Optical Spectral Fingerprinting Enables Sensitive Detection of Anthracycline Chemotherapeutics in Synthetic Clinical Biofluids
) species at increasing concentrations of each of the four anthracyclines. The spectral responses were then used to develop machine learning-based classification models to identify different anthracycline types and concentrations. The optimized extreme gradient boosting model was able to classify high levels of each anthracycline with 100% accuracy. Concentration-based classification by PCA was performed for each anthracycline, distinguishing low (≤ 5 μM) and high (> 5 μM) concentrations. Finally, we validated the sensor performance using synthetic urine and sweat. Our findings demonstrate the potential of carbon nanotube-based sensor array to measure the pharmacokinetics of anthracyclines in patients with the goal of enhancing anti-tumor efficacy and monitoring off-target toxicities.
Authors
- Ryan M. Williams (ORCID: https://orcid.org/0000-0002-2381-8732)
- Adnan Arnaout
- Myesha Thahsin
- Amelia K. Ryan (ORCID: https://orcid.org/0000-0003-2197-6531)
- Atara R Israel
- Minjung Kim (ORCID: https://orcid.org/0000-0002-1732-3418)
- Syeda Rahman
- Zachary Cohen (ORCID: https://orcid.org/0000-0002-1615-8705)
- Yumna Ahmed
- Yunjung Kim
Institutions
- City College of New York (US)
- Georgia Institute of Technology (US)
- Stony Brook Medicine (US)
- Hanyang University (KR)
- Stony Brook University (US)
Publication Details
- Journal
- bioRxiv (Cold Spring Harbor Laboratory)
- Published
- 2026-04-11
- DOI
- https://doi.org/10.64898/2026.04.08.717324
- Primary Topic
- Spectroscopy Techniques in Biomedical and Chemical Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- State University of New York
- National Research Foundation
- National Research Foundation of Korea
- Stony Brook University
- National Institutes of Health
- Advanced Research Projects Agency
- National Institute of General Medical Sciences
- National Institute of Biomedical Imaging and Bioengineering