Machine Learning-Assisted Molecularly Imprinted Polymer Sensor for Point-of-Care Vancomycin Monitoring in Serum.

Optimizing vancomycin dosage is critical for treating severe infections and combating antimicrobial resistance, yet it is hampered by slow, centralized laboratory testing. To address this clinical gap, we have developed a low-cost, disposable electrochemical sensor for the rapid quantification of vancomycin directly in undiluted human serum. Our platform integrates a selective molecularly imprinted polymer using phenol red as a redox-active functional monomer with a signal-amplifying highly porous gold nanostructure on a scalable printed circuit board. To address non-linear responses and matrix interference inherent to complex biological samples, the sensor output is processed by a Random Forest machine learning regression model. The sensor achieved a limit of detection of 0.848 μg mL-1 within a clinically relevant dynamic range (0-100 μg mL-1). As a preliminary proof-of-concept for clinical application, the sensor was tested using patient serum samples, demonstrating good correlation (R2 = 0.98) and agreement when compared against gold-standard liquid chromatography-tandem mass spectrometry (LC-MS/MS). This work presents a data-driven sensor system that offers a robust alternative to conventional methods, paving the way for real-time, personalized vancomycin therapy at the point of care.