Analysis of fluidized bed granulation process using conventional and novel modeling techniques

Petrović, Jelena; Chansanroj, K.; Meier, Brigitte; Ibrić, Svetlana; Betz, Gabriele

doi:10.1016/j.ejps.2011.07.013

Cited by 36 publications

(8 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GRNNs are involved in many aspects of pharmaceutical science research [32][33][34][35][36][37]. It is possible to predict drug release based on GRNNs.…”

Section: Generalized Regression Neural Networkmentioning

confidence: 99%

State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

Wang¹,

Di²,

Wang

et al. 2022

Pharmaceutics

View full text Add to dashboard Cite

During the development of a pharmaceutical formulation, a powerful tool is needed to extract the key points from the complicated process parameters and material attributes. Artificial neural networks (ANNs), a promising and more flexible modeling technique, can address real intricate questions in a high parallelism and distributed pattern in the manner of biological neural networks. The data mined and analyzing based on ANNs have the ability to replace hundreds of trial and error experiments. ANNs have been used for data analysis by pharmaceutics researchers since the 1990s and it has now become a research method in pharmaceutical science. This review focuses on the latest application progress of ANNs in the prediction, characterization and optimization of pharmaceutical formulation to provide a reference for the further interdisciplinary study of pharmaceutics and ANNs.

show abstract

“…GRNNs are involved in many aspects of pharmaceutical science research [32][33][34][35][36][37]. It is possible to predict drug release based on GRNNs.…”

Section: Generalized Regression Neural Networkmentioning

confidence: 99%

State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

Wang¹,

Di²,

Wang

et al. 2022

Pharmaceutics

View full text Add to dashboard Cite

show abstract

“…A successful development of a pharmaceutical formulation is dependent on both formulation ingredients and process parameters (Djuris, 2013). Application of chemometric tools such as experimental design and multivariate data analysis enables to increase the product and process knowledge and consequently allows for appropriate excipients selection as well as proper identification and optimization of critical process parameters (Djuris et al, 2012;Ibrić et al 2002;Petrović et al, 2011;Verma et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

A data mining approach to optimize pellets manufacturing process based on a decision tree algorithm

Ronowicz

Thommes

Kleinebudde

et al. 2015

European Journal of Pharmaceutical Sciences

View full text Add to dashboard Cite

“…The wet granulation process is known to have a significant impact on the properties of the resulting tablets by manipulating the granule's properties via modifying the powder properties. [15][16][17][18] In addition, there are several reports that powder properties (particle size, powder density, flowability and others) affected the weight variation of mini-tablets. [19][20][21] However, systematic knowledge concerning the weight variation of mini-tablets is yet to be obtained.…”

Section: Introductionmentioning

confidence: 99%

Effects of Manufacturing Process Variables on the Tablet Weight Variation of Mini-tablets Clarified by a Definitive Screening Design

Usuda

Masukawa²,

Leong

et al. 2021

Chem. Pharm. Bull.

View full text Add to dashboard Cite

This study investigated the effect of manufacturing process variables of mini-tablets, in particular, the effect of process variables concerning fluidized bed granulation on tablet weight variation. Test granules were produced with different granulation conditions according to a definitive screening design (DSD). The five evaluated factors assigned to DSD were: the grinding speed of the sample mill at the grinding process of the active pharmaceutical ingredient (X1), microcrystalline cellulose content in granules (X2), inlet air temperature (X3), binder concentration (X4) and the spray speed of the binder solution (X5) at the granulation process. First, the relationships between the evaluated factors and the granule properties were investigated. As a result of the DSD analysis, the mode of action of granulation parameters on the granule properties was fully characterized. Subsequently, the variation in tablet weight was examined. In addition to mini-tablets (3 mm diameter), this experiment assessed regular tablets (8 mm diameter). From the results for regular tablets, the variation in tablet weight was affected by the flowability of granules. By contrast, regarding the mini-tablets, no significant effect on the variation of tablet weight was found from the evaluated factors. From this result, this study further focused on other important factors besides the granulation process, and then the effect of the die-hole position of the multiple-tip tooling on tablet weight variation was proven to be significant. Our findings provide a better understanding of manufacturing mini-tablets.

show abstract

Analysis of fluidized bed granulation process using conventional and novel modeling techniques

Cited by 36 publications

References 32 publications

State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

A data mining approach to optimize pellets manufacturing process based on a decision tree algorithm

Effects of Manufacturing Process Variables on the Tablet Weight Variation of Mini-tablets Clarified by a Definitive Screening Design

Contact Info

Product

Resources

About