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3D bioprinted microparticles: Optimizing loading efficiency using advanced DoE technique and machine learning

Jiawei Wang1, Niloofar Heshmati Aghda1, Junhuang Jiang2

  • 1Pharmaceutical Engineering and 3D Printing (PharmE3D) Lab, Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX 78712, USA.

International Journal of Pharmaceutics
|October 19, 2022
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Summary
This summary is machine-generated.

This study introduces a new 3D printing method (SMART) for developing microparticles (MPs) and uses design of experiment (DoE) and machine learning to predict drug loading efficiency (DLE). The approach streamlines MP development for programmable pharmaceutical attributes.

Keywords:
3D printingDesign of experimentMachine learningMicroparticle

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Area of Science:

  • Pharmaceutical Sciences
  • Materials Science
  • Biotechnology

Background:

  • Traditional microparticle (MP) development relies on inefficient trial-and-error methods.
  • Optimizing formulation factors for drug loading efficiency (DLE) is crucial but challenging.
  • A need exists for systematic and predictive approaches in MP formulation.

Purpose of the Study:

  • To develop a systemic method for evaluating MP formulation factors and predicting DLE.
  • To introduce a novel 3D printing technology for fabricating drug-loaded MPs.
  • To compare the predictive performance of design of experiment (DoE) and machine learning models for DLE.

Main Methods:

  • Fabrication of poly (lactide-co-glycolide) (PLGA) microparticles loaded with 6-thioguanine (6-TG) using a novel Sprayed Multi Adsorbed-droplet Reposing Technology (SMART) 3D printing approach.
  • Utilizing Design of Experiments (DoE) to assess the significance of formulation factors (drug amount, printing speed, pressure, nozzle size).
  • Employing machine learning models, including decision trees (DT), to predict drug loading efficiency (DLE) and validate DoE findings.

Main Results:

  • SMART technology enabled the fabrication of spherical MPs (1-3 μm) with high drug release (~100% at 10h) and maintained drug efficacy.
  • DoE analysis identified drug amount as the most influential factor affecting MP formulation.
  • Machine learning models, particularly decision trees, significantly outperformed DoE regression models in predicting DLE.

Conclusions:

  • The integration of DoE and machine learning with the novel SMART 3D printing technology offers a powerful, predictive approach for microparticle formulation.
  • This systemic method streamlines the development of MPs with programmable pharmaceutical attributes, heralding a new era in digital pharmaceutical science.
  • The findings demonstrate a paradigm shift from empirical development to data-driven optimization in microparticle drug delivery systems.