical testing; the living review collates animal evidence on receptor mechanisms alongside emerging human trial data to judge whether the translational case remains sound. The other concerns pro-dopaminergic interventions for anhedonia, the blunting of pleasure and motivation that pervades depression and schizophrenia, where animal work on dopaminergic circuitry must be reconciled with early human studies. In both cases, the SET and GATE machinery makes explicit how strong each strand of evidence is, where uncertainty concentrates and what future experiments would be most informative.

Independent experts in evidence synthesis are likely to welcome the framework’s insistence on transparency. By laying out, in tabular form, exactly what is known from each species, how credible each finding is, and where the streams converge or diverge, the approach makes prioritisation decisions auditable rather than opaque. This matters in mental health particularly, where the translational failure rate has been devastatingly high and where the DSM-based grouping of patients may obscure biological heterogeneity that animal models capture differently. The authors are candid about the epistemological challenges: animal models are approximations, human diagnostic categories are constructs, and integrating them involves assumptions that must be stated, not hidden. The framework’s contribution is precisely to force those assumptions into the open.

The implications extend beyond psychiatry. The general architecture of co-produced questions, living systematic reviews, source-specific synthesis, and structured triangulation through SETs and GATE could be applied in any field where preclinical and clinical evidence must be weighed together, from neurology to oncology. For funders and research commissioners, the framework offers a defensible mechanism for deciding where scarce research money goes; for scientists at the bench, it offers a clearer picture of which of their findings the clinical world considers robust; for patients and people with lived experience, it offers a seat at the table where research agendas are set. The GALENOS team presents the approach as a working system under continuous refinement, with its living reviews and prioritisation outputs publicly accessible, inviting the research community to test, adapt and challenge it as the evidence accumulates.

Subject of Research: A framework for collating and synthesizing evidence from human and animal studies

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

Article References: Chiocchia, V., Higgins, J. P., Siafis, S., Macleod, M., Thomas, J., Furukawa, T. A., Downs, J., Cipriani, A., Egger, M., & Salanti, G. (2026). A framework for collating and synthesizing evidence from human and animal studies: the GALENOS approach. BMC Medicine. https://doi.org/10.1186/s12916-026-05205-z

Image Credits: AI Generated

DOI: 10.1186/s12916-026-05205-z

Keywords: GALENOS, evidence synthesis, living systematic reviews, triangulation, animal studies, human studies, mental health, research prioritisation, TAAR1, co-production, framework, collating