Evidence map›Paper›PMID 12113641›Full record

ReviewDrug safety2002

Genotyping of drug targets: a method to predict adverse drug reactions?

Cüneyt Güzey, Olav Spigset

Abstract readReview
PubMed Publisher
In one paragraph

Review in Drug safety, 2002. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Genetics in clinical trials.Frontiers of neurology and neuroscience · 2009
    Article
  5. Article
  6. Review
  7. Article
  8. 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

2 authors.

Cüneyt GüzeyDepartment of Clinical Pharmacology, St Olav's University Hospital, Trondheim, Norway.
Olav Spigset

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the last decades, advances in molecular biology have led to modern pharmacogenetics, which started as a science that focused on investigating drug metabolising enzymes and genetic determinants of pharmacokinetic variability. As more evidence has become available on the structure of drug targets and the genes coding for them, increasing attention has been directed towards pharmacodynamic explanations of variability in therapeutic response as well as in the risk for adverse drug reactions. Traditionally, genetic drug safety research has focused on variations in single genes whose functions are known to be related to given adverse drug reactions. A few such examples, malignant hyperthermia, the long QT syndrome, venous thromboembolic disease, tardive dyskinesia, and drug addiction, are presented in this article. In the future, results from the Human Genome Project together with tools such as DNA microarray technology, high-output screening systems and advanced bioinformatics, will permit a more thorough elucidation than is currently possible of the genetic components of adverse drug reactions. By screening for a large number of single nucleotide polymorphisms (SNPs), SNP patterns associated with adverse drug reactions can be discovered even though the functions of the SNPs as such are completely unknown. On the basis of these findings, it can be expected that pharmacogenetic research will identify situations where a drug should be avoided in certain individuals in order to reduce the risk for adverse drug reactions. If so, it will be feasible to use molecular diagnostics to select drugs that are safe for the individual patient.

Indexed as

Drug-Related Side Effects and Adverse ReactionsDyskinesiasGenetic TestingHumansLong QT SyndromeMalignant HyperthermiaOligonucleotide Array Sequence AnalysisPharmacogeneticsPolymorphism, Single NucleotideProteomeThromboembolismProteome

Identifiers

What OpenQuestion holds

Textmetadata
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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.