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Fast, Spectroscopy-Based Prediction of In Vitro Dissolution Profile of Extended Release Tablets Using Artificial Neural Networks

1
Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rakpart 3, H-1111 Budapest, Hungary
2
Directorate General for Medicine Authorization and Methodology, Strategy, Development and Methodology Division, National Institute of Pharmacy and Nutrition, Zrínyi u. 3, H-1051 Budapest, Hungary
*
Author to whom correspondence should be addressed.
Pharmaceutics 2019, 11(8), 400; https://doi.org/10.3390/pharmaceutics11080400
Received: 28 June 2019 / Revised: 28 July 2019 / Accepted: 5 August 2019 / Published: 9 August 2019
PDF [2955 KB, uploaded 9 August 2019]
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Abstract

The pharmaceutical industry has never seen such a vast development in process analytical methods as in the last decade. The application of near-infrared (NIR) and Raman spectroscopy in monitoring production lines has also become widespread. This work aims to utilize the large amount of information collected by these methods by building an artificial neural network (ANN) model that can predict the dissolution profile of the scanned tablets. An extended release formulation containing drotaverine (DR) as a model drug was developed and tablets were produced with 37 different settings, with the variables being the DR content, the hydroxypropyl methylcellulose (HPMC) content and compression force. NIR and Raman spectra of the tablets were recorded in both the transmission and reflection method. The spectra were used to build a partial least squares prediction model for the DR and HPMC content. The ANN model used these predicted values, along with the measured compression force, as input data. It was found that models based on both NIR and Raman spectra were capable of predicting the dissolution profile of the test tablets within the acceptance limit of the f2 difference factor. The performance of these ANN models was compared to PLS models using the same data as input, and the prediction of the ANN models was found to be more accurate. The proposed method accomplishes the prediction of the dissolution profile of extended release tablets using either NIR or Raman spectra.
Keywords: dissolution prediction; artificial neural networks; extended release formulation; Raman spectroscopy; NIR spectroscopy; tablet compression dissolution prediction; artificial neural networks; extended release formulation; Raman spectroscopy; NIR spectroscopy; tablet compression
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Galata, D.L.; Farkas, A.; Könyves, Z.; Mészáros, L.A.; Szabó, E.; Csontos, I.; Pálos, A.; Marosi, G.; Nagy, Z.K.; Nagy, B. Fast, Spectroscopy-Based Prediction of In Vitro Dissolution Profile of Extended Release Tablets Using Artificial Neural Networks. Pharmaceutics 2019, 11, 400.

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