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.

Assessment of Pharmaceutical Powder Flowability using Shear Cell-Based Methods and Application of Jenike's Methodology.

Jenike's approach to hopper design for a large-scale (3150 L) conical hopper was applied to pharmaceutical powders to evaluate flow issues, such as funnel flow or cohesive arching. Seven grades of microcrystalline cellulose (MCC) and six powder blends were tested. A Schulze Ring Shear Tester measured the flow function, wall friction (using stainless steel coupons with a #2B or #8 finish) and compressibility.