However, if the data supports a common intercept and slope, the supported shelf life is then based on the overall mean response and can therefore be much longer. As also displayed in Table I, the proportions of combination sets corresponding to batch mean distributions being completely within acceptance criteria are 33.7% and 17.0% for models 1 and 2, respectively, assuming individual slope estimates among batches. This relationship is also depicted in Fig. For those batch combination sets where a common slope is fitted, models 3 and 4, the corresponding proportions are 2.2% and 0.0%, respectively. 39 39 The shelf life distribution is defined with respect to the storage time, depicted as the horizontal axis in Fig. Shelf life estimates that are too long result in an unacceptable percentage of nonconforming batches at expiry while those that are too short put the manufacturer at risk of possibly having to prematurely discard safe and efficacious drug product. From Figs. The ICH guidance specifies that a poolability assessment should be conducted to determine the best fitted simple linear regression model to characterize the trend demonstrated by the batches. The shelf life of formulations of aceclofenac were determined by accelerated stability studies on the basis of first order degradation kinetics and t 0.9 (the time required to degrade 10 % of drug at 25°C). It includes both between-batch and within-batch variation and is represented by the dots in Fig. In addition, results from the industry data set showed a high percentage of the batch mean distribution being completely conformable to acceptance criteria at expiry. You are currently offline. The vertical axis is the frequency or number of batches with mean potency response in each bracket. ICH Q1E guideline provides guidance for the estimation of the shelf life of the pharmaceutical products and substances. The batch mean distribution describes the variation among the batch mean responses derived from the regression analysis at any given storage time although, like the product distribution, usually just expiry is of interest. For either three or six batches, if a regression model with separate slopes among batches is selected, the supported shelf life tends to be in the lower tail of the overall supported shelf life distribution with mean supported shelf lives that are less than the overall mean. For example, in Fig. However, consideration of both the batch mean distribution and shelf life distribution is needed to properly manage risk. 0000001036 00000 n 2013;23(4):881–96. In practice, when there is between batch and within-batch variation, if the mean of the product distribution is used to define the true product shelf life, half of the product distribution will fall out of specification at expiry, which appears to be in conflict with the ICH philosophy. Schematic of the relationship between the batch mean distribution and the shelf life distribution. Neither result is optimal from either the patient or manufacturer perspective. A relationship between the two distributions was described through the quantiles of each. 0000085551 00000 n In general, quantiles of the individual distributions do not correspond to each other unless there is no variation among the batch slopes (Quinlan et al. To evaluate the poolability testing process in terms of the ICH methods, comparisons are made among the supported shelf life distributions for each of the variations of the simple linear regression model. In the next section, an industry data set is presented in which the response decreases over time. Based on the analysis of the industry data set, the ICH-recommended approach did not produce supported shelf lives that effectively managed risk. The shelf life distribution is defined by the distribution of shelf lives obtained through the batch mean distribution corresponding to each batch’s shelf life. The shelf life distribution for the industry data set is summarized in Fig. From Table I, if the mean response trends are characterized by individually estimated batch slopes, models 1 and 2, the average proportion of nonconforming batches at expiry is 6.1 and 7.0%, respectively. Analyzing all the possible combinations of six batches is not manageable. 9a, b). If you have previously obtained access with your personal account, please log in. This variation along with a consideration of risk leads to supported shelf lives that can be much shorter than the true product shelf life of a drug product. 4 is a depiction of an example where 50% of the distribution of batch means is out of specification at a storage time corresponding to when the overall mean response trend intersects the acceptance criteria. However, there is an overall shift to shorter storage times in the supported shelf life distributions comparing models 1 and 2 (separate slopes) to models 3 and 4 (common slopes). (6)) have proposed defining the true product shelf life to ensure that a predefined proportion of dosage units meet the acceptance criterion at expiry, until an agreed upon framework is developed, it is necessary to continue to apply the historic interpretation of true product shelf life discussed previously. Distribution of supported shelf lives based on three batches corresponding to a model 1: separate intercepts and separate slopes, b model 2: common intercept and separate slopes, c model 3: separate intercepts and common slope, d model 4: common intercept and common slope. 0000125062 00000 n Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. In the industry data set, if a standard is set that requires the entire batch mean distribution to conform to acceptance criteria at expiry, which reflects the spirit of the ICH guidance documents, then the supported shelf life cannot be larger than about 18 months. Little Consulting 12401 N Wildflower Lane Highland, UT 84003 1-925-285-1847 drlittle@dr-tom.com The Need for Stability Analysis Stability assessment is a critical aspect of all drug development activities. Evaluating Current Practices in Shelf Life Estimation. A randomly selected representative sample of 20,000 combination sets of six batches is used to produce Fig. Aust NZ J Stat. Thus, when evaluating the performance of any estimation procedure, the proportion of nonconforming drug product batches at expiry must be considered in order to manage the risk/benefit ratio of estimating a product shelf life. Distribution of supported shelf lives based on six batches corresponding to a model 1: separate intercepts and separate slopes, b model 2: common intercept and separate slopes, c model 3: separate intercepts and common slope, d model 4: common intercept and common slope (to better exhibit the relationship among the bars, the inset displays the same data but on a frequency scale of 0 to 150). Note that all of the combination sets associated with model 4 contained at least one nonconforming batch. J Biopharm Stat. 0000004091 00000 n Kiermeier A, Jarrett R, Verbyla A. Again, assume there is a business need to have a supported shelf life that is no less than 24 months. For this discussion, it is assumed that (1) a simple linear regression model is appropriate to describe the stability data from each of the 26 batches, (2) the stability data themselves represent true batch mean responses, and (3) the determination of the overall regression line provides the true response trend among the 26 batches. For a given storage time, T, the fraction of batches with shelf lives less than or equal to T is the same as the fraction of batches with batch means less than or equal to the lower acceptance criterion. x�b```f``ia`c`�� �� @16� G��{K%�^ٍ��D10ę�nr=���rX����4���{B�.j�����6�ru����4�Q�=2+|ղ���}]�E�F�^[�z(xDD�ń�7�R�|]� Characteristics of the medicinal product up to the end of shelf life of the finished product at release 1. In 2006, the Product Quality Research Institute (PQRI) established a Stability Shelf Life Working Group (referred to as the “Working Group” in this article) with the mandate to investigate current statistical methods for estimating shelf life based on stability data. 8a, b, respectively, the mean supported shelf life and related distributional quantiles for each model are similar. Current efforts center on how to best incorporate methods based on tolerance intervals and/or calibration techniques. Learn more Check out. However, with six batches, there is a lesser chance of concluding that a common slope model is appropriate for characterizing the batches versus a model where the common slope assumption is not justified, 45 versus 55%, respectively. Learn more. This paper is a step towards developing a coherent framework to discuss the true product shelf life. For the combination sets fit by model 3, the corresponding mean (range) of the supported shelf lives is 14.9 (0.0–17.6) months. volume 19, pages668–680(2018)Cite this article. The proportion of the 26 batches from the industry data set that are nonconforming is recorded for each combination set of batches. The Working Group is composed of pharmaceutical, regulatory, and statistical scientists from industry, government, and academia. 0000006846 00000 n If you do not receive an email within 10 minutes, your email address may not be registered, The primary purpose of this paper is to evaluate the ICH methods for determining the supported shelf life in terms of their distributional properties and in terms of the proportion of batches conforming to specifications at the supported shelf life. AAPS PharmSciTech. To validate any shelf life estimate and quantify the risk to the patient, how well the product distribution and batch mean distribution are managed with respect to the acceptance criteria must be considered. This pattern is not as evident for models 3 and 4, as shown in Fig. Correspondence to Predicting the stability of biological standards and products.

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