Evidence map›Paper›PMID 28721015›Full record

ReviewDrug design, development and therapy2017

Cell and small animal models for phenotypic drug discovery.

Mihaly Szabo, Sara Svensson Akusjärvi, Ankur Saxena, Jianping Liu, Gayathri Chandrasekar, Satish S Kitambi

Abstract readReview
In one paragraph

Review in Drug design, development and therapy, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Review
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  11. Review
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  13. Mucopolysaccharidosis IVA: Current Disease Models and Drawbacks.International journal of molecular sciences · 2023
    Review
  14. Review
  15. Review
  16. Article
  17. Article
  18. Observational
  19. Review
  20. The Resurrection of Phenotypic Drug Discovery.ACS medicinal chemistry letters · 2020
    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

6 authors.

Mihaly SzaboDepartment of Microbiology Tumor, and Cell Biology.
Sara Svensson AkusjärviDepartment of Microbiology Tumor, and Cell Biology.
Ankur SaxenaDepartment of Microbiology Tumor, and Cell Biology.
Jianping LiuDepartment of Biochemistry and Biophysics, Karolinska Institutet, Solna, Sweden.
Gayathri ChandrasekarDepartment of Microbiology Tumor, and Cell Biology.
Satish S KitambiDepartment of Microbiology Tumor, and Cell Biology.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The phenotype-based drug discovery (PDD) approach is re-emerging as an alternative platform for drug discovery. This review provides an overview of the various model systems and technical advances in imaging and image analyses that strengthen the PDD platform. In PDD screens, compounds of therapeutic value are identified based on the phenotypic perturbations produced irrespective of target(s) or mechanism of action. In this article, examples of phenotypic changes that can be detected and quantified with relative ease in a cell-based setup are discussed. In addition, a higher order of PDD screening setup using small animal models is also explored. As PDD screens integrate physiology and multiple signaling mechanisms during the screening process, the identified hits have higher biomedical applicability. Taken together, this review highlights the advantages gained by adopting a PDD approach in drug discovery. Such a PDD platform can complement target-based systems that are currently in practice to accelerate drug discovery.

Indexed as

Cells, CulturedDisease Models, AnimalPhenotypeAnimalsDrug DiscoveryDrug Evaluation, PreclinicalHumansModels, AnimaldiscoverydrugPDDphenotypescreeningzebrafish

Identifiers

PMID28721015
PMCPMC5500539

What OpenQuestion holds

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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.