SCH: Toward a smart, home-use system for noninvasive, personalized monitoring of drug levels
- Institution
- Oregon State University, OR
- Principal investigator
- Stephen A. Ramsey
- Funding
- $290K
- Duration
- 2024–2028
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Abstract
PROJECT SUMMARY (See instructions): To optimize drug dosing, clinicians may order laboratory testing of drug levels in patient blood samples, but the clinical benefit of such testing is limited because testing is slow and infrequent. While field-use measurement systems are a partial solution, a major problem is that a person's drug pharmacokinetics (PK) can vary over time and from person to person. Through innovative use of new tools from Al and data science, we propose to develop a personalized therapeutic drug monitoring (pTDM) system incorporating (1) frequent noninvasive field measurements of drug levels and (2) a platform that delivers in real-time a profile of the circulating drug levels and patient-reported clinical information. We will assess the system's accuracy in the context of measuring salivary levels of anti-seizure medications for people with epilepsy. In our four research aims, we will develop a personalized drug titration PK model; electrochemistry assays for two anti-seizure drugs in saliva; electronic and electrochemical components and computational tools needed for a field-use platform; and an integrated and validated field-use measurement platform. In our two broader impacts aims, we will train students in transdisciplinary science and engage with caregivers and patients to obtain feedback and ensure equitable benefits from pTDM. Successful completion of our project's research aims will address critical knowledge gaps in how to (i) account for varying pharmacokinetics in saliva-based personalized therapeutic drug monitoring; (ii) address the challenges of electrochemical drug measurement in saliva given sample-to-sample variation in background matrix; and (iii) address the challenges of integrated field-use pTDM system development with low-power microelectronics. Further, the integrated system that we develop will demonstrate the benefits of using machine-learning to overcome the challenges of variable PK and saliva background matrix in the context of personalized therapeutic drug monitoring for chronic conditions and would be transformative for the field. As such, our project is in excellent alignment with the NIBIB's mission to transform, through garnering knowledge and technology development, disease treatment.
Research team
- Elain FuPI
- Matthew L JohnstonPI
- Stephen A. RamseyPI
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