A Practical Framework Toward Prediction of Breaking Force and Disintegration of Tablet Formulations Using Machine Learning Tools.

Enabling the paradigm of quality by design requires the ability to quantitatively correlate material properties and process variables to measureable product performance attributes. Conventional, quality-by-test methods for determining tablet breaking force and disintegration time usually involve destructive tests, which consume significant amount of time and labor and provide limited information. Recent advances in material characterization, statistical analysis, and machine learning have provided multiple tools that have the potential to develop nondestructive, fast, and accurate approaches in drug product development.

Development of a targeted polymorph screening approach for a complex polymorphic and highly solvating API.

Elucidation of the most stable form of an active pharmaceutical ingredient (API) is a critical step in the development process. Polymorph screening for an API with a complex polymorphic profile can present a significant challenge. The presented case illustrates an extensively polymorphic compound with an additional propensity for forming stable solvates.