Application of artificial neural networks for Process Analytical Technology-based dissolution testing

Nagy, Zsombor Kristóf; Petra, Dulichár; Galata, Dorián László; Démuth, Balázs; Borbás, Enikő; Marosi, György; Farkas, Attila

doi:10.1016/j.ijpharm.2019.118464

Cited by 57 publications

(31 citation statements)

References 43 publications

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“…Other NIR methods have capitalised on the detection of strain 6 and chemical information of disintegrants 9 for predicting dissolution performance of tablets. Alternative methods that employ the use of magnetic resonance imaging techniques as well as different kinds of mathematical modelling methods to predict the dissolution of given drug substances from tablets and also to study the effect of process parameters on the dissolution profile of a drug from a tablet have been reported [10][11][12][13][14][15] . However, the use of the above methods to reliably predict the dissolution profiles is still challenging.…”

Section: Introductionmentioning

confidence: 99%

A Fast and Non-destructive Terahertz Dissolution Assay for Immediate Release Tablets

Bawuah

Markl

Turner

et al. 2021

Journal of Pharmaceutical Sciences

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Section: Introductionmentioning

confidence: 99%

A Fast and Non-destructive Terahertz Dissolution Assay for Immediate Release Tablets

Bawuah

Markl

Turner

et al. 2021

Journal of Pharmaceutical Sciences

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“…In opposition to the traditional statistical methods, ML tools offer a distinct opportunity to model complex relationships between several input and output data, thus gaining valuable insights on the process of interest and allowing accurate predictions. Historically, ML tools, such as artificial neural networks (ANN), have been predominantly used to optimize formulation composition and/or processing parameters, based on product properties that are routinely assessed (e.g., drug release profile, tablet disintegration time, hardness and friability) as indicators of a formulation performance [ 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. In addition, some review papers also highlight various applications of ML methods in the development of solid dosage forms [ 22 , 23 , 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine-Learning Algorithms for Better Understanding of Tableting Properties of Lactose Co-Processed with Lipid Excipients

Djuriš

Cirin-Varadjan²,

Aleksić

et al. 2021

Pharmaceutics

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Co-processing (CP) provides superior properties to excipients and has become a reliable option to facilitated formulation and manufacturing of variety of solid dosage forms. Development of directly compressible formulations with high doses of poorly flowing/compressible active pharmaceutical ingredients, such as paracetamol, remains a great challenge for the pharmaceutical industry due to the lack of understanding of the interplay between the formulation properties, process of compaction, and stages of tablets’ detachment and ejection. The aim of this study was to analyze the influence of the compression load, excipients’ co-processing and the addition of paracetamol on the obtained tablets’ tensile strength and the specific parameters of the tableting process, such as (net) compression work, elastic recovery, detachment, and ejection work, as well as the ejection force. Two types of neural networks were used to analyze the data: classification (Kohonen network) and regression networks (multilayer perceptron and radial basis function), to build prediction models and identify the variables that are predominantly affecting the tableting process and the obtained tablets’ tensile strength. It has been demonstrated that sophisticated data-mining methods are necessary to interpret complex phenomena regarding the effect of co-processing on tableting properties of directly compressible excipients.

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“…Additionally, f 2 is usually used to evaluate the similarity of drug dissolution profiles. Nagy et al [96] compared four threelayer ANN models to the traditional PLS regression to predict the dissolution profile of extended release anhydrous caffeine tablets using the NIR and Raman spectra. To be specific, as depicted in Figure 5, the NIR and Raman spectra of each tablet and the experimental dissolution data using the paddle method were measured.…”

Section: Prediction Of Drug Release Behavior In Vitromentioning

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

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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.

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Application of artificial neural networks for Process Analytical Technology-based dissolution testing

Cited by 57 publications

References 43 publications

A Fast and Non-destructive Terahertz Dissolution Assay for Immediate Release Tablets

A Fast and Non-destructive Terahertz Dissolution Assay for Immediate Release Tablets

Application of Machine-Learning Algorithms for Better Understanding of Tableting Properties of Lactose Co-Processed with Lipid Excipients

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

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