Assessing Shelf Life Using Real-Time and Accelerated Stability Tests - Although Accelerated Tests Are Needed, Real-Time Tests Are the Ultimate Proof - BioPharm International

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Assessing Shelf Life Using Real-Time and Accelerated Stability Tests
Although Accelerated Tests Are Needed, Real-Time Tests Are the Ultimate Proof


BioPharm International


Biopharmaceutical products in storage change as they age, but they are considered to be stable as long as their characteristics remain within the manufacturer's specifications. The number of days that the product remains stable at the recommended storage conditions is referred to as the shelf life. The experimental protocols commonly used for data collection that serve as the basis for estimation of shelf life are called stability tests.

Shelf life is commonly estimated using two types of stability testing: real-time stability tests and accelerated stability tests. In real-time stability testing, a product is stored at recommended storage conditions and monitored until it fails the specification. In accelerated stability tests, a product is stored at elevated stress conditions (such as temperature, humidity, and pH). Degradation at the recommended storage conditions can be predicted using known relationships between the acceleration factor and the degradation rate.


Figure 1. A simulated set of stability results also showing the estimated degradation and 95% confidence limits.
Temperature is the most common acceleration factor used for chemicals, pharmaceuticals, and biological products because its relationship with the degradation rate is characterized by the Arrhenius equation. Several methods of predicting shelf life based on accelerated stability testing are described in the article. Humidity and pH also have acceleration effects but, because they are complex, they will not be discussed in detail here. Also, details on statistical modeling and estimation are outside the scope of the article, but we provide references to computer routines.

Regulations and History The assessment of shelf life has evolved from examining the data and making an educated guess, through plotting, to the application of rigorous physical-chemical laws and statistical techniques. Regulators now insist that adequate stability testing be conducted to provide evidence of the performance of a drug or a biopharmaceutical product at different environmental conditions and to establish the recommended storage conditions and shelf life.1-3 Recently, Tsong reviewed the latest approaches to statistical modeling of stability tests,4 and ICH has published some guidelines for advanced testing design and data analysis.5,6


Table 1. Estimates of the degradation model and Table 2. Estimates of degradation rates, days of stability and 95% confidence limits.
Modeling has become easier due to availability of standard statistical software that can perform the calculations. However, an understanding of the general principles of stability testing is necessary to apply these programs correctly and obtain appropriate results. Thus, the purpose of this paper is to provide an outline of the basic approaches to stability testing, as well as to create a foundation for advanced statistical modeling and shelf life prediction.

Stability and Degradation Since degradation is usually defined in terms of loss of activity or performance, a product is considered to be degrading when any characteristic of interest (for example potency or performance) decreases. Degradation usually follows a specific pattern depending on the kinetics of the chemical reaction. The degradation pattern can follow zero-, first-, and second-order reaction mechanisms.6 In zero-order reactions, degradation is independent of the concentration of remaining intact molecules; in first-order reactions, degradation is proportional to that concentration.6,7 Zero- and first-order reactions involve only one kind of molecule, and can be described with linear or exponential relationships. Second- and higher-order reactions involve multiple interactions of two or more kinds of molecules and are characteristic of most biological materials that consist of large and complex molecular structures. Although it is common to approximate these reactions with an exponential relationship, sometimes their degradation pattern needs to be modeled more precisely, and no shortcuts will suffice.

The degradation rate depends on the activation energy for the chemical reaction and is product specific. We don't always have to deal with higher-order equations; in many cases, the observed responses of different orders of reactions are indistinguishable for products that degrade slowly.

The degradation rate depends on the conditions where the chemical reaction takes place. Products degrade faster when subjected to acceleration factors such as temperature, humidity, pH, and radiation. Modeling of the degradation pattern and estimation of the degradation rate are important for assessing shelf life. Experimental protocols used for data collection are called stability tests. In practice, evaluators use both real-time stability tests and accelerated stability tests. The real-time stability test is preferable to regulators. However, since it can take up to two years to complete, the accelerated tests are often used as temporary measures to expedite drug introduction.

Real-Time Stability Tests In real-time stability tests, a product is stored at recommended storage conditions and monitored for a period of time (ttest). Product will degrade below its specification, at some time, denoted ts, and we must also assure that ts is less than or equal to ttest. The estimated value of ts can be obtained by modeling the degradation pattern. Good experimental design and practices are needed to minimize the risk of biases and reduce the amount of random error during data collection. Testing should be performed at time intervals that encompass the target shelf life and must be continued for a period after the product degrades below specification. It is also required that at least three lots of material be used in stability testing to capture lot-to-lot variation, an important source of product variability.1,2


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