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

Abstract

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)/H 2 O/CH 3 OH 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.

Fast Predictions of Reaction Barrier Heights: Toward Coupled-Cluster Accuracy

“…Indeed, recently several research groups have constructed models to quantitatively predict the time-evolution of diverse chemical systems, based largely on parameters derived from quantum chemistry rather than experiment. For example, earlier this year our group has published models for combustion, pyrolysis, , and the degradation of pharmaceutical compounds . However, because quantum chemistry calculations of rate coefficients are slow, only a small fraction of the numerous rate coefficients in these models have been computed accurately, which can make the predictions somewhat erratic.…”

“…For example, earlier this year our group has published models for combustion, 10 pyrolysis, 11,12 and the degradation of pharmaceutical compounds. 13 However, because quantum chemistry calculations of rate coefficients are slow, only a small fraction of the numerous rate coefficients in these models have been computed accurately, which can make the predictions somewhat erratic. The vision of reliable predictive chemical kinetics can only be realized if accurate rate coefficients can be calculated much more rapidly than is possible today.…”

RMG Database for Chemical Property Prediction

“…In the 300–650 K range, rate predictions tend to be less accurate due to higher sensitivity to the errors in estimates of activation barriers and enthalpies. The lower accuracy makes prediction of product yields and overall reaction rates much less robust, although successful examples exist. , Most small nitrogen and sulfur functional groups are covered in the database. RMG can handle their small-molecule chemistries quite well, including some nitrogen–sulfur interactions.…”

“…The lower accuracy makes prediction of product yields and overall reaction rates much less robust, although successful examples exist. 76,77 Most small nitrogen and sulfur functional groups are covered in the database. RMG can handle their small-molecule chemistries quite well, including some nitrogen−sulfur interactions.…”

Best Practices for the Collection of Robust Time Course Reaction Profiles for Kinetic Studies

“…There are many programs that can be used for reaction modeling, such as DynoChem, , COPASI, , and Zeneth , and many valid approaches to kinetic modeling. As a starting point, we recommend first defining a minimal set of all kinetically relevant elementary steps to be input into the modeling software.…”