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Developing imprinted polymer nanoparticles for the selective separation of antidiabetic drugs
Author(s) -
Haq Isma,
Mujahid Adnan,
Afzal Adeel,
Iqbal Naseer,
Bajwa Sadia Zafar,
Hussain Tajamal,
Shehzad Khurram,
Ashraf Hadia
Publication year - 2015
Publication title -
journal of separation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.72
H-Index - 102
eISSN - 1615-9314
pISSN - 1615-9306
DOI - 10.1002/jssc.201500506
Subject(s) - molecularly imprinted polymer , nanoparticle , molecular imprinting , polymer , separation (statistics) , chemistry , chromatography , nanotechnology , materials science , selectivity , organic chemistry , computer science , catalysis , machine learning
In this study, new molecularly imprinted polymer (MIP) nanoparticles are designed for selective recognition of different drugs used for the treatment of type 2 diabetes mellitus, i.e. sitagliptin (SG) and metformin (MF). The SG‐ and MF‐imprinted polymer nanoparticles are synthesized by free‐radical initiated polymerization of the functional monomers: methacrylic acid and methyl methacrylate; and the crosslinker: ethylene glycol dimethacrylate. The surface morphology of resultant MIP nanoparticles is studied by atomic force microscopy. Fourier transform infrared spectra of MIP nanoparticles suggest the presence of reversible, non‐covalent interactions between the template and the polymer. The effect of pH on the rebinding of antidiabetic drugs with SG‐ and MF‐imprinted polymers is investigated to determine the optimal experimental conditions. The molecular recognition characteristics of SG‐ and MF‐imprinted polymers for the respective drug targets are determined at low concentrations of SG (50–150 ppm) and MF (5–100 ppm). In both cases, the MIP nanoparticles exhibit higher binding response compared to non‐imprinted polymers. Furthermore, the MIPs demonstrate high selectivity with four fold higher responses toward imprinted drugs targets, respectively. Recycled MIP nanoparticles retain 90% of their drug‐binding efficiency, which makes them suitable for successive analyses with significantly preserved recognition features.