Considerations for Updates to ICH Q1 and Q5C Stability Guidelines: Embracing Current Technology and Risk Assessment Strategies.

In consideration of the recent ICH Quality Discussion Group (QDG) recommended revision to the ICH series of stability guidelines, the IQ Consortium (International Consortium for Innovation and Quality in Pharmaceutical Development) Science- and Risk-based Stability Working Group conducted a comprehensive review of ICH Q1A, Q1B, Q1C, Q1D, Q1E, and Q5C to identify areas where the guidelines could be clarified, updated, and amended to reflect the potential knowledge gained from current risk-based predictive stability tools and to consider other science- and risk-based stability strategies in accordance with ICH Q8-12. The recommendations propose a holistic approach to stability understanding, utilizing historical data, prior knowledge, modeling, and a risk assessment process to expand the concept of what could be included (or would be acceptable) in the core stability data package, including type and amount of stability evidence, assignment of retest period and shelf-life for a new product, and assessment of the impact of post-approval changes.

Topical drug classification.

Current definitions of lotions, gels, creams and ointments vary depending on literature source, market history or traditional use. This often leads to confusion when deciding which dosage form to prescribe and/or purchase. The existing classification of topical dosage forms needs to be re-examined to ensure that definitions for different dosage forms are based on consistent scientific principles and that dosage forms can be distinguished from one another. The purpose of this study is to obtain a scientifically based, systematic classification of dosage forms for topical drugs. A variety of prescription and over-the-counter topical products currently marketed as lotions, gels, creams, and ointments are evaluated using different techniques including rheology (viscosity and shear rate versus shear stress), loss on drying (LOD), specific gravity, surface tension, thermogravimetric analysis (TGA), water absorption, dilution properties, microscopic evaluation, transmittance of visible light, appearance and composition. Rheology is the most discriminating property separating creams and lotions. Water plus volatiles (as measured by LOD) and composition separate ointments and creams. Composition and thermal behavior separate gels from the other dosage forms. Based on these findings, new definitions and a decision tree are presented to assist in the determination of the appropriate nomenclature for a topical dosage form.

Overview of stability study designs.

Stability requirements for the worldwide registration of pharmaceutical products have changed dramatically in the past few years. A series of guidelines on the design, conduct, and data analysis of stability studies of pharmaceuticals have been published by the International Conference on Harmonization (ICH); however, the statistical discussion on study design is limited. In this paper, stability designs including full, bracketing, and matrixing designs will be exemplified. The statistical aspects of these stability designs will be discussed in relation to the recent ICH guidelines. The statistical and regulatory considerations on the selection of stability design will also be presented. The stability study should be well designed so the shelf life of the drug product can be estimated with a high degree of accuracy and precision. Several commonly used criteria for design comparison will be presented. Finally, a case study is presented to illustrate the potential problems of an overreduced design from the statistical and regulatory perspectives.

ANCOVA approach for shelf life analysis of stability study of multiple factor designs.

For a traditional multiple batch stability design with no other factor, the conventional analysis is analysis of covariance (ANCOVA) modeling using F-tests based on type I sum of squares to determine whether the batches may be pooled for a common estimate of the linear regression line(s). In the last decade, many multiple factor designs were proposed in stability studies. With the objective of model selection, the generalization of the conventional ANCOVA model using type I sum of squares to designs with multiple factors requires a prespecified hierarchical pooling test ordering to determine whether any of the factors may be eliminated. Different shelf life estimates may be derived using different hierarchical pooling test orderings. On the other hand, setting the hierarchical ordering can be subjective and controversial. The stepwise modeling based on F-tests using type III sum of squares for model determination and factor elimination is proposed to eliminate such difficulties.

Shelf life determination based on equivalence assessment.

In a regular analysis of covariance (ANCOVA) approach to stability analysis, the decision for pooling data from different batches plays a key role in the determination of the shelf life of the drug product. Conventionally, the decision to pool data for the estimate of slope and intercept of common or individual regression lines is made by "no evidence to reject the null hypothesis of no difference." With typically limited observations, a significance level of much higher than 0.05 was recommended for the pooling tests in order to avoid inflation of type-I error rate of the shelf life testing. This logic of the pooling test decision making discouraged the use of replicates to improve power of testing and precision of estimation. The concept of pooling by equivalence test was originally proposed by Ruberg and Hsu in their 1990 article "Multiple comparison procedures for pooling batches in stability studies" Such a concept has evolved to pooling batches based on the shelf life equivalence test by Yoshioka et al. in their 1996 article "Power of analysis of variance for assessing batch-variation of stability data of pharmaceuticals." In this article, an approximation test of shelf life equivalence and a test of chemical value equivalence for the data pooling decision are proposed as an alternative to the conventional ANCOVA approach.