The adhesion mechanism of mucoadhesive tablets with dissimilar chain flexibility on viscoelastic hydrogels.

Mucosal membranes with strong variability in their viscoelastic properties line numerous organs and are often targeted by mucoadhesive formulations, e.g., highly swellable hydroxypropylmethylcellulose (HPMC) and slightly cross-linked poly(acrylic acid) (PAA) tablets.

The current state-of-the art in pharmaceutical continuous film coating - A review.

In this decade, one of the major trends in the pharmaceutical industry is the adoption of continuous manufacturing. This requires the development of continuous equivalents of essential pharmaceutical processes such as film coating. The process of film coating is the last step of the processing of solid dosage forms and is critical because it determines the visual appearance of the end product, along with ensuring its stability and possibly even defining the rate of drug release.

Simultaneous prediction of the API concentration and mass gain of film coated tablets using Near-Infrared and Raman spectroscopy and data fusion.

This study investigates the simultaneous prediction of active pharmaceutical ingredient (API) concentration and mass gain in film-coated tablets using Partial Least Squares (PLS) regression combined with three data fusion (DF) techniques: Low-Level (LLDF), Mid-Level (MLDF), and High-Level (HLDF). Near-Infrared (NIR) and Raman spectroscopy were utilized in both reflection and transmission modes, providing four types of spectral data per tablet. Transmission models proved more effective for API prediction by capturing data from the entire tablet, while reflection models excelled in assessing mass gain by focusing on the surface layer.

Automated tablet defect detection and the prediction of disintegration time and crushing strength with deep learning based on tablet surface images.

This paper presents novel measurement methods, where deep learning was used to detect tableting defects and determine the crushing strength and disintegration time of tablets on images captured by machine vision. Five different classes of defects were used and the accuracy of the real-time defect recognition performed with the deep learning algorithm YOLOv5 was 99.2 %.