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.

The vision of Immune Safety Avatar (imSAVAR) is to develop a platform for integrated nonclinical assessments of immunomodulatory therapy safety and efficacy. Existing nonclinical models do not adequately represent the complexity of the immune system and its interactions in both immunoncology and immunmediated diseases. They also do not accurately reflect the diversity of response to new therapies that is seen in clinical medicine. We will, thus, constantly refine existing and develop new nonclinical models with the final goal of validation aiming at: (i) understanding the value of nonclinical models for predicting efficacy and safety of immunomodulators incorporating cellular high throughput assays, complex organisms models and micro physiological systems, (ii) developing new endpoints and better monitoring approaches for immune function tests, and (iii) designing cellular and molecular biomarkers for early detection of adverse effects. The platform imSAVAR will be based upon case studies for prioritized therapeutic modalities and has been built around institutes of the Fraunhofer-Gesellschaft which has strong track records in applied science and in particular toxicology. The consortium will improve the prediction of the transferability of safety and efficacy of immunomodulators from pre-clinical models to first-in-human studies in collaboration with the private sector, pharma, regulators and technology providers. We will share experience on customized models that can be deployed (w.r.t. the 3Rs principles), establish the necessary infrastructure, conduct the analyses and provide wider disease domain expertise. This conjoint effort assures that the platform imSAVAR constantly benefits the field of immune safety evaluation, and will generate opportunities for European businesses. A guiding principle of this consortium is the meaningful engagement of multiple stakeholders including patients and regulators. A multi-stakeholder community will be established.

Sepsis and COVID-19 are both placing a major burden on societies and populations worldwide. Deregulated host response to infection is the hallmark supporting the routine use of corticosteroids (CS), a low-cost and highly efficient class of immuno-modulators, in sepsis/COVID-19. Stratifying patients based on individual immune response may improve the balance of benefit to risk of CS treatment. This proposal will integrate different approaches to define the CS sensitivity/resistance of individual patients. The partners will elaborate signatures from different characterizations of biological systems in patients with sepsis/COVID-19. Targeted approaches will define whether characteristics at the level of DNA, RNA, proteins such as cytokines and hormones, or metabolite compounds, support predicting individual patient’s CS responsiveness. Methods of artificial intelligence will integrate the high dimensional multi-level data from previous studies of this consortium and from data to be newly generated. An exploratory adaptive trial will include patients with sepsis/COVID-19 in multiple arms based on novel CS responsiveness signatures to be tested. Within each biomarker-defined cohort, patients will be randomized to receive corticosteroids or placebo allowing the evaluation of the efficiency of signatures elaborated by the partners. Signatures of CS responsiveness will be integrated for predictive enrichment of CS sensitivity and resistance of each individual patient defining personalized treatment rules, and thereby improving their chance to survive in good health. We will test the robustness of the personalized corticotherapy across subsets of patients based on gender, social categories and ethnicity. We will also ensure that the proposed personalized corticotherapy for sepsis/COVID-19 can be accessed for routine care of patients in low- and middle-income countries.

The ARTEMIs project aims to consolidate existing computational mechanistic and machine-learning models at different scales to deliver ‘virtual twins’ embedded in a clinical decision support system (CDSS). The CDSS will provide clinically meaningful information to clinicians, for a more personalised management of the whole spectrum of Metabolic Associated Fatty Liver Disease (MAFLD). MAFLD, with an estimated prevalence of about 25%, goes from an undetected sleeping disease, to inflammation (hepatitis), to fibrosis development (cirrhosis) and/or hepatocellular carcinoma (HCC), decompensated cirrhosis and HCC being the final stages of the disease. However, many MAFLD patients do not die from the liver disease itself, but from cardiovascular comorbidities or complications. The ARTEMIs will contribute to the earlier management of MAFLD patients, by prognosing the development of more advanced forms of the disease and cardiovascular comorbidities, promoting active surveillance of patients at risk. The system will predict the impact of novel drug treatments or procedures, or simply better life habits. The system will therefore not only serve as a clinical decision aid tool, but also as an educational tool for patients, to promote better nutritional and lifestyle behaviors. In more advanced forms of the disease, therapeutic interventions include TIPPS to manage portal hypertension, partial hepatectomy, partial or complete liver transplant. ARTEMIs will contribute to predict per- or post-intervention heart failure, building on existing microcirculation hemodynamics models. The model developers will benefit from a large distributed patient cohort and data exploration environment to identify patterns in data, draw new theories on the liver-heart metabolic axis and validate the performance of their models. The project includes a proof-of-concept feasibility study assessing the utility of the integrated virtual twins and CDSS in the clinical context.