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Effect of roll compaction on granule size distribution of microcrystalline cellulose–mannitol mixtures: computational intelligence modeling and parametric analysis

Authors Kazemi P, Khalid MH, Pérez Gago A, Kleinebudde P, Jachowicz R, Szlęk J, Mendyk A

Received 14 October 2016

Accepted for publication 25 November 2016

Published 18 January 2017 Volume 2017:11 Pages 241—251

DOI https://doi.org/10.2147/DDDT.S124670

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Cristian Vilos

Peer reviewer comments 2

Editor who approved publication: Dr James Janetka

Pezhman Kazemi,1 Mohammad Hassan Khalid,1 Ana Pérez Gago,2 Peter Kleinebudde,2 Renata Jachowicz,1 Jakub Szlęk,1 Aleksander Mendyk1

1Department of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, Jagiellonian University Medical College, Krakow, Poland; 2Institute of Pharmaceutics and Biopharmaceutics, Heinrich-Heine-University, Düsseldorf, Germany

Abstract:
Dry granulation using roll compaction is a typical unit operation for producing solid dosage forms in the pharmaceutical industry. Dry granulation is commonly used if the powder mixture is sensitive to heat and moisture and has poor flow properties. The output of roll compaction is compacted ribbons that exhibit different properties based on the adjusted process parameters. These ribbons are then milled into granules and finally compressed into tablets. The properties of the ribbons directly affect the granule size distribution (GSD) and the quality of final products; thus, it is imperative to study the effect of roll compaction process parameters on GSD. The understanding of how the roll compactor process parameters and material properties interact with each other will allow accurate control of the process, leading to the implementation of quality by design practices. Computational intelligence (CI) methods have a great potential for being used within the scope of quality by design approach. The main objective of this study was to show how the computational intelligence techniques can be useful to predict the GSD by using different process conditions of roll compaction and material properties. Different techniques such as multiple linear regression, artificial neural networks, random forest, Cubist and k-nearest neighbors algorithm assisted by sevenfold cross-validation were used to present generalized models for the prediction of GSD based on roll compaction process setting and material properties. The normalized root-mean-squared error and the coefficient of determination (R2) were used for model assessment. The best fit was obtained by Cubist model (normalized root-mean-squared error =3.22%, R2=0.95). Based on the results, it was confirmed that the material properties (true density) followed by compaction force have the most significant effect on GSD.

Keywords: computational intelligence, milling, roll compaction, dry granulation, neural network, Cubist

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