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. In this work, a methodology to predict the breaking force and disintegration time of tablet formulations using nondestructive ultrasonics and machine learning tools was developed. The input variables to the model include intrinsic properties of formulation and extrinsic process variables influencing the tablet during manufacturing. The model has been applied to predict breaking force and disintegration time using small quantities of active pharmaceutical ingredient and prototype formulation designs. The novel approach presented is a step forward toward rational design of a robust drug product based on insight into the performance of common materials during formulation and process development. It may also help expedite drug product development timeline and reduce active pharmaceutical ingredient usage while improving efficiency of the overall process.

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. Hopper Index (HI, maximum hopper angle required for mass flow) and Arching Index (AI, minimum hopper outlet size to prevent cohesive arch formation) were computed using Mathcad(©) . For MCC, a linear relationship was observed between median particle size and the Jenike flow function coefficient. A curvilinear relationship was observed for powder blends indicating more complex flow behavior than based on median particle size alone. Powder bulk density had a minimal effect on AI for MCC grades. Overestimation of AI can occur with this method for pharmaceutical powders because the true shape of the flow function is not defined at very low consolidation pressures and linear extrapolation becomes unrepresentative. This instrumental limitation underscores the need for a precise and accurate test method to determine powder flow functions at very low levels of consolidation stress for pharmaceutical applications.