Modeling, design, and machine learning-based framework for optimal injectability of microparticle-based drug formulations
Name
eabb6594.full.pdf
Description
Published version
Size
4.21 MB
Format
Unknown
Checksum (MD5)
8009e9b516688856375f82b1529343a5
Author(s)
Sarmadi, Morteza
Behrens, Adam M
McHugh, Kevin J
Contreras, Hannah TM
Tochka, Zachary L
Lu, Xueguang
Langer, Robert S
Jaklenec, Ana
Date Issued
2020
Journal
Science Advances
Publisher
American Association for the Advancement of Science (AAAS)
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Koch Institute for Integrative Cancer Research at MIT
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1126/SCIADV.ABB6594
