Evidence map›Paper›PMID 27096073›Full record

ArticleJournal of biomedical semantics2016

An accurate and precise representation of drug ingredients.

Josh Hanna, Jiang Bian, William R Hogan

Open access · goldAbstract read
In one paragraph

Article in Journal of biomedical semantics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.2field-weighted citation impact, top 20% of its field
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

6 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. 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 at 1 institution in 1 country.

Josh HannaBiomedical Informatics Program, Department of Health Outcomes and Policy, University of Florida, Gainesville, FL USA.ORCID 0000-0002-4600-1258
Jiang BianBiomedical Informatics Program, Department of Health Outcomes and Policy, University of Florida, Gainesville, FL USA.
William R HoganBiomedical Informatics Program, Department of Health Outcomes and Policy, University of Florida, Gainesville, FL USA.
University of Florida Health · US

Funding

Using social networks to map and evaluate team science across CTSA hubsUL1TR001427 · NCATS · UNIVERSITY OF FLORIDA · PI MITCHELL, DUANE A. · 2015 to 2024
$37.2M
UF Clinical and Translational Science AwardUL1TR000064 · NCATS · UNIVERSITY OF FLORIDA · PI NELSON, DAVID R · 2012 to 2014
$12.2M
Apollo: Increasing Access and Use of Epidemic Models Through the Development andR01GM101151 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI HOGAN, WILLIAM R, WAGNER, MICHAEL MATTHEW · 2012 to 2015
$2.2M
NCATS NIH HHS UL1 TR000064NCATS NIH HHS UL1 TR001427NIGMS NIH HHS R01 GM101151
6 · The paper itself

Abstract

backgroundIn previous work, we built the Drug Ontology (DrOn) to support comparative effectiveness research use cases. Here, we have updated our representation of ingredients to include both active ingredients (and their strengths) and excipients. Our update had three primary lines of work: 1) analysing and extracting excipients, 2) analysing and extracting strength information for active ingredients, and 3) representing the binding of active ingredients to cytochrome P450 isoenzymes as substrates and inhibitors of those enzymes.

methodsTo properly differentiate between excipients and active ingredients, we conducted an ontological analysis of the roles that various ingredients, including excipients, have in drug products. We used the value specification model of the Ontology for Biomedical Investigations to represent strengths of active ingredients and then analyzed RxNorm to extract excipient and strength information and modeled them according to the results of our analysis. We also analyzed and defined dispositions of molecules used in aggregate as active ingredients to bind cytochrome P450 isoenzymes.

resultsOur analysis of excipients led to 17 new classes representing the various roles that excipients can bear. We then extracted excipients from RxNorm and added them to DrOn for branded drugs. We found excipients for 5,743 branded drugs, covering ~27% of the 21,191 branded drugs in DrOn. Our analysis of active ingredients resulted in another new class, active ingredient role. We also extracted strengths for all types of tablets, capsules, and caplets, resulting in strengths for 5,782 drug forms, covering ~41% of the 14,035 total drug forms and accounting for ~97 % of the 5,970 tablets, capsules, and caplets in DrOn. We represented binding-as-substrate and binding-as-inhibitor dispositions to two cytochrome P450 (CYP) isoenzymes (CYP2C19 and CYP2D6) and linked these dispositions to 65 compounds. It is now possible to query DrOn automatically for all drug products that contain active ingredients whose molecular grains inhibit or are metabolized by a particular CYP isoenzyme. DrOn is open source and is available at http://purl.obolibrary.org/obo/dron.owl.

Indexed as

Biological OntologiesCytochrome P-450 Enzyme InhibitorsCytochrome P-450 Enzyme SystemExcipientsIsoenzymesPharmaceutical PreparationsCytochrome P-450 Enzyme InhibitorsCytochrome P-450 Enzyme SystemExcipientsIsoenzymesPharmaceutical Preparations

Identifiers

PMID27096073
PMCPMC4836073
OpenAlexW2337023886

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.