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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

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