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

Abstract

A compressed pharmaceutical oral solid dosage (OSD) form is a strongly micro-viscoelastic material composite arranged as a network of agglomerated particles due to its constituent powders and their bonding and fractural mechanical properties. An OSD product’s Critical Quality Attributes, such as disintegration, drug release (dissolution) profile, and structural strength (“hardness”), are influenced by its micro-scale properties. Ultrasonic evaluation is direct, non-destructive, rapid, and cost-effective. However, for practical process control applications, the simultaneous extraction of the micro-viscoelastic and scattering properties from a tablet’s ultrasonic response requires a unique solution to a challenging inverse mathematical wave propagation problem. While the spatial progression of a pulse traveling in a composite medium with known micro-scale properties is a straightforward computational task when its dispersion relation is known, extracting such properties from the experimentally acquired waveforms is often non-trivial. In this work, a novel Machine Learning (ML)-based micro-property extraction technique directly from waveforms, based on Multi-Output Regression models and Neural Networks, is introduced and demonstrated. Synthetic waveforms with a given set of micro-properties of virtual tablets are computationally generated to train, validate, and test the developed ML models for their effectiveness in the inverse problem of recovering specified micro-scale properties. The effectiveness of these ML models is then tested and demonstrated for a set of physical OSD tablets. The micro-viscoelastic and micro-structural properties of physical tablets with known properties have been extracted through experimentally acquired waveforms to exhibit their consistency with the generated ML-based attenuation results.