Towards Precision Medicine: Platform for Transdiagnostic Stratification of Brain Dysfunction

Objectives

• Applicants are expected to address all four main objectives of the topic in their proposal:
  • Adapt and extend an existing federated data platform that is sustainable, enabling collaborative curation, access and analysis of clinical datasets and samples (as mentioned above). Consolidate existing multimodal datasets and samples from cohorts with relevant disorders1 into the adapted platform;
  • Collect additional new clinical datasets and samples to address gaps and integrate these into the adapted platform;
  • Test hypotheses for candidate markers and endpoints within a defined context (e.g. patient selection, diagnosis, or treatment monitoring) in a transdiagnostic patient population presenting symptom domains of RM&I, including those with AD, MDD, and obesity;
  • Establish a collaborative platform to bring together people with LE, HCPs, regulators, HTA bodies and payers to achieve consensus on the value of candidate markers and endpoints, how to operationalise them into new diagnostic and treatment frameworks and achieve readiness in the healthcare system.

Scope

• Current diagnosis and patient stratification in health disorders with Central Nervous System (CNS)-driven symptoms are based on DSM-5/ICD-11 codes, which are not aligned with underlying biological processes and mechanisms. Subsequent suboptimal disease classification and patient stratification is a key reason for the low PoS of clinical development and the historical lack of new and more efficacious treatments. This topic aims to address these challenges by adopting a holistic, transdiagnostic approach focused on the common underlying biology of RM&I symptom domains across the relevant disorders listed in the first expected outcome.
• The topic seeks to build on an existing federated data platform to consolidate, curate, link and analyse robust, multimodal datasets from relevant patient populations. Thus, activities related to building a new platform or a biorepository from scratch are out of scope. The data platform must enable data/sample discovery, access, and support advanced computational analysis including artificial intelligence (AI)/machine learning (ML) technologies while ensuring interoperability with other global data platforms to illuminate the biological basis of the RM&I symptom domains and identify related candidate markers and endpoints. The hypotheses will be prospectively tested in clinical case studies focusing on but not limited to AD, MDD, and obesity. Post-project, the platform will be available as open access for ongoing research and validation.
• The topic also priorities collaboration with relevant stakeholders, including people with lived experience (LE), carers, HCPs, providers, regulators, HTA bodies and payers, to prepare the healthcare system for this transformative shift. People with LE can provide unique insights and expertise that comes as the result of first-hand experience of health challenges. Integrating LE expertise improves research by bringing an understanding beyond academic and clinical knowledge. The perspectives of people with LE across the relevant symptom domains must be represented within the consortium and applied wherever appropriate.
• Applicants must outline their approach to inclusive and equitable practices throughout the initiative, possibly through a risk register and appropriate mitigations. Example areas for consideration include data representation, bias mitigation, stakeholder engagement, ethics, and feedback mechanisms.

Funding Information

• Budget (EUR) – Year 2025: 20 202 000
• Contributions: around 20202000

Eligible Activities

• Activities under objective 1:
  • The success of this topic hinges on access to a large of amount of high-quality, multimodal data and biological samples collated from applicants and other partners (including industry). The applicants must list the datasets and samples that they will bring and confirm that they will be made accessible to the whole public-private partnership (PPP) from the start of the action.
    • Collate existing multimodal, longitudinal and transdiagnostic datasets at an individual level, including relevant parameters outlined under 1.2. These datasets can come from public or private databases, observational studies, clinical trials, real-world evidence (RWE) studies, biobanks, electronic health records, registries, and/or other digital health technologies and platforms. In their short proposal applicants must include a strategy to utilise relevant data from the European Platform for Neurodegenerative Diseases (EPND) catalogue2 as much as possible as well as other relevant datasets available from previous projects (including pre-clinical data).
    • Relevant multimodal datasets ideally include as many as possible from the following: neurophysiology data (e.g. electroencephalography (EEG), magnetoencephalography (MEG)), brain imaging data (e.g. functional magnetic resonance imaging (fMRI), MRI), qualitative subjective assessments, behavioural data, real-world data, medical claims and billing data, routine clinical data (from medical and psychological assessments including data on metabolic status), physiological/activity monitoring data (polysomnography, actigraphy, digital data from wearables, etc.), speech/language data, patient reported outcome data (e.g. questionnaires), molecular biodata (e.g. “-omics”), and potentially data gained via therapeutic protocols (drugs, neuromodulation (deep brain stimulation, transcranial magnetic stimulation, transcranial functional ultrasound, etc.)). Biological samples (e.g. blood, urine, stools, cerebrospinal fluid) from biobanks should be leveraged. Datasets should be from individuals with relevant disorders as well as healthy controls.
    • Propose a strategy to integrate and connect the datasets from different sources.
    • Outline an approach to inclusive and equitable practices, including data representation, including but not limited to gender, ethnicity, and age (e.g. paediatric and adolescent populations).
    • Adapt and extend a federated data platform by building on existing infrastructures proven effective in PPPs, including the AD Workbench3 and the EPND hub4 (made available via the pre-identified industry consortium). The adapted platform should leverage available resources (including standard operating procedures) from EPND. The adapted platform must be scalable and adaptable to curate high-quality, multimodal, retrospective, prospective and longitudinal data as mentioned under 1.1 and 1.2. It must enable data/sample discovery, access, and support AI analysis, while ensuring interoperability with other global data platforms.
    • Ensure high data quality by verifying the robustness of methodologies before integration into the adapted platform. This could be achieved by establishing a Data Quality Assessment Committee.
    • Implement fair and transparent governance for data- and sample-sharing including model interpretability, data provenance, and traceability of AI decision-making processes. Applicants must explain how they will develop a consensus on data sharing principles, complying with legal and ethical standards (e.g. General Data Protection Regulation (GDPR) and intellectual property rights (IPR)) and ensuring robust protection of data volunteers’ rights. For example, leveraging the Data Sharing Playbook and setting up a Data Access Review Committee.
• Activities under objective 2:
  • Collect new prospective multimodal and ideally longitudinal data from transdiagnostic cohorts, focussing on individuals affected by RM&I abnormalities in the relevant disorders, and ideally also collect biological samples. The datasets should close data gaps identified under 1.8 and be integrated into the adapted platform, meeting the same criteria described in objective 1.
  • Continue to recognise and fill data gaps to expand and maintain the adapted data platform, keeping it current with technological and scientific advancements. Whenever appropriate, utilise AI/ML, such as synthetic data generation, image analysis, natural language processing etc., to enhance the dataset.
• Activities under objective 3:
  • The short proposal should propose an initial pilot clinical case study designed to test a scientifically robust and data-supported hypothesis on candidate markers and/or endpoints in RM&I symptom domains during the project’s initial year. It must include transdiagnostic populations from AD, MDD, and obesity. The case study must include as a minimum neurophysiological data (e.g. EEG or MEG) and brain imaging data (e.g. MRI, fMRI) from each subject. In addition, datasets should include as many parameters as possible from the list described in 1.2. The precise scope of the initial clinical case study will be developed by the full consortium during the preparation of the full proposal. (Additional case studies are described under 3.4).
  • In the first 6 months, prepare a systematic literature review (white paper) of the available potential markers in RM&I symptom domains in relevant disorders to support hypothesis generation and subsequent testing. This should be kept up to date throughout the action.
  • Apply suitable statistical methods, advanced computational analytics (including, whenever appropriate, AI/ML as part of the statistical/analytical toolbox), modelling, and simulation across the multimodal data in the adapted platform to cluster biologically similar subjects across disorders/diseases, stratified independently of their conventional diagnostic classification. This should enable to identify and confirm clinically significant, quantitative candidate markers for RM&I symptom domains in relevant disorders, incorporating hypothesis-driven and data-driven approaches. It should also establish the foundation for a new transdiagnostic framework based on phenotypes/biotypes to enable detection of factors for susceptibility, risk stratification, diagnostic precision, disease monitoring, treatment response prediction, and overall patient outcomes. In addition, it should elucidate the biological underpinnings of the relationship between psychiatric and physical health (e.g. for obesity, understanding the interplay between metabolic disturbances, mental health and eating behaviours).
  • Test putative transdiagnostic markers and endpoint hypotheses derived from 3.2 and 3.3 through additional non-sequential pilot clinical case studies, incorporating insights from stakeholder consultations as mentioned in objective 4. These case studies must test the same transdiagnostic marker/endpoints in separate pre-defined patient populations in two or more of the relevant disorders to strengthen the transdiagnostic approach. As a preference, the three priority disorders should be included in at least one study each as a lead indication. For instance, one study with AD, one with MDD and one with obesity as the lead indication, each including at least one additional relevant disorder. Each study must include neurophysiological and brain imaging data and include as many other parameters as possible from the list outlined under 1.2. Studies must be powered sufficiently to allow analyses both within and across the included disorders. All results must be integrated into the adapted platform. The studies should enhance the platform’s ability to accelerate hypothesis testing of new candidate markers and endpoints within a defined context of use (e.g. patient selection, diagnosis, or treatment monitoring) in representative patient populations. These studies must not involve the development of new in vitro diagnostic tools or digital sensors. The resulting evidence from pilot case studies (including the initial pilot clinical study under 3.1) should:
    • be verifiable and applicable for patient stratification and/or monitoring in future clinical trials;
    • demonstrate clinical utility to foster new patient pathways and clinical guidelines;
    • contribute to bridging the gap between health care needs and capacity.
• Activities under objective 4:
  • Create an efficient collaborative platform to support seamless communication and collaboration among key stakeholders in the field of the relevant disorders. This includes innovators, researchers, clinicians, people with LE, carers, patient advocates, HCPs, regulators, scientific societies, HTA bodies, payers, and policy makers to collectively define and implement a new framework for the diagnosis and treatment of these disorders.
  • Form advisory/working groups comprising different stakeholders to support activities under objectives 1, 2 and 3, and co-create solutions. Ensure active and meaningful participation of people with LE, carers, and advocacy organisations throughout the activities and governance.
  • Engage with regulators (via experts with relevant expertise), e.g. EMA and/or national competent authorities, proactively initiating early consultations as appropriate. This should set the basis for continuation towards full validation of markers and endpoints beyond the action. Applicants are expected to consider the potential regulatory impact of the results and as relevant, develop a regulatory strategy and interaction plan early on to define a strategic approach to evidence collection and analysis where feasible (including case studies under objective 3) for generating appropriate evidence, as well as engaging with regulators in a timely manner (e.g. national competent authorities, EMA Innovation Task Force, qualification advice). Similarly, appropriately engage with HTA bodies and payers on the value of new transdiagnostic framework, candidate markers and endpoints when used to support claims of effectiveness of new therapies, paving the way for future reimbursement.
  • Craft evidence-based clinical guidelines through consultations with stakeholders, including people with LE, regulators, HTA bodies, payers, and medical organisations. Achieve consensus on best practices for implementing the new transdiagnostic framework. Develop recommendations and provide proposals for updates to the classification of disorders.
  • Design and implement a comprehensive training programme for HCPs to adopt the new transdiagnostic framework. Create educational materials and implement trainings for people with LE, families and carers in multiple languages, ensuring readiness across the healthcare system for the paradigm shift in healthcare delivery throughout Europe and helping to reduce stigma.

Eligibility Criteria

• Entities eligible to participate:
  • Any legal entity, regardless of its place of establishment, including legal entities from nonassociated third countries or international organisations (including international European research organisations) is eligible to participate (whether it is eligible for funding or not), provided that the conditions laid down in the Horizon Europe Regulation have been met, along with any other conditions laid down in the specific call/topic.
  • A ‘legal entity’ means any natural or legal person created and recognised as such under national law, EU law or international law, which has legal personality and which may, acting in its own name, exercise rights and be subject to obligations, or an entity without legal personality.
• To become a beneficiary, legal entities must be eligible for funding.
• To be eligible for funding, applicants must be established in one of the following countries:
  • the Member States of the European Union, including their outermost regions:
    • Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden.
  • the Overseas Countries and Territories (OCTs) linked to the Member States:
    • Aruba (NL), Bonaire (NL), Curação (NL), French Polynesia (FR), French Southern and Antarctic Territories (FR), Greenland (DK), New Caledonia (FR), Saba (NL), Saint Barthélemy (FR), Sint Eustatius (NL), Sint Maarten (NL), St. Pierre and Miquelon (FR), Wallis and Futuna Islands (FR).
  • countries associated to Horizon Europe;
    • Albania, Armenia, Bosnia and Herzegovina, Canada, Faroe Islands, Georgia, Iceland, Israel, Kosovo, Moldova, Montenegro, New Zealand, North Macedonia, Norway, Serbia, Tunisia, Türkiye, Ukraine, United Kingdom.

For more information, visit EC.