Integration of diverse data sources for prediction of adverse drug events.

D R Abernethy, J P F Bai, K Burkhart, H-G Xie, P Zhichkin

Abstract readComment

In one paragraph

Article in Clinical pharmacology and therapeutics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it

0cells of the map it votes in

5citing 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

5 citing papers in PubMed.

Target-Adverse Event Profiles to Augment Pharmacovigilance: A Pilot Study With Six New Molecular Entities.CPT: pharmacometrics & systems pharmacology · 2018
Article
Translating New Science Into the Drug Review Process: The US FDA's Division of Applied Regulatory Science.Therapeutic innovation & regulatory science · 2018
Article
Use of Biomedical Ontologies for Integration of Biological Knowledge for Learning and Prediction of Adverse Drug Reactions.Gene regulation and systems biology · 2017
Review
The inevitable QSAR renaissance.Journal of computer-aided molecular design · 2012
Article
Of possible cheminformatics futures.Journal of computer-aided molecular design · 2012
Article

4 · The record

Corrections and comments

Comment on
Linking pharmacology to clinical reports: cyclobenzaprine and its possible association with serotonin syndrome.2011

5 · Who and what money

Authors and funding

5 authors.

D R AbernethyOffice of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA. darrell.abernethy@fda.hhs.gov

J P F Bai

K Burkhart

H-G Xie

P Zhichkin

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid evolution of large biological, pharmacological, and chemical databases has led to optimism that such data resources can be leveraged for prediction of drug action based on molecular descriptors of the drug. Challenges to realize this possibility include organization of each type of database in a manner that allows extraction of information across disparate data sources and the linkage of information across the biological, pharmacological, and chemical domains.

Indexed as

AmitriptylineAntidepressive Agents, TricyclicHumansSerotonin SyndromeAmitriptylineAntidepressive Agents, Tricycliccyclobenzaprine

Identifiers

PMID22012310

DOI10.1038/clpt.2011.171

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.