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.

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. The compression forces of 5-25 kN were used to compress round, beveled edge, and oval shape tablets. Compression speeds of 25, 40, and 80 rpm were chosen as representative ranges for bench-to-manufacturing-scale processing. Tablets were tested by in-house developed nondestructive ultrasonic method. The measurements revealed that elastic modulus vary with different testing orientations that indicated elastic modulus anisotropy in tablets. It was shown that altering process conditions such as tooling, compression force, and compression speed significantly impact the capping tendencies of formulations. A systematic approach has been applied to develop a predictive tool to assess capping tendencies of formulations. The developed tool is fast, material sparing, and has potential to flag the risk of manufacturability issues and provide insight into the performance of formulations during early development.