Reliability of Artificial Neural Network Predictions — A Case Study in Drug Release Profile Predictions
Artificial neural networks have been widely used in pharmaceutical research such as for estimation of process coefficients and pharmacokinetic parameters. In this paper, we present a study on the use of the Radial Basis Function-based Gaussian mixture model to predict dissolution profiles of a matrix controlled release theophylline pellet preparation. Performance of the network has been assessed using similarity factor—an index for profile comparison in pharmaceutical research. In addition, we also investigate the phenomena of interpolation and extrapolation of the test data sets that will affect the reliability of network predictions. The Parzen-window approach has been employed to determine, based on the calculated data densities, whether the trained network produces interpolated or extrapolated predictions. The experimental results are discussed and analyzed.
KeywordsGaussian Mixture Model Hide Node Radial Basis Function Network Dissolution Profile Trained Network
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- 4.D.E. Rumelhart, G.E. Hinton, and R.J. Williams, R.J. (1986) Learning internal representation by error propagation. In D.E. Rumelhart, and J.L. McLelland, J.L. (Eds.), Parallel Distributed Processing, I, (pp. 318–362 ). Cambridge, MA: MIT Press.Google Scholar
- 6.J.A. Leonard, M.A. Kramer, L.H. Unger. (1992) A neural network architecture that computes its own reliability, Comput. Chem. Eng. 16 (9) 819–835.Google Scholar
- 7.Ian T. Nabney et al. (1997) Practical Assessment of Neural Network Applications. In SafeComp 97, Proceedings of the 16th International Conference on Computer Safety, Reliability and Security, ed. Ian T. Nabney et al, pp. 357–368. Springer Verlag.Google Scholar
- 11.Bishop Christopher M. (1995) Neural Networks for Pattern Recognition. Clarendon Press, Oxford (UK).Google Scholar
- 12.FDA Guidance for Industry: Modified release solid dosage forms: Scale-up and Post Approval Changes (SUPAC-MR): Chemistry, Manufacturing and Controls. In vitro dissolution testing and in vivo bioequivalence documentation, September, (1997).Google Scholar