Recommendations for robust and reproducible preclinical research in personalised medicine
Fosse, Vibeke Samuelsen; Oldoni, Emanuela; Bietrix, Florence; Budillon, Alfredo; Daskalopoulos, Evnagelos P.; Fratelli, Maddalena; Gerlach, Björn; Groenen, Peter M.A.; Hölter, Sabine M.; Menon, Julia M.L.; Mobasheri, Ali; Osborne, Nikki; Ritskes-Hoitinga, Merel; Ryll, Bettina; Schmitt, Elmar; Ussi, Anton; Andreu, Antonio L.; Mccormack, Emmet Matin
Journal article, Peer reviewed
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Original versionBMC Medicine. 2023, 21, 14. https://doi.org/10.1186/s12916-022-02719-0
Background Personalised medicine is a medical model that aims to provide tailor-made prevention and treatment strategies for defined groups of individuals. The concept brings new challenges to the translational step, both in clinical relevance and validity of models. We have developed a set of recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine. Methods These recommendations have been developed following four main steps: (1) a scoping review of the literature with a gap analysis, (2) working sessions with a wide range of experts in the field, (3) a consensus workshop, and (4) preparation of the final set of recommendations. Results Despite the progress in developing innovative and complex preclinical model systems, to date there are fundamental deficits in translational methods that prevent the further development of personalised medicine. The literature review highlighted five main gaps, relating to the relevance of experimental models, quality assessment practices, reporting, regulation, and a gap between preclinical and clinical research. We identified five points of focus for the recommendations, based on the consensus reached during the consultation meetings: (1) clinically relevant translational research, (2) robust model development, (3) transparency and education, (4) revised regulation, and (5) interaction with clinical research and patient engagement. Here, we present a set of 15 recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine. Conclusions Appropriate preclinical models should be an integral contributor to interventional clinical trial success rates, and predictive translational models are a fundamental requirement to realise the dream of personalised medicine. The implementation of these guidelines is ambitious, and it is only through the active involvement of all relevant stakeholders in this field that we will be able to make an impact and effectuate a change which will facilitate improved translation of personalised medicine in the future.