Digital Twin Technology to Predict Individual Response to Pharmacological Treatments in Ovarian Cancer

Personalised medicine (PM) is a novel approach to medical care that customises each treatment not just to the disease but also to the patient.

While the advantages of this framework are widely recognised and its development is acknowledged as a fundamental societal challenge by the H2020 program, clinical applications are still limited, due to the increased complexity and costs, with respect to the current standard of care.

ITHACA aims at developing an innovative PM toolkit that, relying on an advanced computational simulator, innovative in-vitro techniques and standard clinical data could provide an efficient and ethical framework for the screening of different ovarian cancer therapies and the identification of the most effective for each subject.

This project builds on my experience with computational modelling and multidisciplinary research environments, to develop a programmable in-silico simulator capable of reproducing the patient-specific response to standard and novel ovarian cancer treatments.

This tool will be extensively validated with both standard and primary cell lines and will then be used to determine patient-specific therapy outcome (RECIST 1.1 criteria) in women affected by ovarian cancer. Additionally it will be integrated in a novel bioreactor system for the in-vitro study of metastasization in HGSC.

This compound device will allow for automatic treatment optimisation within an accurate and unique experimental model for this disease, and will thus provide an effective platform for personalised treatments development.

Funding of this action will be a fundamental opportunity of career development, that will support my training in a highly innovative, interdisciplinary field while fostering the achievement of professional independence.

Additionally the results of ITHACA will lead to high profile, impactful publications and provide the scientific community with enabling tools for the extensive application of PM in the clinic.