Speaker
Description
Ourobionics is pioneering a transformative paradigm in regenerative medicine and tissue engineering through the development of a predictive biofabrication platform that integrates 3D Bio-Electrospraying (3D-BES), 3D Cell-Electrospinning (3D-CE) to create complex human tissue with biomarker biosensors connected to generative artificial intelligence (AI).
Unlike conventional extrusion-based 3D bioprinting, which is often limited by low resolution, prolonged fabrication times, and high shear-induced cell death, our platform enables rapid, high-fidelity construction of complex, heterogeneous human tissue architectures with significantly enhanced cell viability (>95%). 3D-BES and 3D-CE enable the deposition of living cells, extracellular matrix (ECM) components, and functionalized nanofibers at micro- to nanoscale precision, replicating native tissue microenvironments with high spatial control and tunable mechanical properties.
By embedding real-time biosensing elements—including sel-healing, printable, bio-electronic biomarker biosensors—directly into the printed constructs, we achieve dynamic monitoring of tissue functionality, metabolic state, and host–biomaterial interactions. Previous work includes the use of machine learning models to determine the optimal electric fields and biomaterial/ cell compositions to create the most reproducible and effective components for the advanced tissue fabrication for drug development and tissue therapeutics.
This closed-loop feedback is harnessed by a generative AI framework trained on multimodal datasets encompassing omics, imaging, and biomechanical inputs. The AI predicts optimal fabrication parameters and tissue maturation pathways, enabling on-demand personalization and adaptive refinement of tissue constructs pre- and post-fabrication. This convergence of advanced biofabrication and AI not only overcomes key bioprinting limitations such as limited vascularization potential, post-printing degradation, and scalability, but also opens the path to intelligent, sensor-integrated human tissue systems for regenerative therapies, in vitro modeling, and human–machine interfaces.
The convergence of complex human tissue biofabrication, integrated biosensors, and generative AI digital twins represents a frontier in regenerative medicine poised to overcome long-standing clinical and engineering bottlenecks. By embedding multi-modal biomarker biosensors—such as electrochemical, optical, and piezoelectric—into tissue constructs, real-time physiological monitoring becomes possible at cellular and subcellular resolution. These biosensors continuously collect data on key biomarkers including pH, oxygen, cytokines, and metabolites, offering unprecedented insight into tissue viability, inflammatory responses, and therapeutic performance post-implantation.
When coupled with generative AI, this data fuels dynamic digital twins—computational models that learn and evolve alongside the living tissue. These twins simulate tissue behavior under various physiological conditions, predict degeneration or failure points, and guide personalized therapeutic interventions in silico before clinical application. This real-time feedback loop enables adaptive control over both fabrication and therapeutic deployment, optimizing tissue maturation, integration, and long-term function.
This predictive, self-adaptive approach addresses critical limitations of current biofabrication methods, including graft rejection, lack of tissue vascularization, and static design protocols. It accelerates translational timelines while enhancing patient safety and treatment efficacy. Ultimately, the fusion of smart tissue constructs with AI-driven digital twins could redefine regenerative medicine—from reactive treatment to proactive, precision-engineered cellular therapy.
Ourobionics’ predictive biofabrication platform marks a critical step toward the autonomous generation of functional, patient-specific tissues and organs with real-time physiological insight.
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