Implementation of an artificial neural network as a PAT tool for the prediction of temperature distribution within a pharmaceutical fluidized bed granulator

Korteby, Yasmine; Mahdi, Yassine; Azizou, Amel; Daoud, Kamel; Regdon, Géza

doi:10.1016/j.ejps.2016.03.010

Cited by 21 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to the applications mentioned above, ANNs can be used to determine drug concentration [76,80], predict transdermal permeability [135,136], determine the critical quality attributes that affect a certain formulation property [75,[137][138][139][140], predict the intrinsic solubility of drugs [141,142], control the granulation process within a fluidized bed [50], predict the stability of dosage forms [143][144][145], characterize physicochemical properties [146], control drug quality in multicomponent formulation with overlapping spectra [65,147] and predict complex colloidal delivery systems phase behavior [37,[148][149][150][151] so as to improve the efficiency of conducting various related processes.…”

Section: Other Applications Of Artificial 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

Section: Other Applications Of Artificial 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

“…Moreover, it collects information by identifying patterns and relationships in the data (23). Formulation development and optimization studies are carried out using ANNs and have become increasingly more important in drug development studies, especially in the digital era (24).…”

Section: Introductionmentioning

confidence: 99%

Effect of Mannitol Particle Size on Melatonin Dissolution and Tablet Properties using a Quality by Design Framework

Mesut¹,

Tok²,

Alkan³

et al. 2023

Dissolution Technol.

View full text Add to dashboard Cite

The objective of the current study was to develop an optimized formulation for orally disintegrating tablets (ODTs) containing melatonin. Different particle sizes of mannitol (i.e., filler) were used to study the effects on dissolution and tablet properties using a quality by design (QbD) approach. The quality target product profile was identified, then critical quality attributes (CQAs) were determined. Risk assessment was performed using the Failure Mode Effect Analysis (FMEA) method to identify and rank critical material attributes and process parameters. Thirty-four formulations were prepared and tested. Box-Behnken design (BBD) with response surface methodology was applied to assess the effect of independent factors on CQAs. Finally, the most suitable formulation in terms of tablet properties was determined with Minitab. Specification tests were applied to confirm that the optimized formula met USP requirements. Mannitol with a small particle size had the fastest disintegration time and dissolution rate in ODT formulations containing melatonin.

show abstract

“…In the pharmaceutical field, ANN has been used to predict the amounts of active pharmaceutical ingredients in the dosage [12,13,16]. Recently, some researchers have extended ANN for predicting the parameters in the industrial production scale [17][18][19][20][21][22][23]. Meanwhile, the SVR model is a generalization of a well-known classification algorithm, called support vector machine (SVM), for prediction using kennel transformations and a series of linear equations to separate data by attribute.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network and Support Vector Regression Modeling for Prediction of Mixing Time in Wet Granulation

2021

View full text Add to dashboard Cite

Purpose Every successful pharmaceutical product requires a rapid and reliable scale-up process from small laboratory quantities to a large commercial production to ensure the qualities of products. Despite best efforts, a pharmaceutical company must incur losses in material costs and working-hours before achieving optimal process parameters. One possible approach to shorten this tedious process is to leverage knowledge and experience using a computational algorithm that predicts the process parameters using related data. Method This study aimed to demonstrate the approach of embedded successful material attributes and process parameters of wet granulation as inputs into an artificial neural network (ANN) model and support vector regression (SVR) model to predict the total number of the impeller revolution for dry mixing and wet massing to produce the granules with the same qualities. ResultsThe SVR model performed better for prediction with RMSEs of 3.2552-4.0066. Using the SVR model using Gaussian kernel, which had the least RMSE, in the training stage and validation stage gave the values of MAPE of 17.12% and 12.32%, respectively. Conclusion The model can be implemented for the prediction of the number of impeller revolution by using the proposed parameters.

show abstract

Implementation of an artificial neural network as a PAT tool for the prediction of temperature distribution within a pharmaceutical fluidized bed granulator

Cited by 21 publications

References 16 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

Effect of Mannitol Particle Size on Melatonin Dissolution and Tablet Properties using a Quality by Design Framework

Artificial Neural Network and Support Vector Regression Modeling for Prediction of Mixing Time in Wet Granulation

Contact Info

Product

Resources

About