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  1. Topological characterization of a bacterial cellulose-acrylic acid polymeric matrix
    Halib N, Mohd Amin MC, Ahmad I, Abrami M, Fiorentino S, Farra R, et al.
    Eur J Pharm Sci, 2014 Oct 1;62:326-33.
    PMID: 24932712 DOI: 10.1016/j.ejps.2014.06.004
    This paper focuses on the micro- and nano-topological organization of a hydrogel, constituted by a mixture of bacterial cellulose and acrylic acid, and intended for biomedical applications. The presence of acrylic acid promotes the formation of two interpenetrated continuous phases: the primary "pores phase" (PP) containing only water and the secondary "polymeric network phase" (PNP) constituted by the polymeric network swollen by the water. Low field Nuclear Magnetic Resonance (LF NMR), rheology, Scanning Electron Microscopy (SEM) and release tests were used to determine the characteristics of the two phases. In particular, we found that this system is a strong hydrogel constituted by 81% (v/v) of PP phase the remaining part being occupied by the PNP phase. Pores diameters span in the range 10-100 μm, the majority of them (85%) falling in the range 30-90 μm. The high PP phase tortuosity indicates that big pores are not directly connected to each other, but their connection is realized by a series of interconnected small pores that rend the drug path tortuous. The PNP is characterized by a polymer volume fraction around 0.73 while mesh size is around 3 nm. The theoretical interpretation of the experimental data coming from the techniques panel adopted, yielded to the micro- and nano-organization of our hydrogel.
    Matched MeSH terms: Theophylline/chemistry
  2. Use of artificial neural networks to predict drug dissolution profiles and evaluation of network performance using similarity factor
    Peh KK, Lim CP, Quek SS, Khoh KH
    Pharm Res, 2000 Nov;17(11):1384-8.
    PMID: 11205731
    PURPOSE: To use artificial neural networks for predicting dissolution profiles of matrix-controlled release theophylline pellet preparation, and to evaluate the network performance by comparing the predicted dissolution profiles with those obtained from physical experiments using similarity factor.

    METHODS: The Multi-Layered Perceptron (MLP) neural network was used to predict the dissolution profiles of theophylline pellets containing different ratios of microcrystalline cellulose (MCC) and glyceryl monostearate (GMS). The concepts of leave-one-out as well as a time-point by time-point estimation basis were used to predict the rate of drug release for each matrix ratio. All the data were used for training, except for one set which was selected to compare with the predicted output. The closeness between the predicted and the reference dissolution profiles was investigated using similarity factor (f2).

    RESULTS: The f2 values were all above 60, indicating that the predicted dissolution profiles were closely similar to the dissolution profiles obtained from physical experiments.

    CONCLUSION: The MLP network could be used as a model for predicting the dissolution profiles of matrix-controlled release theophylline pellet preparation in product development.

    Matched MeSH terms: Theophylline/chemistry*
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