Development of Molecularly Imprinted Olanzapine Nano-particles: In Vitro Characterization and In Vivo Evaluation

Abstract

Abstract. Molecularly imprinted nano-particles (MINPs) selective for olanzapine were prepared using methacrylic acid (MA) as monomer, ethylene glycol dimethacrylate (EGDMA) as a cross-linker, and 2,2-azobis (2-isobutyronitrile) (AIBN) as the initiator in 36 different ratios. The reaction runs with considerable fine powder formation were selected for further binding and selectivity studies. The MINP with the best selectivity (MINP-32) was chosen for further structural characterization by Fourier transform infrared spectroscopy (FT-IR), thermal gravimetric analysis (TGA), scanning electron microscopy (SEM), adsorption-desorption isotherm for specific surface area, volume and average pore diameter determination. All characterization methods confirmed the successful formation of MINP. The optimum conditions for maximum template loading on the MINP-32 were found by experimental design using response surface methodology (RSM) and choosing absorbent amount, pH, and time as the main factors. MINPs with maximum template loading also indicated significant selectivity between template and its analog (clozapine). The release profile demonstrated a maximum release of about 95% after 288 h for MINP-32 in comparison with about 94% after 120 h for non-MINP-32. The same slow release of drug from MINP-32 was also observed during animal study of the plasma level of template, 20-28 μg/ml versus 5-10 μg/ml. The MINP-32 of this study represents a desirable ability to keep the memory of the template with significant selectivity and good capability to control the release of template in vitro and in vivo and hence could be a promising drug delivery system.

The Use of Computational Methods for the Development of Molecularly Imprinted Polymers

“…Similar to the situation in the pre-polymerization mixture, the possible combinations of experimental parameters (e.g., analyte concentration, solvent, pH, temperature, flow rate, incubation time) are nearly endless, making this an area highly suited for multivariate optimization. Thus, different combinations of experimental designs and response surface modeling have been used for optimization of parameters when using MIPs in adsorption, separation or sensing applications [ 214 , 267 , 375 , 467 , 468 , 470 , 475 , 476 , 477 , 520 , 521 , 522 , 523 , 524 , 525 , 526 , 527 , 528 , 529 , 530 , 531 , 532 , 533 , 534 , 535 , 536 , 537 , 538 , 539 , 540 , 541 , 542 , 543 , 544 , 545 , 546 , 547 , 548 , 549 , 550 , 551 , 552 , 553 , 554 , 555 , 556 , 557 , …”