Osteoarthritis is a common and debilitating disease that affects more than 15% of the population, with increasing prevalence due to an aging population.
There is significant variability between patients, and no drugs are available that can fundamentally alter the disease.
Personalized models linking mechanical load (patient movement) to biology (cell regulation) allow for the exploration of the best treatment strategy, ranging from tailored physiotherapy to identifying potential (personalized) pharmaceutical targets.
A multi-scale model encompassing both elements is being validated using AI techniques and lab-on-chip technology. Work initiated during the previous DigiTwin4PH project includes the development of personalized models based on existing cohort data and the start of in silico clinical studies.
These aspects of validation and in silico clinical studies are being further elaborated in the current DigiTwin4PH project.
Special attention will be paid to the connection between measurable biomarkers (in blood or in patient movement patterns) and in silico parameters obtained from the in silico studies.