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Updated: Jan 8, 2026

Use of Enzymatic Biosensors to Quantify Endogenous ATP or H2O2 in the Kidney
Published on: October 12, 2015
Toward clinical translation of biosensors for chronic kidney disease
Ronil J Rath1, Sepehr Talebian2, Beatriz Mayol3
1School of Chemical and Biomolecular Engineering, The University of Sydney, Sydney, NSW, 2006, Australia; The University of Sydney, Sydney Nano Institute, Sydney, NSW, 2006, Australia; Department of Biochemistry and Chemistry, School of Agriculture, Biomedicine and Environment, La Trobe University, Melbourne, Victoria, 3086, Australia; The Biomedical and Environmental Sensor Technology (BEST) Research Centre, Biosensors Program, La Trobe Institute for Molecular Science (LIMS), La Trobe University, Melbourne, Victoria, 3086, Australia; ARC Research Hub for Molecular Biosensors at Point-of-Use (MOBIUS), La Trobe University, Melbourne, Victoria, 3086, Australia.
Abstract:
Chronic kidney disease (CKD) is a progressive disorder that affects one in ten individuals globally and ranks as the seventh leading cause of mortality. Its rising prevalence, driven by lifestyle factors and ultra-processed diets, underscores the need for prevention and early detection. Current assessments of kidney function are invasive, time-consuming, and centralized, thus limiting scalability and accessibility. Point-of-care and wearable biosensors offer a paradigm shift by enabling decentralized, real-time monitoring; however, their clinical translation remains challenging. This review provides a roadmap for advancing CKD biosensor research toward clinical implementation. We examine (i) key biomarkers and risk factors for early detection, (ii) their partitioning in alternative body fluids such as sweat, interstitial fluid, saliva, and tears, (iii) sensor technologies validated through animal and human proof-of-concept studies. Beyond technical aspects, we highlight strategies for navigating institutional review boards, securing ethics approval, and designing clinical trials tailored to CKD biosensors. Finally, we discuss current challenges, opportunities, and future directions, offering practical guidance for integrating these technologies into clinical workflows.
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