Recipient organisationNewcastle UniversitySource-published name: Newcastle University
Funding£420K
PeriodJun 2025 — Jun 2028
In plain English
AI plain-English summary
Every four to six hours, a patient at risk of tumour lysis syndrome must have blood drawn and sent to a lab, with results taking roughly two hours to return—a delay that can prove fatal when biomarker levels shift rapidly. Tumour lysis syndrome occurs when cancer cells break open during treatment, flooding the bloodstream with uric acid, potassium, and phosphate, which can trigger sudden organ failure or death. Current monitoring is invasive, slow, and ill-suited for the real-time decisions needed to prevent this emergency. This project will build electrochemical biosensors—small devices using novel electroactive materials—that measure these biomarkers continuously and non-invasively, potentially through a wearable patch. The sensor data will feed machine learning models that predict risk and help clinicians adjust therapy before a crisis unfolds. If successful, the technology could replace frequent blood tests with ambulatory monitoring, allowing medium-risk patients to be managed at home rather than in hospital. That would reduce inpatient stays, ease pressure on the NHS, and cut healthcare costs. For high-risk patients, real-time alerts could prevent the unexpected death that currently follows rapid biomarker surges.
View original technical description
Tumour lysis syndrome (TLS) is a life-threatening oncologic emergency that occurs when malignant cells lyse spontaneously or in response to therapy for haematologic malignancies and other rapidly proliferating tumours. For high-risk TLS patients, the levels of critical biomarkers (e.g., uric acid, potassium, and phosphate) must be monitored every 4-6 hours. This is currently carried out using frequent blood draws and subsequent in vitro laboratory analyses that are invasive, resource-intensive, not suitable for point-of-care testing, and fail to provide real-time data necessary for immediate intervention. Moreover, the turnaround time of ~2 hours for blood analysis offers limited temporal resolution and does not capture the dynamic changes of biomarkers while rapid changes in biomarker levels commonly take place, increasing the risk factors associated with TLS. The lack of careful monitoring during the therapy can lead to the unexpected death of TLS patients. Because of the potential severity of complications resulting from TLS, it is necessary to have preventative measures for at-risk patients and rapid treatment in place when symptoms develop. This project will develop novel biosensors that will facilitate early TLS detection and timely intervention through adjustments of therapy, preventative measures, and referral to critical care. The biosensors will be made using a combination of novel electroactive materials which will allow for rapid and easy-to-use electrochemical measurement techniques. The data from the biosensors will then be used to train and develop machine learning and artificial intelligence models for identification and better management of the patients at risk. The models will reliably predict the TLS risk factors during care to help clinicians adjust the therapy as necessary. As a result, the unexpected lysis of malignant cells can be avoided, preventing severe organ dysfunction or unexpected death. The development of advanced biosensor systems for the point-of-care monitoring and prevention of TLS is a significant leap forward in TLS patient care and will revolutionise the way TLS is monitored. The biosensors this project will develop will ultimately allow for integration with wearable platforms that enable non-invasive and continuous monitoring of biomarkers. This will allow for out of hospital ambulatory monitoring, reducing the need for costly inpatient care for medium risk patients. Additionally, wearable biosensors can collect data over extended periods, providing valuable insights into long-term trends and potential risk factors for individual patients. The TLS biosensors this project will develop will ultimately reduce mortality, improve patient outcomes, and enhance quality of life. Given the high healthcare resources (e.g., hospitalisation, medications, laboratory testing, and monitoring) needed for TLS management, TLS prevention for at-risk patients through real-time monitoring of biomarkers will be extremely beneficial as it will reduce inpatient time, decrease burden on NHS, cut NHS waiting times, and lead to savings in healthcare costs.
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