Prediction of dissolution performance of uncoated solid oral dosage forms via optical coherence tomography

Abstract

Real-time prediction of the dissolution behavior of solid oral dosage forms is an important research topic. Although methods such as Terahertz and Raman can provide measurements that can be linked to the dissolution performance, they typically require a longer time off-line for analysis. In this paper, we present a novel strategy for analyzing uncoated compressed tablets by means of optical coherence tomography (OCT). Using OCT, which is fast and in-line capable, makes it possible to predict the dissolution behavior of tablets based on images. In our study, OCT images were obtained of individual tablets from differently produced batches. Differences between tablets or batches in these images were hardly visible to the human eye. Advanced image analysis metrics were developed to quantify the light scattering behavior captured by the OCT probe and depicted in the OCT images. Detailed investigations assured the repeatability and robustness of the measurements. A correlation between these measurements and the dissolution behavior was established. A tree-based machine learning model was used to predict the amount of dissolved active pharmaceutical ingredient (API) at certain time points for each immediate-release tablet. Our results indicate that OCT, which is a non-destructive and real-time technology, can be used for in-line monitoring of tableting processes.