AIMC Topic: Excipients
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Predicting Powder Blend Flowability from Individual Constituent Properties Using Machine Learning.
Pharmaceutical research
Apr 17, 2025
PURPOSE: Predicting powder blend flowability is necessary for pharmaceutical manufacturing but challenging and resource-intensive. The purpose was to develop machine learning (ML) models to help predict flowability across multiple flow categories, id...
Machine Learning-Based Prediction of Drug Solubility in Lipidic Environments: The Sol_ME Tool for Optimizing Lipid-Based Formulations with a Preliminary Apalutamide Case Study.
AAPS PharmSciTech
Feb 3, 2025
Lipid-based formulations are essential for enhancing drug solubility and bioavailability, yet selecting optimal lipid excipients for specific drugs remains challenging. This study introduces Sol_ME, a machine learning-based model designed to predict ...
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...
Representative training data sets are critical for accurate machine-learning classification of microscopy images of particles formed by lipase-catalyzed polysorbate hydrolysis.
Journal of pharmaceutical sciences
Jan 15, 2025
Polysorbate 20 (PS20) is commonly used as an excipient in therapeutic protein formulations. However, over the course of a therapeutic protein product's shelf life, minute amounts of co-purified host-cell lipases may cause slow hydrolysis of PS20, rel...
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...
Machine learning strengthened formulation design of pharmaceutical suspensions.
International journal of pharmaceutics
Nov 19, 2024
Many different formulation strategies have been investigated to oppose suboptimal treatment of long-term or chronic conditions, one of which are the nano- and microsuspensions prepared as long-acting injectables to prolong the release of an active ph...
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
Nov 1, 2024
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...
Roller compaction: Measuring ribbon porosity by terahertz spectroscopy and machine learning.
International journal of pharmaceutics
Oct 28, 2024
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 ...
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