Sensing Mechanisms of GPCR- and OBP-Based Biosensors for Addictive Drug Detection

Addictive drug abuse remains a major global public health concern, creating a need for rapid and reliable approaches for drug screening and on-site analysis. Conventional analytical techniques provide accurate chemical identification but generally require laboratory-based instrumentation and sample processing. Biomimetic sensors incorporating biological recognition elements offer an alternative approach by exploiting molecular recognition and, in some cases, receptor-mediated signaling. This review summarizes recent progress in the use of G-protein-coupled receptors (GPCRs) and odorant-binding proteins (OBPs) as recognition elements for addictive drug-related sensing. The structural basis of ligand recognition and the native signaling mechanisms of GPCRs and OBPs are discussed, together with structural and computational studies that provide insight into drug-recognition element interactions. Recent sensing strategies based on electrical, optical, electrochemical, and acoustic transduction are then examined in terms of recognition mechanism, analytical performance, and application characteristics. Reported systems include direct measurements of drug–protein interactions, as well as functional assays that monitor receptor activation and downstream signaling. These studies demonstrate the potential of GPCR- and OBP-based recognition elements for sensitive detection of addictive drugs and related ligands, but also reveal limitations in discriminating structurally related compounds. In particular, receptor activation or ligand binding does not necessarily provide definitive chemical identification, which limits the direct application of activity-based sensing to forensic and regulatory analysis. Other challenges include maintaining functional recognition elements following immobilization, achieving reproducible coupling between molecular recognition and signal generation, and establishing performance in complex samples. Future development will require improved control of recognition-element interfaces, broader use of multiplexed recognition, and complementary analytical confirmation to improve the reliability and practical applicability of biomimetic sensors for addictive drug detection.