Modeling, design, and machine learning-based framework for optimal injectability of microparticle-based drug formulations
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 est...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Association for the Advancement of Science (AAAS)
2021
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| Online Access: | https://hdl.handle.net/1721.1/135398 |
| _version_ | 1811074520665030656 |
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| author | Sarmadi, Morteza Behrens, Adam M McHugh, Kevin J Contreras, Hannah TM Tochka, Zachary L Lu, Xueguang Langer, Robert Jaklenec, Ana |
| author_facet | Sarmadi, Morteza Behrens, Adam M McHugh, Kevin J Contreras, Hannah TM Tochka, Zachary L Lu, Xueguang Langer, Robert Jaklenec, Ana |
| author_sort | Sarmadi, Morteza |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T09:51:06Z |
| format | Article |
| id | mit-1721.1/135398 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:51:06Z |
| publishDate | 2021 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | dspace |
| spelling | mit-1721.1/1353982021-10-28T04:39:36Z Modeling, design, and machine learning-based framework for optimal injectability of microparticle-based drug formulations Sarmadi, Morteza Behrens, Adam M McHugh, Kevin J Contreras, Hannah TM Tochka, Zachary L Lu, Xueguang Langer, Robert Jaklenec, Ana 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. 2021-10-27T20:23:17Z 2021-10-27T20:23:17Z 2020 2021-06-22T15:46:16Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135398 en 10.1126/SCIADV.ABB6594 Science Advances Creative Commons Attribution NonCommercial License 4.0 https://creativecommons.org/licenses/by-nc/4.0/ application/pdf American Association for the Advancement of Science (AAAS) Science Advances |
| spellingShingle | Sarmadi, Morteza Behrens, Adam M McHugh, Kevin J Contreras, Hannah TM Tochka, Zachary L Lu, Xueguang Langer, Robert Jaklenec, Ana Modeling, design, and machine learning-based framework for optimal injectability of microparticle-based drug formulations |
| title | Modeling, design, and machine learning-based framework for optimal injectability of microparticle-based drug formulations |
| title_full | Modeling, design, and machine learning-based framework for optimal injectability of microparticle-based drug formulations |
| title_fullStr | Modeling, design, and machine learning-based framework for optimal injectability of microparticle-based drug formulations |
| title_full_unstemmed | Modeling, design, and machine learning-based framework for optimal injectability of microparticle-based drug formulations |
| title_short | Modeling, design, and machine learning-based framework for optimal injectability of microparticle-based drug formulations |
| title_sort | modeling design and machine learning based framework for optimal injectability of microparticle based drug formulations |
| url | https://hdl.handle.net/1721.1/135398 |
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