AcceleRating the Translation of virtual twins towards a personalised Management of fatty liver patients
SUMMARY
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 (MASLD). MASLD, 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 MASLD patients do not die from the liver disease itself, but from cardiovascular comorbidities or complications.
The ARTEMIs will contribute to the earlier management of MASLD 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.
CHALLENGES
The main challenges that ARTEMIs will address to bring Virtual Twins closer to the clinical practise are:
▪ The focus on relevant clinical use cases, gearing complex computational models to give responses to specific clinical questions, providing actionable knowledge to the clinicians;
▪ The integration of models ranging from the molecular to cell, tissue, organ level and capturing intracellular signalling, intercellular communication, tissue components adaptation, blood perfusion and transport in and between organs, in multi-organ systems, for dynamic simulations of the disease evolution and prediction of comorbidities;
▪ The capacity of virtual twins to exploit patient real-world data to simulate the individual patient pathophysiology, and thus be suitable for personalised patient management when input with his/her multimodal (patient history, lab studies, digitalised histology, imaging, omics) data.
▪ The access to the necessary multimodal data, at the required volume and quality, representative of different populations of patients and disease management, using a common data model and complying with data protection and data sovereignty requirements;
▪ The evaluation in a relevant context of use, of the utility of these models to support and improve patient management, implying integrating the models in comprehensible
decision support tools, providing access to the right information at the right time without deviating the clinicians from their routine clinical workflow, and leveraging clinicians´ trust on the accuracy and reliability of the models
▪ The involvement of clinical Key Opinion Leaders (KOL) for guiding the developments and leading the POC feasibility studies of the Virtual Twins, to ensure clinical relevance of
the results and raising acceptance in the clinical community;
▪ The involvement of patients to ensure acceptance of the use of Virtual Twins in the management of their disease;
▪ The involvement of industrialists including SMEs, experienced in bringing new technologies to the healthcare market (regulatory aspects, user interface design, interoperability with IT systems, …), which can accompany the commercial potential of Virtual Twins for patient management