Evidence map›Paper›PMID 32923598›Full record

ArticleScience advances2020

Modeling, design, and machine learning-based framework for optimal injectability of microparticle-based drug formulations.

Morteza Sarmadi, Adam M Behrens, Kevin J McHugh, Hannah T M Contreras, Zachary L Tochka, Xueguang Lu, Robert Langer, Ana Jaklenec

Open access · goldAbstract read
In one paragraph

Article in Science advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
3.6field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 67 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Morteza SarmadiDepartment of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.ORCID 0000-0001-9018-4066
Adam M BehrensDavid H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Kevin J McHughDavid H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.ORCID 0000-0001-6801-4431
Hannah T M ContrerasDavid H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.ORCID 0000-0002-9429-3750
Zachary L TochkaDavid H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.ORCID 0000-0002-9626-6190
Xueguang LuDavid H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.ORCID 0000-0002-1069-7265
Robert LangerDepartment of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.ORCID 0000-0003-4255-0492
Ana JaklenecDavid H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.ORCID 0000-0001-6096-0538
Massachusetts Institute of Technology · USHarvard–MIT Division of Health Sciences and Technology · US

Funding

VIRUS PRODUCTION COREP30CA014051 · NCI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Angela Nicole Koehler · 1985 to 2026
$93.9M
Oncolytic Virotherapy of Meningeal CancerR01CA140510 · NCI · DUKE UNIVERSITY · PI GROMEIER, MATTHIAS · 2009 to 2013
$1.6M
Fluorescence-based molecular imaging of in vivo release kineticsF32EB022416 · NIBIB · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI MCHUGH, KEVIN JAMES · 2017 to 2018
$118k
NCI NIH HHS P30 CA014051NCI NIH HHS R01 CA140510NIBIB NIH HHS F32 EB022416
6 · The paper itself

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.

Indexed as

Machine LearningSyringesDrug CompoundingInjectionsViscosity

Identifiers

PMID32923598
PMCPMC7455482
OpenAlexW3040908771

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.