Electrochemical platform based on Ti₃C₂T_x MXene-supported molecularly imprinted polymers for low-level and selective...

Pomalidomide (POM), a third-generation immunomodulatory drug, is widely used in the therapeutic regimen of multiple myeloma and other hematologic malignancies. Owing to its narrow therapeutic index and substantial interindividual pharmacokinetic variability, accurate quantification of POM in biological matrices is essential to optimize dosing regimens and minimize adverse effects. Conventional analytical systems like HPLC and LC-MS/MS, while highly accurate, can be time-consuming, costly, and technically demanding.

This study covers the design of a molecularly imprinted polymer (MIP)-based electrochemical sensor incorporating two-dimensional Ti₃C₂T_X MXene nanaolayers (Ti₃C₂T_x NLs) for sensitive and selective detection and quantification of POM. The sensor's performance was enhanced by the integration of Ti₃C₂T_x NLs, followed by modifying a GCE via electropolymerization of its surface with 3-thienylboronic acid (3-TBA) as the functional monomer and aniline (ANI) as a co-monomer. Critical parameters, including the template: monomer ratio, the number of polymerization cycles, and the rebinding time, were systematically optimized. EIS, CV, and SEM analyses were used to confirm the formation of selective recognition cavities and the sensor's electrochemical functionality. The developed MIP sensor exhibited excellent linearity across a concentration range of 1.75 × 10^-13 M to 1.00 × 10^-12 M. It demonstrated outstanding selectivity for POM, even in the presence of structurally similar analogues, co-administered drugs, common biomolecules, and frequently encountered inorganic ions.

Overall, this highly sensitive, selective, and cost-effective MIP-based sensor, which demonstrates excellent stability and performance even in complex matrices, provides a robust, practical analytical tool for routine therapeutic drug monitoring of POM in clinical settings. Moreover, the sensor's straightforward fabrication, low operational cost, and reproducible analytical behavior make it a promising alternative to conventional methods, ultimately contributing to more efficient, accessible, and patient-centered therapeutic monitoring platforms.