AI Medical Compendium Journal:
International journal of pharmaceutics

Showing 41 to 50 of 74 articles

Machine learning using multi-modal data predicts the production of selective laser sintered 3D printed drug products.

International journal of pharmaceutics
Three-dimensional (3D) printing is drastically redefining medicine production, offering digital precision and personalized design opportunities. One emerging 3D printing technology is selective laser sintering (SLS), which is garnering attention for ...

Interpretable artificial neural networks for retrospective QbD of pharmaceutical tablet manufacturing based on a pilot-scale developmental dataset.

International journal of pharmaceutics
As the pharmaceutical industry increasingly adopts the Pharma 4.0. concept, there is a growing need to effectively predict the product quality based on manufacturing or in-process data. Although artificial neural networks (ANNs) have emerged as power...

A novel soft robotic pediatric in vitro swallowing device to gain insights into the swallowability of mini-tablets.

International journal of pharmaceutics
Soft robotics could help providing a better understanding of the mechanisms underpinning the swallowability of solid oral dosage forms (SODF), especially by vulnerable populations such as the elderly or children. In this study a novel soft robotic in...

Processability evaluation of multiparticulate units prepared by selective laser sintering using the SeDeM Expert System approach.

International journal of pharmaceutics
3D printing in dosage forms fabrication is in the focus of researchers, however, the attempts in multiparticulate units (MPUs) preparation are scarce. The aim of this study was to fabricate different size MPUs by selective laser sintering (SLS), usin...

Premexotac: Machine learning bitterants predictor for advancing pharmaceutical development.

International journal of pharmaceutics
Bitter taste receptors were recently found to be involved in numerous physiological and pathological conditions other than taste and are suggested as potential drug targets. In vivo and in vitro techniques for screening bitterants as ligands come wit...

Real-time coating thickness measurement and defect recognition of film coated tablets with machine vision and deep learning.

International journal of pharmaceutics
This paper presents a system, where images acquired with a digital camera are coupled with image analysis and deep learning to identify and categorize film coating defects and to measure the film coating thickness of tablets. There were 5 different c...

UV/VIS imaging-based PAT tool for drug particle size inspection in intact tablets supported by pattern recognition neural networks.

International journal of pharmaceutics
The potential of machine vision systems has not currently been exploited for pharmaceutical applications, although expected to provide revolutionary solutions for in-process and final product testing. The presented paper aimed to analyze the particle...

Reliable stability prediction to manage research or marketed vaccines and pharmaceutical products. "Avoid any doubt for the end-user of vaccine compliance at time of administration".

International journal of pharmaceutics
A major challenge for the pharmaceutical/vaccine industry is to anticipate and test/control product stability, regardless of the time/temperature profile of the product, from release to administration. Current empirical stability protocols performed ...

Machine learning predicts the effect of food on orally administered medicines.

International journal of pharmaceutics
Food-mediated changes to drug absorption, termed the food effect, are hard to predict and can have significant implications for the safety and efficacy of oral drug products in patients. Mimicking the prandial states of the human gastrointestinal tra...

Application of machine learning to a material library for modeling of relationships between material properties and tablet properties.

International journal of pharmaceutics
This study investigates the usefulness of machine learning for modeling complex relationships in a material library. We tested 81 types of active pharmaceutical ingredients (APIs) and their tablets to construct the library, which included the followi...