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Tablets

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QCL Infrared Spectroscopy Combined with Machine Learning as a Useful Tool for Classifying Acetaminophen Tablets by Brand.

Molecules (Basel, Switzerland)
The development of new methods of identification of active pharmaceutical ingredients (API) is a subject of paramount importance for research centers, the pharmaceutical industry, and law enforcement agencies. Here, a system for identifying and class...

Explainable deep recurrent neural networks for the batch analysis of a pharmaceutical tableting process in the spirit of Pharma 4.0.

International journal of pharmaceutics
Due to the continuously increasing Cost of Goods Sold, the pharmaceutical industry has faced several challenges, and the Right First-Time principle with data-driven decision-making has become more pressing to sustain competitiveness. Thus, in this wo...

Optimization and evaluation of modified release solid dosage forms using artificial neural network.

Scientific reports
This study aims to optimize and evaluate drug release kinetics of Modified-Release (MR) solid dosage form of Quetiapine Fumarate MR tablets by using the Artificial Neural Networks (ANNs). In training the neural network, the drug contents of Quetiapin...

Hybrid modeling of T-shaped partial least squares regression and transfer learning for formulation and manufacturing process development of new drug products.

International journal of pharmaceutics
T-shaped partial least squares regression (T-PLSR) is a valuable machine learning technique for the formulation and manufacturing process development of new drug products. An accurate T-PLSR model requires experimental data with multiple formulations...

3D printed dosage forms, where are we headed?

Expert opinion on drug delivery
INTRODUCTION: 3D Printing (3DP) is an innovative fabrication technology that has gained enormous popularity through its paradigm shifts in manufacturing in several disciplines, including healthcare. In this past decade, we have witnessed the impact o...

Advancing pharmaceutical Intelligence via computationally Prognosticating the in-vitro parameters of fast disintegration tablets using Machine Learning models.

European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V
The field of Machine Learning (ML) has garnered significant attention, particularly in healthcare for predicting disease severity. Recently, the pharmaceutical sector has also adopted ML techniques in various stages of drug development. Tablets are t...

Virtual screening of drug materials for pharmaceutical tablet manufacturability with reference to sticking.

International journal of pharmaceutics
The manufacturing of pharmaceutical solid dosage forms, such as tablets involves a large number of successive processing operations including crystallisation of the drug substance, granulation, drying, milling, mixing of the formulation, and compacti...

Evaluation of floatability characteristics of gastroretentive tablets using VIS imaging with artificial neural networks.

European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V
Gastroretentive dosage forms are recommended for several active substances because it is often necessary for the drug to be released from the carrier system into the stomach over an extended period. Among gastroretentive dosage forms, floating tablet...

Roller compaction: Measuring ribbon porosity by terahertz spectroscopy and machine learning.

International journal of pharmaceutics
Roller compaction is a crucial unit operation in pharmaceutical manufacturing, with its ribbon porosity widely recognised as a critical quality attribute. Terahertz spectroscopy has emerged as a fast and non-destructive technique to measure porosity ...

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

International journal of pharmaceutics
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 wer...