There is a very high need for improving the management of pain. Acute and persistent pain of different origins represent a common medical, social, and economic burden, and its pharmacotherapy is often inadequate. To advance management of pain patients and support decision making in clinical practice, more predictive assessments of treatment success are needed. The development of analgesics is onerous because promising preclinical data often do not translate into the clinic. Improved pharmacodynamic biomarkers could define whether nociceptive signalling is adequately modulated by a new drug, so increasing the chance of successful translation and greatly reducing the risk in initiating clinical development. Further, the pathophysiology of chronic pelvic pain indications is poorly understood and no adequate preclinical models are available, precluding focused preclinical research and leaving affected patients with little hope of relief. IMI-PainCare aims at making advances in these three pain areas in a complementary manner. Three subprojects will address specific scientific challenges. Subproject PROMPT will identify Patient Reported Outcome Measures as tools to standardise assessments of treatment success of acute and chronic pain in Real World conditions and controlled trials, and so improve its management; subproject BioPain will validate the translatability of pharmacodynamic biomarkers and PK-PD modelling in pain pathways of healthy subjects and preclinical species, thereby offering tools to improve drug development; subprojectTRiPP will identify biomarkers and novel therapeutic pathways of clinical phenotypes of patients with chronic pelvic pain, which after back-translation, can improve how preclinical models reflecting human diseases. The goal of IMI-PainCare is to improve the care of patients with acute or chronic pain by providing a toolbox to streamline the development process for novel analgesic drugs and to improve treatment quality in clinical practice.

Data-driven innovation will bring enormous benefits for citizens, affecting all sectors of activity and the daily lives of all Europeans. It will enable, for example, AI-driven services to find causes for diseases or share attributes of individuals’ identity to quickly participate, for instance, in social or education activities across Europe. However, identity compromises, leakages or abuses of this kind of data pose serious threats that may compromise the proliferation of data spaces involving personal data, and reduce the opportunities offered by the data economy. The goal of the TrustED project is to design and develop a robust self-sovereign identity management method with advanced features as well as a set of combined privacy enhancing technologies (PETs) to enable TWO highly scalable and reliable services on personal data within data spaces with privacy guarantees that exceed the requirements of the GDPR, eIDASv2 and EUDI wallet: (1) A trustworthy federated learning service combined with several PETs that will allow AI-powered studies of siloed datasets with privacy guarantees. (2) A scalable and reliable self sovereign identity management service combined with methods for AI-based document validation, multimodal biometrics and ZKP-based selective disclosure techniques, that will enable electronic attestation, revocation of credentials and sharing of specific attributes of identity in a privacy preserving way. The components will be piloted, demonstrated and validated in a minimum viable data space that follows the implementations and deployments defined under the EDC Framework, which is powered by the specifications of the Gaia-X AISBL Trust Framework and the IDSA Data space protocol. The consortium is composed of 10 interdisciplinary partners: 2 Research Organizations, 3 SMEs, 2 Large Entities, 2 NGOs and 1 Clinical partner with extensive experience and expertise to guarantee the correct performance of the activities and the achievement of the results.