BIGPICTURE, a pathology-led consortium, has the vision to become the catalyst in digital transformation in Pathology. Our mission is to create the first European GDPR compliant platform, in which both quality-controlled Whole Slide Imaging (WSI) data and advanced Artificial intelligence (AI) algorithms will exist. The BIGPICTURE platform will be built on existing assets of ELIXIR EU data infrastructure, including the federated European Genome-phenome Archive (EGA) technology for managing the exchange of confidential information between contributors and users. The consortium will use Cytomine, an established open-source, cross-platform framework to develop unique tools for access to WSI, including annotations and visualisation of algorithm results, while we will develop new and generic models to facilitate AI development and mining of WSI data. By engaging and building consensus with all the relevant stakeholders, we will contribute to the development of a regulatory framework for digital slides and AI-based methods. Finally, BIGPICTURE envisions sustainability of its platform through a community- based model which relies on reciprocity, value creation and inclusiveness. To achieve our vision, we have brought together Europe’s leaders in the field of computational pathology who have access to national and European high-performance computing infrastructures as well as Europe’s fully digitalised pathology departments. Additionally, the consortium has currently access to approximately 4.5 million clinical WSI covering a wider range of indications through 17 partners and 23 third parties from the largest European and international pathology and trial groups. Our consortium is further strengthened by the presence of the European Society of Pathology, Digital Pathology Association, FDA and 9 SMEs as partners, while we are further supported by professional societies, and patient advocates.

VICT3R, a public-private partnership running under the European Innovative Health Initiative, aims to significantly reduce the number of animals used in experimental studies performed during the nonclinical drug and chemical safety evaluation by replacing the animals of the concurrent controls groups (CCGs) with Virtual Control Groups (VCGs). These VCGs will be generated by means of state-of-the-art statistical or artificial intelligence (AI) approaches that optimally exploit the wealth of historical data from control animals accumulated over decades by pharmaceutical companies and other relevant industrial and academic sectors. The VCG concept was conceived and prototyped during the recently finished eTRANSAFE IMI2 project for its application in the nonclinical safety assessment of the pharmaceutical industry. A preliminary evaluation of the VCG concept carried out in the eTRANSAFE project demonstrated that it is generally feasible, yet scientifically and operationally challenging and must therefore be refined before its adoption for regulatory hazard and risk assessment. The main challenges consist of adequate data collection and curation, identification of key variables to achieve optimal matching between VCGs and CCGs, and validation of procedures including compliance with Good Laboratory Practice (GLP). These challenges will be systematically tackled in VICT3R for achieving the full development and regulatory acceptance of the VCG concept. VICT3R will collect, curate and analyse large data sets of control animals from different species to produce a large high-quality database. The database will be made available to VICT3R partners, regulators, and policy makers with the purpose to allow maturation of the VCG concept and to prove its validity, reproducibility and robustness. While VICT3R will be primarily focused on repeated dose toxicity studies, the extension of the VCG concept to other types of studies involving animals will also be tackled. VICT3R will promote that its database and software platform is maintained and expanded long term.

Drug discovery is a time consuming, expensive and risky process. Each drug to market must undergo safety/ toxicity testing in animals which yields thousands of tissue sections that currently must be assessed manually by a trained veterinary pathology. However, there is a looming crisis due to the lack pathologists. The PATH-TOX consortium is led by Irish SME, Deciphex Ltd, and includes key partners such as Janssen Pharmaceuticals in Belgium and Pathology Data Systems Ltd a Swiss based SME. They are creating PATHOLYTIX-TOX a computer aided diagnostic system to streamline the pathology review process. Using state-of-the-art artificial intelligence (AI) image analysis tools it will automatically identify the normal and abnormal tissues upfront, allowing the pathologist to focus mainly on the abnormal cases, hence accelerate workflow. To date, we have built a working prototype of the image analysis engine, which can identify abnormal tissue in liver. In this project we will optimise the engine further to improve performance and expand its use into other tissues. We will also develop other features such as the user interface, data management and cloud framework. Once developed, we will perform a comprehensive validation and benchmarking study to compare to manual pathology assessment. There is a large, growing, global target market for PATHOLYTIX-TOX. Pharma and CROs worldwide routinely perform thousands of animal toxicology tests each year, who are potential customers. With regards to competitors, there is no direct competition. Competitors offer individual components of the pathological workflow, not fit-for-purpose solutions for the toxicology pathology market. Overall, PATHOLYTIX would have a big impact on drug development, by reducing the time required for pathology review and associated costs. This should have a knock on effect for the global and EU markets, reducing time and cost for new drugs to get to market.

Routine diagnosis on biopsies and tissues is performed by expert pathologists. However, the pathology sector faces a major crises, due to soaring numbers of biopsies for diagnosis, coupled with decreasing numbers of trained pathologists, which is affecting patients. Technology can alleviate this crises by using computer aided diagnosis (CAD) to increase productivity, reduce costs and streamline workflow. Deciphex Ltd is an Irish company developing an innovative Computer Aided Diagnosis (CAD) system which will revolutionize the medical diagnosis pathology sector. Our ‘PATHOLYTIX’ system uses state-of-the-art artificial intelligence techniques to automate the laborious and costly pathology assessment (currently done via microscope). By transforming this centuries old clinical practice, we aim to become a market leader in new CAD industry. We currently have prototype technology and preliminary data, to identify different tissue and cell types. In this feasibility study we will perform pilot studies in a US based clinical pathologist, to demonstrate our tool can identify various diseases. In Phase 2 of the SME Instrument, we will undergo additional development, extensive validation, and build the data management framework and user interface. There is a large global target market for PATHOLYTIX, which is growing each year driven by an increasing population and higher prevalence of diseases (such as cancer). Hence hospitals and clinics worldwide process hundreds of millions of biopsies per year. With regards to competitors, there is no direct competition. Competitors offer individual components of the pathological workflow, not fit-for-purpose solution like PATHOLYTIX. Overall, PATHOLYTIX would have a big impact on the pathology sector, by increasing productivity and reducing costs. This should have a knock on effect for the global and EU markets, particularly for patients as diagnosis becomes more efficient.