Researchers at New York University Abu Dhabi (NYUAD) have announced the development of Spheromatrix, a simple and low-cost technology that enables 3D tumor models to be grown, frozen, and stored for future cancer drug testing.

The team reported that Spheromatrix is made from specially engineered filter paper designed to support the growth of tumor spheroids in a controlled and reproducible way. Unlike conventional methods, which are complex, expensive, and cannot be preserved, this platform allows researchers to create biobanks of “off-the-shelf” 3D tumor models that can be thawed and tested on demand.

The study was led by Associate Professor of Mechanical Engineering and Bioengineering Mohammad A. Qasaimeh, with first author Ayoub Glia, a Postdoctoral Associate, and colleagues at NYUAD’s Advanced Microfluidics and Microdevices Laboratory (AMMLab).

Qasaimeh revealed that Spheromatrix represents a significant advance in cancer research. He explained that its fiber-based structure provides a biocompatible environment for cells, enabling tumor models to behave more like real cancers. He added that preserving these models for long-term use may speed up preclinical testing, reduce reliance on animal models, and open new opportunities for patient-focused studies.

The research team tested Spheromatrix with commercial chemotherapy drugs on brain tumor models. They reported that preserved tumors responded in ways closely resembling real patient cancers. This demonstrated the platform’s potential to produce scalable, realistic, and reproducible systems for drug screening.

Glia stated that the goal was to design a platform that is simple, reliable, and affordable while addressing a bottleneck in cancer drug development. He noted that by engineering paper to support tumor spheroids, researchers can grow, freeze, and reuse models for multiple experiments. He also revealed that the team is now exploring the use of patient-derived samples to advance personalized cancer therapies.

According to the researchers, Spheromatrix could help reduce costs and timelines for preclinical testing. It may also provide humane alternatives to animal models and support progress in precision oncology.

Key points reported by the team include:

• The platform allows long-term storage of 3D tumor models.
• It supports reproducible drug screening with preserved tumor responses.
• It offers a low-cost, scalable alternative to existing systems.

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