Evidence map›Paper›PMID 26377814›Full record

ArticleBMC systems biology2015

Systematic synergy modeling: understanding drug synergy from a systems biology perspective.

Di Chen, Xi Liu, Yiping Yang, Hongjun Yang, Peng Lu

Abstract read
In one paragraph

Article in BMC systems biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

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  8. Review
  9. Mathematical Modeling Support for Lung Cancer Therapy-A Short Review.International journal of molecular sciences · 2023
    Review
  10. Article
  11. Article
  12. Article
  13. Frontiers in systems biology · 2023
    Article
  14. Article
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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

5 authors.

Di ChenInstitute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. di.chen@ia.ac.cn.
Xi LiuInstitute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. xi.liu@ia.ac.cn.
Yiping YangInstitute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. yiping.yang@ia.ac.cn.
Hongjun YangInstitute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China. hongjun0420@vip.sina.com.
Peng LuInstitute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. peng.lu@ia.ac.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Owing to drug synergy effects, drug combinations have become a new trend in combating complex diseases like cancer, HIV and cardiovascular diseases. However, conventional synergy quantification methods often depend on experimental dose-response data which are quite resource-demanding. In addition, these methods are unable to interpret the explicit synergy mechanism. In this review, we give representative examples of how systems biology modeling offers strategies toward better understanding of drug synergy, including the protein-protein interaction (PPI) network-based methods, pathway dynamic simulations, synergy network motif recognitions, integrative drug feature calculations, and "omic"-supported analyses. Although partially successful in drug synergy exploration and interpretation, more efforts should be put on a holistic understanding of drug-disease interactions, considering integrative pharmacology and toxicology factors. With a comprehensive and deep insight into the mechanism of drug synergy, systems biology opens a novel avenue for rational design of effective drug combinations.

Indexed as

Drug SynergismHumansModels, BiologicalProtein Interaction MappingSystems Biology

Identifiers

PMID26377814
PMCPMC4574089

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Registered trials

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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.