AIMC Topic: Drug Liberation

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RSM and AI based machine learning for quality by design development of rivaroxaban push-pull osmotic tablets and its PBPK modeling.

Scientific reports
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
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
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
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
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
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
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...

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
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...

Computational analysis of controlled drug release from porous polymeric carrier with the aid of Mass transfer and Artificial Intelligence modeling.

Scientific reports
Controlled release of a desired drug from porous polymeric biomaterials was analyzed via computational method. The method is based on simulation of mass transfer and utilization of artificial intelligence (AI). This study explores the efficacy of thr...

Data-driven insights into the properties of liquisolid systems based on machine learning algorithms.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Liquisolid systems (LS) represent a formulation approach where liquid drug or its dispersion is transformed into a powder with good flowability and compactibility, leading to enhanced drug dissolution and bioavailability. Many research groups have fo...