Identification of Out-of-Trend Stability Results, Part II PhRMA CMC Statistics, Stability Expert Teams - Pharmaceutical Technology

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Identification of Out-of-Trend Stability Results, Part II PhRMA CMC Statistics, Stability Expert Teams

Pharmaceutical Technology

High-quality stability data are critical to the pharmaceutical and biopharmaceutical industries. These data form the basis for justifying specification limits (also called acceptance criteria in the International Conference on Harmonization [ICH] Q1 and Q6 guidances), for setting and extending product expiration dates, and for establishing product label storage statements. Annual, routine stability studies also can be used to support product or process modifications and are vital for ensuring the continuous quality of production batches. To facilitate the prompt identification of potential issues, and to ensure data quality, it is advantageous to use objective (often statistical) methods that detect potential out-of-trend (OOT) stability data quickly.

In general, OOT stability data can be described as a result or sequence of results that are within specification limits but are unexpected, given the typical analytical and sampling variation and a measured characteristic's normal change over time (e.g., an increase in degradation product on stability).

OOT stability results may have a significant business and regulatory impact. For example, if an OOT result occurs in the annual production batch placed on stability, the entire year's production could be affected, depending on the OOT alert level. Systems that are useful for detecting OOT results provide easy access to earlier results so that they can track stability data and trigger an alert when a potential OOT situation arises. Procedures describing OOT limits, responsibilities for all aspects of an OOT system, and investigational directions are all components of a company's quality system.

Representatives from the US Food and Drug Administration and 77 individuals from more than 30 Pharmaceutical Research and Manufacturers of America (PhRMA) member companies met in October 2003 to address important stability OOT alert issues. Their discussions underscored the need to track OOT stability data, associated issues, and potential statistical or other approaches to establish OOT alert limits. Breakout sessions captured participants' perspectives on FDA expectations and guidances, acceptable OOT identification methodologies, and implementation challenges.

Workshop attendees concluded that the main focus for OOT identification and investigation should be annual, routine production stability studies rather than primary new drug application (NDA) batches because historical data are usually needed to determine appropriate OOT alert limits.

In addition, attendees clearly expressed a desire for a simple approach to tracking stability data that would allow at-the-bench, real-time detection of OOT results. This capability is important from an implementation standpoint so that a timely investigation can be initiated. Timeliness is especially important when an analytical error is suspected of causing OOT results. It was recognized that an efficient approach with such properties might be difficult to achieve.

Another recommendation was the need for standard operating procedures (SOPs) that address how to handle (e.g., identify, investigate, conclude, take action) OOT data. Workshop participants believed that a separate FDA guidance is not needed to develop such SOPs because the existing out-of-specification (OOS) guidance should suffice.

Three types of OOT results—which determine the degree and scope of an OOT investigation—were identified at the workshop. This article expounds on these outcomes and addresses other questions raised during the workshop and in a previous article (1). Recommendations put forward in this article are not requirements, but rather, are possible alternatives for handling OOT data.

Types of OOT data

Figure 1: An analytical alert (theoretical data, see endnote).
OOT alerts can be classified into three categories to help identify the appropriate depth for an investigation. In this article, OOT stability alerts are referred to as analytical, process control, and compliance alerts, in order of implied severity. As the alert level increases from analytical to process control to compliance alert, the depth of investigation should increase.

Figure 2: A process control alert (theoretical data, see endnote). Results from multiple time points for a study do not follow the same trend over the 36-month shelf-life expiration as do other studies. The study is not expected to fall below the specification limit through the 36-month expiration.
Historical data are needed to identify OOT alerts. An analytical alert is observed when a single result is aberrant but within specification limits (i.e., outside normal analytical or sampling variation and normal change over time) (see Figure 1).

A process control alert occurs when a succession of data points shows an atypical pattern that was possibly caused by changes to the laboratory or manufacturing process. These data points might originate from the same stability study (see Figure 2) or from multiple studies assayed within a reasonably close timeframe (within a few weeks) (see Figure 3). As these Figures 2 and 3 indicate, the trends for the batches in question vary substantially from those of comparable batches. Despite the deviating trends, no pending potential OOS situation occurs.

Lastly, a compliance alert defines a case in which an OOT result suggests the potential or likelihood for OOS results to occur before the expiration date within the same stability study (or for other studies) on the same product (see Figures 4 and 5).

Figure 3: A process control alert (theoretical data, see endnote). Within the past few months, results from two studies are unexpectedly low, each initiating an analytical alert. Neither study is expected to fall short of the specification limit through the 30-month expiration.
Similarly, tests with multiple stages can be classified into the three OOT alert levels, but this task is much more challenging and less straightforward. For example, consider the three-stage USP dissolution test. If Stage 2 testing is rarely required, then an analytical alert for USP dissolution testing might be a single, unusual minimum or average dissolution. If the product usually passes Stage 1 and a new stability study goes to Stage 2 several times, then this result might signal a process control alert. If the study's dissolution average shows a decreasing trend with time that indicates difficulty remaining above the registered Q-value through expiration, this fact could indicate a compliance alert.


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