Abstract
In this paper, the radial basis function-based Gaussian mixture model (GMM) is applied to model and predict drug dissolution profiles in a time-series approach. The Parzen-window method is embedded into the GMM for determining whether the network predictions are derived from interpolation or extrapolation of the training data. A benchmark study on time-series prediction is first used to evaluate and compare the GMM performance with those from other models. The GMM is then used to predict dissolution profiles of a matrix-controlled release theophylline pellet preparation. Performance of the GMM is assessed using the difference and similarity factors, as recommended by the United States Food and Drug Administration for dissolution profile comparison. In addition, bootstrapping is employed to estimate the confidence intervals of the network predictions. The experimental results are analyzed and compared, and implications of the GMM for pharmaceutical product formulation tasks are discussed.
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Acknowledgements
The corresponding author gratefully acknowledges the research grants provided by University of Science Malaysia and the Ministry of Science, Technology, and Innovation Malaysia (No. 06-02-05-8002 & 04-02-05-0010) that have in part resulted in this article.
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Lim, C.P., Quek, S.S. & Peh, K.K. Application of the Gaussian mixture model to drug dissolution profiles prediction. Neural Comput & Applic 14, 345–352 (2005). https://doi.org/10.1007/s00521-005-0471-2
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DOI: https://doi.org/10.1007/s00521-005-0471-2