Machine learning framework for extracting micro-viscoelastic and micro-structural properties of compressed oral solid dosage forms

Machine learning modeling for ultrasonic quality attribute assessment of pharmaceutical tablets for continuous manufacturing and real-time release testing

In in-process quality monitoring for Continuous Manufacturing (CM) and Critical Quality Attributes (CQA) assessment for Real-time Release (RTR) testing, ultrasonic characterization is a critical technology for its direct, non-invasive, rapid, and cost-effective nature. In quality evaluation with ultrasound, relating a pharmaceutical tablet's ultrasonic response to its defect state and quality parameters is essential. However, ultrasonic CQA characterization requires a robust mathematical model, which cannot be obtained with traditional first principles-based modeling approaches. Machine Learning (ML) using experimental data is emerging as a critical analytical tool for overcoming such modeling challenges. In this work, a novel Deep Neural Network-based ML-driven Non-Destructive Evaluation (ML-NDE) modeling framework is developed, and its effectiveness for extracting and predicting three CQAs, namely defect states, compression force levels, and amounts of disintegrant, is demonstrated. Using a robotic tablet handling experimental rig, each attribute's distinct waveform dataset was acquired and utilized for training, validating, and testing the respective ML models. This study details an advanced algorithmic quality assessment framework for pharmaceutical CM in which automated RTR testing is expected to be critical in developing cost-effective in-process real-time monitoring systems. The presented ML-NDE approach has demonstrated its effectiveness through evaluations with separate (unused) test datasets.