AI-Powered Raman Spectroscopy Method Enables Rapid Drug Detection in Blood

Traditional techniques for monitoring blood drug levels often face interference from serum biomolecules, requiring extensive sample processing. Conventional methods, such as liquid chromatography (LC) and mass spectrometry (MS), involve complex sample preparation and laboratory settings, which limits their efficiency in clinical practice. However, a new study introduces an alternative: a surface-enhanced Raman spectroscopy (SERS)-based platform, enhanced with "molecular hooks" and powered by artificial intelligence (AI)-driven spectral analysis. This innovative approach offers a rapid, highly sensitive, and selective method for detecting drugs in the blood, with significant potential for personalized medicine. The combination of SERS and AI provides a new level of sensitivity in diagnostic medicine.

The newly developed SERS-based approach was the result of collaborative research by scientists from Harbin Medical University (Harbin, China) and the University of Oulu (Oulu, Finlandi). It overcomes the challenges of traditional blood drug monitoring by using self-assembled silver nanoparticles functionalized with an A13 molecule. This "molecular hook" selectively binds small drug molecules while excluding larger biomolecules like hemoglobin, ensuring accurate analyte detection. The study, published in Biosensors and Bioelectronics, demonstrated the technique by detecting two cardiovascular drugs—dobutamine hydrochloride and milrinone—commonly used in the treatment of acute heart failure. The method achieved detection limits as low as 10 pg/mL for dobutamine hydrochloride and 10 ng/mL for milrinone, well below their therapeutic thresholds, making it one of the most sensitive non-invasive drug detection techniques available.

The platform enhances Raman signals by creating dense electromagnetic "hotspot" regions. When calcium ions are introduced, the nanoparticles aggregate, further intensifying these hotspots and boosting the drug-specific Raman signals. To ensure precision and efficiency, the researchers integrated AI, allowing for automated spectral analysis that minimizes human error and speeds up the detection process. Advanced characterization techniques, such as transmission electron microscopy (TEM), scanning electron microscopy (SEM), and X-ray diffraction (XRD), were employed to confirm the uniformity and stability of the nanoparticles. The researchers found that the "molecular hook" substrate maintained high SERS activity for at least five days, ensuring its reliability for clinical use. Further validation experiments comparing the new method with conventional techniques showed that the SERS-based platform provided superior selectivity and sensitivity. Even in complex biological samples, the technique successfully differentiated dobutamine hydrochloride from other compounds, generating a clear Raman fingerprint.

This advancement holds significant promise for personalized medicine. By enabling real-time monitoring of drug concentrations, clinicians can tailor treatments with greater precision, reducing the risk of under- or overdosing. This is especially crucial for patients with cardiovascular conditions, where the efficacy and safety of drugs depend heavily on individual metabolic differences. Moreover, the SERS-AI approach could be applied beyond cardiovascular drugs to other therapeutic agents, including antibiotics, chemotherapy drugs, psychiatric medications, and diagnostic tests. Future research will focus on broadening the range of detectable substances and refining the AI models for even greater accuracy. The integration of surface-enhanced Raman spectroscopy with molecular hook technology and AI represents a paradigm shift in clinical diagnostics, offering rapid, precise, and minimally invasive drug detection. This method could revolutionize patient care, paving the way for more effective and personalized treatment strategies.

“The combination of SERS technology and AI significantly improves drug monitoring accuracy and speed, paving the way for real-time clinical applications,” the study authors reported.

Related Links:
Harbin Medical University
University of Oulu