Evidence map›Paper›PMID 23457168›Full record

ArticleEuropean respiratory review : an official journal of the European Respiratory Society2013

The impact of personalised therapies on respiratory medicine.

J Stuart Elborn

Abstract read
In one paragraph

Article in European respiratory review : an official journal of the European Respiratory Society, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

1 author.

J Stuart ElbornCentre for Infection and Immunity, School of Medicine, Dentistry and Biomedical Sciences, Queens University, Belfast, UK. s.elborn@qub.ac.uk

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Stratified approaches to treating disease are very attractive, as efficacy is maximised by identifying responders using a companion diagnostic or by careful phenotyping. This approach will spare non-responders form potential side-effects. This has been pioneered in oncology where single genes or gene signatures indicate tumours that will respond to specific chemotherapies. Stratified approaches to the treatment of asthma with biological therapies are currently being extensively studied. In cystic fibrosis (CF), therapies have been developed that are targeted at specific functional classes of mutations. Ivacaftor, the first of such therapies, potentiates dysfunctional cystic fibrosis transmembrane conductance regulator (CFTR) protein Class III mutations and is now available in the USA and some European countries. Pivotal studies in patients with a G551D mutation, the most common Class III mutation, have demonstrated significant improvements in clinically important outcomes such as spirometry and exacerbations. Sweat chloride was significantly reduced demonstrating a functional effect on the dysfunctional CFTR protein produced by the G551D mutation. Symptom scores are also greatly improved to a level that indicates that this is a transformational treatment for many patients. This stratified approach to the development of therapies based on the functional class of the mutations in CF is likely to lead to new drugs or combinations that will correct the basic defect in many patients with CF.

Indexed as

Molecular Targeted TherapyMutationPrecision MedicinePulmonary MedicineAminophenolsChloridesCystic FibrosisCystic Fibrosis Transmembrane Conductance RegulatorGenetic Predisposition to DiseaseHumansLungPatient SelectionPhenotypeQuinolonesRespiratory System AgentsSweatAminophenolsCFTR protein, humanChloridesCystic Fibrosis Transmembrane Conductance RegulatorivacaftorQuinolonesRespiratory System Agents

Identifiers

PMID23457168
PMCPMC9487431

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.