Zhipeng Zhang, Ying Liu, Huizi Zheng, Guanlan Wu, Xiaolin Zhu, Jiao Qu
Conventional in vitro cytotoxicity assays, when used as biosensing platforms, suffer from high reagent consumption, poor signal reproducibility, and low throughput, limiting their utility for generating high-quality training data for computational models. Herein, we develop a novel cell-based microfluidic electrochemical biosensing platform featuring a 9-channel PDMS/glass microfluidic chip for uniform monolayer culture, and a MWCNT-COOH/Ti3C2Tx/ionic liquid nanocomposite-modified screen-printed electrode transducer. This biosensor detects electroactive purine metabolites (including xanthine/guanine) secreted by HepG2 cells, enabling label-free monitoring of cellular metabolic status. The single-channel working volume is only 200 μL, reducing reagent and cell consumption by over 90% versus traditional systems, while the single-compound assay cycle is shortened to approximately 48 h. As a demonstration, we systematically evaluated the cytotoxicity of eight chlorinated phenylacetonitrile (CPAN) isomers. The biosensor yielded consistent dose-response results with conventional methods but with improved repeatability (RSD = 3.17%). To test whether the electrochemical signals faithfully reflect cellular stress, we performed molecular docking-assisted regression, which established a class-specific correlation between multi-target binding affinity and the biosensor's cytotoxicity readout (R2 = 0.85, Q2 = 0.79). Mechanistic analysis suggests that chlorine substitution patterns are associated with cytotoxic potency, potentially via synergistic hydrogen/halogen bond geometries, which is consistent with the differential purine metabolic disruption detected by the sensor. This work not only provides a robust and low-cost biosensing toolkit for emerging contaminants but also demonstrates a phenotype-to-mechanism workflow that bridges experimental sensing with computational insight. The modular chip design offers a flexible template for scalable screening and is readily adaptable to other contaminant classes or dynamic exposure scenarios.