Rapid and precise assessment of drug sensitivity is essential for optimizing chemotherapy and improving patient outcomes. However, conventional chemosensitivity tests are often slow, require large sample volumes, and struggle to handle complex biological samples.
In a study published in Biosensors and Bioelectronics, a research team led by Prof. WANG Hongzhi from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences developed a programmable plasmonic ring biosensor, MetaRing, which is capable of rapidly and accurately identifying paclitaxel sensitivity in breast cancer patients.
Researchers designed the MetaRing biosensor based on the coffee-ring effect. By carefully controlling nanoparticle concentration and evaporation temperature, the MetaRing biosensing platform achieved deterministic nanoparticle assembly, forming hierarchical structures with dense and stable nanogaps. This design enhanced detection stability and robustness across a variety of biological environments including water, buffer solutions, protein-rich media, and complex cell lysates.
Moreover, this platform integrated surface-enhanced Raman spectroscopy to capture molecular “fingerprints” with high sensitivity, enabling rapid acquisition of metabolic spectra that reflect tumor cell responses to paclitaxel. This design required only trace amounts of biological samples and eliminated the need for labeling or cell culture expansion.
Experimental results showed that MetaRing could reliably identify paclitaxel sensitivity signatures in drug-resistant breast cancer cell lines, xenograft models, and patient-derived biopsy tissues. When paired with a lightweight one-dimensional convolutional neural network, the integrated MetaRing-AI system completed drug sensitivity assessments within 10 minutes, achieving over 92% classification accuracy in clinical cohorts.
This study provides a practical strategy for rapidly evaluating paclitaxel sensitivity, supporting personalized chemotherapy and addressing challenges posed by inter-patient variability and tumor heterogeneity, with strong potential for clinical translation.