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Related Concept Videos

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

Modified-Release Drug Delivery Systems: Rate-Programmed II

139
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...
139
Modified-Release Drug Delivery Systems: Drug Release Characteristics01:22

Modified-Release Drug Delivery Systems: Drug Release Characteristics

263
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...
263
Modified-Release Drug Delivery Systems: Classification01:23

Modified-Release Drug Delivery Systems: Classification

318
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...
318
Modified-Release Drug Delivery Systems: Rate-Programmed I01:22

Modified-Release Drug Delivery Systems: Rate-Programmed I

174
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,...
174
Modified-Release Drug Delivery Systems: Stimuli-Activated01:30

Modified-Release Drug Delivery Systems: Stimuli-Activated

184
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...
184
Site-Targeted Drug Delivery Systems: Polymeric Carriers01:24

Site-Targeted Drug Delivery Systems: Polymeric Carriers

165
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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An Injectable and Drug-loaded Supramolecular Hydrogel for Local Catheter Injection into the Pig Heart
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Modeling Polymeric Drug Release: The Emerging Role of Machine Learning.

Ryan N Woodring1, Kristy M Ainslie1,2,3

  • 1Division of Pharmacoengineering & Molecular Pharmaceutics, Eshelman School of Pharmacy, UNC, Chapel Hill, North Carolina, USA.

Wiley Interdisciplinary Reviews. Nanomedicine and Nanobiotechnology
|March 2, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) advances drug delivery by predicting polymeric drug release kinetics, overcoming limitations of traditional models. This enables faster development of novel polymer-based drug formulations.

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Area of Science:

  • Polymer science
  • Drug delivery systems
  • Computational modeling

Background:

  • Traditional mechanistic and empirical models for polymeric drug release rely on transport phenomena but often use simplifying assumptions.
  • These models are typically limited to retrospective analysis of in vitro data, posing challenges for accurate, predictive characterization.
  • Accurate characterization of time-dependent drug release from polymeric formulations remains a significant challenge in pharmaceutical development.

Purpose of the Study:

  • To explore the transition from traditional mathematical models to machine learning (ML) approaches for characterizing and predicting drug release from polymeric systems.
  • To provide a practical roadmap for applying ML in formulation development, integrating AI with established knowledge.
  • To highlight the potential of ML in accelerating the design and translation of advanced polymer-based drug delivery systems.

Main Methods:

  • Review of traditional mathematical and physical principles (diffusion, swelling, erosion) used in drug release modeling.
  • Exploration of recent advances and applications of artificial intelligence (AI), specifically machine learning (ML), in drug release characterization and prediction.
  • Discussion of key trends in ML applications, including data compilation, processing, architecture selection, and performance metrics.

Main Results:

  • Machine learning models offer a new frontier for characterizing and predicting drug release kinetics from polymeric systems.
  • ML can unveil key formulation parameters that govern unique drug release profiles, supporting efficient development.
  • Recent ML applications demonstrate significant potential in enhancing the predictive power beyond traditional modeling approaches.

Conclusions:

  • Machine learning provides powerful tools to overcome the limitations of traditional models in characterizing time-dependent drug release.
  • Integrating ML with existing knowledge can accelerate the development and translation of next-generation polymer-based drug delivery systems.
  • This review offers a roadmap for scientists to leverage ML for advanced formulation development and innovation.