Understanding the chemical stability of active pharmaceutical ingredients (API) is of great importance, as it affects the quality, safety, robustness, and efficacy of a drug product. Streamlining early identification processes of potential API degradation products are paramount to ensure that drug quality and safety is retained over its shelf life. To improve product development strategy it is crucial to be able to predict impurity profiles as early as possible in the drug design process. Computational chemistry approaches could complement experimental API stress-testing in answering questions pertaining to reaction mechanisms and honing questions that can be investigated experimentally. While pharmaceutical stress-testing beckons with many opportunities for applying computational chemistry, relatively little work has been published in this area.<br/><br/>In this talk we will discuss the first work to apply quantitative chemical kinetic models to predict the chemical stability of APIs based on ab-initio computations. We have generated a chemical kinetic model for a common stress testing system, and we show which radicals are likely to attack an API at different pH levels. We also generated a predictive chemical kinetic model for the degradation if imipramine – a modest size API that is computationally tractable and its intrinsic susceptibility toward oxidative degradation is an active area of research.<br/><br/>Compared with the state-of-the-art commercially-available expert system predictions, our ab initio modeling approach provides quantitative predictions that: (1) enable more precise identification of key degradation products, which is crucial for both designing forced degradation experiments and analytical method development; (2) offer mechanistic insight into the API degradation process, which is difficult to obtain experimentally, but may be valuable for improving formulation stability; (3) can be iteratively and systematically improved by refining the thermodynamic and kinetic parameters used to construct the model with a higher level of theory in quantum chemistry or accurate experimental values; and (4) is readily extensible to additional pharmaceutical chemical space through ab initio calculations.

Designing robust active pharmaceutical ingredients: Quantitative kinetic predictions of degradation chemistry

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