AIMC Topic: Tablets
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Development of machine learning models for estimation of disintegration time on fast-disintegrating tablets.
European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
May 23, 2025
The disintegration time for solid dosage oral formulations is directly influenced by diverse factors such as molecular properties, physical characteristics, excipient compositions, and formulation-specific attributes. This research addresses the chal...
Prediction of tablet disintegration time based on formulations properties via artificial intelligence by comparing machine learning models and validation.
Scientific reports
Apr 21, 2025
This research assesses multiple predictive models aimed at estimating disintegration time for pharmaceutical oral formulations, based on a dataset comprising nearly 2,000 data points that include molecular, physical, compositional, and formulation at...
RSM and AI based machine learning for quality by design development of rivaroxaban push-pull osmotic tablets and its PBPK modeling.
Scientific reports
Mar 7, 2025
The study is based on applying Artificial Neural Network (ANN) based machine learning and Response Surface Methodology (RSM) as simultaneous bivariate approaches in developing controlled-release rivaroxaban (RVX) osmotic tablets. The influence of dif...
Intelligent characterization multi-components in Yangxinshi tablet by online comprehensive two-dimensional liquid chromatography-quadrupole time-of-flight mass spectrometry combined with deep learning-assisted mass defect filtering classification and multidimensional data annotation strategy.
Talanta
Feb 25, 2025
A comprehensive characterization strategy for the intelligent analysis of multiple chemical components in Yangxinshi Tablet (YXST) was established. The strategy developed the deep learning-assisted mass defect filtering intelligent classification, pr...
Integrating hyperspectrograms with class modeling techniques for the construction of an effective expert system: Quality control of pharmaceutical tablets based on near-infrared hyperspectral imaging.
Journal of pharmaceutical and biomedical analysis
Jan 26, 2025
Near-infrared hyperspectral imaging (NIR-HSI) integrated with expert systems can support the monitoring of active pharmaceutical ingredients (APIs) and provide effective quality control of tablet formulations. However, existing quality control method...
Deep learning-based defect detection in film-coated tablets using a convolutional neural network.
International journal of pharmaceutics
Jan 18, 2025
Film-coating is a critical step in pharmaceutical manufacturing. Traditional visual inspections for film-coated tablet defect assessment are subjective, inefficient, and labor-intensive. We propose a novel approach utilizing machine learning and imag...
Optimizing critical quality attributes of fast disintegrating tablets using artificial neural networks: a scientific benchmark study.
Drug development and industrial pharmacy
Dec 8, 2024
OBJECTIVE: The objective of this study is to create predictive models utilizing machine learning algorithms, including Artificial Neural Networks (ANN), k-nearest neighbor (kNN), support vector machines (SVM), and linear regression, to predict critic...
Modelling the effect of base component properties and processing conditions on mixture products using probabilistic, knowledge-guided neural networks.
International journal of pharmaceutics
Dec 4, 2024
Development of materials by mixing different base components is a widespread methodology to create materials with improved properties compared to those of its base components. However, efficient determination of the properties of mixture-based materi...
A non-linear modelling approach to predict the dissolution profile of extended-release tablets.
European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Nov 28, 2024
This study proposes a novel non-linear modelling approach to predict the dissolution profiles of extended-release tablets, by combining a full-factorial design, curve fitting to the dissolution profiles, and artificial neural networks (ANN), with lin...
In silico formulation optimization and particle engineering of pharmaceutical products using a generative artificial intelligence structure synthesis method.
Nature communications
Nov 7, 2024
Pharmaceutical drug dosage forms are critical for ensuring the effective and safe delivery of active pharmaceutical ingredients to patients. However, traditional formulation development often relies on extensive lab and animal experimentation, which ...
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