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A functional human liver tissue model: 3D bioprinted co-culture discoids
Vignesh Subramaniam1, Carolina Abrahan2, Brett R Higgins3
1Department of Mechanical and Aerospace Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, United States of America.
Biomaterials Advances
|March 19, 2025
Summary
Researchers developed 3D bioprinted liver tissue models called discoids. These functional models accurately predict drug absorption, distribution, metabolism, and excretion (ADME) and toxicity, reducing drug development costs.
Area of Science:
- Biotechnology
- Regenerative Medicine
- Drug Development
Background:
- Developing new drugs is costly and time-consuming.
- Existing human liver tissue models have limitations.
- Improved models are needed for accurate pharmacological and toxicological testing.
Purpose of the Study:
- To create functional 3D human liver tissue models using bioprinting.
- To assess the precision, accuracy, and scalability of the bioprinting method.
- To evaluate the performance of these models for drug development applications.
Main Methods:
- Fabrication of disc-shaped liver tissue models (discoids) using 3D bioprinting.
- Embedding cells and collagen-1 in a polyethylene glycol (PEG) microgel support medium.
- Histologic, immunohistochemical, and gene expression analysis of printed tissues.
- Assessment of albumin and urea synthesis, and metabolic activity over three weeks in culture.
Main Results:
- The bioprinting method is precise, accurate, and scalable, producing up to 100 discoids/hour.
- Printed discoids exhibit self-organization, cell cohesion, and express key liver markers.
- Tissues stably synthesize albumin and urea, outperforming spheroid models.
- Expressed over 100 absorption, distribution, metabolism, and excretion (ADME) genes at human liver-relevant levels.
- Demonstrated enzymatic metabolite formation upon exposure to test compounds.
Conclusions:
- 3D bioprinted liver discoids offer a promising, functional human liver tissue model.
- These models can accurately predict drug metabolism and toxicity.
- The technology has significant potential for accelerating drug discovery and development.
