Evidence map›Paper›PMID 22235860›Full record

ReviewAnnual review of pharmacology and toxicology2012

Systems pharmacology: network analysis to identify multiscale mechanisms of drug action.

Shan Zhao, Ravi Iyengar

Open access · greenAbstract readReview
In one paragraph

Review in Annual review of pharmacology and toxicology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 160 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
160citing papers in PubMed, 1 pooled it
35.5field-weighted citation impact, top 1% 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

160 citing papers in PubMed, 1 synthesis or guideline pooled it, 355 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Review
  9. New Personalized Medicine Model for Medication Management.Journal of personalized medicine · 2026
    Review
  10. Article
  11. Article
  12. Article
  13. Review
  14. Protective role ofFrontiers in pharmacology · 2026
    Article
  15. Article
  16. Review
  17. Article
  18. Review
  19. Review
  20. Article

100 more citing papers are in PubMed but not listed here.

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 at 1 institution in 1 country.

Shan ZhaoDepartment of Pharmacology and Systems Therapeutics, and Systems Biology Center New York, Mount Sinai School of Medicine, New York, New York 10029, USA.
Ravi Iyengar
Icahn School of Medicine at Mount Sinai · US

Funding

System Biology Center in New YorkP50GM071558 · NIGMS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI ZAPH, SUSANA NEVES · 2007 to 2017
$22.8M
MOLECULAR MECHANISM OF DESENSITIZATIONR01GM054508 · NIGMS · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI IYENGAR, SRINIVAS RAVI V · 1995 to 2016
$8.0M
Teaching biomedical and pharmacological trainees to produce FAIR data for AI & ML applicationsT32GM062754 · NIGMS · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI SCHLESSINGER, AVNER · 2001 to 2023
$6.2M
NIGMS NIH HHS GM54508NIGMS NIH HHS P50 GM071558NIGMS NIH HHS P50-GM071558NIGMS NIH HHS R01 GM054508NIGMS NIH HHS T32 GM062754NIGMS NIH HHS T32-GM062754
6 · The paper itself

Abstract

Systems approaches have long been used in pharmacology to understand drug action at the organ and organismal levels. The application of computational and experimental systems biology approaches to pharmacology allows us to expand the definition of systems pharmacology to include network analyses at multiple scales of biological organization and to explain both therapeutic and adverse effects of drugs. Systems pharmacology analyses rely on experimental "omics" technologies that are capable of measuring changes in large numbers of variables, often at a genome-wide level, to build networks for analyzing drug action. A major use of omics technologies is to relate the genomic status of an individual to the therapeutic efficacy of a drug of interest. Combining pathway and network analyses, pharmacokinetic and pharmacodynamic models, and a knowledge of polymorphisms in the genome will enable the development of predictive models of therapeutic efficacy. Network analyses based on publicly available databases such as the U.S. Food and Drug Administration's Adverse Event Reporting System allow us to develop an initial understanding of the context within which molecular-level drug-target interactions can lead to distal effectors in a process that results in adverse phenotypes at the organ and organismal levels. The current state of systems pharmacology allows us to formulate a set of questions that could drive future research in the field. The long-term goal of such research is to develop polypharmacology for complex diseases and predict therapeutic efficacy and adverse event risk for individuals prior to commencement of therapy.

Indexed as

Drug DiscoveryPharmacokineticsAnimalsDatabases, FactualDrug Delivery SystemsHumansMetabolomicsPharmaceutical PreparationsProteomicsSystems BiologyPharmaceutical Preparations

Identifiers

PMID22235860
PMCPMC3619403
OpenAlexW2158293950

What OpenQuestion 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 OpenQuestion graph.