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Kinetic Modeling of API Oxidation: (2) Imipramine Stress Testing

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journal contribution
posted on 18.04.2022, 13:09 by Haoyang Wu, Alon Grinberg Dana, Duminda S. Ranasinghe, Frank C. Pickard, Geoffrey P. F. Wood, Todd Zelesky, Gregory W. Sluggett, Jason Mustakis, William H. Green
Gauging the chemical stability of active pharmaceutical ingredients (APIs) is critical at various stages of pharmaceutical development to identify potential risks from drug degradation and ensure the quality and safety of the drug product. Stress testing has been the major experimental method to study API stability, but this analytical approach is time-consuming, resource-intensive, and limited by API availability, especially during the early stages of drug development. Novel computational chemistry methods may assist in screening for API chemical stability prior to synthesis and augment contemporary API stress testing studies, with the potential to significantly accelerate drug development and reduce costs. In this work, we leverage quantum chemical calculations and automated reaction mechanism generation to provide new insights into API degradation studies. In the continuation of part one in this series of studies [Grinberg Dana et al., Mol. Pharm. 2021 18 (8), 3037−3049], we have generated the first ab initio predictive chemical kinetic model of free-radical oxidative degradation for API stress testing. We focused on imipramine oxidation in an azobis­(isobutyronitrile) (AIBN)/H2O/CH3OH solution and compared the model’s predictions with concurrent experimental observations. We analytically determined iminodibenzyl and desimipramine as imipramine’s two major degradation products under industry-standard AIBN stress testing conditions, and our ab initio kinetic model successfully identified both of them in its prediction for the top three degradation products. This work shows the potential and utility of predictive chemical kinetic modeling and quantum chemical computations to elucidate API chemical stability issues. Further, we envision an automated digital workflow that integrates first-principle models with data-driven methods that, when actively and iteratively combined with high-throughput experiments, can substantially accelerate and transform future API chemical stability studies.