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In silico methods in stability testing of hydrocortisone, powder for injections: Multiple regression analysis versus dynamic neural network

DOI: 10.2298/hemind120207023s

Keywords: hydrocortisone , stability , multiple regression analysis , dynamic neural network

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This article presents the possibility of using of multiple regression analysis (MRA) and dynamic neural network (DNN) for prediction of stability of Hydrocortisone 100 mg (in a form of hydrocortisone sodium succinate) freeze-dried powder for injection packed into a dual chamber container. Degradation products of hydrocortisone sodium succinate: free hydrocortisone and related substances (impurities A, B, C, D and E; unspecified impurities and total impurities) were followed during stress and formal stability studies. All data obtained during stability studies were used for in silico modeling; multiple regression models and dynamic neural networks as well, in order to compare predicted and observed results. High values of coefficient of determination (0.950.99) were gained using MRA and DNN, so both methods are powerful tools for in silico stability studies, but superiority of DNN over mathematical modeling of degradation was also confirmed.


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