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.

Quantitative correlation of the effect of process conditions on the capping tendencies of tablet formulations.

Capping is a mechanical defect in tablet formulation and manufacture. Understanding what influences tablet capping in terms of process variables and formulation properties and developing specialized techniques to correlate these variables with mechanical failures are practical interests of the pharmaceutical industry. Tablet compression emulator was used to rank order capping tendencies of a diverse sample set.