APOEɛ4 has long been known as a risk factor of LOAD, yet the biological mechanisms through which it acts remain largely unknown and affect both the vasculature and the brain. This complexity and pleiotropic influence demands an integrated hypothesis-free approach to embark on a fundamental study of the gene, the phenotype and modulation by other risk factors. ADAPTED proposes to leverage extreme genotypes from consortium cohorts dating back more than 25 years to generate, and use existing, lines of human induced pluripotent stem cells (iPSC). These cells, with blood cells, will form the backbone of the research programme. We will differentiate them to the most relevant cells for APOE and AD studies and with gene editing create bespoke homozygote ɛ3 and ɛ2 cells for a highly focussed effort on APOE biology. A series of experiments including neuron-astrocyte and macrophage-endothelium co-culture and Organ on a Chip models combined with state-of-the-art omics including quantitative proteomics assays will generate data for rigorous integrated analysis to uncover new signalling pathways related to APOE. Lipid homeostasis, endocytosis, metabolism and immune systems pathways will be investigated in a broad approach. The findings are expected to lead to identification of new treatment approaches and blood based AD signatures with a temporal dimension from the earliest stages of risk identification and progression through MCI to AD. Testing and validation of biomarkers will be performed by examining their influence and predictive capacity in a longitudinal ADAPTED cohort harmonised using a combination of approaches for marker and diagnostic consistency. The impact of this work can be expected to include seminal new finding to illuminate the research path towards new diagnostics and therapies to attenuate the rising tide of suffering from AD. The vision is a follow on with clinical proof of concept validating utility of the results within two years of the end of ADAPTED.

The aim of ROADMAP is to provide the foundation for a Europe-wide real world evidence (RWE) platform in AD by piloting multi-modal data integration tools and engaging with all key stakeholder groups for consensual definition of patient outcomes, tools and methods that are actionable and relevant. ROADMAP will leverage best practice and exploit synergies with other projects and initiatives at the national and European levels in pursue of scalable and transferable solutions for dataset characterisation, outcome classification, data standards, data sourcing, software application and guidelines on the handling and interpretation of RWE data. In parallel, the project will deepen understanding of the ethical, legal and social implications (ELSI) and health economics (HE) impact of a RWE approach for a meaningful contribution to the Big Data for Better Outcomes programme in IMI2. The ROADMAP programme will consist of 8 integrated work packages by which the Consortium will work to (1) identify relevant AD outcomes and progression markers; (2) identify and pool AD-related RWE across data sources; (3) develop and validate disease progression models combining diverse datasets and strategies; (4) develop risk/value-based costing and health economics models for HTA/regulators, service providers, industry and carers; (5) establish stakeholder-based guidelines for RWE; (6) develop a communication strategy focussing on the needs of patients and professionals; (7) develop an ELSI framework for the development and application of RWE in AD. The ROADMAP consortium brings together outstanding expertise, experience and traction in the field, including many of Europe’s top institutions, authorities, companies, experts in AD, biomedical informatics and analysis of epidemiologic and routinely collected health data sources, providing direct connections to key initiatives such as DPUK, EMIF, EPAD and others.

Background: Long term conditions require monitoring of patients, traditionally conducted in the clinic, to monitor treatment effects, adverse events and disease course. This can be inefficient and cumbersome – clinic visits may be too infrequent to identify individuals at risk of significant changes in disease state (e.g. relapse) and place unnecessary burden on patients and providers. Smartphone and wearable technologies have led to an exponential growth in the amount of information which can be collected on patients unobtrusively and at low cost. Sensors collect data passively, and active monitoring, using experience sampling, provides information on multiple parameters. Such technologies could monitor long term outcomes of patients, at scale, and provide fine-grained information on outcomes, available in real time and at low-cost, enabling services to offer more responsive and efficient care. Whilst there is growing interest in the application of RMT in health, the field is in its infancy. The private-public partnership fostered by IMI2 is an ideal way to overcome the inherent challenges of this field. Our ultimate goal is to improve patient outcomes through remote assessment. To achieve this we will create a pipeline for developing, testing and implementing RMT in depression, multiple sclerosis and epilepsy. The pipeline will include a data management and modelling infrastructure applicable to other disorders and with the flexibility of design to be future-proofed against further technological innovation. We will provide data on implementation barriers and facilitators gleaned from patients, clinicians, regulator and payers which will optimise the pathways for regulatory approval and uptake. Program of Work: In order to test the feasibility and predictive power of RMT four main components are required: (1) Excellent project oversight, management, and a dissemination and exploitation strategy;

Fatigue and sleep disturbances are two common and disabling symptoms that affect patients with neurodegenerative disorders (NDD) and immune-mediated inflammatory diseases (IMID). These symptoms are major predictors of poor quality of life and increased healthcare cost. Current questionnaire-based approaches to measure these symptoms have key limitations preventing them from being used as reliable endpoints in clinical trials to evaluate the effect of therapies. IDEA-FAST aims to address these issues by identifying novel digital endpoints for fatigue and sleep disturbances that will provide more objective, sensitive and reliable measures of the severity and impact of these symptoms in ecological settings. Such digital endpoints will not only help to gain insight into the underpinning mechanisms of fatigue and sleep disturbances, but will also vastly improve the efficiency of clinical trials, ultimately reducing the time and cost to bring new therapies to patients. To identify these digital endpoints, we will follow the recommendations of the Clinical Trials Transformation Initiative (CTTI). We will identify the characteristics that fatigue and sleep disturbances will have impact, then select the digital measures (endpoints) to quantify them, followed by choosing the appropriate digital device/technology accordingly. We will then perform a pilot study to prioritise a few of these candidate digital endpoints for validation. We will test the performance of these digital endpoints in two NDD and four IMID in a large longitudinal study during which extensive relevant clinical data will be collected. If these digital endpoints were validated, we will seek support from EMA/FDA for their qualification. Patient users’ perspective, ethical, data privacy, legal and other regulatory issues will be taken into consideration in all aspects of our proposal. The resultant digital biobank from the longitudinal study will become an invaluable resource for future exploitation.