Abstract
BACKGROUND: Despite extensive investment, the development of effective treatments for Alzheimer's disease (AD) has been largely unsuccessful. To improve translation, it is crucial to ensure the quality and reproducibility of foundational evidence generated from laboratory models. Systematic reviews play a key role in providing an unbiased overview of the evidence, assessing rigour and reporting, and identifying factors that influence reproducibility. However, the sheer pace of evidence generation is prohibitive to evidence synthesis and assessment. NEW METHOD: To address these challenges, we have developed AD-SOLES, an integrated workflow of automated tools that collect, curate, and visualise the totality of evidence from in vivo experiments. RESULTS: AD-SOLES is a publicly accessible interactive dashboard aiming to surface and expose data from in vivo experiments. It summarises the latest evidence, tracks reporting quality and transparency, and allows research users to easily locate evidence relevant to their specific research question. COMPARISON WITH EXISTING METHODS: Using automated screening methodologies within AD-SOLES, systematic reviews can begin at an accelerated starting point compared to traditional approaches. Furthermore, through text-mining approaches within the full-text of publications, users can identify research of interest using specific models, outcomes, or interventions without relying on details in the title and/or abstract. CONCLUSIONS: By automating the collection, curation, and visualisation of evidence from in vivo experiments, AD-SOLES addresses the challenges posed by the rapid pace of evidence generation. AD-SOLES aims to offer guidance for research improvement, reduce research waste, highlight knowledge gaps, and support informed decision making for researchers, funders, patients, and the public.