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.

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. In all, 5 anhydrous forms and 66 solvated forms have been discovered. After early polymorph screening using common techniques yielded mostly solvates and failed to uncover several key anhydrous forms, it became necessary to devise new approaches based on an advanced understanding of crystal structure and conformational relationships between forms. With the aid of this analysis, two screening approaches were devised which targeted high-temperature desolvation as a means to increase conformational populations and enhance overall probability of anhydrous form production. Application of these targeted approaches, comprising over 100 experiments, produced only the known anhydrous forms, without appearance of any new forms. The development of these screens was a critical and alternative approach to circumvent solvation issues associated with more conventional screening methods. The results provided confidence that the current development form was the most stable polymorph, with a low likelihood for the existence of a more-stable anhydrous form.