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

AI-powered Raman spectroscopy method for rapid drug detection in blood © angellodeco - stock.adobe.com

Accurately monitoring drug levels in blood is crucial for effective treatment, particularly in cardiovascular disease management. Conventional methods, such as liquid chromatography (LC) and mass spectrometry (MS), require complex sample preparation and laboratory settings, limiting their efficiency in clinical practice. A new study, published in the journal Biosensors and Bioelectronics, presents an alternative: a surface-enhanced Raman spectroscopy (SERS)-based platform enhanced with "molecular hooks" and artificial intelligence (AI)-driven spectral analysis. This breakthrough, developed by researchers from Harbin Medical University in China and the University of Oulu in Finland, provides a rapid, highly sensitive, and selective means of detecting drugs in blood, with potential implications for personalized medicine (1). SERS combined with AI is providing new sensitive analysis for diagnostic medicine (1,2).

A New Approach to Drug Detection

Traditional blood drug monitoring techniques suffer from interference caused by serum biomolecules, necessitating labor-intensive sample processing. The newly developed SERS-based approach, however, overcomes this challenge by utilizing self-assembled silver nanoparticles functionalized with an A13 molecule. This "molecular hook" selectively binds small drug molecules while excluding larger biomolecules like hemoglobin, ensuring precise analyte detection (1).

The research team demonstrated this technique by detecting two cardiovascular drugs—dobutamine hydrochloride and milrinone—commonly used in treating acute heart failure. The method achieved detection limits as low as 10 pg/mL for dobutamine hydrochloride and 10 ng/mL for milrinone, significantly below their therapeutic thresholds, making it one of the most sensitive non-invasive drug detection techniques available (1).

Enhancing Sensitivity with "Hotspots" and AI

The novel platform enhances Raman signals by generating dense electromagnetic "hotspot" regions. By introducing calcium ions, the nanoparticles aggregate, further intensifying these hotspots and amplifying drug-specific Raman signals. To ensure accuracy and efficiency, the researchers integrated AI, enabling automated spectral analysis that eliminates human error and accelerates the detection process (1).

“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 (1).

Experimental Validation and Stability

The research team employed advanced characterization techniques, including transmission electron microscopy (TEM), scanning electron microscopy (SEM), and X-ray diffraction (XRD), to confirm the uniformity and stability of the nanoparticles. Their findings revealed that the "molecular hook" substrate maintained high SERS activity for at least five days, ensuring reliability for clinical applications (1).

In further validation experiments, the researchers compared their new method with conventional techniques and found that their SERS-based platform exhibited superior selectivity and sensitivity. Even in complex biological samples, the technique successfully distinguished dobutamine hydrochloride from other compounds, providing a clear Raman fingerprint (1).

Clinical Implications and Future Applications

This development has significant implications for personalized medicine. By enabling real-time monitoring of drug concentrations, clinicians can tailor treatments more precisely, reducing the risk of under- or overdosing. This is especially crucial for patients with cardiovascular conditions, where drug efficacy and safety are highly dependent on individual metabolic differences (1).

Moreover, the SERS-AI approach has the potential to extend beyond cardiovascular drugs to other therapeutic agents, including antibiotics, chemotherapy drugs, psychiatric medications, and diagnostic testing (1,2). Future research will focus on expanding the range of detectable substances and refining the AI models for even greater accuracy (1).

The integration of surface-enhanced Raman spectroscopy with molecular hook technology and AI represents a paradigm shift in clinical diagnostics. By providing rapid, precise, and minimally invasive drug detection, this method could revolutionize patient care and pave the way for more effective and personalized treatment strategies (1).

References

(1) Wei, Q.; Zhou, L.; Sun, J.; Wu, G.; Gong, S.; Gao, Z.; Wu, J.; Wang, Y.; Xiao, Y.; Li, Y. Rapid Detection of Drugs in Blood Using “Molecular Hook” Surface-Enhanced Raman Spectroscopy and Artificial Intelligence Technology for Clinical Applications. Biosens. Bioelectron. 2025, 267, 116855. DOI: 10.1016/j.bios.2024.116855

(2) Workman, Jr, J. Machine Learning-Enhanced SERS Technology Advances Cancer Detection. Available at: https://www.spectroscopyonline.com/view/machine-learning-enhanced-sers-technology-advances-cancer-detection (accessed 2025-02-05).