Can life be predicted? Strituvad and digital twin technologies presented at Pharmaday 2022

The second edition of the event for academics and industries organized by the Department of Health and Drug Science of the University of Catania has been held on 1 June 2022


Can life be predicted? With this question Prof Viceconti from partner University of Bologna introduced his opening lecture on modeling and simulation technologies in medicine in general and for drug development, highlighting the opportunities that these technologies can offer to researchers, industries, healthcare professionals and, ultimately, to patients.


In silico technologies, and in particular digital twin applications, can be used for drug trial design, augmented randomized controlled trials, and for virtual placebo and virtual follow-up designs among others. Another example of in silico model is the agent-based model UISS-TB developed by Prof Pappalardo from the partner University of Catania, chair of the first session of the Pharmaday 2022.


The Strituvad project has brought different research groups together, including the team of Prof Juarez from the University of Sheffield, to develop a design for augmented in silico clinical trials with a virtual cohort of tuberculosis patients to be used for the credibility assessment of the UISS-TB simulation platform, presented during the second part of the workshop.


Pharmaday also brings together students and industries to foster the dialogue between the different key players in the R&D field. In the afternoon, some spin-offs that formed from research in the academia were presented, like the newborn MIMESIS, an innovative startup for the design, development and marketing of high-tech simulation software for evaluating the efficacy and safety of medicinal products.


“An event like this shows and advocate for the need of an open dialogue between the academia and the industry of such highly innovative fields like in silico medicine – Prof Pappalardo commented – in this way we can break down the barriers to the adoption of in silico technologies and speed up the development of new and better treatments for patients.”




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