TY - JOUR
T1 - Near infrared spectroscopic (NIRS) analysis of drug-loading rate and particle size of risperidone microspheres by improved chemometric model
AU - Song, Jia
AU - Xie, Jing
AU - Li, Chenliang
AU - Lu, Jia Hui
AU - Meng, Qing Fan
AU - Yang, Zhaogang
AU - Lee, Robert J.
AU - Wang, Di
AU - Teng, Le Sheng
N1 - Funding Information:
This work was supported by the ‘Research and Development of Industrial Technology’ Program of Jilin Province, PR China (grant no. 2013C015-3 ).
PY - 2014/9/10
Y1 - 2014/9/10
N2 - Microspheres have been developed as drug carriers in controlled drug delivery systems for years. In our present study, near infrared spectroscopy (NIRS) is applied to analyze the particle size and drug loading rate in risperidone poly(d,l-lactide-co-glycolide) (PLGA) microspheres. Various batches of risperidone PLGA microspheres were designed and prepared successfully. The particle size and drug-loading rate of all the samples were determined by a laser diffraction particle size analyzer and high performance liquid chromatography (HPLC) system. Monte Carlo algorithm combined with partial least squares (MCPLS) method was applied to identify the outliers and choose the numbers of calibration set. Furthermore, a series of preprocessing methods were performed to remove signal noise in NIR spectra. Moving window PLS and radical basis function neural network (RBFNN) methods were employed to establish calibration model. Our data demonstrated that PLS-developed model was only suitable for drug loading analysis in risperidone PLGA microspheres. Comparatively, RBFNN-based predictive models possess better fitting quality, predictive effect, and stability for both drug loading rate and particle size analysis. The correlation coefficients of calibration set (Rc 2) were 0.935 and 0.880, respectively. The performance of optimum RBFNN models was confirmed by independent verification test with 15 samples. Collectively, our method is successfully performed to monitor drug-loading rate and particle size during risperidone PLGA microspheres preparation.
AB - Microspheres have been developed as drug carriers in controlled drug delivery systems for years. In our present study, near infrared spectroscopy (NIRS) is applied to analyze the particle size and drug loading rate in risperidone poly(d,l-lactide-co-glycolide) (PLGA) microspheres. Various batches of risperidone PLGA microspheres were designed and prepared successfully. The particle size and drug-loading rate of all the samples were determined by a laser diffraction particle size analyzer and high performance liquid chromatography (HPLC) system. Monte Carlo algorithm combined with partial least squares (MCPLS) method was applied to identify the outliers and choose the numbers of calibration set. Furthermore, a series of preprocessing methods were performed to remove signal noise in NIR spectra. Moving window PLS and radical basis function neural network (RBFNN) methods were employed to establish calibration model. Our data demonstrated that PLS-developed model was only suitable for drug loading analysis in risperidone PLGA microspheres. Comparatively, RBFNN-based predictive models possess better fitting quality, predictive effect, and stability for both drug loading rate and particle size analysis. The correlation coefficients of calibration set (Rc 2) were 0.935 and 0.880, respectively. The performance of optimum RBFNN models was confirmed by independent verification test with 15 samples. Collectively, our method is successfully performed to monitor drug-loading rate and particle size during risperidone PLGA microspheres preparation.
KW - Microspheres
KW - Near infrared spectroscopy
KW - Partial least squares
KW - Radical basis function neural network
KW - Risperidone
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U2 - 10.1016/j.ijpharm.2014.06.033
DO - 10.1016/j.ijpharm.2014.06.033
M3 - Article
C2 - 24954726
AN - SCOPUS:84903719794
SN - 0378-5173
VL - 472
SP - 296
EP - 303
JO - International Journal of Pharmaceutics
JF - International Journal of Pharmaceutics
IS - 1-2
ER -