Evidence map›Paper›PMID 32051340›Full record

ArticleJCI insight2020

Computational discovery of therapeutic candidates for preventing preterm birth.

Brian L Le, Sota Iwatani, Ronald J Wong, David K Stevenson, Marina Sirota

Open access · goldAbstract read
In one paragraph

Article in JCI insight, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
2.5field-weighted citation impact, top 10% 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

20 citing papers in PubMed, 32 citations in OpenAlex.

  1. Article
  2. Article
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  4. Article
  5. Review
  6. Single-cell-level digital twins for preterm birth prevention strategies.bioRxiv : the preprint server for biology · 2025
    Article
  7. Article
  8. Review
  9. Article
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  11. Article
  12. Review
  13. Article
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  15. Article
  16. Next generation strategies for preventing preterm birth.Advanced drug delivery reviews · 2021
    Review
  17. Article
  18. Article
  19. Article
  20. Review
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

5 authors at 2 institutions in 1 country.

Brian L LeBakar Computational Health Sciences Institute and.
Sota IwataniDepartment of Pediatrics, Stanford University, Stanford, California, USA.
Ronald J WongDepartment of Pediatrics, Stanford University, Stanford, California, USA.
David K StevensonDepartment of Pediatrics, Stanford University, Stanford, California, USA.
Marina SirotaBakar Computational Health Sciences Institute and.
Stanford University · USUniversity of California, San Francisco · US

Funding

Elucidating the Role of the Genetic and Environmental Determinants of Preterm Birth Using Integrative Computational ApproachesK01LM012381 · NLM · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SIROTA, MARINA · 2016 to 2018
$531k
NLM NIH HHS K01 LM012381
6 · The paper itself

Abstract

Few therapeutic methods exist for preventing preterm birth (PTB), or delivery before completing 37 weeks of gestation. In the US, progesterone (P4) supplementation is the only FDA-approved drug for use in preventing recurrent spontaneous PTB. However, P4 has limited effectiveness, working in only approximately one-third of cases. Computational drug repositioning leverages data on existing drugs to discover novel therapeutic uses. We used a rank-based pattern-matching strategy to compare the differential gene expression signature for PTB to differential gene expression drug profiles in the Connectivity Map database and assigned a reversal score to each PTB-drug pair. Eighty-three drugs, including P4, had significantly reversed differential gene expression compared with that found for PTB. Many of these compounds have been evaluated in the context of pregnancy, with 13 belonging to pregnancy category A or B - indicating no known risk in human pregnancy. We focused our validation efforts on lansoprazole, a proton-pump inhibitor, which has a strong reversal score and a good safety profile. We tested lansoprazole in an animal inflammation model using LPS, which showed a significant increase in fetal viability compared with LPS treatment alone. These promising results demonstrate the effectiveness of the computational drug repositioning pipeline to identify compounds that could be effective in preventing PTB.

Indexed as

Computational BiologyDrug RepositioningFemaleFetusHumansInfant, NewbornLansoprazolePregnancyPremature BirthLansoprazoleBioinformaticsReproductive BiologyTherapeutics

Identifiers

PMID32051340
PMCPMC7098786
OpenAlexW3005892426

What OpenQuestion holds

Textmetadata
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.