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Neural Computing and Applications

Erschienen in:

01.12.2005 | Original Article

Application of the Gaussian mixture model to drug dissolution profiles prediction

verfasst von: Chee Peng Lim, Siow San Quek, Koh Khiang Peh

Erschienen in: Neural Computing and Applications | Ausgabe 4/2005

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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.

Vorheriger Artikel The optimisation of neural network parameters using Taguchi’s design of experiments approach: an application in manufacturing process modelling

Nächster Artikel Neural network analysis of ophthalmic arterial doppler signals with Uveitis disease

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Titel: Application of the Gaussian mixture model to drug dissolution profiles prediction
verfasst von: Chee Peng Lim
Siow San Quek
Koh Khiang Peh
Publikationsdatum: 01.12.2005
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 4/2005
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-005-0471-2

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