An optimization-based model discrimination framework for selecting an appropriate reaction kinetic model structure during early phase pharmaceutical process development

Abstract

The value of reaction kinetic models for manufacturing APIs (active pharmaceutical ingredients) has been well established in the quality by design (QbD) paradigm. Creating such models during the early phase of development when data and material are scarce is challenging. In this work, we present a model-based design of experiments framework for selecting a “fit for purpose” kinetic model from limited data. The framework leverages an estimability analysis to facilitate parameterizing candidate models. The essential elements can be applied in other domains where model selection is required, but an illustrative case study is presented for selecting the best of three proposed kinetic models for the 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU)-catalyzed N-methylation of a key intermediate in an API process using dimethyl carbonate (DMC). The case study concludes by selecting a mechanism that invokes an N-methylated DBU species as a key intermediate over other plausible mechanisms previously suggested in the literature. The framework is conceptually straightforward and requires minimal coding and computational time to execute.