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AAPS PharmSciTech. 2012 Jun;13(2):611-22. doi: 10.1208/s12249-012-9782-7. Epub 2012 Apr 26.

Evaluation of anticancer drug-loaded nanoparticle characteristics by nondestructive methodologies.

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  • 1Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, 2300 4th street, N.W., Washington, District of Columbia 20059, USA.


The purpose of this study was to utilize near-infrared (NIR) spectroscopy and near-infrared chemical imaging (NIR-CI) as non-invasive techniques to evaluate the drug loading in letrozole-loaded PLGA nanoparticle formulations prepared by the emulsification-solvent evaporation method. A Plackett-Burman design was applied to evaluate the main effects of amount of drug (X(1)), amount of polymer (X(2)), stirring rate (X(3)), emulsifier concentration (X(4)), organic to aqueous phase volume ratio (X(5)), type of organic solvent (X(6)), and homogenization time (X(7)) on drug entrapment efficiency. The influence of three different spectral pretreatment methods (multiplicative scatter correction, standard normal variate, and Savitzky-Golay second derivative transformation with third-order polynomial) and two different regression methods (PLS regression and principal component regression (PCR)) on model prediction ability were compared. PLS of spectra that were pretreated with Savitzky-Golay second derivative transformation provided better model prediction than PCR as it revealed better linear correlation (correlation coefficient of 0.991) for both calibration and prediction models. Relatively low values of root mean square errors of calibration (RMSEC = 0.748) and prediction (RMSEP = 0.786) and low standard errors of calibration (SEC = 0.758) and prediction (SEP = 0.589) suggested good predictability for estimation of the loading of letrozole in PLGA nanoparticles. NIR-CI analysis also revealed mutual homogenous distribution of both polymer and drug and was capable of clearly distinguishing the 12 formulations both quantitatively and qualitatively. In conclusion, NIR and NIR-CI could be potentially used to characterize anticancer drug-loaded nanoparticulate matrix.

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