AIMC Topic: Drug Delivery Systems

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Exploration of Predictive Potential of AI-enabled Portable System in Anticancer Drug Delivery: A Comparative Study with Modified Gompertz like Biphasic Response Model.

AAPS PharmSciTech
Mathematical models are conventionally used to understand the of tumor behaviors, but they generally lack in precisely correlating drug efficacy with tumor response. Artificial intelligence (AI) has forged a new avenue in cancer management, but requi...

Picoeukaryote-based biohybrid microrobots for active delivery in the kidney.

Science advances
Confined spaces in the human body pose substantial challenges for biomedical procedures. Navigating these ultrasmall environments is essential for precise drug delivery, improving treatment outcomes and reducing adverse effects. Microrobots offer a p...

A formulation dataset of poly(lactide-co-glycolide) nanoparticles for small molecule delivery.

Scientific data
Poly(lactide-co-glycolide) (PLGA) nanoparticles are promising drug delivery systems, widely recognized for their ability to overcome various limitations associated with conventional formulations. However, designing and optimizing such formulations is...

Recent advances of engineering cell membranes for nanomedicine delivery across the blood-brain barrier.

Journal of nanobiotechnology
The blood-brain barrier (BBB) poses a major challenge to the effective delivery of therapeutic agents for the treatment of central nervous system (CNS) disorders. The integration of cell membrane engineering with nanotechnology has recently enabled t...

Implementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery application.

Scientific reports
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 ...

Predicting PLGA nanoparticle size and zeta potential in synthesis for application of drug delivery via machine learning analysis.

Scientific reports
This study employed multiple machine learning (ML) methods to model and predict key attributes of PLGA nanoparticles, specifically particle size and zeta potential. The predictions were based on input variables, including PLGA polymer type, PLGA conc...

FiBar: A tool for analyzing fiber diameters in complex drug delivery systems from scanning electron microscopy images.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Electrospinning increases opportunities to facilitate the production of drug delivery systems (DDSs), such as complex biomaterials. However, the manual measurement of fiber diameters remains a critical bottleneck, hindering efficiency and scalability...

Polymer microparticles in an evolving drug delivery landscape: challenges and the role of machine learning.

International journal of pharmaceutics
Polymer microparticles (MPs) have long been a cornerstone of long-acting injectable (LAI) drug delivery, offering controlled drug release, reduced dosing frequency, and improved patient adherence. Among these, poly(lactide-co-glycolide) (PLGA)-based ...

Recent advances in nanopharmaceutical strategies for cancer treatment.

Biochemical and biophysical research communications
Next-generation cancer nanomedicines are revolutionizing therapeutic precision through multifunctional, adaptive, and tumor-specific strategies. This review discusses emerging innovations in cancer nanomedicine, including stimuli-responsive nanomedic...

Analysis of an electrically responsive drug delivery system for ibuprofen on-demand release using a machine learning approach.

Computers in biology and medicine
This study aims to optimize ibuprofen-based Drug Delivery Systems (DDSs) to address their short half-life and enhance controlled release. Advanced machine learning techniques, including Artificial Neural Networks, Random Forest, and CatBoost, were em...