Full Text:   <1146>

Summary:  <1332>

CLC number: R932

On-line Access: 2020-11-05

Received: 2020-07-29

Revision Accepted: 2020-08-14

Crosschecked: 2020-10-15

Cited: 0

Clicked: 2182

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Wen-long Li

https://orcid.org/0000-0001-7961-7975

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE B 2020 Vol.21 No.11 P.897-910

http://doi.org/10.1631/jzus.B2000417


A near-infrared spectroscopy-based end-point determination method for the blending process of Dahuang soda tablets


Author(s):  Si-jun Wu, Ping Qiu, Pian Li, Zheng Li, Wen-long Li

Affiliation(s):  College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; more

Corresponding email(s):   wshlwl@tjutcm.edu.cn

Key Words:  Process analytical technology, Blending process, Near-infrared spectroscopy, End-point determination


Si-jun Wu, Ping Qiu, Pian Li, Zheng Li, Wen-long Li. A near-infrared spectroscopy-based end-point determination method for the blending process of Dahuang soda tablets[J]. Journal of Zhejiang University Science B, 2020, 21(11): 897-910.

@article{title="A near-infrared spectroscopy-based end-point determination method for the blending process of Dahuang soda tablets",
author="Si-jun Wu, Ping Qiu, Pian Li, Zheng Li, Wen-long Li",
journal="Journal of Zhejiang University Science B",
volume="21",
number="11",
pages="897-910",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B2000417"
}

%0 Journal Article
%T A near-infrared spectroscopy-based end-point determination method for the blending process of Dahuang soda tablets
%A Si-jun Wu
%A Ping Qiu
%A Pian Li
%A Zheng Li
%A Wen-long Li
%J Journal of Zhejiang University SCIENCE B
%V 21
%N 11
%P 897-910
%@ 1673-1581
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B2000417

TY - JOUR
T1 - A near-infrared spectroscopy-based end-point determination method for the blending process of Dahuang soda tablets
A1 - Si-jun Wu
A1 - Ping Qiu
A1 - Pian Li
A1 - Zheng Li
A1 - Wen-long Li
J0 - Journal of Zhejiang University Science B
VL - 21
IS - 11
SP - 897
EP - 910
%@ 1673-1581
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B2000417


Abstract: 
Objectives: This study is aimed to explore the blending process of Dahuang soda tablets. These are composed of two active pharmaceutical ingredients (APIs, emodin and emodin methyl ether) and four kinds of excipients (sodium bicarbonate, starch, sucrose, and magnesium stearate). Also, the objective is to develop a more robust model to determine the blending end-point. Methods: Qualitative and quantitative methods based on near-infrared (NIR) spectroscopy were established to monitor the homogeneity of the powder during the blending process. A calibration set consisting of samples from 15 batches was used to develop two types of calibration models with the partial least squares regression (PLSR) method to explore the influence of density on the model robustness. The principal component analysis-moving block standard deviation (PCA-MBSD) method was used for the end-point determination of the blending with the process spectra. Results: The model with different densities showed better prediction performance and robustness than the model with fixed powder density. In addition, the blending end-points of APIs and excipients were inconsistent because of the differences in the physical properties and chemical contents among the materials of the design batches. For the complex systems of multi-components, using the PCA-MBSD method to determine the blending end-point of each component is difficult. In these conditions, a quantitative method is a more suitable alternative. Conclusions: Our results demonstrated that the effect of density plays an important role in improving the performance of the model, and a robust modeling method has been developed.

基于近红外光谱技术的大黄苏打片混合工艺终点判断方法的研究

目的:探究密度效应对模型性能的影响,旨在建立一种稳健性更好的模型来实现大黄苏打片混合终点的准确判断.
创新点:通过将密度差异变量引入模型校正集中的方法,建立了一种稳健性更好的原辅料多组分定量校正模型.
方法:利用15批样品建立包含密度效应和未包含密度效应的偏最小二乘回归校正模型,并利用模型对3个未知批次样品进行终点监测.同时,使用主成分分析-移动块标准偏差算法对3批样品混合终点进行定性判别.分别使用基于近红外光谱技术的定性、定量分析方法,实现对大黄苏打片混合终点进行准确监测的目的.
结论:粉体密度效应对模型预测性能的提高起到了重要作用.与普通模型相比,本研究所开发的压力不敏感模型展示了更加稳健的预测性能,这种稳健建模策略具有一定的推广应用前景.

关键词:过程分析技术;混合过程;近红外光谱;终点判断

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Abe H, Otsuka M, 2012. Effects of lubricant-mixing time on prolongation of dissolution time and its prediction by measuring near infrared spectra from tablets. Drug Dev Ind Pharm, 38(4):412-419.

[2]Barone A, Glassey J, Montague G, 2019. Towards online near-infrared spectroscopy to optimise food product mixing. J Food Eng, 263:227-236.

[3]Bodson C, Dewé W, Hubert P, et al., 2006. Comparison of FT-NIR transmission and UV-vis spectrophotometry to follow the mixing kinetics and to assay low-dose tablets containing riboflavin. J Pharm Biomed Anal, 41(3):783-790.

[4]Chen H, Tan C, Lin Z, et al., 2019. Quantifying several adulterants of notoginseng powder by near-infrared spectroscopy and multivariate calibration. Spectrochim Acta Part A Mol Biomol Spectrosc, 211:280-286.

[5]Chen ZP, Morris J, Martin E, et al., 2006. Extracting chemical information from spectral data with multiplicative light scattering effects by optical path-length estimation and correction. Anal Chem, 78(22):7674-7681.

[6]de Leersnyder F, Peeters E, Djalabi H, et al., 2018. Development and validation of an in-line NIR spectroscopic method for continuous blend potency determination in the feed frame of a tablet press. J Pharm Biomed Anal, 151: 274-283.

[7]Deconinck E, van Campenhout R, Aouadi C, et al., 2019. Combining attenuated total reflectance-infrared spectroscopy and chemometrics for the identification and the dosage estimation of MDMA tablets. Talanta, 195:142-151.

[8]de Leersnyder F, Vanhoorne V, Kumar A, et al., 2019. Evaluation of an in-line NIR spectroscopic method for the determination of the residence time in a tablet press. Int J Pharm, 565:358-366.

[9]Goh HP, Heng PWS, Liew CV, 2018. Comparative evaluation of powder flow parameters with reference to particle size and shape. Int J Pharm, 547(1-2):133-141.

[10]Hossain MN, Igne B, Anderson CA, et al., 2019. Influence of moisture variation on the performance of Raman spectroscopy in quantitative pharmaceutical analyses. J Pharm Biomed Anal, 164:528-535.

[11]Järvinen K, Hoehe W, Järvinen M, et al., 2013. In-line monitoring of the drug content of powder mixtures and tablets by near-infrared spectroscopy during the continuous direct compression tableting process. Eur J Pharm Sci, 48(4-5):680-688.

[12]Kona R, Qu HB, Mattes R, et al., 2013. Application of in-line near infrared spectroscopy and multivariate batch modeling for process monitoring in fluid bed granulation. Int J Pharm, 452(1-2):63-72.

[13]Lee WB, Widjaja E, Heng PWS, et al., 2019. Near infrared spectroscopy for rapid and in-line detection of particle size distribution variability in lactose during mixing. Int J Pharm, 566:454-462.

[14]Li WL, Han HF, Cheng ZW, et al., 2016. A feasibility research on the monitoring of traditional Chinese medicine production process using NIR-based multivariate process trajectories. Sensor Actuat B Chem, 231:313-323.

[15]Lin YQ, Li WY, Xu J, et al., 2015. Development of a NIR-based blend uniformity method for a drug product containing multiple structurally similar actives by using the quality by design principles. Int J Pharm, 488(1-2):120-126.

[16]Liu P, Wang J, Li Q, et al., 2019. Rapid identification and quantification of Panax notoginseng with its adulterants by near infrared spectroscopy combined with chemometrics. Spectrochim Acta Part A Mol Biomol Spectrosc, 206:23-30.

[17]Martínez L, Peinado A, Liesum L, et al., 2013. Use of near-infrared spectroscopy to quantify drug content on a continuous blending process: influence of mass flow and rotation speed variations. Eur J Pharm Biopharm, 84(3):606-615.

[18]Mehmood T, 2016. Hotelling T2 based variable selection in partial least squares regression. Chemometr Intell Lab Syst, 154:23-28.

[19]Mohan S, Momose W, Katz JM, et al., 2018. A robust quantitative near infrared modeling approach for blend monitoring. J Pharm Biomed Anal, 148:51-57.

[20]Momose W, Imai K, Yokota S, et al., 2011. Process analytical technology applied for end-point detection of pharmaceutical blending by combining two calibration-free methods: simultaneously monitoring specific near-infrared peak intensity and moving block standard deviation. Powder Technol, 210(2):122-131.

[21]Otsuka M, Tanabe H, Osaki K, et al., 2007. Chemoinformetrical evaluation of dissolution property of indomethacin tablets by near-infrared spectroscopy. J Pharm Sci, 96(4):788-801.

[22]Pauli V, Roggo Y, Pellegatti L, et al., 2019. Process analytical technology for continuous manufacturing tableting processing: a case study. J Pharm Biomed Anal, 162:101-111.

[23]Pawar P, Talwar S, Reddy D, et al., 2019. A “Large-N” content uniformity process analytical technology (PAT) method for phenytoin sodium tablets. J Pharm Sci, 108(1):494-505.

[24]Sánchez-Paternina A, Sierra-Vega NO, Cárdenas V, et al., 2019. Variographic analysis: a new methodology for quality assurance of pharmaceutical blending processes. Comput Chem Eng, 124:109-123.

[25]Scheibelhofer O, Balak N, Wahl PR, et al., 2013a. Monitoring blending of pharmaceutical powders with multipoint NIR spectroscopy. AAPS Pharm Sci Tech, 14:234-244.

[26]Scheibelhofer O, Balak N, Koller DM, et al., 2013b. Spatially resolved monitoring of powder mixing processes via multiple NIR-probes. Powder Technol, 243:161-170.

[27]Scheibelhofer O, Grabner B, Bondi RW Jr, et al., 2015. Designed blending for near infrared calibration. J Pharm Sci, 104(7):2312-2322.

[28]Short SM, Cogdill RP, Wildfong PLD, et al., 2009. A near-infrared spectroscopic investigation of relative density and crushing strength in four-component compacts. J Pharm Sci, 98(3):1095-1109.

[29]Sibik J, Chalus P, Maurer L, et al., 2017. Mechanistic approach in powder blending PAT: bi-layer mixing and asymptotic end point prediction. Powder Technol, 308:306-317.

[30]Sierra-Vega NO, Román-Ospino A, Scicolone J, et al., 2019. Assessment of blend uniformity in a continuous tablet manufacturing process. Int J Pharm, 560:322-333.

[31]Sun XD, Subedi P, Walsh KB, 2020. Achieving robustness to temperature change of a NIRS-PLSR model for intact mango fruit dry matter content. Postharvest Biol Technol, 162:111117.

[32]Vanarase AU, Osorio JG, Muzzio FJ, et al., 2013. Effects of powder flow properties and shear environment on the performance of continuous mixing of pharmaceutical powders. Powder Technol, 246:63-72.

[33]Vargas JM, Nielsen S, Cárdenas V, et al., 2018. Process analytical technology in continuous manufacturing of a commercial pharmaceutical product. Int J Pharm, 538(1-2):167-178.

[34]Xiong HS, Gong XC, Qu HB, et al., 2012. Monitoring batch-to-batch reproducibility of liquid-liquid extraction process using in-line near-infrared spectroscopy combined with multivariate analysis. J Pharm Biomed Anal, 70:178-187.

[35]Xu L, Shou JY, Gill RA, et al., 2019. Effects of ZJ0273 on barley and growth recovery of herbicide-stressed seedlings through application of branched-chain amino acids. J Zhejiang Univ-Sci B (Biomed & Biotechnol), 20(1):71-83.

[36]http://doi.org/10.1631/jzus.B1700375

[37]Xue SW, Lee TW, Guo YH, 2018. Spontaneous activity in medial orbitofrontal cortex correlates with trait anxiety in healthy male adults. J Zhejiang Univ-Sci B (Biomed & Biotechnol), 19(8):643-653.

[38]http://doi.org/10.1631/jzus.B1700481

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2022 Journal of Zhejiang University-SCIENCE