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Supervised machine learning for predicting drug release from acetalated dextran nanofibers.

Ryan N Woodring1, Elizabeth G Gurysh1, Tanvi Pulipaka1

  • 1Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina at, Chapel Hill, USA. ainsliek@email.unc.edu.

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This study introduces a machine learning model to predict drug release from acetalated dextran (Ace-DEX) nanofibers. This approach streamlines in vitro testing, accelerating the development of advanced drug delivery systems.

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Area of Science:

  • Biomaterials Science
  • Drug Delivery Systems
  • Computational Biology

Background:

  • Electrospun nanofibers offer enhanced drug bioavailability and targeted delivery.
  • Acetalated dextran (Ace-DEX) is a biocompatible polymer enabling controlled drug release.
  • Current in vitro drug release characterization is labor-intensive and limits clinical translation.

Purpose of the Study:

  • To develop a novel machine learning workflow for assessing in vitro drug release from Ace-DEX nanofibers.
  • To create a predictive model that streamlines the characterization of drug release kinetics.
  • To establish a drug-agnostic method for predicting fractional drug release over time.

Main Methods:

  • Development and application of a Gaussian process regression (GPR) model.
  • Training, validation, and optimization of the GPR model using in vitro release data from 30 electrospun Ace-DEX scaffolds.
  • Assessment of model performance across various Ace-DEX formulations.

Main Results:

  • The developed GPR model accurately predicts in vitro drug release from Ace-DEX nanofibers.
  • The model demonstrated consistent performance across all tested Ace-DEX formulations.
  • The predictive approach proved to be drug-agnostic, applicable to different therapeutic agents.

Conclusions:

  • Machine learning, specifically GPR, offers an efficient and reliable method for characterizing drug release from electrospun nanofibers.
  • This novel workflow significantly reduces the labor associated with in vitro drug release studies.
  • The drug-agnostic predictive model accelerates the translation of electrospun drug delivery systems for clinical applications.