Evidence map›Paper›PMID 33902757›Full record

ArticleBMC biomedical engineering2021

Drug target ranking for glioblastoma multiforme.

Radhika Saraf, Shaghayegh Agah, Aniruddha Datta, Xiaoqian Jiang

Open access · goldAbstract read
In one paragraph

Article in BMC biomedical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 25% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Article
  3. 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

4 authors at 3 institutions in 1 country.

Radhika Saraf *Department of Electrical and Computer Engineering, Texas A&M University, College Station, US. saraf.radhika@tamu.edu.ORCID http://orcid.org/0000-0002-3026-3421
Shaghayegh Agah *University of Texas Health Science Center at Houston, School of Biomedical Informatics, Houston, US.
Aniruddha DattaDepartment of Electrical and Computer Engineering, Texas A&M University, College Station, US.
Xiaoqian JiangUniversity of Texas Health Science Center at Houston, School of Biomedical Informatics, Houston, US.
The University of Texas Health Science Center at Houston · USTexas A&M University · USThe University of Texas Health Science Center · US

Funding

Open Health Natural Language Processing CollaboratoryU01TR002062 · NCATS · MAYO CLINIC ROCHESTER · PI JIANG, XIAOQIAN, LIU, HONGFANG · 2017 to 2021
$7.6M
Finding Combinatorial Drug Repositioning Therapy For Alzheimer'S Disease And Related DementiasR01AG066749 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI JIANG, XIAOQIAN, ZHENG, WENJIN JIM · 2020 to 2024
$4.4M
SHARE: Statistical Health Information Release with Differential PrivacyR01GM114612 · NIGMS · EMORY UNIVERSITY · PI XIONG, LI · 2015 to 2017
$1.0M
CPRIT RR180012National Science Foundation ECCS-1609236National Science Foundation ECCS-1917166NCATS NIH HHS U01 TR002062NIA NIH HHS R01 AG066749NIGMS NIH HHS R01 GM114612NIH HHS R01AG066749NIH HHS R01GM114612NIH HHS U01TR002062Texas A and M Engineering Experiment Station, Texas A and M University TEES-Agrilife CBGSE Start Up FundsUniversity of Texas Medical School at Houston Christopher Sarofim Family ProfessorshipUniversity of Texas Medical School at Houston Stars AwardUniversity of Texas Medical School at Houston Start Up
6 · The paper itself

Abstract

backgroundGlioblastoma Multiforme, an aggressive primary brain tumor, has a poor prognosis and no effective standard of care treatments. Most patients undergoing radiotherapy, along with Temozolomide chemotherapy, develop resistance to the drug, and recurrence of the tumor is a common issue after the treatment. We propose to model the pathways active in Glioblastoma using Boolean network techniques. The network captures the genetic interactions and possible mutations that are involved in the development of the brain tumor. The model is used to predict the theoretical efficacies of drugs for the treatment of cancer.

resultsWe use the Boolean network to rank the critical intervention points in the pathway to predict an effective therapeutic strategy for Glioblastoma. Drug repurposing helps to identify non-cancer drugs that could be effective in cancer treatment. We predict the effectiveness of drug combinations of anti-cancer and non-cancer drugs for Glioblastoma.

conclusionsGiven the genetic profile of a GBM tumor, the Boolean model can predict the most effective targets for treatment. We also identified two-drug combinations that could be more effective in killing GBM cells than conventional chemotherapeutic agents. The non-cancer drug Aspirin could potentially increase the cytotoxicity of TMZ in GBM patients.

Indexed as

Boolean network modelingCancerDrug repurposingDrug resistanceDrug target rankingGlioblastoma

Identifiers

PMID33902757
PMCPMC8074458
OpenAlexW3159497393

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.