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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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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 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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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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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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Predictive modeling of controlled drug release from polysaccharide-based systems using gradient boosting and

Ahmed H Albariqi1, Abdullah Alsalhi1, Meshal Alshamrani1

  • 1Department of Pharmaceutics, College of Pharmacy, Jazan University, Jazan, 45142, Saudi Arabia.

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Summary

A new hybrid machine learning model accurately predicts drug release from polysaccharide systems using Raman spectroscopy and formulation data. This approach enhances formulation design by linking polymer structure to drug release kinetics.

Keywords:
Controlled drug releaseMetaheuristic optimizationPolysaccharide-based formulationRaman spectroscopyRelease kinetics modeling

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Area of Science:

  • Pharmaceutical Science
  • Computational Chemistry
  • Materials Science

Background:

  • Accurate prediction of drug release kinetics is crucial for designing effective polysaccharide-based drug delivery systems.
  • Existing methods often lack the precision needed for rational formulation development.
  • Understanding the relationship between polymer structure and drug release is key.

Purpose of the Study:

  • To develop a hybrid machine learning framework integrating Raman spectroscopy and formulation descriptors to model drug release profiles.
  • To optimize and evaluate the predictive performance of advanced machine learning models for drug release kinetics.
  • To identify key spectral features and formulation parameters influencing drug release.

Main Methods:

  • A dataset of 155 experimental instances across 13 formulation groups was utilized, including Raman spectral data, medium descriptors, and temporal information.
  • Feature selection identified 17 informative Raman peaks, combined with medium and time as model inputs.
  • Four hybrid predictors (XGSO, XGQO, LGSO, LGQO) were developed by optimizing Extreme Gradient Boosting (XGB) and Light Gradient Boosting (LGB) models using Swarm-Assisted Bayesian Optimization (SABO) and Quantum-Inspired Optimization (QIO).

Main Results:

  • The optimized hybrid models significantly outperformed single learners in predicting drug release kinetics.
  • The XGSO and LGSO models achieved the lowest prediction errors, with test RMSE values of 0.065 and 0.077, and R² values of 0.961 and 0.939, respectively.
  • SHapley Additive exPlanations (SHAP) highlighted the strong influence of Raman bands (940-990 cm⁻¹ and 470-510 cm⁻¹) related to glycosidic backbone vibrations, along with time and medium effects.

Conclusions:

  • The proposed hybrid machine learning framework accurately predicts drug release kinetics from polysaccharide matrices.
  • The study successfully linked specific polysaccharide structural vibrations (via Raman spectroscopy) to macroscopic drug release behavior.
  • This data-driven approach offers a powerful tool for optimizing drug delivery formulations and advancing rational design principles.