A Practical Framework Toward Prediction of Breaking Force and Disintegration of Tablet Formulations Using Machine Learning Tools

Abstract

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.

A “Large-N” Content Uniformity Process Analytical Technology (PAT) Method for Phenytoin Sodium Tablets

“…The decrease in the compaction force, and hence tablet crushing strength from 15 kP to 5 kP was observed along LV2. The compression force was observed to significantly affect the compact crushing strength as demonstrated earlier by Akseli et al 50 The direction of the arrow suggests increasing crushing strength.…”

A Review of Disintegration Mechanisms and Measurement Techniques

“…Akseli et al published several articles about the use of ultrasonic methods to analyse mechanical properties of tablets [133, 134] and recently they utilised ultrasonic measurements to predict the breaking force and disintegration time of tablets [135]. The authors applied machine learning concepts (neural networks, genetic algorithms, support vector machines and random forest) to predict the disintegration time from ultrasonic measurements and several other tablet properties (tablet diameter, thickness, weight, porosity and breaking force) as well as process parameters (compression force and tablet compaction speed).…”

Performance appraisal of Trichoderma viride based novel tablet and powder formulations for management of Fusarium wilt disease in chickpea

“…The performance of a tablet formulation depends on several factors such as dissolution, tablet breaking force, disintegration, the homogeneity of composition, etc. (Akseli et al, 2017) Similarly, the effect of Trichoderma based formulation depends on different factors namely p H , moisture content, inoculum concentration, storage temperature, inoculum age, and the incubation period (Mulatu et al, 2021). Therefore, integrating knowledge of material science and fungal physiological behavior is paramount for the development of antagonistic fungi-based tablet bioformulations.…”