Electrochemical biosensors are central to wearable diagnostics, point-of-care testing, and continuous health monitoring due to their low power requirements, compatibility with miniaturized electronics, and proven clinical impact. Despite these advantages, the development of new electrochemical biosensors remains slow, constrained by limited throughput, complex electrode–biomolecule interfaces, and challenges associated with selectivity and performance in chemically complex environments. This perspective outlines how the next generation of electrochemical biosensors can be enabled by decoupling high-throughput front-end discovery and optimization from electrochemical readouts using nonelectrochemical surrogate assays. Optical, affinity, and cell-sorting platforms, including SELEX, fluorescence-activated cell sorting, and chemically coupled fluorescence assays, allow orders-of-magnitude expansion in accessible design space for recognition elements, enzymes, and redox mediators. These approaches enable data-rich exploration of sequence–function relationships and provide scalable inputs for directed evolution, de novo protein design, and machine-learning-guided optimization. Top-performing constructs obtained from these nonelectrochemical surrogate assays can then be screened and validated electrochemically, ensuring translation into functional electrochemical biosensors. Together, these strategies outline a path toward data-driven, scalable, and predictive electrochemical biosensor design that moves beyond trial-and-error development and accelerates deployment in real-world settings.
Ricks et al. (Mon,) studied this question.