The PREFER project will deliver an overview and evaluation of preference elicitation methods to be applied in the entire drug life cycle, i.e. in the early stages of identifying medical needs, in clinical testing, to guide decisions on reimbursement and to make decisions on withdrawal of drugs from the market. A broad array of (combinations of) patient preference methods will be tested prospectively in a large number of case studies. The availability of large patient cohorts will enable to test new methods or deviations from existing methods in a randomized manner, by comparing well-known methods with newer ones. The use of simulation studies will both contribute to smarter design of case studies and to exploring the sensitivity of outcomes of preference studies. Based on discussions with a broad representation of stakeholders e.g. patients, patient organisations, regulatory authorities, HTA bodies and reimbursement agencies, suitable methods will be tested and their contributions to improved decision making will be discussed in recommendations adapted to the needs of all relevant stakeholders. The recommendations from PREFER are expected to lead to changed practices, in that industry will routinely assess whether a preference study would add value at key decision points in the medicinal product life cycle and, if so, implement patient-preference elicitation studies according to the PREFER project recommendations. The PREFER consortium consist of 16 industry partners and 16 academic and SME members including representation from academia, patient organizations, HTA bodies, reimbursement agencies, and project management.

This project aims to create a multi-scale patient-specific human virtual twin for patients who are eligible for CAR T cell therapy, which can be extended to cellular immunotherapies in general. The virtual twin will be fully integrated into the ecosystem under the Digital Europe Programme and will be available as a research-use-only prototype in relevant application environments with technology readiness level 6. The virtual twin will support decision-making throughout the patient's journey, from diagnosis to pre-treatments, cellular immunotherapy, and late patient monitoring. This will enable patients and health experts to identify the most effective course of therapy. The virtual twin for cellular immunotherapies will take multi-scale modelling to the next level by providing methodological concepts for integrating "living drugs" such as CAR T cells into the human virtual twin ecosystem. To achieve this goal, the project builds on existing data models for hematological malignancies and multiple myeloma (MM) to create a virtual twin for CAR T cell therapy for MM patients. The implementation process follows software development principles for software as medical devices, ensuring the exploitation of the research-use-only prototype. Patient's, SSH, gender differences, and other stakeholders' perspectives are fully integrated into the design, specification, and implementation process. Throughout the implementation process, a modular software architecture and extensible ontologies/terminologies are utilized, making the reference model easily transferable to other indications that can be treated with CAR T cell therapy or other cellular immunotherapies. Overall, the CERTAINTY virtual twin will provide decision support for patients and health experts, improving the effectiveness of cellular immunotherapies and advancing precision medicine.

Thera4Care represents a large unique consortium, gathering well-established European academic radiotheranostic centres, strong industry partners, European and medical societies focusing on training and education, a patient advocacy group spearheaded by an experienced project management group. Thera4Care aims to establish a European network of radiotheranostics centres able to rapidly develop and implement radiotheranostics tools and solutions to drive precision health. The overarching vision of Thera4Care is to revolutionise radiotheranostics procedures by establishing, implementing, and disseminating standardised scalable methods to produce and efficiently administer key radiotheranostics solutions. Thera4Care focus is on developing diagnostic and therapeutic ligands tailored to key disease areas (such as solid tumours), aligning with the growing significance of multi-modal radiotheranostics solutions dominated by radionuclide-based therapy and companion diagnostics. Thera4Care project is scheduled for 5 years and consists of 9 work packages. WP1 handles Project Management, WP2 focuses on Regulatory aspects, WP3 plans Preclinical studies, WP4 provides Supply chain, WP5 develops Clinical Trials, WP6 generates AI models, WP7 focuses on Dosimetry, WP8 develops Precision Imaging components, while WP9 focuses on Education and Dissemination. Thera4Care will advocate for the progress of radiotheranostics, with the goal of increasing medical knowledge and enhancing treatment efficacy for improved patient outcomes. Concurrently, aims to coordinate scientific initiatives across European landscapes, leveraging a comprehensive and multimodal approach to cancer therapy applicable to various cancer types. Lastly, will work on the establishment of sustainable supply and production networks and the upscaling of production methodologies and supply chains across the public and private sectors, to enhance the availability of radionuclides.

Hypertension, or high blood pressure (BP), is a serious medical condition, and the single biggest contributor to circulatory diseases which continue to dominate as the leading cause of death and morbidity across the EU. It accounts for almost 10 percent of all healthcare-related costs. Systolic hypertension leads to a broad variety of diseases with an immense impact on both patients and healthcare systems. HYPERMARKER will unleash the potential of pharmacometabolomics to provide a ‘smart’ prescription of antihypertensive therapy. Well-phenotyped cohorts from eleven European countries will provide metabolomic profiles and blood samples for pharmacometabolomic assessments to identify predictors of treatment response in hypertension using advanced AI and deep learning methods. Prediction models for individual treatment responses to antihypertensive medication will be clinically validated and refined through an innovative RCT across 4 sites in Europe. The result is a clinical decision support tool that will give clinicians the ability to make an informed selection of whether the patient they are treating will best respond to the use of angiotensin inhibition, calcium antagonists, beta-blockers, or a range of other existing drugs with evidence-based for BP control. To ensure sustainability, the project will also develop a framework for the uptake of this tool in routine care for patients with hypertension across Europe and beyond. HYPERMARKER will be implemented by a group of world-class scientists and clinicians from a diversity of disciplines who have collaborated multiple times and have a track record of leading key national and EU-funded initiatives to deliver high-impact results.