Using a material database and data fusion method to accelerate the process model development of high shear wet granulation

Abstract High shear wet granulation (HSWG) has been wildly used in manufacturing of oral solid dosage (OSD) forms, and process modeling is vital to understanding and controlling this complex process. In this paper, data fusion and multivariate modeling technique were applied to develop a formulation...

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Autores principales:	Zheng Wang, Junjie Cao, Wanting Li, Yawen Wang, Gan Luo, Yanjiang Qiao, Yanling Zhang, Bing Xu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0bcb1ae5262d4551a73870bc314a63e9
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spelling	oai:doaj.org-article:0bcb1ae5262d4551a73870bc314a63e92021-12-02T19:06:42ZUsing a material database and data fusion method to accelerate the process model development of high shear wet granulation10.1038/s41598-021-96097-x2045-2322https://doaj.org/article/0bcb1ae5262d4551a73870bc314a63e92021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96097-xhttps://doaj.org/toc/2045-2322Abstract High shear wet granulation (HSWG) has been wildly used in manufacturing of oral solid dosage (OSD) forms, and process modeling is vital to understanding and controlling this complex process. In this paper, data fusion and multivariate modeling technique were applied to develop a formulation-process-quality model for HSWG process. The HSWG experimental data from both literature and the authors’ laboratory were fused into a single and formatted representation. A material database and material matching method were used to compensate the incomplete physical characterization of literature formulation materials, and dimensionless parameters were utilized to reconstruct process variables at different granulator scales. The exploratory study on input materials properties by principal component analysis (PCA) revealed that the formulation data collected from different articles generated a formulation library which was full of diversity. In prediction of the median granule size, the partial least squares (PLS) regression models derived from literature data only and a combination of literature data and laboratory data were compared. The results demonstrated that incorporating a small number of laboratory data into the multivariate calibration model could help significantly reduce the prediction error, especially at low level of liquid to solid ratio. The proposed data fusion methodology was beneficial to scientific development of HSWG formulation and process, with potential advantages of saving both experimental time and cost.Zheng WangJunjie CaoWanting LiYawen WangGan LuoYanjiang QiaoYanling ZhangBing XuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zheng Wang Junjie Cao Wanting Li Yawen Wang Gan Luo Yanjiang Qiao Yanling Zhang Bing Xu Using a material database and data fusion method to accelerate the process model development of high shear wet granulation
description	Abstract High shear wet granulation (HSWG) has been wildly used in manufacturing of oral solid dosage (OSD) forms, and process modeling is vital to understanding and controlling this complex process. In this paper, data fusion and multivariate modeling technique were applied to develop a formulation-process-quality model for HSWG process. The HSWG experimental data from both literature and the authors’ laboratory were fused into a single and formatted representation. A material database and material matching method were used to compensate the incomplete physical characterization of literature formulation materials, and dimensionless parameters were utilized to reconstruct process variables at different granulator scales. The exploratory study on input materials properties by principal component analysis (PCA) revealed that the formulation data collected from different articles generated a formulation library which was full of diversity. In prediction of the median granule size, the partial least squares (PLS) regression models derived from literature data only and a combination of literature data and laboratory data were compared. The results demonstrated that incorporating a small number of laboratory data into the multivariate calibration model could help significantly reduce the prediction error, especially at low level of liquid to solid ratio. The proposed data fusion methodology was beneficial to scientific development of HSWG formulation and process, with potential advantages of saving both experimental time and cost.
format	article
author	Zheng Wang Junjie Cao Wanting Li Yawen Wang Gan Luo Yanjiang Qiao Yanling Zhang Bing Xu
author_facet	Zheng Wang Junjie Cao Wanting Li Yawen Wang Gan Luo Yanjiang Qiao Yanling Zhang Bing Xu
author_sort	Zheng Wang
title	Using a material database and data fusion method to accelerate the process model development of high shear wet granulation
title_short	Using a material database and data fusion method to accelerate the process model development of high shear wet granulation
title_full	Using a material database and data fusion method to accelerate the process model development of high shear wet granulation
title_fullStr	Using a material database and data fusion method to accelerate the process model development of high shear wet granulation
title_full_unstemmed	Using a material database and data fusion method to accelerate the process model development of high shear wet granulation
title_sort	using a material database and data fusion method to accelerate the process model development of high shear wet granulation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0bcb1ae5262d4551a73870bc314a63e9
work_keys_str_mv	AT zhengwang usingamaterialdatabaseanddatafusionmethodtoacceleratetheprocessmodeldevelopmentofhighshearwetgranulation AT junjiecao usingamaterialdatabaseanddatafusionmethodtoacceleratetheprocessmodeldevelopmentofhighshearwetgranulation AT wantingli usingamaterialdatabaseanddatafusionmethodtoacceleratetheprocessmodeldevelopmentofhighshearwetgranulation AT yawenwang usingamaterialdatabaseanddatafusionmethodtoacceleratetheprocessmodeldevelopmentofhighshearwetgranulation AT ganluo usingamaterialdatabaseanddatafusionmethodtoacceleratetheprocessmodeldevelopmentofhighshearwetgranulation AT yanjiangqiao usingamaterialdatabaseanddatafusionmethodtoacceleratetheprocessmodeldevelopmentofhighshearwetgranulation AT yanlingzhang usingamaterialdatabaseanddatafusionmethodtoacceleratetheprocessmodeldevelopmentofhighshearwetgranulation AT bingxu usingamaterialdatabaseanddatafusionmethodtoacceleratetheprocessmodeldevelopmentofhighshearwetgranulation
_version_	1718377129321693184

Using a material database and data fusion method to accelerate the process model development of high shear wet granulation

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