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Related Concept Videos

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

Modified-Release Drug Delivery Systems: Rate-Programmed II

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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...
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Modified-Release Drug Delivery Systems: Stimuli-Activated01:30

Modified-Release Drug Delivery Systems: Stimuli-Activated

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Stimuli-activated drug delivery systems are designed to release drugs in response to specific physical, chemical, or biological stimuli. These systems often utilize hydrogels—three-dimensional, hydrophilic polymer networks capable of swelling in aqueous environments and retaining significant fluid volumes. Upon exposure to particular stimuli, these hydrogels undergo structural transitions that allow the embedded drug to be released. Due to this adaptive behavior, such systems are also...
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Modified-Release Drug Delivery Systems: Rate-Programmed I01:22

Modified-Release Drug Delivery Systems: Rate-Programmed I

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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,...
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Modified-Release Drug Delivery Systems: Classification01:23

Modified-Release Drug Delivery Systems: Classification

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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...
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Modified-Release Drug Delivery Systems: Drug Release Characteristics01:22

Modified-Release Drug Delivery Systems: Drug Release Characteristics

53
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...
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Modified-Release Drug Delivery Systems: Influencing Factors01:20

Modified-Release Drug Delivery Systems: Influencing Factors

47
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...
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Related Experiment Video

Updated: Feb 24, 2026

Preparation and Characterization of Individual and Multi-drug Loaded Physically Entrapped Polymeric Micelles
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Machine Learning for Predicting Drug Release Behavior of PLGA Microspheres.

Andrew F Catapano1, Ling Zheng1, Xudong Yuan2

  • 1Monmouth University, West Long Branch, NJ, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|February 23, 2026
PubMed
Summary

Machine learning accurately predicts drug release from poly(lactic-co-glycolic acid) (PLGA) microspheres. This approach optimizes long-acting drug formulations, reducing inefficient trial-and-error development.

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Area of Science:

  • Biomaterials Science
  • Pharmaceutical Technology
  • Computational Chemistry

Background:

  • Poly(lactic-co-glycolic acid) (PLGA) microspheres are crucial for sustained drug delivery, enhancing patient compliance.
  • Current development relies on inefficient trial-and-error methods due to complex formulation factors influencing drug release.

Purpose of the Study:

  • To develop and validate machine learning models for predicting in vitro drug release profiles from PLGA microspheres.
  • To identify key formulation parameters influencing drug release kinetics.

Main Methods:

  • A comprehensive dataset of 113 PLGA formulations was compiled from scientific literature.
  • Multiple machine learning algorithms were trained and evaluated for predictive performance.
  • Feature importance analysis was performed to understand critical factors affecting release.

Main Results:

  • The optimal machine learning model achieved high accuracy, with R² = 0.9415, RMSE = 6.99%, and MAE = 4.35%.
  • The model demonstrated robust predictive capability for in vitro drug release from PLGA microspheres.
  • Key factors influencing drug release were identified through feature importance analysis.

Conclusions:

  • Machine learning provides a powerful tool for the rational design and optimization of PLGA-based drug delivery systems.
  • Accurate prediction of drug release profiles can accelerate formulation development and improve therapeutic outcomes.
  • This data-driven approach facilitates the efficient development of long-acting injectable formulations.