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-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.

Recycling of Contaminated Marine Sediment and Industrial By-Products through Combined Stabilization/Solidification and Granulation Treatment

“…From the particle distribution, it was possible to determine the sizes of the 10%, 50%, and 90% (by volume) of the smallest particles, denoted Dv 10 , Dv 50 , and Dv 90 , respectively. The span, which is calculated as (Dv 90 − Dv 10 )/Dv 50 , was 2.93, and showed the usability of the ashes; a uniform particle size distribution would favor a layering mechanism of smaller particles onto the surfaces of larger ones in the granulation phase, leading to an uneven growth process [ 50 ].…”

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

“…The second category of applications used as predictors variables that do not evolve over time. To this respect, process conditions, raw material attributes, and multivariate data (spectroscopy) have been fused to predict granule quality [ 159 ], content uniformity [ 104 , 134 ], powder flowability [ 134 ], coating thickness [ 135 ], and the dissolution of the API [ 91 , 102 , 105 , 135 , 145 ].…”

“… [ 79 ] Pharmaceutical Predict quality model for HSWG process-based formulations Literature data; Process data in HSWG LLDF PLS - /; complementary univariate sources LLDF > individual model e.d. [ 159 ] Predict Beta-carotene, Riboflavin, ferrous fumarate, ginseng, and ascorbic acid content in powder blends; quantify powder flow behavior Light-induced fluorescence spectroscopy; NIR; RGB color imaging LLDF MB-PLS - MB-PLS LLDF > individual model e.d. [ 134 ] Predict the thickness of microsphere coating and API dissolution performance Raw material data; Process data; NIR; Raman; FBRM MLDF MB-PLS - MB-PLS MLDF ≈ Raman individual model* / [ 135 ] Predict meloxicam content in nanofibers NIR; Raman; Colorimetry; Image analysis MLDF PLS; ANN PCA/OPLS scores from raw or preprocessed data OPLS MLDF > individual model Accuracy profiles [ 104 ] …”

Adaptive modeling optimized by the data fusion strategy: Real‐time dying cell percentage prediction using capacitance spectroscopy

“…Numerous applications of data fusion are presented in the informatic domain, including robotics, remote sensing, image analysis, and analytical chemistry 42–45 . However, data fusion in biopharmaceutical process modeling is at the developing stage 46 . This paper hypothesized that data fusion is a powerful tool to combine data from the Cole–Cole model, conductivity/its derivatives, and Mahalanobis distance.…”

“…[42][43][44][45] However, data fusion in biopharmaceutical process modeling is at the developing stage. 46 This paper hypothesized that data fusion is a powerful tool to combine data from the Cole-Cole model, conductivity/its derivatives, and Mahalanobis distance. Based on the authors' knowledge, concatenating the different data blocks followed by variable selection 47 is the most promising data fusion approach to generate a single partial least squares (PLS) model for the quantitative determination of dying cells in the upstream biopharmaceutical process.…”