Evidence mapPaperPMID 25670520Full record

ArticleClinical pharmacology and therapeutics2015

Systems pharmacology augments drug safety surveillance.

T Lorberbaum, M Nasir, M J Keiser, S Vilar, G Hripcsak, N P Tatonetti

Abstract read
In one paragraph

Article in Clinical pharmacology and therapeutics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 3 pooled it
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

27 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Artificial Intelligence for Drug Toxicity and Safety.Trends in pharmacological sciences · 2019
    Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability.Journal of the American Medical Informatics Association : JAMIA · 2016
    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

6 authors.

T LorberbaumDepartment of Physiology and Cellular Biophysics, Columbia University, New York, New York, USA; Department of Biomedical Informatics, Columbia University, New York, New York, USA; Departments of Systems Biology and Medicine, Columbia University, New York, New York, USA.
M Nasir
M J Keiser
S Vilar
G Hripcsak
N P Tatonetti

Funding

DISCOVERING AND APPLYING KNOWLEDGE IN CLINICAL DATABASESR01LM006910 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HRIPCSAK, GEORGE M · 2000 to 2023
$10.6M
Training in Cardiovascular Translational ResearchT32HL120826 · NHLBI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI MARKS, ANDREW ROBERT, TABAS, IRA A · 2014 to 2023
$4.7M
Drug Effect Discovery Through Data Mining and Integrative Chemical BiologyR01GM107145 · NIGMS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI TATONETTI, NICHOLAS P · 2014 to 2018
$2.5M
Training Program in Computational BiologyT32GM082797 · NIGMS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HONIG, BARRY H · 2008 to 2015
$1.4M
A platform to predict side-effect targets for drugsR44GM093456 · NIGMS · SEACHANGE PHARMACEUTICALS, INC. · PI HODGE, CARL NICHOLAS, KEISER, MICHAEL JAMES · 2013 to 2014
$910k
Relating GPCRs by biased ligands for enhanced therapeutic efficacyR43MH099712 · NIMH · SEACHANGE PHARMACEUTICALS, INC. · PI HODGE, CARL NICHOLAS, KEISER, MICHAEL JAMES · 2013 to 2013
$301k
Drug Repurposing using Pharmacological NetworksR43GM093456 · NIGMS · SEACHANGE PHARMACEUTICALS, INC. · PI KEISER, MICHAEL JAMES · 2010 to 2010
$203k
NHLBI NIH HHS T32 HL120826NHLBI NIH HHS T32HL120826NIGMS NIH HHS GM93456NIGMS NIH HHS R01 GM107145NIGMS NIH HHS R01GM107145NIGMS NIH HHS R43 GM093456NIGMS NIH HHS R44 GM093456NIGMS NIH HHS T32 GM082797NIMH NIH HHS MH099712NIMH NIH HHS R43 MH099712NLM NIH HHS R01 LM006910
6 · The paper itself

Abstract

Small molecule drugs are the foundation of modern medical practice, yet their use is limited by the onset of unexpected and severe adverse events (AEs). Regulatory agencies rely on postmarketing surveillance to monitor safety once drugs are approved for clinical use. Despite advances in pharmacovigilance methods that address issues of confounding bias, clinical data of AEs are inherently noisy. Systems pharmacology-the integration of systems biology and chemical genomics-can illuminate drug mechanisms of action. We hypothesize that these data can improve drug safety surveillance by highlighting drugs with a mechanistic connection to the target phenotype (enriching true positives) and filtering those that do not (depleting false positives). We present an algorithm, the modular assembly of drug safety subnetworks (MADSS), to combine systems pharmacology and pharmacovigilance data and significantly improve drug safety monitoring for four clinically relevant adverse drug reactions.

Indexed as

Patient SafetyPharmacologyPharmacovigilanceSystems BiologyAlgorithmsDrug-Related Side Effects and Adverse ReactionsGenomicsHumansModels, Biological

Identifiers

PMID25670520
PMCPMC4325423

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.