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Related Concept Videos

Modified-Release Drug Delivery Systems: Rate-Programmed II01:19

Modified-Release Drug Delivery Systems: Rate-Programmed II

Rate-programmed drug delivery systems release drugs in a controlled manner to maintain therapeutic levels. Three main designs include reservoir, matrix, and hybrid systems.Reservoir systems consist of a drug core enclosed within a membrane that controls drug release. In non-swelling reservoir systems, polymers like ethyl cellulose or polymethacrylates are used. These do not hydrate in aqueous media and control release through membrane thickness, porosity, or insolubility. This type includes...
Modified-Release Drug Delivery Systems: Drug Release Characteristics01:22

Modified-Release Drug Delivery Systems: Drug Release Characteristics

Drug release from modified-release dosage forms is designed to achieve specific therapeutic effects by controlling the rate and extent of drug release. The classification of these drug release systems is based on key pharmacokinetic assumptions: drug disposition follows first-order kinetics, drug release is the rate-limiting step in absorption, and the released drug is rapidly and completely absorbed.There are four major models of drug release patterns. The first model is the slow zero-order...
Modified-Release Drug Delivery Systems: Classification01:23

Modified-Release Drug Delivery Systems: Classification

Modified-release drug delivery systems improve drug efficacy and minimize side effects by controlling the rate and location of drug release. These systems fall into three categories: rate-programmed, stimuli-activated, and site-targeted.Rate-programmed systems release drugs at a predetermined rate, maintaining consistent therapeutic levels and reducing fluctuations that could lead to toxicity or subtherapeutic effects. These systems use polymeric matrices, reservoir-based designs, or osmotic...
Modified-Release Drug Delivery Systems: Rate-Programmed I01:22

Modified-Release Drug Delivery Systems: Rate-Programmed I

Rate-programmed drug delivery systems (DDS) are designed to release drugs at specific, controlled rates to maintain consistent therapeutic levels. These systems are categorized based on their release mechanisms, including dissolution-controlled DDS, diffusion-controlled DDS, and combined dissolution-diffusion-controlled DDS.In dissolution-controlled DDS, the release rate depends on the slow dissolution of the drug itself or the surrounding matrix. Drugs with inherently slow dissolution rates,...
Modified-Release Drug Delivery Systems: Influencing Factors01:20

Modified-Release Drug Delivery Systems: Influencing Factors

Modified-release drug delivery systems are designed to optimize the therapeutic effect of drugs by minimizing side effects, reducing the dosage required, and controlling drug release to align with pharmacokinetic and pharmacodynamic needs. The system depends on two key factors: the drug's release from the formulation and its movement through the body to the target site. Unlike conventional dosage forms, where absorption is the limiting step, the rate of drug release is the key determinant in...
Site-Targeted Drug Delivery Systems: Polymeric Carriers01:24

Site-Targeted Drug Delivery Systems: Polymeric Carriers

Polymeric carriers enhance targeted drug delivery by increasing efficacy while minimizing off-target effects. These carriers comprise a biodegradable polymeric backbone integrated with functional elements that enable targeting, improve physicochemical properties, and regulate drug release.Targeting MechanismsThe targeting ability of polymeric carriers is mediated by a homing device, which is a molecular recognition component designed to selectively bind to specific tissues or cells. Monoclonal...

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Related Experiment Video

Updated: Jul 15, 2026

Preparation and Characterization of Individual and Multi-drug Loaded Physically Entrapped Polymeric Micelles
07:32

Preparation and Characterization of Individual and Multi-drug Loaded Physically Entrapped Polymeric Micelles

Published on: August 28, 2015

Predicting drug release from polymeric long-acting injectables using a machine learning approach to decode

Tianqi Wang1, Tianyu Liu2, Xiaoying Xu3

  • 1Department of Pharmacy and Pharmaceutical Sciences, Faculty of Science, National University of Singapore, 18 Science Drive 4, Singapore, 117543, Singapore.

Drug Delivery and Translational Research
|July 13, 2026
PubMed
Summary

A new hybrid AI framework predicts polymeric long-acting injectable (LAI) performance by combining machine learning with release theory. This approach decodes LAI behavior, improving formulation development and overcoming standardization challenges.

Keywords:
Drug deliveryDrug releaseFormulation developmentLong-acting injectablesMachine learningMathematical modelsMicrospherePolymer

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Injectable Supramolecular Polymer-Nanoparticle Hydrogels for Cell and Drug Delivery Applications
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Last Updated: Jul 15, 2026

Preparation and Characterization of Individual and Multi-drug Loaded Physically Entrapped Polymeric Micelles
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Published on: August 28, 2015

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A Freeze-Thawing Method to Prepare Chitosan-Poly(vinyl alcohol) Hydrogels Without Crosslinking Agents and Diflunisal Release Studies

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Injectable Supramolecular Polymer-Nanoparticle Hydrogels for Cell and Drug Delivery Applications
09:39

Injectable Supramolecular Polymer-Nanoparticle Hydrogels for Cell and Drug Delivery Applications

Published on: February 7, 2021

Area of Science:

  • Drug Delivery Systems
  • Polymer Science
  • Artificial Intelligence

Background:

  • Polymeric long-acting injectables (LAIs) are crucial but unpredictable drug delivery systems.
  • LAI performance depends on complex interactions between polymer microstructure, manufacturing, and testing conditions.
  • Current methods lack standardization, hindering reliable prediction of drug release kinetics.

Purpose of the Study:

  • To develop a hybrid machine learning framework for systematic decoding of LAI performance.
  • To integrate data-driven prediction with mechanistically relevant release theory.
  • To improve the rational development and precision of polymeric LAIs.

Main Methods:

  • A hybrid AI framework combining random forest models with interpretable SHAP analysis.
  • Utilized a curated dataset of 1,537 literature-derived LAI release profiles and 35 descriptors.
  • Validated the framework using the commercial product Risperdal Consta® and in-house donepezil-loaded PLGA microspheres.

Main Results:

  • The random forest model achieved robust prediction performance (R² = 0.86 ± 0.16).
  • SHAP analysis revealed the significant impact of the release apparatus on measured kinetics.
  • High prediction accuracy (MAE = 0.06) was achieved for intermediate-release formulations, with improvements for slow-release formulations using early-stage data.

Conclusions:

  • Hybrid AI offers a promising strategy for the rational development of polymeric LAIs.
  • The framework bridges polymer science and drug release, enabling data-driven precision over empirical methods.
  • Addressing method-dependent shifts and improving prediction for various release profiles are key advancements.