shelf life determination of pharmaceutical products pdf

10. Figure 6 is a similar summary of the supported shelf lives from combination sets of six batches. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. ICH puts emphasis on the batch mean distribution to define an appropriate shelf life estimate since according to the guidance, the objective of a stability study is to give assurance that future batches will be safe and efficacious at expiry. The above results imply that if a separate slope model is chosen, there is only about a 50% chance of attaining the required shelf life (median storage time ≈ 24 months in Fig. Acceptance criteria are set at 90 and 110%, as is typical for a potency specification in the USA. Kiermeier A, Verbyla A, Jarrett R. Estimating a single shelf life for multiple batches. 0000124890 00000 n The schematic in Fig.

X���H�! The evaluation of the ICH methods for estimating shelf life presented in this paper is based on an industry data set. There are 230,230 unique combinations when selecting sets of six batches from the 26-batch industry data set. Through the poolability assessment, the best fitted simple linear regression model or system of models characterizing each individual batch was determined, allowing for all four possible alternative models. Typically, three batches (occasionally more) of drug product are represented in a stability study for a New Drug Application (NDA) or Marketing Authorization Application (MAA) filing and are referred to as the registration batches. Because it is often not feasible to adequately characterize the product distribution at expiry, for both regulatory and industry reasons, a surrogate definition of patient risk is used, namely, the proportion of the batch mean distribution outside the acceptance criteria at expiry, as shown in Fig. The horizontal axis represents storage time in months where the midpoint of each bracket is labeled. In this case, though, even if pooling cannot be achieved, the effect on the supported shelf life is not as dramatic because the worst case batch still demonstrates a typical rate of degradation. Key results from these discussions are published in the Working Group’s first paper (1). 0000003201 00000 n Application of kinetic models and Arrhenius methods to product stability evaluation, Kinetic principles and stability testing. The batch response data for the industry data set are shown in Fig. 1 and 2, it is not surprising that all of the maximum supported shelf lives listed in the last column in Table I are approximately 18 months or less since the minimum true shelf life is 18.2 months. First, the industry data set is used assuming the 26 individual stability batches represent a common pharmaceutical product from a production process under control. 0000006846 00000 n

From Table III, it is apparent that there is a dependence of the results of the poolability tests for selecting the best fitted regression model on not only the mean of the supported shelf lives (models 1 and 2 versus models 3 and 4), but also on the corresponding quantiles of the overall supported shelf life distribution. 9a–d, respectively. 0000001629 00000 n Historically, for practical purposes, the true product shelf life has been defined as the storage time at which the mean of the product distribution, following some response trend across storage time, intersects the acceptance criteria. <<1A01FFFDFA9CC545994B13DBCC63149C>]>> These data were offered to the Working Group for their use from an anonymous PQRI member company. %PDF-1.4 %���� 39 0 obj <> endobj Note that neither excessively short nor excessively long supported shelf life estimates are desirable. volume 19, pages668–680(2018)Cite this article. Similarly, the 75th quantile of the shelf life distribution is 47.6 months where 20/26 (76.9%) of the batch means fall below the 90% specification limit. 2003. Google Scholar. Third, as stated in both ICH Q1A and Q1E, a supported shelf life of a drug product must define the storage time during which drug product batches are expected to remain within specifications. Traditionally, this is done at expiry as defined by the supported shelf life. A schematic of this relationship is depicted in Fig. 0000023550 00000 n 8a, b, respectively, the mean supported shelf life and related distributional quantiles for each model are similar. The response measure is percent assay (potency). Recall that the batch mean distribution is defined by the intersection points of the individual batch mean responses, derived from a regression analysis, at expiry. %PDF-1.4 %���� Second, the random batch analysis does not require poolability testing. There is an interesting relationship between the batch mean distribution and the shelf life distribution. PubMed  0000002301 00000 n 0000142107 00000 n 10 is not defined by where the mean response intersects the acceptance criteria. 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). 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.

The poolability testing strategy is then conducted, allowing for all four possible regression models as described previously. The full text of this article hosted at iucr.org is unavailable due to technical difficulties. DOSAGE FORMS OF HERBAL MEDICINAL PRODUCTS AND THEIR STABILITY CONSIDERATIONS-AN OVERVIEW, Shelf life evaluation of Laghu Sutashekhara Rasa – A preliminary assessment. 6 0 obj <> endobj In the next section, an industry data set is presented in which the response decreases over time. 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. This second paper is a report on the continuation of those discussions which again are meant to raise public awareness of the existing different interpretations of shelf life and to stimulate a broader public discussion on these topics, which are relevant for drug products, drug substances, clinical supplies, etc.

The horizontal axis represents product potency expressed as percent assay where the midpoint of each bracket is labeled. Research continues on addressing these remaining issues.

In particular, the product shelf life is the true but unknown limit on the period of storage time during which the pharmaceutical or drug product remains within specifications and is therefore considered effective and fit for use. The industry data set represents 26 individual batches of a common pharmaceutical or drug product, where most batches remained on stability for a 24-month period and came from a manufacturing process assumed to be under statistical control. 3, the 5th quantile of the shelf life distribution is 25.1 months. While some author’s (e.g., Kiermeier, et al. Any method used to estimate the product shelf life has to account for both the risk associated with patients using a potentially subpotent product (patient risk) and the risk associated with a manufacturer having to set a short shelf life, which could lead to discarding good product (business or industry risk). 0000035604 00000 n Capen R, Christopher D, Forenzo F, Ireland C, Liu O, Lyapustina S, et al. Figure 5 is the summary distribution of the 2600 supported shelf lives truncated to 48 storage months for ease of presentation. The batch mean distribution shown in Fig. 9a, b). Correspondingly, if batches demonstrating slower degradation than typical are included in the sample, this can also prevent the pooling of the slopes and/or intercepts.

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. Services Google Scholar.

Results for sampling both three and six batches from the industry data set ranged from a nominal to a high percentage of the batch mean distribution being out of specification at expiry. 7 which displays the cumulative distributions of the ICH-supported shelf lives for both three and six batches. (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. From Figs. J Biopharm Stat. Stability Requirements in GMP Regulations and Guidelines, Stability Requirements for Drug Substances (APIs), Evaluation of Stability Data and Shelf Life Determination, Assignment of Climatic Zones and Recommended Storage Conditions, Stability Testing Parameters for Different Dosage Forms, Pharmaceutical Manufacturing Handbook: Regulations and Quality. USE OF MODELS IN DETERMINING CHEMICAL PHARMACEUTICAL STABILITY. Second, a model selection procedure is outlined in the ICH guidance documents, commonly referred to as the “poolability” tests, and is a major component to the ICH methods for estimating shelf life.

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