Machine learning-assisted nanozyme nanosensor for instantaneous quantitative analysis of antitubercular drugs

The dynamic concentration of drugs in the blood reflect pharmacokinetic characteristics and serves as crucial indicators of therapeutic efficacy and medication safety. This underscores the necessity of real-time monitoring to guide individualized dosing and reduce adverse effects, thus requiring rapid and accurate therapeutic drug monitoring (TDM) to optimize patient outcomes and minimize side effects. To this end, a colorimetric nanozyme-based nanosensor was constructed for the detection of isoniazid (INH). In the presence of INH, ·O₂^- generated by the nanozyme with excellent oxidase-like activity were effectively scavenged, thereby inhibiting the chromogenic reaction of the nanozyme substrate and enabling the rapid quantification of INH in biological samples. In this study, two methods were developed: UV-Visible spectrophotometry and a smartphone-based RGB extraction method for rapid quantification of INH in tablets and biological samples. Smartphone imaging integrated with machine learning compared Convolutional Neural Network (CNN), Random Forest (RF), and Logistic Regression (LR) models under dark box condition, followed by training the best model under dark box and ambient lighting. The results showed that under optimized conditions (pH 5 buffer, 25℃, 10 min), UV-Visible spectrophotometry achieved a detection range of 1-30 µM (LOD: 0.582 µM), while the smartphone-based RGB method achieved 3-30 µM (LOD: 1.368 µM). Spiked recovery rates were 98.08%-111.25% for INH tablets and 97.10%-100.51% for rat serum. Machine learning results showed CNN outperforming RF and LR under dark-box conditions, with consistent prediction accuracy under dark-box and ambient lighting (MAE: 1.230 and 1.160 µM, respectively), validating robustness under varying environments. Ultimately, this integrated nanoprobe-based detection platform stands to significantly improve clinical anti-tuberculosis therapeutic drug monitoring by substantially increasing the accessibility, reliability, and scalability of INH concentration detection.