Computational modelling analysis and synthesis of molecularly imprinted polymers (MIPs) with two functional monomers using bulk polymerization

Abstract

Sulfamethoxazole (SMX) is a well-known antibiotic used for the treatment and prevention of diseases for dairy-producing animals. However, the overuse of antibiotics in milk-producing herd resulted in detectable traces in milk which can cause severe effect to human health and also pose a risk of antimicrobial resistance through milk consumption. Therefore, a simple and rapid detection method is required to monitor the level of SMX in milk. Molecularly imprinted polymer (MIP) is a synthetic recognition element producing structurally artificial recognition sites “cavities” in polymeric matrices which are complementary to SMX structure for rebinding purposes through a process known as molecular imprinting. This method allows the capture of SMX with high affinity and selectivity even in the presence of antibiotics with very similar molecular structures to SMX. Herein, this study aims to synthesize and characterize MIP with specific binding to SMX through computational predictions and experimental verification using two different functional monomers which are methacrylic acid (MAA) and acrylamide (AA). The docking analysis using PyRx – Virtual Screening Tool showed higher binding affinity for SMX-MAA compared to SMX-AA which were −2.0 and −1.7 kcal/mol, respectively. Smaller binding affinity values indicate a greater interaction between the MIP and the analyte. This modelling data was further validated through experimental study by synthesizing MIP using MAA and AA monomers which presented an imprinting factor (IF) of 1.46 and 0.98 in ultra-pure water (UPW), and 1.02 and 0.93 in phosphate buffer solution (PBS) media respectively. IF values in both mediums showed higher affinity for SMX-MAA compared to SMX-AA and in agreement with the predicted values from the docking analysis. All in all, MIP synthesized using MAA as the monomer can be used for selective detection of SMX as a recognition element in sensors. This method is simple, stable and relatively cheap compared to the conventional detection methods such as immunoassays, GC/MS and HPLC.