Full stability study design

Full Stability Study Design Assume that a stability study includes three factors batch, strength (denoted SI, S2, and S3 in what follows), and packaging size (denoted PI, P2, and P3). Each value of a factor is normally referred to as level. Therefore, if there are four packaging sizes, the factor called packaging size has four levels. It should be noted that the FDA requires at least three batches and consequently the batch factor will have three levels. If the other two factors have three levels each, then the required number of experiments is 27. Table 2 shows the 27 experiments that should be performed at each point in time. The number of combinations, C, can be readily calculated by multiplying the number of levels of each factor ... 

TABLE 3 Testing Schedule for Full Stability Study Design... 

A flow diagram on how to analyze and evaluate longterm stability data for appropriate quantitative test attributes from a study with a multifactor full or reduced design is provided in Appendix A. The statistical method used for data analysis should consider the stability study design to provide a valid statistical inference for the estimated retest period or shelf fife. 

Matrixing design [10] may involve elimination of some stability sample pull time points to achieve reduced testing strategy. For example, a one-half reduction in time points eliminates one in every two time points from full study design, and one-third reduction eliminates one in every three time points. However, such a scenario must include full testing at initial, 12-month, and final time points under a 36-month shelf life study [10]. 

For this matrixing design, the total number of samples needed for the whole stability study is 3 x 18 + 5 x 12, or 114, compared to 216 samples needed for the full 3k design. Furthermore, for the case where both strength and packaging size have... 

The basic concepts of stability data evaluation are the same for single- vs. multifactor studies and for full- vs. reduced-design studies. Data evaluation from the formal stability studies and, as appropriate, supporting data should be used to determine the critical quality attributes likely to influence the quality and performance of the drug substance or product. Each attribute should be assessed separately and an overall assessment made of the findings for the purpose of proposing a retest period or shelf life. The retest period or shelf life proposed should not exceed that predicted for any single attribute. 

For an attribute that is known to decrease over time, the lower one-sided 95% confidence limit should be compared to the acceptance criterion. For an attribute that is known to increase over time, the upper one-sided 95% confidence limit should be compared to the acceptance criterion. If the attribute increases or decreases over time or whose direction of change is not known, a two-sided 95 % confidence limit should be calculated and compared to the upper and lower acceptance criteria. Other examples of statistical approaches to the analysis of stability data from single or multifactor, full or reduced design studies can be found in ICH QIE Evaluation for Stability Data ... 

In this chapter difference schemes for the simplest time-dependent equations are studied, namely, for the heat conduction equation with one or more spatial variables, the one-dimensional transfer equation and the equation of vibrations of a string. Two-layer and three-layer schemes are designed for the first, second and third boundary-value problems. Stability is investigated by different methods such as the method of separation of variables and the method of energy inequalities as well as by means of the maximum principle. Asymptotic stability of difference schemes is discovered for the heat conduction equation in ascertaining the viability of difference approximations. Finally, stability theory is being used, increasingly, to help us understand a variety of phenomena, so it seems worthwhile to discuss it in full details. 

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