Computational-Assisted Development of Molecularly Imprinted Polymers for Synthetic Cannabinoid Recognition

Abstract

Synthetic cannabinoids (SCs), a prominent class of new psychoactive substances, pose growing challenges to public health due to their severe toxic effects and widespread global presence. In this study, we employed computational methods to develop molecularly imprinted polymers (MIPs) for the selective recognition of seven SCs, chosen based on seizure reports from the Narcotics Examination Unit of the Scientific Police of the State of São Paulo. Density functional theory and extended tight binding for geometry, frequency, and noncovalent model 2 (GFN2-xTB) calculations were used to optimize the molecular geometries and predict ideal monomer–solvent combinations for MIP synthesis. We assessed six solvents─acetone, acetonitrile, dichloromethane, chloroform, diethyl ether, and dimethyl sulfoxide─based on their solvation energy, identifying suitable candidates for the polymerization step. Hydrogen bonding interaction sites were mapped, guiding the selection of functional monomers such as acrylic acid (AA), 4-vinylbenzoic acid (BA), 2-(trifluoromethyl)acrylic acid (TFAA), and methacrylic acid. Our findings suggest that TFAA and BA offer the most stable complexation with SCs, influenced by their acidity and aromatic interactions. These computational predictions pave the way for resource-efficient experimental validation and enhance the development of MIPs as tools for the extraction of SCs in complex matrices, contributing to efforts to combat the global SC epidemic.