Evolving Organs-on-Chip from developmental engineering to “mechanical re-evolution”

Descripción del proyecto

Regeneración tisular mediante mecanobiología

Muchas enfermedades siguen siendo intratables porque los científicos carecen de modelos fiables para estudiar cómo se desarrollan y regeneran los tejidos. Los métodos tradicionales de laboratorio no consiguen reproducir las complejas fuerzas mecánicas que influyen en la formación de tejidos, lo cual limita los avances en medicina regenerativa. Aunque la ingeniería del desarrollo ha avanzado imitando el desarrollo embrionario, ignora en gran medida las fuerzas mecánicas como factor clave. Los actuales montajes «in vitro» funcionan de manera fija, sin tener en cuenta cómo responden naturalmente los tejidos a su entorno mecánico y cómo lo modifican. El proyecto EvOoC, financiado por el Consejo Europeo de Investigación, pretende transformar este planteamiento desarrollando plataformas inteligentes de órgano en chip que apliquen, controlen y ajusten activamente las fuerzas mecánicas. Al integrar la microfabricación, la mecanobiología y el aprendizaje automático, el equipo de EvOoC podría allanar el camino a terapias revolucionarias en la reparación de tejidos.

Objetivo

EvOoC aims at developing smart mechanically active Organs-on-Chip platforms as clinically relevant in vitro setups to unravel mechanisms underlying tissue regeneration and progression of unmet diseases.
A decade ago, developmental engineering (DE) proposed to model in vitro clinically relevant tissues replica by recapitulation of embryonic developmental events. Despite physical forces have recently been suggested as main driver of developmental processes, mechanical conditioning never prevailed as key DE strategy. This is related to a lack in current in vitro mechanobiology setups, mainly based on open loop systems, which disregard the fact that native mechanical environment varies in time as function of tissue state itself.
EvOoC vision is to elevate mechanobiology as leading DE approach through a ground-breaking paradigm, named mechanical re-evolution, based on the high-risk/high-gain hypothesis that an iterative manipulation of mechanical forces is necessary to guide in vitro adult tissue development at unprecedented levels.
Towards this vision, I will deliver a new method (Evolving OoC, EvOoC), integrating three enabling functions:
“Move” - to apply native-inspired mechanical forces to tissues in vitro;
“Sense” – to monitor their comprehensive effect on tissue development;
“Adapt” – to modulate forces as a function of tissue responses through machine learning (ML)-based algorithms, towards an unsupervised tissue evolution.
I will take advantages of two paradigmatic test-cases (cartilage and heart) to showcase the power of mechanical re-evolution in guiding in vitro tissue physiological and pathological states, towards the identification of a brand-new class of mechanotherapeutics for unmet pathologies.
By combining principles of microfabrication, DE, mechanobiology and ML, EvOoC will revolutionize basic studies in tissue development and disease modeling, facilitating innovative translational strategies to tackle tissue repair in manifold applications.