Evidence map›Paper›PMID 20423335›Full record

ReviewBritish journal of pharmacology2010

High-throughput screening and small animal models, where are we?

Jean Giacomotto, Laurent Ségalat

Abstract readReview
In one paragraph

Review in British journal of pharmacology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 125 papers.

0numbers the graph read from it
0cells of the map it votes in
125citing papers in PubMed
–field-weighted citation impact
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

125 citing papers in PubMed.

  1. A Landscape ofBiology · 2026
    Review
  2. Article
  3. Mitochondrial complex I deficiency-associated diseases and models.Cellular and molecular life sciences : CMLS · 2026
    Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. Review
  9. Review
  10. Review
  11. Characterization of LaBioengineering (Basel, Switzerland) · 2025
    Article
  12. Article
  13. Murine model for investigating severe trauma.World journal of emergency medicine · 2025
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Insects · 2024
    Review
  20. Article

65 more citing papers are in PubMed but not listed here.

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

2 authors.

Jean GiacomottoUniversité Claude Bernard Lyon, Villeurbanne, France. giacomottojean@gmail.com <giacomottojean@gmail.com>
Laurent Ségalat

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current high-throughput screening methods for drug discovery rely on the existence of targets. Moreover, most of the hits generated during screenings turn out to be invalid after further testing in animal models. To by-pass these limitations, efforts are now being made to screen chemical libraries on whole animals. One of the most commonly used animal model in biology is the murine model Mus musculus. However, its cost limit its use in large-scale therapeutic screening. In contrast, the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and the fish Danio rerio are gaining momentum as screening tools. These organisms combine genetic amenability, low cost and culture conditions that are compatible with large-scale screens. Their main advantage is to allow high-throughput screening in a whole-animal context. Moreover, their use is not dependent on the prior identification of a target and permits the selection of compounds with an improved safety profile. This review surveys the versatility of these animal models for drug discovery and discuss the options available at this day.

Indexed as

Drug Delivery SystemsDrug DesignAnimalsDrug DiscoveryHigh-Throughput Screening AssaysHumansModels, AnimalSmall Molecule LibrariesSmall Molecule Libraries

Identifiers

PMID20423335
PMCPMC2874843

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.