posted on 2022-04-18, 13:09authored byHaoyang 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.