Researchers said the advance could help pharmaceutical companies save time and money when developing new drugs.
"We've made a tool that pharmaceutical companies could use to do pilot studies on their new drugs, and they won't have to wait until animal or human trials to test a drug's safety and efficacy on patients," said Shaochen Chen, professor at the University of California, San Diego.
Existing liver models for drug screening so far lack the complex micro-architecture and diverse cell makeup.
"We've engineered a functioning liver tissue that matches what you'd see under a microscope," said Chen.
"The liver is unique in that it receives a dual blood supply with different pressures and chemical constituents," said Shu Chien, professor at UC San Diego.
"Our model has the potential of reproducing this intricate blood supply system, providing unprecedented understanding of the complex coupling between circulation and metabolic functions of the liver in health and disease," said Chien.
The liver tissue was printed in two steps. First, the team printed a honeycomb pattern of 900-micrometre-sized hexagons, each containing liver cells derived from human induced pluripotent stem cells.
An advantage of human induced pluripotent stem cells is they are patient-specific, which makes them ideal materials for building patient-specific drug screening platforms.
Since these cells are derived from a patient's skin cells, researchers do not need to extract any cells from the liver to build liver tissue.
The entire structure a 3X3 millimetre square, 200 micrometres thick - takes just seconds to print. This is a vast improvement over other methods to print liver models, which typically take hours.
The structure was cultured in vitro for at least 20 days. The researchers then tested the resulting tissue's ability to perform various liver functions, such as albumin secretion and urea production, and compared it to other models.
They found that their model was able to maintain these functions over a longer time period than other liver models.
The study was published in the journal PNAS.
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