American Chemical Society
op3c00344_si_002.txt (441.8 kB)

In Silico Prediction of Pharmaceutical Degradation Pathways: A Benchmarking Study Using the Software Program Zeneth

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posted on 2024-02-19, 20:37 authored by Rachel Hemingway, Steven W. Baertschi, David Benstead, John M. Campbell, Maggie Coombs, Zongyun Huang, Ryan Khalaf, Martin A. Ott, David J. Ponting, Darren L. Reid, Neil G. Stevenson, Samuel J. Webb, Angela White, Todd Zelesky
Zeneth, a software application for the prediction of chemical degradation of small organic molecules, incorporates a knowledge base of rules to predict degradation pathways. In addition, the knowledge base contains property predictors that modulate the predicted likelihood of a given degradation product. In this study, a C–H bond dissociation energy (C–H BDE) predictor, which has been integrated into the software, was utilized. To determine this software’s predictive capabilities [using its knowledge base (2020.1.0 KB)], experimentally derived degradation profiles for 25 drug substances were compared to Zeneth predictions. These degradation profiles were derived from forced degradation studies, including accelerated and long-term stability studies, aligned with International Council for Harmonisation (ICH) guidelines. In addition, two case studies highlighting how prediction data can be utilized to confirm experimental data or assist with the identification of unknown degradation products have been presented. The specificity of prediction results was evaluated; transformation types that often predict degradation products not observed experimentally were identified, and an assessment of the causes is presented. The sensitivity for the study group was also evaluated using a historic knowledge base (2012.2.0 KB), enabling an assessment of how the predictive capabilities have improved over this period; the comparison demonstrated a 40% increase in sensitivity. This study has demonstrated that the ongoing expansion and optimization of this in silico tools knowledge base continues to result in improvements in its predictive capability and its ability to impart insight into the drug degradation knowledge space to aid pharmaceutical development.