Fast, Spectroscopy-Based Prediction of In Vitro Dissolution Profile of Extended Release Tablets Using Artificial Neural Networks
| Autor: | Lilla Alexandra Mészáros, Andrea Pálos, Dorián László Galata, Edina Szabó, Zsombor Kristóf Nagy, Zsófia Könyves, Brigitta Nagy, Attila Farkas, György Marosi, István Csontos |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Materials science
lcsh:RS1-441 Pharmaceutical Science 02 engineering and technology 030226 pharmacology & pharmacy Article lcsh:Pharmacy and materia medica 03 medical and health sciences symbols.namesake 0302 clinical medicine Partial least squares regression medicine Spectroscopy Dissolution Artificial neural network Near-infrared spectroscopy dissolution prediction NIR spectroscopy extended release formulation 021001 nanoscience & nanotechnology Raman spectroscopy symbols 0210 nano-technology Biological system Drotaverine Extended release tablets artificial neural networks tablet compression medicine.drug |
| Zdroj: | Pharmaceutics Volume 11 Issue 8 Pharmaceutics, Vol 11, Iss 8, p 400 (2019) |
| ISSN: | 1999-4923 |
| DOI: | 10.3390/pharmaceutics11080400 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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