Analysis of paclitaxel concentration in rat plasma by Raman spectrums combined with partial least square_Journal of Biomedical Engineering

Authors：

TENG Meiyu ^1,2 , SONG Jia ^2,3 , ZHAO Yi ⁴ , LU Chengyu ² , XING Gaoyang ² , LI Lanzhou ² , YAN Guodong ² ,  WANG Di ²

1. Jilin JiCe Detective Technical Co.LTD, Changchun 130012, P.R.China;
2. College of Life Science, Jilin University, Changchun 130012, P.R.China;
3. College of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, P.R.China;
4. Trauma Department of Orthopedics, The First Hospital of Jilin University, Changchun 130012, P.R.China;

Corresponding author：

WANG Di, Email: jluwangdi@jlu.edu.cn

Keywords：

Raman spectroscopy; partial least square; paclitaxel

DOI：

10.7507/1001-5515.201607051

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Partial least square (PLS) combining with Raman spectroscopy was applied to develop predictive models for plasma paclitaxel concentration detection. In this experiment, 312 samples were scanned by Raman spectroscopy. High performance liquid chromatography (HPLC) was applied to determine the paclitaxel concentration in 312 rat plasma samples. Monte Carlo partial least square (MCPLS) method was successfully performed to identify the outliers and the numbers of calibration set. Based on the values of degree of approach (D_a), moving window partial least square (MWPLS) was used to choose the suitable preprocessing method, optimum wavelength variables and the number of latent variables. The correlation coefficients between reference values and predictive values in both calibration set (R_c²) and validation set (R_p²) of optimum PLS model were 0.933 1 and 0.926 4, respectively. Furthermore, an independent verification test was performed on the prediction model. The results showed that the correlation error of the 20 validation samples was 9.36%±2.03%, which confirmed the well predictive ability of established PLS quantitative analysis model.

Citation： TENG Meiyu, SONG Jia, ZHAO Yi, LU Chengyu, XING Gaoyang, LI Lanzhou, YAN Guodong, WANG Di. Analysis of paclitaxel concentration in rat plasma by Raman spectrums combined with partial least square. Journal of Biomedical Engineering, 2018, 35(4): 578-582. doi: 10.7507/1001-5515.201607051 Copy

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7.	Hu Yaxi, Feng Shaolong, Gao Fang, et al. Detection of melamine in milk using molecularly imprinted polymers-surface enhanced Raman spectroscopy. Food Chem, 2015, 176(49): 123-129.
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12.	杨涛, 崔福德, 金大德. RP-HPLC 法测定紫杉醇在大鼠血浆中的含量. 沈阳药科大学学报, 2008, 25(1): 48-51, 55.
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14.	Song Jia, Xie Jing, Li Chenliang, et al. Near infrared spectroscopic (NIRS) analysis of drug-loading rate and particle size of risperidone microspheres by improved chemometric model. Int J Pharm, 2014, 472(1/2): 296-303.
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1. Panchagnula R. Pharmaceutical aspects of paclitaxel. Int J Pharm, 1998, 172(1/2): 1-15.
2. He L, Jagtap P G, Kingston D G, et al. A common pharmacophore for Taxol and the epothilones based on the biological activity of a taxane molecule lacking a C-13 side chain. Biochemistry, 2000, 39(14): 3972-3978.
3. Guenard D, Gueritte-Voegelein F, Potier P. Taxol and taxotere: discovery, chemistry, and structure-activity relationships. Acc Chem Res, 1993, 26(4): 160-167.
4. Gangadevi V, Muthumary J. Preliminary studies on cytotoxic effect of fungal taxol on cancer cell lines. African Journal of Biotechnology, 2007, 6(12): 1382-1386.
5. De Beer T, Burggraeve A, Fonteyne M, et al. Near infrared and Raman spectroscopy for the in-process monitoring of pharmaceutical production processes. Int J Pharm, 2011, 417(1/2): 32-47.
6. Zhang Bin, Maiti A, Shively S, et al. Microtubule-binding drugs offset tau sequestration by stabilizing microtubules and reversing fast axonal transport deficits in a tauopathy model. Proc Natl Acad Sci U S A, 2005, 102(1): 227-231.
7. Hu Yaxi, Feng Shaolong, Gao Fang, et al. Detection of melamine in milk using molecularly imprinted polymers-surface enhanced Raman spectroscopy. Food Chem, 2015, 176(49): 123-129.
8. Scheier R, Scheeder M, Schmidt H. Prediction of pork quality at the slaughter line using a portable Raman device. Meat Sci, 2015, 103: 96-103.
9. Berhe D T, Lawaetz A J, Engelsen S B, et al. Accurate determination of endpoint temperature of cooked meat after storage by Raman spectroscopy and chemometrics. Food Control, 2015, 52: 119-125.
10. Tenenhaus M, Vinzi V E, Chatelin Y M, et al. PLS path modeling. Comput Stat Data Anal, 2005, 48(1): 159-205.
11. Mariani N C T, de Almeida Teixeira G H, de Lima K M G, et al. NIRS and iSPA-PLS for predicting total anthocyanin content in jaboticaba fruit. Food Chemistry, 2015, 174: 643-648.
12. 杨涛, 崔福德, 金大德. RP-HPLC 法测定紫杉醇在大鼠血浆中的含量. 沈阳药科大学学报, 2008, 25(1): 48-51, 55.
13. Li Jiangbo, Huang Wenqian, Chen Liping, et al. Variable selection in visible and near-infrared spectral analysis for noninvasive determination of soluble solids content of ‘Ya’ pear. Food Anal Methods, 2014, 7(9): 1891-1902.
14. Song Jia, Xie Jing, Li Chenliang, et al. Near infrared spectroscopic (NIRS) analysis of drug-loading rate and particle size of risperidone microspheres by improved chemometric model. Int J Pharm, 2014, 472(1/2): 296-303.
15. Haughey S A, Galvin-King P, Ho Y C, et al. The feasibility of using near infrared and Raman spectroscopic techniques to detect fraudulent adulteration of chili powders with Sudan dye. Food Control, 2015, 48(SI): 75-83.

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