Scaling from microphysiological systems to humans using scalable digital twins

Type 2 diabetes and liver inflammation (NASH) are some of our most studied diseases, often using overfeeding experiments in rodents, despite known differences compared to humans.

Therefore, we and others have pioneered advanced experimental alternatives, including ex vivo and multi-organoid microphysiological systems (MPS).

Nevertheless, important differences compared to humans will always remain, both regarding functionality, dynamics, volumes, and missing organs.

For this reason, we have also developed world-unique digital twin models, which simulate metabolism in and between the main organs in humans.

Recent publications show proof-of-principle for how the twins can be scaled, to make the computer copy of the MPS transform to a human.

In this project, we will improve these preliminary results by: i) validation of the intracellular mechanisms, by experimentally testing predictions for e.g. new drugs; ii) scaling up the volumes and re-adding the removed organs in the computer, and compare to human data; iii) quantification of the improvements with this new approach, e.g. for knowledge-driven drug development, so we can disseminate and spread easy-to-use versions among researchers, pharma, and regulatory agencies.

Because we have developed the only existing data-driven and multi-organ digital twin, and are well-networked in the 3R community, we can both pioneer this new generalizable replacement possibility, and spread it in a new replacement-based research infrastructure.