Abstract
Anthracycline chemotherapeutics are commonly used as frontline treatments for a wide array of cancers. However, their administration to patients results in substantial side effects, primarily cardiotoxicity, as well as myelosuppression and gastrointestinal toxicity. Current clinical management of such side effects is solely based on a lifetime dosage limit, which inhibits their anti-tumor efficacy. Many individualized factors, including age, family history of cardiovascular disease, treatment regimen, and other co-morbidities influence drug pharmacology. Despite this heterogeneity, there is no method for determining actual organ or tumor exposure to the treatment in an individual. Here, we developed an optical nanosensor array for four anthracyclines: doxorubicin, daunorubicin, epirubicin, and idarubicin. We used single-walled carbon nanotubes as the signal transducer due to their tunable near-infrared fluorescence. We screened twelve distinct ssDNA sequences paired with seven SWCNT (n,m) 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 (less than 5 micromolar) and high (greater than 5 micromolar) 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.