Abstract
In pharmaceutical industry, dissolution testing is part of the target product quality that are essentials in the approval of new products. The prediction of the dissolution profile based on spectroscopic data is an alternative to the current destructive and time-consuming method. Raman and near infrared (NIR) spectroscopies are two complementary methods, that provide information on the physical and chemical properties of the tablets and can help in predicting their dissolution profiles. This work aims to use the information collected by these methods by creating an artificial neural network model that can predict the dissolution profiles of the scanned tablets. The ANN models created used the spectroscopies data along with the measured compression curves as an input to predict the dissolution profiles. It was found that ANN models were able to predict the dissolution profile within the acceptance limit of the f1 and f2 factors.
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References
Lawrence, X.Y.: Pharmaceutical quality by design: product and process development, understanding, and control. Pharm. Res. 25(4), 781–791 (2008)
Susto, G.A., McLoone, S.: Slow release drug dissolution profile prediction in pharmaceutical manufacturing: a multivariate and machine learning approach. In: 2015 IEEE International Conference on Automation Science and Engineering (CASE), pp. 1218–1223. IEEE (2015)
Patadia, R., Vora, C., Mittal, K., Mashru, R.: Dissolution criticality in developing solid oral formulations: from inception to perception. Crit. Rev. Ther. Drug Carrier Syst. 30(6), 495–534 (2013)
Hédoux, A.: Recent developments in the Raman and infrared investigations of amorphous pharmaceuticals and protein formulations: a review. Adv. Drug Deliv. Rev. 100, 133–146 (2016)
Porep, J.U., Kammerer, D.R., Carle, R.: On-line application of near infrared (NIR) spectroscopy in food production. Trends Food Sci. Technol. 46(2), 211–230 (2015)
Zannikos, P.N., Li, W.-I., Drennen, J.K., Lodder, R.A.: Spectrophotometric prediction of the dissolution rate of carbamazepine tablets. Pharm. Res. 8(8), 974–978 (1991)
Donoso, M., Ghaly, E.S.: Prediction of drug dissolution from tablets using near-infrared diffuse reflectance spectroscopy as a nondestructive method. Pharm. Dev. Technol. 9(3), 247–263 (2005)
Freitas, M.P., et al.: Prediction of drug dissolution profiles from tablets using NIR diffuse reflectance spectroscopy: a rapid and nondestructive method. J. Pharm. Biomed. Anal. 39(1–2), 17–21 (2005)
Hernandez, E., et al.: Prediction of dissolution profiles by non-destructive near infrared spectroscopy in tablets subjected to different levels of strain. J. Pharm. Biomed. Anal. 117, 568–576 (2016)
Szaleniec, M., Witko, M., Tadeusiewicz, R., Goclon, J.: Application of artificial neural networks and DFT-based parameters for prediction of reaction Kinetics of ethylbenzene dehydrogenase. J. Comput. Aided Mol. Des. 20(3), 145–157 (2006)
Drăgoi, E.N., Curteanu, S., Fissore, D.: On the use of artificial neural networks to monitor a pharmaceutical freeze-drying process. Drying Technol. 31(1), 72–81 (2013)
Jouyban, A.G., Soltani, S., Asadpour, Z.K.: Solubility prediction of drugs in supercritical carbon dioxide using artificial neural network. Iranian J. Pharm. Res. (IJPR) 6(4), 243 (2007)
Ebube, N.K., McCall, T., Chen, Y., Meyer, M.C.: Relating formulation variables to in vitro dissolution using an artificial neural network. Pharm. Dev. Technol. 2(3), 225–232 (1997)
Galata, D.L., et al.: Fast, spectroscopy-based prediction of in vitro dissolution profile of extended release tablets using artificial neural networks. Pharmaceutics 11(8), 400 (2019)
Moore, J., Flanner, H.: Mathematical comparison of dissolution profiles. Pharm. Technol. 20(6), 64–74 (1996)
Acknowledgments
Project no. FIEK_16-1-2016-0007 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the Centre for Higher Education and Industrial Cooperation Research infrastructure development (FIEK_16) funding scheme.
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Mrad, M.A., Csorba, K., Galata, D.L., Nagy, Z.K., Nagy, B. (2021). Spectroscopy-Based Prediction of In Vitro Dissolution Profile Using Artificial Neural Networks. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2021. Lecture Notes in Computer Science(), vol 12854. Springer, Cham. https://doi.org/10.1007/978-3-030-87986-0_13
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