More than 20 years of experience
According to ICH Q1A and Q5C guidelines
Scientific expertise
Stability studies play a vital role in the development of drug products, ensuring that they uphold their quality, efficacy, and safety over time, even when exposed to a range of environmental conditions. Notably, biologics, biosimilars, viruses, vaccines, and biofunctionalized devices are especially susceptible to the influence of environmental factors.
The main goal of stability testing is to establish the shelf life of a drug product during storage. These studies evaluate the critical quality attributes (CQAs) associated with the drug substance (DS) or drug product (DP) at carefully selected intervals. By doing so, they ensure that the product maintains its desired attributes throughout its designated shelf life, meeting the stringent quality criteria demanded by regulatory standards.
Stability studies evaluate the stability of a drug substance (DS) or drug product (DP) under various conditions such as storage, light, or mechanical stress over time. An evaluation encompassing both in-vitro and in-silico methods as an integrated approach enables a comprehensive understanding of the product's stability profile. Leukocare specializes in providing these advanced testing and prediction techniques. Our team performs stability studies according to ICH Q1A guidelines and can support the data set for registration applications.
Stability study expertise
With more than 20 years of scientific and regulatory experience, and guided by the internationally recognized ICH Q1A-E guidelines, our team executes stability studies with precision, generating robust data that not only assures product stability but also serves as a valuable asset for registration applications.
Stability testing services cover temperature stress (Freezer and ICH Q1A storage cabinets covering temperatures from -80 °C to +40 °C), light stress to assess photostability (ICH Q1B light tester) and various models of mechanical stress. With a broad analytical toolbox optimized for stability indicating analyses of the CQAs, we evaluate the stability at specific time points. For long-term stability prediction, we can provide regression analysis (linear and multiple) and kinetic modeling based on either in-house stability data or data from your GMP laboratory. With kinetic modeling, we can even extend the quality of extrapolation even beyond ICH Q1E guidelines.
In essence, stability studies stand as a cornerstone of our commitment to delivering drug products that endure the test of time and environmental challenges.
Case Study
Combination of accelerated aging and DoE speeds up the formulation development process.
Design of Experiments (DoE) based formulation development allows to apply regression analysis to statistically estimate the stabilizing effects of excipients. Accelerated aging at high temperature (37 °C, left) showed predictive power in detecting statistically significant (p< 0.01) stabilizing effects of methionine on the titer.
Reinauer et al. (2020), J Pharm Sci
Case Study
Shelf-life prediction as an enabler for faster decision making
Current ICH guidelines support the use of Linear Regression as golden standard to predict shelf life. However, Kinetic Modeling can lead to more accurate stability predictions of DS such as antibodies and viral vectors that exhibit complex multi-step degradation patterns and autocatalytic behavior.
Case Study
Kinetic Modeling: Enhancing Shelf-life Predictions for Biologics
Previous research shows that alternative methods to linear regression could better tackle the degradation of antibodies and viral vectors, which exhibit complex degradation patterns composed of multiple steps and autocatalytic behavior, leading to superior stability predictions for selection candidates. Here, we compared the accuracy of three predictive methods on the stability of 2 antibodies and 2 viral vectors after storage at 5°C and 25°C for 6 months: linear, multiple regression, and kinetic modeling.
In-vitro testing
Real-time, intermediate and accelerated stability storage (non-GMP)
Long-term stability studies (non-GMP)
In-use stability studies
Stability-indicating analytics
In-silico long-term stability prediction
Linear and multiple regression analysis for long-term shelf-life prediction
Kinetic Modeling for long-term shelf-life prediction
