Journal
SCIENCE ADVANCES
Volume 6, Issue 28, Pages -Publisher
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/sciadv.abb6594
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Funding
- Bill & Melinda Gates Foundation, Seattle, WA [OPP1095790]
- Koch Institute Support (core) Grant from the National Cancer Institute [P30-CA14051]
- NIH Ruth L. Kirschestein National Research Service Award [F32EB022416]
- Bill and Melinda Gates Foundation [OPP1095790] Funding Source: Bill and Melinda Gates Foundation
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Inefficient injection of microparticles through conventional hypodermic needles can impose serious challenges on clinical translation of biopharmaceutical drugs and microparticle-based drug formulations. This study aims to determine the important factors affecting microparticle injectability and establish a predictive framework using computational fluid dynamics, design of experiments, and machine learning. A numerical multiphysics model was developed to examine microparticle flow and needle blockage in a syringe-needle system. Using experimental data, a simple empirical mathematical model was introduced. Results from injection experiments were subsequently incorporated into an artificial neural network to establish a predictive framework for injectability. Last, simulations and experimental results contributed to the design of a syringe that maximizes injectability in vitro and in vivo. The custom injection system enabled a sixfold increase in injectability of large microparticles compared to a commercial syringe. This study highlights the importance of the proposed framework for optimal injection of microparticle-based drugs by parenteral routes.
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