AIMC Topic: Tablets
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UV imaging for the rapid at-line content determination of different colourless APIs in their tablets with artificial neural networks.
International journal of pharmaceutics
May 1, 2024
This paper presents a novel high-resolution and rapid (50 ms) UV imaging system, which was used for at-line, non-destructive API content determination of tablets. For the experiments, amlodipine and valsartan were selected as two colourless APIs with...
A prediction model based on artificial intelligence techniques for disintegration time and hardness of fast disintegrating tablets in pre-formulation tests.
BMC medical informatics and decision making
Mar 27, 2024
BACKGROUND: The pharmaceutical industry is continually striving to innovate drug development and formulation processes. Orally disintegrating tablets (ODTs) have gained popularity due to their quick release and patient-friendly characteristics. The c...
Machine vision-based non-destructive dissolution prediction of meloxicam-containing tablets.
International journal of pharmaceutics
Mar 17, 2024
Machine vision systems have emerged for quality assessment of solid dosage forms in the pharmaceutical industry. These can offer a versatile tool for continuous manufacturing while supporting the framework of process analytical technology, quality-by...
Microextraction by packed sorbent of N-nitrosamines from Losartan tablets using a high-throughput robot platform followed by liquid chromatography-tandem mass spectrometry.
Journal of separation science
Jul 3, 2023
The development of a fast, cost-effective, and efficient microextraction by packed sorbent setup was achieved by combining affordable laboratory-repackable devices of microextraction with a high-throughput cartesian robot. This setup was evaluated fo...
Application of unsupervised and supervised learning to a material attribute database of tablets produced at two different granulation scales.
International journal of pharmaceutics
May 20, 2023
The purpose of this study is to demonstrate the usefulness of machine learning (ML) for analyzing a material attribute database from tablets produced at different granulation scales. High shear wet granulators (scale 30 g and 1000 g) were used and da...
Convolutional neural network-based evaluation of chemical maps obtained by fast Raman imaging for prediction of tablet dissolution profiles.
International journal of pharmaceutics
Apr 30, 2023
In this work, the capabilities of a state-of-the-art fast Raman imaging apparatus are exploited to gain information about the concentration and particle size of hydroxypropyl methylcellulose (HPMC) in sustained release tablets. The extracted informat...
Investigations into the use of machine learning to predict drug dosage form design to obtain desired release profiles for 3D printed oral medicines.
Pharmaceutical development and technology
Feb 16, 2023
Three-dimensional (3D) printing, digitalization, and artificial intelligence (AI) are gaining increasing interest in modern medicine. All three aspects are combined in personalized medicine where 3D-printed dosage forms are advantageous because of th...
An overview of the implementation of SeDeM and SSCD in various formulation developments.
International journal of pharmaceutics
Feb 8, 2023
The Sediment Delivery Model explains experimental analysis and quantitative assessment of the powdered substance characterizing parameters, which offer pertinent data about the material's appropriateness for direct compression (DC) of tablet, which i...
Interpretable artificial neural networks for retrospective QbD of pharmaceutical tablet manufacturing based on a pilot-scale developmental dataset.
International journal of pharmaceutics
Jan 18, 2023
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
Nov 6, 2022
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...
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