Evidence map›Paper›PMID 32612262›Full record

ReviewNature reviews. Drug discovery2020

Leveraging preclinical models for the development of Alzheimer disease therapeutics.

Kimberly Scearce-Levie, Pascal E Sanchez, Joseph W Lewcock

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Drug discovery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.

0numbers the graph read from it
0cells of the map it votes in
65citing papers in PubMed
9.2field-weighted citation impact, top 1% 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

65 citing papers in PubMed, 117 citations in OpenAlex.

  1. Article
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  16. Review
  17. In vivo validation of late-onset Alzheimer's disease genetic risk factors.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024
    Article
  18. Review
  19. Article
  20. Article

5 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 at 1 institution in 1 country.

Kimberly Scearce-Levie *Denali Therapeutics, South San Francisco, CA, USA. scearce-levie@dnli.com.ORCID http://orcid.org/0000-0003-0568-802X
Pascal E Sanchez *Denali Therapeutics, South San Francisco, CA, USA. sanchez@dnli.com.ORCID http://orcid.org/0000-0002-2620-5165
Joseph W Lewcock *Denali Therapeutics, South San Francisco, CA, USA. lewcock@dnli.com.ORCID http://orcid.org/0000-0003-3012-7881
Denali Therapeutics (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A large number of mouse models have been engineered, characterized and used to advance biomedical research in Alzheimer disease (AD). Early models simply damaged the rodent brain through toxins or lesions. Later, the spread of genetic engineering technology enabled investigators to develop models of familial AD by overexpressing human genes such as those encoding amyloid precursor protein (APP) or presenilins (PSEN1 or PSEN2) carrying mutations linked to early-onset AD. Recently, more complex models have sought to explore the impact of multiple genetic risk factors in the context of different biological challenges. Although none of these models has proven to be a fully faithful reproduction of the human disease, models remain essential as tools to improve our understanding of AD biology, conduct thorough pharmacokinetic and pharmacodynamic analyses, discover translatable biomarkers and evaluate specific therapeutic approaches. To realize the full potential of animal models as new technologies and knowledge become available, it is critical to define an optimal strategy for their use. Here, we review progress and challenges in the use of AD mouse models, highlight emerging scientific innovations in model development, and introduce a conceptual framework for use of preclinical models for therapeutic development.

Indexed as

Disease Models, AnimalAlzheimer DiseaseAnimalsBiomarkersDrug DevelopmentDrug Evaluation, PreclinicalGenetic Predisposition to DiseaseHumansMiceMutationRisk FactorsBiomarkers

Identifiers

PMID32612262
OpenAlexW3015437269

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.