# Comprehensive, Real Time Monitoring of the Accumulation and Clearance of Small Molecules in Kidney Disease

> **NIH NIH R56** · UNIVERSITY OF CALIFORNIA SANTA BARBARA · 2023 · $150,000

## Abstract

:
Current methods for monitoring kidney function and the effectiveness of renal replacement therapies, which rely on the ex-vivo measurement of plasma creatinine and urea, are inadequate on several fronts. Here, we propose to adapt electrochemical aptamer-based (EAB) sensors, the first platform molecular measurement technology shown to work in vivo, to enabling real-time, seconds-resolved monitoring of creatinine and urea in living subjects. To achieve this goal, we propose two specific aims. Our aim 1 goal is the validation of our existing, creatinine-detecting in-vivo EAB sensor for plasma measurements by comparison to ex-vivo analysis and to adapt it to the monitoring of creatinine in the subcutaneous space. Aim 2 will develop an in-vivo EAB sensor that supports multi-hour, drift-free, high temporal resolution measurements of plasma urea. The expected outcome of this work is the production of optimized EAB sensors against the two mostly clinically important biomarkers of renal function and renal replacement efficacy. Future development of these tools will set the stage to significantly improve our ability to study, detect, monitor, and treat all stages of kidney disease.

## Key facts

- **NIH application ID:** 10863011
- **Project number:** 1R56DK137421-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA SANTA BARBARA
- **Principal Investigator:** Tod Edward Kippin
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $150,000
- **Award type:** 1
- **Project period:** 2023-08-21 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10863011

## Citation

> US National Institutes of Health, RePORTER application 10863011, Comprehensive, Real Time Monitoring of the Accumulation and Clearance of Small Molecules in Kidney Disease (1R56DK137421-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10863011. Licensed CC0.

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