Pharmaceutical Forced Degradation (Stress Testing) Endpoints: A Scientific Rationale and Industry Perspective.

ABSTRACT

Forced degradation (i.e., stress testing) of small molecule drug substances and products is a critical part of the drug development process, providing insight into the intrinsic stability of a drug that is foundational to the development and validation of stability-indicating analytical methods. There is a lack of clarity in the scientific literature and regulatory guidance as to what constitutes an "appropriate" endpoint to a set of stress experiments. That is, there is no clear agreement regarding how to determine if a sample has been sufficiently stressed. Notably, it is unclear what represents a suitable justification for declaring a drug substance (DS) or drug product (DP) "stable" to a specific forced degradation condition. To address these concerns and to ensure all pharmaceutically-relevant, potential degradation pathways have been suitably evaluated, we introduce a two-endpoint classification designation supported by experimental data. These two endpoints are 1) a % total degradation target outcome (e.g., for "reactive" drugs) or, 2) a specified amount of stress, even in the absence of any degradation (e.g., for "stable" drugs). These recommended endpoints are based on a review of the scientific literature, regulatory guidance, and a forced degradation data set from ten global pharmaceutical companies. The experimental data set, derived from the Campbell et al. (2022) benchmarking study,1 provides justification for the recommendations. Herein we provide a single source reference for small molecule DS and DP forced degradation stress conditions and endpoint best practices to support regulatory submissions (e.g., marketing applications). Application of these forced degradation conditions and endpoints, as part of a well-designed, comprehensive and a sufficiently rigorous study plan that includes both the DS and DP, provides comprehensive coverage of pharmaceutically-relevant degradation and avoids unreasonably extreme stress conditions and drastic endpoint recommendations sometimes found in the literature.