Time is a major factor for cancer treatment development. Patients often don’t have much time, but developing new therapies takes a lot of it. It’s an issue that scientists, researchers, and doctors continue to work on. Researchers at the UCLA Health Jonsson Comprehensive Cancer Center created a new tool to speed up the process.

They built a system that mixes 3D bioprinting, clear imaging, and artificial intelligence to see exactly how cancer handles different drugs.

Testing Cancer Treatments Before Touching a Patient

3D Tumor
With the help of AI, researchers test cancer treatments on 3D models of tumors; Photo: Butusova Elena/Shutterstock

The UCLA team found a way to take a patient’s cancer cells and grow tiny, 3D replicas of their tumors, called organoids, in the lab. Organoids are helpful because they act like the real tumors inside the body. In the past, scientists struggled to make these replica tumors fast enough or in large enough numbers to test a lot of drugs at once.

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This new platform uses 3D bioprinting to create thousands of these organoids quickly. Then, it uses high-speed cameras to watch them grow and react to different drugs over time. It also does this without using harmful dyes, meaning the cells behave naturally while being watched.

AI Does the Heavy Work

Watching thousands of tiny tumors is a lot of work. That’s where AI steps in. The system uses machine learning to track every single organoid. It measures exactly how each tiny tumor responds to a drug.

“Instead of asking whether a drug works on average for a large number of tumor cells, we can now determine which specific organoids respond and which do not, and, ultimately, have an approach to determine the underlying reasons for unique response profiles,” said Dr. Michael Teitell, director of the UCLA Health Jonsson Comprehensive Cancer Center, professor of pathology and laboratory medicine and co-senior author of the study. “This allows us to measure drug responses across thousands of individual organoids, detect rare resistant tumor populations, track growth and treatment responses over time, and better predict which therapies may work for a particular patient.”