Deadline for pre-proposal submission is: 5 March 2020 (17:00 CET)
1. INTRODUCTION & MOTIVATION
Personalised Medicine (PM) represents a paradigm shift from a “one size fits all” approach to an optimised strategy for prevention, diagnosis and treatment of disease for each person, based on his or her unique characteristics. Accordingly, PM puts the patient at the very centre of health care, aiming for an optimised management of a patient’s disease and/or predisposition to disease. Recent developments in areas such as diagnostic tests, medical imaging, biomarker monitoring to characterise patient phenotypes, omics technologies, interrogation of molecular pathways, lifestyle data, real-time monitoring of parameters associated with disease and compliance in taking medication and integration with smart information technology support this development.
Definition of Personalised Medicine:
ERA PerMed adheres to the definition stated in the Strategic Research and Innovation Agenda (SRIA) of PerMed, adopted from the Horizon2020 advisory group1:
“Personalised Medicine refers to a medical model using characterisation of individuals’ phenotypes and genotypes (e.g. molecular profiling, medical imaging, lifestyle data) for tailoring the right therapeutic strategy for the right person at the right time, and/or to determine the predisposition to disease and/or to deliver timely and targeted prevention.”
Some additional information can be found in the Advice for 2018–2020 of the Horizon 2020 Advisory Group for Societal Challenge 1, “Health, Demographic Change and Well-being”2:
“Different synonymous terms have been used alongside ‘personalised medicine’, most commonly ‘precision medicine’ and ‘stratified medicine’. While there may be subtle differences in the literal meanings of these terms, they usually refer to the same concept when applied in practice. Stratified medicine (mainly used in the UK) is more treatment-dependent, while precision medicine (mostly used in US) has a relatively broad meaning as it refers to 4P (predictive, preventive, personalised and participatory) medicine. We use the term personalised medicine because this term best reflects the ultimate goal of effectively tailoring treatment based on an individual’s ‘personal profile’, as determined by the individual’s genotype and phenotype data. Based on individuals’ profiles, PM aims to identify the optimal treatment regime by avoiding the treatment-failure approach commonly used in current evidence-based medicine.”
The health systems of the European Union occupy a central part of Europe’s high levels of social protection. They contribute to social cohesion, social justice, as well as to the systems sustainable development. The overarching values of universality, access to good quality care, equity and solidarity have been widely accepted in the work of the different EU institutions, while its implementation depends on the different countries and the respective structures and needs.
Current advances in the field of genomics and other omic disciplines, together with technological progress (such as high-performance computing), hold the promise of finally bringing PM into practice and applying preventive and predictive care models.
Besides the possibility of enhancing the lifespan of patients and increasing the quality of clinical practice through more targeted therapies, improvements in PM in the long term may also lead to more efficient use of costs and resources for healthcare systems through early detection, prevention, accurate risk assessment and efficiencies in care delivery.
However, despite recent progress in this field, many challenges remain. The development of PM approaches is complex, interlinked and global in nature. It requires truly multidisciplinary, cross-sectoral and transnational collaborations. To be successfully implemented, these approaches need to include strategies on how to better involve patients and citizens in all stages of the process, and on training the different key players and stakeholders needed for the implementation of PM approaches.
ERA PerMed seeks to facilitate these collaborations, and to foster the sharing of ideas, knowledge, data, and results between academic researchers from different disciplines (e.g. life sciences, physics, bioinformatics, biostatistics, ethics, economics and health-service research), health care providers, industry/pharma, regulatory authorities, as well as health technology assessors.
ERA PerMed3 is an ERA-NET Cofund, supported by 32 partners from 23 countries and co- funded by the European Commission. It aims to align regional and national research strategies and funding activities, promote excellence, reinforce the competitiveness of – and at the same time foster cooperation between – European players in PM, and enhance European collaboration with non-EU countries.
ERA PerMed is closely linked to the International Consortium for Personalised Medicine (ICPerMed4), established in November 2016. The Action Plan5 of ICPerMed builds on the Strategic Research and Innovation Agenda (SRIA) “Shaping Europe’s Vision for Personalised Medicine”6 developed by PerMed in 2015. ERA PerMed will foster the implementation of the Action Plan by funding transnational research projects in the field of PM.
The funding organisations listed below have decided to jointly launch the third ERA PerMed Joint Transnational Call (JTC2020) in order to fund international high-quality research projects in PM. The Joint Call Secretariat (JCS) will centrally coordinate this call for proposals.
The call is opened and supported simultaneously by the following funding organisations in their respective regions/countries:
· Fund for Scientific Research – FNRS, (F.R.S.-FNRS), Belgium
· Quebec Health Research Funds (FRQS), Quebec (Canada)
· Ministry of Science and Education of the Republic of Croatia, (MSE), Croatia
· Innovation Fund Denmark, (InnoFond), Denmark
· Academy of Scientific Research and Technology, (ASRT), Egypt
· Academy of Finland, (AKA), Finland
· The French National Research Agency, (ANR), France
· Federal Ministry of Education and Research, (BMBF) / German Aerospace Center e.V. – Project Management Agency, (DLR), Germany
· Federal Ministry of Health, (BMG) / VDI/VDE Innovation + Technik GmbH, Programme Management Agency, (VDI/VDE-IT), Germany
· Saxon State Ministry for Higher Education, Research and the Arts, (SMWK), Saxony (Germany)
· General Secretariat for Research and Technology, (GSRT), Greece
· National Research, Development and Innovation Office, (NKFIH), Hungary
· Health Research Board, (HRB), Ireland
· Ministry of Health, The Chief Scientist Office, (CSO-MOH), Israel
· Italian Ministry of Health, (IT-MoH), Italy
· Fondazione Regionale per la Ricerca Biomedica, (FRRB), Lombardy (Italy)
· Tuscany Region, (TuscReg), Tuscany (Italy)
· State Education Development Agency, (VIAA), Latvia
· National Research Fund, (FNR), Luxembourg
· Research Council of Norway, (RCN), Norway
· National Secretariat for Science, Technology and Innovation of Panama (SENACYT), Panama
· National Centre for Research and Development, (NCBR), Poland
· Executive Agency for Higher Education, Research, Development and Innovation Funding, (UEFISCDI), Romania
· Ministry of Education, Science and Sport (MIZS), Slovenia
· Centro para el Desarrollo Tecnológico Industrial, E.P.E. (CDTI), Spain
· National Institute of Health Carlos III, (ISCIII), Spain
· The Scientific Foundation of the Spanish Association Against Cancer, (FCAECC), Spain
· Health Department – Generalitat de Catalunya, (DS-CAT), Catalonia (Spain)
· Government of Navarre, (GN), Navarre (Spain)
· Swedish Research Council, (SRC), Sweden
· The Scientific and Technological Research Council of Turkey, (TUBITAK), Turkey
2. TIMELINE OF THE CALL
16 December 2019
expected around 13 May 2020
Mid/end of August 2020
Expected for October 2020
3. AIM OF THE CALL
Opening of the submission system for pre-proposals
Communication of the results of the pre-proposal assessment and invitation for full-proposal stage
Communication of the funding decisions to the applicants
16 December 2019
Publication of the call
5 March 2020 (17:00, CET)
Deadline for pre-proposal submission
15 June 2020 (17:00, CEST)
Deadline for full-proposal submission
Peer Review Panel meeting and CSC meeting for funding recommendation to national funding agencies
End of 2020, beginning of 2021
Expected project start (also subject to regional/national procedures)
With its third transnational call (non-cofunded by the EC), ERA PerMed fosters research and innovation activities that build close linkages between basic biomedical research, clinical research, physical sciences, bioengineering, bioinformatics and biostatistics, epidemiology, socio-economic research, as well as research on the integration of PM into clinical practice and on ethical, legal and social implications across the participating countries and beyond. This implies a wide range of multidisciplinary activities brought together by different stakeholders from academia (e.g. universities and research institutions), clinics (e.g. clinical laboratories, medical professionals), industry (e.g. pharmaceutical industry, biotechnology companies, information technology companies including health information technology – HIT), policy makers, regulatory/health technology assessment (HTA) agencies and patients/patient organisations.
The overarching goal is to improve disease prevention and disease management, based on broader and more efficiently characterised and defined patient stratification, diagnostics and tailored treatment protocols. Early involvement of regulatory authorities and close interaction with the different key players along the value chain should be included right from the project development phase to bridge the gap between first discoveries or inventions until market access. Proposals submitted under this call are expected to demonstrate the applicability of project outcomes to clinical practice. The clinical relevance of the proposed PM approach needs to be convincingly demonstrated. Moreover, proposals are expected to include research on ethical, legal and socio-economic implications, including health economics and regulation, and/or research on the optimisation of health care systems. They may also consider patient and citizen empowerment and training strategies for the different stakeholders in PM.
The overall objectives of the call are to:
– Support translational research projects in the field of Personalised Medicine;
– Encourage and enable interdisciplinary collaborations towards the implementation of PM, combining pre-clinical and/or clinical research with bioinformatics components and research on relevant ethical, legal and social aspects and/or research on the optimisation of health care systems;
– Encourage collaboration between academia (research teams from universities,
higher education institutions, public research institutions), clinical/public health research (research teams from hospital/ public health, health care settings and other health care organisations), private partners e.g. SMEs7 (small and medium-sized enterprises) as well as policy makers, regulatory/HTA agencies and patient organisations.
The JTC2020 of ERA PerMed comprises three Research Areas:
Each project proposal MUST address at least one module of Research Area 3 and at least one module of Research Area 1 or 2:
Assessment of the coherent integration and combination of the different research areas and modules in the proposals is part of the evaluation process.
Research Area 1: “Translating Basic to Clinical Research and Beyond”.
Research proposals should aim to improve the exchange between basic and clinical research. This is needed to allow the transition from bench to bedside (e.g. by translational science, transferring pre-clinical technologies/other predictive tools to clinical application) but also vice versa by using, for example, existing clinical databases, repositories and cohorts, and by sharing experiences obtained in classical and innovative clinical studies/trials. The aim is to achieve a better identification and validation of known biomarkers and therapeutic targets (including omics and other data obtained, for example, by imaging, biomarker monitoring etc.) as well as diagnostic re-classification. This in turn will help to predict in advance how a patient will respond to a specific therapy.
Proposals are expected to thoroughly describe appropriate validation strategies according to the translational gap to be bridged. The inclusion of a strategy to ensure the robustness and reproducibility of results is strongly encouraged.
Research projects on diseases other than cancer are also encouraged.
Module 1A: Pre-clinical Research
· Development and implementation of high-throughput pre-clinical models for (A) validation of data and hypotheses from human population, clinical and molecular studies and/or (B) prediction of clinical outcome. This may include in silico models, cell culture/co-culture, organoids and animal models, etc.
· Classification of diseases at the molecular level, which can be instrumental for successful implementation of PM, including pre-clinical studies for the validation of biomarkers that can be used in diagnosis, prognosis and prediction of response to treatment.
· Validation (in preclinical models, in terms of reproducibility, safety and efficiency) and characterisation of the role of biomarkers in predictive medicine for future prevention, assessment and management of diseases.
Module 1B: Clinical Research
· Improvement, validation and combination of tools (e.g. imaging, physiological monitoring and omics) for diagnostics and integrated analytical methods, allowing the discovery of molecular characteristics involved in disease etiopathogenesis (including co-morbidities and sex-related differences), development and progression, and patient treatment including pharmacokinetics or pharmacodynamics.
· Development and evaluation of concepts for innovative clinical trial methodologies, suitable for PM approaches, taking into account the fact that more flexible and innovative trial design is needed, considering both health benefits and health economics (see also Module 3A). Development of novel strategies that will enable clinical scientists to speed up the transition from clinical observation to diagnostic development.
· Development of new concepts and stratification strategies in exploratory clinical studies (for further indications, see also the blue box on page 13/14).
· Clinical and omics data integration, use of machine learning technology to provide the basis for a more personalised treatment for patients.
Research Area 2: “Integrating Big Data and ICT8 Solutions”.
Systematic integration of different bioinformatics resources (databases, algorithms, etc.), big data and ICT solutions should be an essential part of the research proposals submitted under this call wherever appropriate. The PM approaches to be developed should support the easy flow, robust analysis and interpretation of information such as clinical data (including imaging data and physiological monitoring data), omics data, data on biological samples, as well as patient outcomes among different institutions while ensuring data security and data protection.
The re-use and sharing of data through public databases are encouraged and the re-use or combination of existing tools is also welcome. Applicants are asked to describe both new and existing tools, methodologies, technologies and digital support to be used in the project. This includes ICT solutions (e.g. eHealth and mHealth solutions, and telehealth) for the timely and safe collection and transfer of health information and to facilitate the use of already collected data, including electronic medical records (structured and unstructured sources), by respecting data security, protection and privacy on one hand, and ensuring interoperability, completeness, sufficient documentation and comparability of data on the other.
Outlining how ICT solutions developed/used in the project will be maintained after the end of the project is also encouraged.
Module 2A: Data and ICT – Enabling Technology
· Research on data harmonisation strategies and the development of ICT solutions to address research questions raised in the consortium, e.g. ICT solutions enabling the use of clinical data in research.
· Strategies for the development of common quality standards, semantics and minimal indicators, and metrics for data and metadata, and demonstration of utility of the strategy proposed in the research proposal.
· Development of computational (ICT) tools respecting interoperability of biomedical databases, the FAIR data principles as well as relevant regulations on data protection and security.
· Development of bioinformatics models/methods to integrate information into databases, and to analyse and extract this information, allowing, for example, the (automated or manually curated) integration and processing of data from unstructured sources and the combination of multiple data sources.
· Development of new devices/tools for data collection (e.g. mHealth, wearable devices for continuous online physiological monitoring, haptic devices, etc.) and measurement of patient compliance with therapy. This also includes procedures/algorithms for handling/integrating this data in an interoperable way.
· Development of platforms that will enable clinical scientists to speed up the transition from clinical observation to diagnostic development.
Module 2B: Data and ICT – Towards Application in Health Care
· Research on data integration and interpretation of diseases aimed at advancing PM. Demonstration of the potential clinical benefit of using and combining different kinds of datasets from various sources. These datasets can originate for example from large, multimodal and multi-centre public data repositories or clinical records from different sources. They can comprise data from multiple biological organisation levels or scales, e.g. behavioural, physiologic and molecular data. In addition, different forms of mathematical, statistical and modelling frameworks can be used for exploring and validating data quality and information content. This might include, for example, the development of standardised strategies for cross-validating biomarkers across existing databases.
· Development of innovative and easy-to-handle clinical decision support tools tailored to the needs of healthcare professionals. Such tools should provide reliable and accurate interpretation of complex multifactorial and multimodal data (including e.g. clinically validated data and information on current diagnosis and treatment options).
· Development of telehealth and telemedicine applications to support the implementation of PM, e.g. by innovative use and combination of already validated and novel eHealth and mHealth solutions, such as e.g. new physiological sensor and patient monitoring technologies combined with mHealth solutions for real-time personalised feedback.