Chromatographic behavior of fosinopril sodium and fosinoprilat using neural networks
Abstract
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 / fosinoprilatSource:
Chromatographia, 2008, 67Publisher:
- Springer Heidelberg, Heidelberg
Funding / projects:
DOI: 10.1365/s10337-008-0575-9
ISSN: 0009-5893
WoS: 000256533700017
Scopus: 2-s2.0-43449139973
Collections
Institution/Community
PharmacyTY - JOUR AU - Jančić, Biljana AU - Medenica, Mirjana AU - Ivanović, Darko AU - Malenović, Anđelija AU - Popović, Igor PY - 2008 UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/1070 AB - 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. PB - Springer Heidelberg, Heidelberg T2 - Chromatographia T1 - Chromatographic behavior of fosinopril sodium and fosinoprilat using neural networks VL - 67 DO - 10.1365/s10337-008-0575-9 ER -
@article{ author = "Jančić, Biljana and Medenica, Mirjana and Ivanović, Darko and Malenović, Anđelija and Popović, Igor", year = "2008", abstract = "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.", publisher = "Springer Heidelberg, Heidelberg", journal = "Chromatographia", title = "Chromatographic behavior of fosinopril sodium and fosinoprilat using neural networks", volume = "67", doi = "10.1365/s10337-008-0575-9" }
Jančić, B., Medenica, M., Ivanović, D., Malenović, A.,& Popović, I.. (2008). Chromatographic behavior of fosinopril sodium and fosinoprilat using neural networks. in Chromatographia Springer Heidelberg, Heidelberg., 67. https://doi.org/10.1365/s10337-008-0575-9
Jančić B, Medenica M, Ivanović D, Malenović A, Popović I. Chromatographic behavior of fosinopril sodium and fosinoprilat using neural networks. in Chromatographia. 2008;67. doi:10.1365/s10337-008-0575-9 .
Jančić, Biljana, Medenica, Mirjana, Ivanović, Darko, Malenović, Anđelija, Popović, Igor, "Chromatographic behavior of fosinopril sodium and fosinoprilat using neural networks" in Chromatographia, 67 (2008), https://doi.org/10.1365/s10337-008-0575-9 . .