Scientists at the University of Utah announced on Mar. 26 the development of a new lab-on-a-chip device that uses artificial intelligence to quickly predict how cancer cells respond to targeted therapies for children with T-cell acute lymphoblastic leukemia (T-ALL). The device, called μPharma, is designed to deliver results in under four hours and is not yet used in clinical settings.
This technology could help reduce unnecessary treatments and side effects by rapidly identifying which therapies are most likely to be effective for each patient. By enabling same-day precision medicine, it may offer a critical advantage for children facing aggressive cancers like T-ALL.
The μPharma platform automates labor-intensive laboratory steps using digital microfluidics, which moves tiny droplets across the chip. This process reduces the number of cells and reagents needed while minimizing human error. It identifies drug-response profiles without directly exposing cancer cells to drugs, potentially streamlining care and making it more precise.
Luke Maese, pediatric oncologist at Huntsman Cancer Institute and associate professor of pediatrics, said: “Innovation in treatment selection is a pressing need within pediatric malignancies. Personalized treatment selection accomplished in ‘real-time’ will be part of the future of cancer therapeutics, and μPharma represents an encouraging step in that direction.”
In a study published March 13 in Med, researchers showed that μPharma accurately predicted responses to two targeted therapies currently being investigated for T-ALL—dasatinib and venetoclax—and revealed a previously unrecognized link between drug response and a key molecular marker for T-ALL. Yue Lu, assistant professor of molecular pharmaceutics at HCI’s Experimental Therapeutics Program, said: “Our team has worked hard to develop this technology, and seeing it perform well is a key step toward bringing it into the clinic to help patients.”
The platform can detect differences in drug susceptibility at the single-cell level. This could help clinicians identify medications that target all parts of a patient’s cancer more effectively. Alphonsus Ng added: “If we can rapidly and accurately monitor the sensitivity of cancer cells and tailor treatment appropriately, we believe it can significantly improve outcomes.”
Makala Pace, pharmacy director at Huntsman Cancer Institute said: “A tool that can predict drug response in hours and help clinical teams prioritize therapies with the best chance of benefit—while streamlining care and minimizing unnecessary toxicity for our youngest cancer patients—is exactly the kind of precision we strive for in oncology pharmacy practice.”
Looking ahead, researchers plan further validation using primary leukemia cells from real-world clinical environments.
