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Predicting acetalated dextran nanoparticle features: Controlled synthesis, formulation, and testing in a

Thorben Köhler1, Sreekanth Kunchapu2, Antje Vollrath1

  • 1Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstraße 10, 07743, Jena, Germany; Jena Center for Soft Matter (JCSM), Friedrich Schiller University Jena, Philosophenweg 7, 07743, Jena, Germany.

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Summary
This summary is machine-generated.

Acetalated dextran (Ac(e)Dex) nanoparticles show promise for drug delivery. This study links their structural properties to nanoparticle stability, creating a predictive model to guide future nanomedicine development.

Keywords:
DextranDrug deliveryHigh-throughputMachine learningNanoparticlesStability predictionpH-responsiveness

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Area of Science:

  • Biomaterials Science
  • Nanomedicine
  • Polymer Chemistry

Background:

  • Acetalated dextran (Ac(e)Dex) is a promising hydrophobic, pH-labile, and biodegradable material for nanomedicine-based drug delivery.
  • A lack of fundamental structure-property relationship knowledge hinders the clinical translation of Ac(e)Dex.
  • Understanding these relationships is crucial for optimizing Ac(e)Dex for preclinical and clinical applications.

Purpose of the Study:

  • To investigate the structure-property relationships of acetalated dextran (Ac(e)Dex) derivatives.
  • To develop a predictive model for Ac(e)Dex nanoparticle stability based on synthesis and formulation parameters.
  • To enable standardized comparisons and support the preclinical development of Ac(e)Dex.

Main Methods:

  • Synthesis of 36 Ac(e)Dex derivatives with varied molar masses, types, and functionalization degrees.
  • High-throughput formulation screening (>1000 formulations) using automated liquid handling.
  • Machine learning model development for predicting nanoparticle degradation based on synthesis/formulation data.

Main Results:

  • Established correlations between Ac(e)Dex structural parameters (molar mass, type, functionalization) and formulation properties (size, dispersity, repeatability).
  • Developed a machine learning model capable of predicting Ac(e)Dex nanoparticle stability.
  • Demonstrated the influence of synthesis and formulation on particle stability.

Conclusions:

  • The integrated synthesis-to-prediction pipeline provides a novel approach for Ac(e)Dex development.
  • Elucidated structure-property relationships enable standardized comparisons of Ac(e)Dex materials.
  • This work supports the advancement of Ac(e)Dex for preclinical drug delivery applications.