Chromatographic behavior of fosinopril sodium and fosinoprilat using neural networks
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In this paper, the chromatographic characterization of fosinopril sodium and fosinoprilat is presented. The first stept was pK(a) determination for the active substance and its degradation product using RP-LC. It was followed by optimization employing the combination of experimental design and artificial neural networks. For the definition of input and output variables, the central composite design for three factors was built. Back propagation algorithm was applied to model the system, and then the optimization of the experimental conditions was carried out in the neural network with 3-8-2 structure, which confirmed to be able to provide the maximum performance. From the method optimization, the most appropriate experimental conditions for fosinopril sodium and fosinoprilat analysis were extracted. The optimized method was validated and applied in the quality control of tablets and for forced degradation studies.
Keywords:column liquid chromatography / experimental design-artificial neural networks / forced degradation studies / fosinoprilat
Source:Chromatographia, 2008, 67
- Springer Heidelberg, Heidelberg