Neuro-genetic multi-objective optimization and computer-aided design of pantoprazole molecularly imprinted polypyrrole sensor

A molecularly imprinted polymer (MIP) of pantoprazole (PNZ) was prepared through electropolymerization of pyrrole on a functionalized multi-walled carbon nanotube modified pencil graphite electrode. The preparation of MIP and quantitative measurements were performed by cyclic voltammetry and differential pulse voltammetry (DPV), respectively. Several important parameters controlling the performance of polypyrrole film. The factors, i.e. pH of buffer solution, cyclic voltammetric scan rate in polymerization step, number of cyclic voltammetric scans, monomer and template concentrations in prepolymerization mixture, nanotube concentration in functionalized multi-walled carbon nanotubes-coating step, uptake time after MIP preparation and uptake step stirring rate were expected to affect MIP preparation and voltammetric measurements. The optimization of parameters was performed using Plackett–Burman design, central composite design, artificial neural network and genetic algorithm. The Pareto plot showed that effects of monomer concentration and pH are most important to the process. The best MIP to NIP response ratio was obtained 17.4. The selection of monomer was performed computationally using ab initio calculations. The calibration curve demonstrated linearity over a concentration range of 5–700 μM with a correlation coefficient (r) of 0.9980. The detection limit of PNZ was obtained 3.75 × 10−7 M. The minimum and maximum recovery (%) through the spiking 0.1–0.4 mM PNZ to a biological and some pharmaceutical matrices were obtained 95.9% (human blood serum) and 106% (PNZ tablet), respectively.

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