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It is recommended to use an appropriate statistical method to analyze the data generated during a stability study The purpose of using statistics is to establish, with a high degree of con dence, the shelf life, that is, the period during which a quantitative attribute of the drug product remains within acceptance criteria for all future batches manufactured, packaged, and stored under similar conditions Stability studies are expensive and time consuming and statistics can certainly help in this aspect Statistical design principles can be applied to reduce the amount of testing required [7] The shelf life for a single batch is usually computed based on regression techniques An appropriate approach to shelf life estimation when using regression analysis is by calculating the earliest time at which the 95% con dence limit for the mean intersects the proposed acceptance criterion [8].

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A detailed description of shelf life calculations is provided in Sections 723 and 724 The analysis of covariance is the conventional statistical tool to determine whether or not the degradation lines from several batches belong to a single population If the degradation lines are statistically different, the minimum criterion is used to determine the shelf life for the current and future batches According to the minimum criterion, the shelf life of future batches corresponds to the shortest of all shelf lives from the tested batches On the other hand, if the batches belong to a single population, regression analysis is used to develop a single degradation line based on pooled data of the tested batches A short-term stability study is conducted under stressed conditions to increase the rate of chemical and physical degradation of the drug product.

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The classical statistical procedure to establish a preliminary shelf life is based on regression techniques, which are used to estimate the parameters of the Arrhenius equation A systematic approach for stability data evaluation should be used to determine whether extrapolation beyond the time period covered in the stability study for the long-term data is appropriate to calculate the shelf life period The approach consists of evaluating any signi cant change at the accelerated condition and, if applicable, at the intermediate condition in order to follow a guideline on how to establish the shelf life period by extrapolation The required relevant supporting data and the commitment to place stability batches to be tested until the end of the extrapolated shelf life should be provided to the regulatory agency.

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The relevant supporting data include satisfactory long-term data from development batches manufactured with close related formulation in a small scale and stored in a packaging con guration similar to the primary stability batches The data evaluation approach is executed as follows [8] Product to be Stored at Room Temperature For products to be stored at room temperatures the extent of extrapolation will depend on the evaluation of the results from the short-term, intermediate, and long-term data If no signi cant change at the accelerated (short-term) condition is observed, then the long-term data are evaluated for change and variability:.

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[167] P.J. Kuhn. Multiqueue systems with non-exhaustive cyclic service. The Bell System Technical Journal, 58(3):671-698, 1979. [168] H. Kwakernaak and R. Sivan. Modern

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public DataSet GetExcel(string fileName) { Application oXL; Workbook oWB; Worksheet ... Value); // get WorkSheet object oSheet = (Microsoft.Office.Interop.

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