Evidence map›Paper›PMID 37945646›Full record

ArticleScientific reports2023

Artificial axons as a biomimetic 3D myelination platform for the discovery and validation of promyelinating compounds.

Anna Jagielska, Kristin Radzwill, Daniela Espinosa-Hoyos, Mingyu Yang, Kavin Kowsari, Jonathan E Farley, Stefanie Giera, Ann Byrne, Guoqing Sheng, Nicholas X Fang and 3 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.0field-weighted citation impact, top 25% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
  2. Rethinking MS Therapeutics: From Disease Pathogenesis Mechanisms to AI-Driven Drug Discovery.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2026
    Review
  3. Article
  4. Article
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

13 authors at 4 institutions in 3 countries.

Anna JagielskaDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. anna.jagielska@gmail.com.
Kristin RadzwillSanofi, Cambridge, MA, USA.
Daniela Espinosa-HoyosDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Mingyu YangHarvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA.
Kavin KowsariDepartment of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Jonathan E FarleySanofi, Cambridge, MA, USA.
Stefanie GieraSanofi, Cambridge, MA, USA.
Ann ByrneSanofi, Cambridge, MA, USA.
Guoqing ShengSanofi, Cambridge, MA, USA.
Nicholas X FangDepartment of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
James C DodgeSanofi, Cambridge, MA, USA.
Carlos E PedrazaSanofi, Cambridge, MA, USA.
Krystyn J Van VlietDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. krystyn.vv@cornell.edu.
Sanofi (United States) · USMassachusetts Institute of Technology · USAssociation pour l'Utilisation du Rein Artificiel · FRUniversity of Massachusetts Boston · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multiple sclerosis (MS), a chronic neurodegenerative disease driven by damage to the protective myelin sheath, is currently incurable. Today, all clinically available treatments modulate the immune-mediated symptoms of the disease but they fail to stop neurodegeneration in many patients. Remyelination, the regenerative process of myelin repair by oligodendrocytes, which is considered a necessary step to protect demyelinated axons and stop neuronal death, is impaired in MS patients. One of the major obstacles to finding effective remyelinating drugs is the lack of biomimetic drug screening platforms that enable quantification of compounds' potential to stimulate 3D myelination in the physiologically relevant axon-like environment. To address this need, we built a unique myelination drug discovery platform, by expanding our previously developed technology, artificial axons (AAs), which enables 3D-printing of synthetic axon mimics with the geometry and mechanical properties closely resembling those of biological axons. This platform allows for high-throughput phenotypic myelination assay based on quantification of 3D wrapping of myelin membrane around axons in response to compounds. Here, we demonstrate quantification of 3D myelin wrapping by rat oligodendrocytes around the axon mimics in response to a small library of known pro-myelinating compounds. This assay shows pro-myelinating activity for all tested compounds consistent with the published in vitro and in vivo data, demonstrating predictive power of AA platform. We find that stimulation of myelin wrapping by these compounds is dose-dependent, providing a facile means to quantify the compounds' potency and efficacy in promoting myelin wrapping. Further, the ranking of relative efficacy among these compounds differs in this 3D axon-like environment as compared to a traditional oligodendrocyte 2D differentiation assay quantifying area of deposited myelin membrane. Together, we demonstrate that the artificial axons platform and associated phenotypic myelin wrapping assay afford direct evaluation of myelin wrapping by oligodendrocytes in response to soluble compounds in an axon-like environment, providing a predictive tool for the discovery of remyelinating therapies.

Indexed as

Multiple SclerosisNeurodegenerative DiseasesAnimalsAxonsBiomimeticsHumansMyelin SheathOligodendrogliaRats

Identifiers

PMID37945646
PMCPMC10636046
OpenAlexW4388523417

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.