Drug Compounding

Optimizing critical quality attributes of fast disintegrating tablets using artificial neural networks: a scientific benchmark study.

Drug development and industrial pharmacy 39648277

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...

Algorithms Chemistry, Pharmaceutical Drug Compounding Excipients Hardness Linear Models Machine Learning Neural Networks, Computer Support Vector Machine Tablets

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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 39881455

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...

Chemistry, Pharmaceutical Drug Compounding Excipients Expert Systems Hyperspectral Imaging Particle Size Powders Principal Component Analysis Quality Control Spectroscopy, Near-Infrared Tablets

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Deep learning-based defect detection in film-coated tablets using a convolutional neural network.

International journal of pharmaceutics 39832574

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...

Convolutional Neural Networks Deep Learning Drug Compounding Image Processing, Computer-Assisted Neural Networks, Computer Tablets Tablets, Enteric-Coated Technology, Pharmaceutical

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'Applications of machine learning in liposomal formulation and development'.

Pharmaceutical development and technology 39780760

Machine learning (ML) has emerged as a transformative tool in drug delivery, particularly in the design and optimization of liposomal formulations. This review focuses on the intersection of ML and liposomal technology, highlighting how advanced algo...

Animals Chemistry, Pharmaceutical Drug Compounding Drug Delivery Systems Drug Development Drug Liberation Humans Liposomes Machine Learning

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Preparation and optimisation of solid lipid nanoparticles of rivaroxaban using artificial neural networks and response surface method.

Journal of microencapsulation 39757376

AIMS: This study aimed to improve rivaroxaban delivery by optimising solid lipid nanoparticles (SLN) for minimal mean diameter and maximal entrapment efficiency (EE), enhancing solubility, bioavailability, and the ability to cross the blood-brain bar...

Biological Availability Drug Compounding Drug Delivery Systems Factor Xa Inhibitors Lipids Nanoparticles Neural Networks, Computer Particle Size Rivaroxaban Solubility

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A deep learning approach to perform defect classification of freeze-dried product.

International journal of pharmaceutics 39756597

Cosmetic inspection of freeze-dried products is an important part of the post-manufacturing quality control process. Traditionally done by human visual inspection, this method poses typical challenges and shortcomings that can be addressed with innov...

Chemistry, Pharmaceutical Cosmetics Deep Learning Drug Compounding Freeze Drying Neural Networks, Computer Quality Control Technology, Pharmaceutical

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Scale-independent solid fraction prediction in dry granulation process using a gray-box model integrating machine learning model and Johanson model.

International journal of pharmaceutics 39961553

We propose a novel approach for predicting the solid fraction after roller compaction processes. It is crucial to predict and control the solid fraction, as it has a significant impact on the product quality. The solid fraction can be theoretically p...

Chemistry, Pharmaceutical Drug Compounding Machine Learning Models, Statistical Particle Size Powders Technology, Pharmaceutical

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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 39900704

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 ...

Benzaldehydes Chemistry, Pharmaceutical Drug Compounding Excipients Lipids Machine Learning Solubility Solvents

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Predicting Powder Blend Flowability from Individual Constituent Properties Using Machine Learning.

Pharmaceutical research 40244512

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...

Algorithms Chemistry, Pharmaceutical Drug Compounding Excipients Machine Learning Particle Size Pharmaceutical Preparations Powders Silicon Dioxide Surface Properties

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Combining High-Throughput Screening and Machine Learning to Predict the Formation of Both Binary and Ternary Amorphous Solid Dispersion Formulations for Early Drug Discovery and Development.

Pharmaceutical research 40180767

OBJECTIVE: Amorphous solid dispersion (ASD) is widely utilized to enhance the solubility and bioavailability of water-insoluble drugs. However, conventional experimental approaches for ASD development are often resource-intensive and time-consuming. ...

Algorithms Chemistry, Pharmaceutical Crystallization Drug Compounding Drug Development Drug Discovery High-Throughput Screening Assays Machine Learning Powder Diffraction Solubility Support Vector Machine X-Ray Diffraction

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AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Optimizing critical quality attributes of fast disintegrating tablets using artificial neural networks: a scientific benchmark study.

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.

Deep learning-based defect detection in film-coated tablets using a convolutional neural network.

'Applications of machine learning in liposomal formulation and development'.

Preparation and optimisation of solid lipid nanoparticles of rivaroxaban using artificial neural networks and response surface method.

A deep learning approach to perform defect classification of freeze-dried product.

Scale-independent solid fraction prediction in dry granulation process using a gray-box model integrating machine learning model and Johanson model.

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.

Predicting Powder Blend Flowability from Individual Constituent Properties Using Machine Learning.

Combining High-Throughput Screening and Machine Learning to Predict the Formation of Both Binary and Ternary Amorphous Solid Dispersion Formulations for Early Drug Discovery and Development.

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