The PERMIT guidelines for designing and implementing all stages of personalised medicine research
Personalised medicine (PM) research programmes represent the modern paradigm of complex cross-disciplinary research, integrating innovative methodologies and technologies. Methodological research is required to ensure that these programmes generate robust and reproducible evidence. The PERMIT project developed methodological recommendations for each stage of the PM research pipeline. A common methodology was applied to develop the recommendations in collaboration with relevant stakeholders. Each stage was addressed by a dedicated working group, specializing in the subject matter. A series of scoping reviews that mapped the methods used in PM research and a gap analysis were followed by working sessions and workshops where field experts analyzed the gaps and developed recommendations. Through collaborative writing and consensus building exercises, the final recommendations were defined. They provide guidance for the design, implementation and evaluation of PM research, from patient and omics data collection and sample size calculation to the selection of the most appropriate stratification approach, including machine learning modeling, the development and application of reliable preclinical models, and the selection and implementation of the most appropriate clinical trial design. The dissemination and implementation of these recommendations by all stakeholders can improve the quality of PM research, enhance the robustness of evidence, and improve patient care.
Authors
- Rita Banzi (ORCID: https://orcid.org/0000-0002-2211-3300)
- Jacques Demotes‐Mainard (ORCID: https://orcid.org/0000-0002-0807-0746)
- Josep María Haro (ORCID: https://orcid.org/0000-0002-3984-277X)
- Paula Garcia (ORCID: https://orcid.org/0000-0003-2549-1136)
- Chiara Gerardi (ORCID: https://orcid.org/0000-0002-2459-4769)
- Judit Subirana-Mirete (ORCID: https://orcid.org/0000-0002-5102-6660)
- Vibeke Fosse (ORCID: https://orcid.org/0000-0001-9021-4843)
- Enrico Glaab (ORCID: https://orcid.org/0000-0003-3977-7469)
- Raphaël Porcher (ORCID: https://orcid.org/0000-0002-5277-4679)
- Emanuela Oldoni (ORCID: https://orcid.org/0000-0002-9105-2601)
- Cecilia Superchi (ORCID: https://orcid.org/0000-0002-5375-6018)
Institutions
- Inserm (FR)
- University of Luxembourg (LU)
- Mario Negri Institute for Pharmacological Research (IT)
- Université Paris Cité (FR)
- Instituto de Salud Carlos III (ES)
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR)
- Centro de Investigación Biomédica en Red de Salud Mental (ES)
- Sorbonne Paris Cité (FR)
- Parc Sanitari Sant Joan de Déu (ES)
- European Clinical Research Infrastructure Network (FR)
- Centre de Recherche Épidémiologie et Statistique (FR)
- University of Bergen (NO)
Publication Details
- Journal
- Scientific Reports
- Published
- 2024-11-13
- DOI
- https://doi.org/10.1038/s41598-024-79161-0
- Primary Topic
- Scientific Computing and Data Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- GlaxoSmithKline
- Sanofi
- Biogen
- Aarhus Universitet
- Helsingin Yliopisto
- Université du Luxembourg
- Universidad de Sevilla
- Merck KGaA
- Istituto di Ricerche Farmacologiche Mario Negri - IRCCS
- University College London
- European Commission
- Erasmus Universiteit Rotterdam
- Universiteit Utrecht
- Semmelweis Egyetem
- Eberhard Karls Universität Tübingen
- KU Leuven
- Università degli Studi di Firenze
- Universidad Autónoma de Madrid
- European Organisation for Research and Treatment of Cancer
- Universitetet i Bergen
- Université de Liège
- Universitat de Barcelona
- Università di Bologna
- Oulun Yliopisto
- Universidade do Porto
- Università degli Studi di Verona
- Idorsia Pharmaceuticals
- Horizon 2020 Framework Programme
- Joint Research Centre
- Instituto de Salud Carlos III
- Belgian Health Care Knowledge Centre