AIMC Topic: Drug Liberation
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Machine Learning Predicts Drug Release Profiles and Kinetic Parameters Based on Tablets' Formulations.
The AAPS journal
Jul 28, 2025
Direct compression (DC) remains a popular manufacturing technology for producing solid dosage forms. However, the formulation optimisation is a laborious process, costly and time-consuming. The aim of this study was to determine whether machine learn...
Implementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery application.
Scientific reports
Jul 2, 2025
A combined methodology was performed based on chemometrics and machine learning regressive models in estimation of polysaccharide-coated colonic drug delivery. The release of medication was measured using Raman spectroscopy and the data was used for ...
PLGA-based long-acting injectable (LAI) formulations.
Journal of controlled release : official journal of the Controlled Release Society
Apr 21, 2025
Long-acting injectable (LAI) formulations, which deliver drugs over weeks or months, have been in use for more than three decades. Most clinically approved LAI products are formulated using poly(lactide-co-glycolide) (PLGA) polymers. Historically, th...
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...
Utilizing machine learning for predicting drug release from polymeric drug delivery systems.
Computers in biology and medicine
Feb 19, 2025
Polymeric drug delivery systems (PDDS) play a crucial role in controlled drug release, providing improved therapeutic outcomes. However, formulating PDDS and predicting their release profiles remain challenging due to their complex structures and the...
Machine learning integrated with in vitro experiments for study of drug release from PLGA nanoparticles.
Scientific reports
Feb 4, 2025
This paper investigates delivery of encapsulated drug from poly lactic-co-glycolic micro-/nano-particles. Experimental data collected from about 50 papers are analyzed by machine learning algorithms including linear regression, principal component an...
Active learning and Gaussian processes for the development of dissolution models: An AI-based data-efficient approach.
Journal of controlled release : official journal of the Controlled Release Society
Jan 13, 2025
In vitro dissolution testing plays a key role in controlling the quality and optimizing the formulation of solid dosage pharmaceutical products. Data-driven dissolution models can improve the efficiency of testing: their predictions can act as surrog...
Drug Release Nanoparticle System Design: Data Set Compilation and Machine Learning Modeling.
ACS applied materials & interfaces
Jan 12, 2025
Magnetic nanoparticles (NPs) are gaining significant interest in the field of biomedical functional nanomaterials because of their distinctive chemical and physical characteristics, particularly in drug delivery and magnetic hyperthermia applications...
'Applications of machine learning in liposomal formulation and development'.
Pharmaceutical development and technology
Jan 11, 2025
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...
Deep Learning for the Accurate Prediction of Triggered Drug Delivery.
IEEE transactions on nanobioscience
Jan 2, 2025
The need to mitigate the adverse effects of chemotherapy has driven the exploration of innovative drug delivery approaches. One emerging trend in cancer treatment is the utilization of Drug Delivery Systems (DDSs), facilitated by nanotechnology. Nano...
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