Evidence map›Paper›PMID 25951530›Full record

ArticleNature medicine2015

Translational value of mouse models in oncology drug development.

Stephen E Gould, Melissa R Junttila, Frederic J de Sauvage

Abstract read
PubMed Publisher
In one paragraph

Article in Nature medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 152 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
152citing papers in PubMed, 2 pooled it
–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

152 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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92 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

3 authors.

Stephen E GouldDepartment of Molecular Oncology at Genentech, Inc., South San Francisco, California, USA.
Melissa R JunttilaDepartment of Molecular Oncology at Genentech, Inc., South San Francisco, California, USA.
Frederic J de SauvageDepartment of Molecular Oncology at Genentech, Inc., South San Francisco, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Much has been written about the advantages and disadvantages of various oncology model systems, with the overall finding that these models lack the predictive power required to translate preclinical efficacy into clinical activity. Despite assertions that some preclinical model systems are superior to others, no single model can suffice to inform preclinical target validation and molecule selection. This perspective provides a balanced albeit critical view of these claims of superiority and outlines a framework for the proper use of existing preclinical models for drug testing and discovery. We also highlight gaps in oncology mouse models and discuss general and pervasive model-independent shortcomings in preclinical oncology work, and we propose ways to address these issues.

Indexed as

Disease Models, AnimalDrug DesignAnimalsCell Line, TumorDrug IndustryGenetic EngineeringHumansImmune SystemMiceMice, TransgenicNeoplasmsNeoplasm TransplantationPredictive Value of TestsRetrospective StudiesTranslational Research, Biomedical

Identifiers

PMID25951530

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.