Evidence map›Paper›PMID 22211661›Full record

ReviewCurrent HIV research2011

Facts and fiction: cellular models for high throughput screening for HIV-1 reactivating drugs.

Vicente Planelles, Frank Wolschendorf, Olaf Kutsch

Abstract readReview
In one paragraph

Review in Current HIV research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. μ-Lat: A mouse model to evaluate human immunodeficiency virus eradication strategies.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2020
    Article
  4. Article
  5. Article
  6. Article
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  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. 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

3 authors.

Vicente PlanellesDepartment of Pathology, University of Utah, Salt Lake City, USA.
Frank Wolschendorf
Olaf Kutsch

Funding

Using NK Cells to eliminate the latent HIV reservoirU19AI096113 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI PLANELLES, VICENTE · 2011 to 2015
$39.5M
A GFP-based HTS Assay for HIV-1 Reactivating AgentsR01AI064012 · NIAID · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI KUTSCH, OLAF · 2005 to 2007
$968k
NIAID NIH HHS R01 AI064012NIAID NIH HHS R01AI064012NIAID NIH HHS U19 AI096113NIAID NIH HHS U19 AI096113-01
6 · The paper itself

Abstract

A curative therapy for HIV-1 infection will have to include measures to eliminate the reservoir of latently HIV- 1 infected cells that allow the virus to persist despite otherwise successful therapy. To date, all efforts to deplete the latent reservoir by triggering viral reactivation have used preexisting drugs that are believed to potentially target molecular mechanisms controlling HIV-1 infection. These therapeutic attempts were not clinically successful. Only in the last few years have cellular models of latent HIV-1 infection suitable for high throughput screening been developed and concerted drug discovery efforts were initiated to discover new HIV-1 reactivating drugs. We here provide a historic overview about the development of cell models with latent HIV-1 infection that lend themselves to drug discovery. We provide an overview from the first reported latently infected cell lines to current in vitro models of latent HIV-1 infection in primary T cells, and compare their potential to be used in future large-scale drug screening efforts.

Indexed as

Anti-HIV AgentsCell LineDrug Evaluation, PreclinicalHIV-1HumansModels, BiologicalT-LymphocytesVirus ActivationVirus LatencyAnti-HIV Agents

Identifiers

PMID22211661
PMCPMC3771330

What OpenQuestion holds

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Registered trials

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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.