Evidence map›Paper›PMID 35462105›Full record

ReviewCurrent opinion in pharmacology2022

Established and novel human translational models to advance cystic fibrosis research, drug discovery, and optimize CFTR-targeting therapeutics.

Deborah M Cholon, Martina Gentzsch

Abstract readReview
In one paragraph

Review in Current opinion in pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Patient-derived cell models for personalized medicine approaches in cystic fibrosis.Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society · 2023
    Review
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.

Deborah M CholonMarsico Lung Institute and Cystic Fibrosis Research Center, School of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA.
Martina GentzschMarsico Lung Institute and Cystic Fibrosis Research Center, School of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Pediatrics, Division of Pediatric Pulmonology, School of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA; Department of Cell Biology and Physiology, School of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA. Electronic address: gentzsch@med.unc.edu.

Funding

Vector CoreP30DK065988 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Scott H Randell · 2004 to 2026
$26.5M
NIDDK NIH HHS P30 DK065988
6 · The paper itself

Abstract

To find a cure for cystic fibrosis, there has been tremendous progress in the development of treatments that target the basic defect in the protein channel, CFTR. However, 10% of cystic fibrosis patients have rare CFTR mutations that are still without an approved CFTR-targeting drug. To identify relevant therapies for these patients, culture models using nasal, bronchial, and rectal tissue from individual patients allow functional, biochemical, and cellular detection of drug-rescued CFTR. Additionally, novel systems such as induced pluripotent stem cell-derived models are utilized to characterize CFTR mutations and identify treatments. State-of-the-art translational models were instrumental for CFTR modulator development and may become important for gene-based drug discovery and other novel therapeutic strategies.

Indexed as

Cystic FibrosisCystic Fibrosis Transmembrane Conductance RegulatorDrug DiscoveryGenetic TherapyHumansMutationCFTR protein, humanCystic Fibrosis Transmembrane Conductance Regulator

Identifiers

PMID35462105
PMCPMC9167699

What OpenQuestion holds

Textmetadata
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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.