Computerised Tomography (CT) scan is one of the most common medical imaging performed in healthcare, Each year, 300 million CT scans are performed globally. Of which, around 180M include use of radiocontrast media (RCM). Contrast Enhanced CTs (CECT) create a significant environmental impact, namely: 42,000 tonnes of single use packaging, 900 Tonnes of surgical steel (needles), 90,000 tonnes of plastic tubing and 150,000,000 kWh in energy consumption. These generate on average 9.2 kg of CO2/ scan. In addition, CECTs generates 200,000 tonnes of iodine contamination in water/yr. This is a recognised form of ‘pharmaceutical pollution’. CECTs may also harm patients: needle insertion, toxicity of iodinated RCMs to kidneys (potentially kidney failure) and allergic reactions, which in some cases can be life-threatening. Healthcare systems are responsible for the 4.4% CO2 global emissions (2 Giga tonnes/yr). Of this, ~3 Mega tonnes/yr are generated from CECTs. The EU has declared its NetZero targets of by 2050 through the European Green Deal. We showed feasibility that artificial intelligence (AI, deep learning methods) can extract high level information from non-contrast CT scans and synthesise contrast ‘digitally’. This avoids the need to administer RCM for CECTs. We seek to develop and validate 5 uses cases of CT ’Digital Contrast’ during this Horizon award. By implementing ‘Digital Contrast’ for scans globally, we aim to reduce 30% of the CO2e and iodine RCM waste generated from CECTs by 2033. NetZeroAICT has a grand vision to define a reference framework for scalable development of AI health tools for a future of sustainable health systems. This builds on our prior efforts of AICT consortium, which was established to make CT imaging safer, more efficient, more equitable and more sustainable. NetZeroAICT will accelerate the EU’s trajectory towards NetZero and advance EU’s globally recognized leadership position on Healthcare sustainability.

The blood-brain barrier (BBB) is a major obstacle in treating diseases of the central nervous system (CNS) such as Parkinson's, Alzheimer's, Schizophrenia and brain cancer, affecting 180 million Europeans with less than 5% of current candidate drugs effectively reaching the brain. NAP4DIVE strives to revolutionize the traditionally expensive and inefficient drug development for these diseases by establishing advanced non-animal alternatives for testing and predicting nanoparticle (NP)-based drug delivery across the human BBB. This approach aligns with EU and global initiatives to reduce animal testing and advance human-based biomedical research models. The project will develop two complementary non-animal tools: a high-throughput BBB-on-Chip and an in silico model based on machine-learning (“NP Design Simulator”). A digital repository of optimized nanoparticle designs “NP Design Library” will be created to gather publicly available and newly obtained NP characterisation data, specialised for BBB delivery. The Design simulator screens thousands of NP designs to recommend the most promising ones, which will be tested in vitro on the microfluidic BBB-on-Chip with real-time measurement of barrier integrity. The accuracy and physiological relevance of both tools will be validated by the pharmaceutical partner through comparison with clinical and pre-clinical data. NAP4DIVE tools will reduce animal use in CNS drug development by up to 95% while saving 30 % of costs. By identifying nanoparticles for cross-BBB drug delivery and offering avenues for new effective treatment options, NAP4DIVE addresses one of the most pressing healthcare challenges of the century. A comprehensive HTA will demonstrate market readiness and cost-effectiveness of the tools, an ethical assessment will analyse harm reduction and engagement with regulators and policy makers will promote non-animal alternatives in preclinical testing on a larger scale.

Non-Communicable Diseases (NCDs) e.g. osteoporosis, osteoarthritis, joint & ligament wear, frailty fractures and associated complications in elderly are a severe threat frequently leading to a permanent decline of the patient’s general health and autonomy. NCD prevention is highly relevant to reduce the need for long-term care. The economic burden is expected to increase by 65% from 2020 to 2040 with a strong negative impact on patient’s quality of life. SmILE will provide a cross-sectoral, early risk detection methodology by holistic analysis of elderly patients’ health data. Co-creation with end-users and consideration of diverse needs, mental and physical abilities, living and socio-economic conditions as well as life-situation of older people are implemented. This includes training and enhanced stream of information to patients and other stakeholders. It will offer an AI-based patient analysis, incl. integration of smart wearables and medical devices incl. implants to support monitoring of general health status. Our approach will disrupt the cycle of frailty by minimizing physical decline and risk of re-fractures. To enhance the AI functions, new data sources will be established by instrumenting medical devices, mainly implants, to augment them into monitoring and actively supporting devices. SMILE will: (1) develop an integrated platform to collect, centralize, manage, analyze and share multimorbid geriatric patient data using technology such as AI & machine learning; (2) establish a pan-European bottom-up model of good governance of health data being patient-centered and patient-controlled in line with current regulations; and (3) establish a solution driven by the needs of citizens and patients of old age. SMILE will establish a new approach of outcome-monitoring to provide patient centered, personalized and integrated treatment of elderly NCD patients to improve quality of life, enable real empowerment and ease the financial burden in an ageing society.

Safe and Sustainable by Design approach requires an entire life cycle monitoring of toxicity of chemicals. However, current testing systems cannot mimic the exposure conditions related to each step and are not compatible with downstream in silico analyses. New sets of instrumentation that enables modular testing capacities with integrated data bridging and progressive in silico model development systems are needed. TOXBOX will provide a device based on a prototype developed in a H2020 project, PANBioRA, with a flexible microfluidic and instrument architecture to provide a plug and play testing platform to ease accessibility and interlaboratory validation. The system will incorporate the following tests, with modifications for each step of life cycle: automated cytotoxicity and genotoxicity tests, connected barrier/metabolic tissue couples with cytokine and real-time electro-chemical read-outs, flow cycle modules with environment mimicking conditions, a testing module based on zebrafish embryo with mechanical stimuli. The system will be validated using metallic 2D structures and nanoparticles, biocides, and known endocrine disruptors. Custom made functional polypeptides (novel biocides) will be used to cover the design phase. Progressive in silico models for long term effects will be iteratively developed and used to predict each new group to be tested until good predictability is achieved with new chemical formulations (comprehensive risk assessment). A data management platform that enables interfacing with the available databases will be developed. After interlaboratory validation of the device by 4 partners, a standardization folder will be prepared, to make the device available for testing with accessibility at all stages of material life cycle assessment to different stakeholders. TOXBOX aspires to bring forth an instrument that will provide reliable toxicity data in relevant conditions for each chemical and enable reliable in silico model development.

Due to population lifestyle changes, i.e. obesity, diabetes and aging population, chronic wounds (CW) which fail to follow the typical healing process is a major medical socioeconomic challenge. Current wound management is clearly insufficient and advanced therapies failed in keeping their promise of reliable skin regeneration. The aim of FORCE REPAIR is to develop a smart and multifunctional wound dressing providing pro-regenerative environment and mechanical stability to treat CW. Thus, FORCE REPAIR will combine state-of-the-art technologies in a biological scaffold tailored to patient’s needs: (1) Antibacterial and bioadhesive bioink with antibiotics and anti-inflammatory loaded nanocapsules, (2) Elastin like polypeptides promoting innervation and vascularization (3) Wharton Gel Complex preventing oxidative stress and boosting key extracellular matrix proteins. Also, the dressing activated by UV light will induce contractile force to help wound closure and activate skin regeneration. A customized 3D bioprinter with a user-friendly 3D trajectory software will help to strategically placed the biological compounds to timely address and mitigate the degenerative process occurring in CW, i.e. infection, inflammation, tension forces to promote skin regeneration. The 3D printed dressing will be tested in relevant in vitro model with a human exudate library and testing relevant key healing steps (i.e. re-epithelization, angiogenesis, cell proliferation…). Selected candidates will be tested in vivo on pig CW models and mice with bacterial infection. To ensure translation to clinical practice and reach patients, regulatory framework, HTA and a business model will be defined for a viable exploitation strategy that will decrease economic burden of wound care management and improve patients’ QoL. Finally, to ensure market acceptance health professional will guide the development of FORCE REPAIR to offer a dressing that treat efficiently CW and can be used by medical staff.