Evidence map›Paper›PMID 31617439›Full record

ArticleJournal of the American Heart Association2019

Novel Screening Method Identifies PI3Kα, mTOR, and IGF1R as Key Kinases Regulating Cardiomyocyte Survival.

Manar Elmadani, Suleiman Khan, Olli Tenhunen, Johanna Magga, Tero Aittokallio, Krister Wennerberg, Risto Kerkelä

Open access · goldAbstract read
In one paragraph

Article in Journal of the American Heart Association, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.1field-weighted citation impact, top 52% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. 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

7 authors at 4 institutions in 1 country.

Manar ElmadaniResearch Unit of Biomedicine Department of Pharmacology and Toxicology University of Oulu Finland.
Suleiman KhanInstitute for Molecular Medicine Finland (FIMM) University of Helsinki Finland.
Olli TenhunenDepartment of Oncology and Radiotherapy Oulu University Hospital University of Oulu Finland.
Johanna MaggaResearch Unit of Biomedicine Department of Pharmacology and Toxicology University of Oulu Finland.
Tero AittokallioInstitute for Molecular Medicine Finland (FIMM) University of Helsinki Finland.
Krister WennerbergInstitute for Molecular Medicine Finland (FIMM) University of Helsinki Finland.
Risto KerkeläResearch Unit of Biomedicine Department of Pharmacology and Toxicology University of Oulu Finland.
Institute for Molecular Medicine Finland · FIOulu University Hospital · FIUniversity of Oulu · FIUniversity of Helsinki · FI

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Small molecule kinase inhibitors (KIs) are a class of agents currently used for treatment of various cancers. Unfortunately, treatment of cancer patients with some of the KIs is associated with cardiotoxicity, and there is an unmet need for methods to predict their cardiotoxicity. Here, we utilized a novel computational method to identify protein kinases crucial for cardiomyocyte viability. Methods and Results One hundred forty KIs were screened for their toxicity in cultured neonatal cardiomyocytes. The kinase targets of KIs were determined based on integrated data from binding assays. The key kinases mediating the toxicity of KIs to cardiomyocytes were identified by using a novel machine learning method for target deconvolution that combines the information from the toxicity screen and from the kinase profiling assays. The top kinases identified by the model were phosphoinositide 3-kinase catalytic subunit alpha, mammalian target of rapamycin, and insulin-like growth factor 1 receptor. Knockdown of the individual kinases in cardiomyocytes confirmed their role in regulating cardiomyocyte viability. Conclusions Combining the data from analysis of KI toxicity on cardiomyocytes and KI target profiling provides a novel method to predict cardiomyocyte toxicity of KIs.

Indexed as

Cell SurvivalMachine LearningAdenosine TriphosphateAnimalsCardiotoxinsCells, CulturedMyocytes, CardiacPhosphatidylinositol 3-KinasesProtein Kinase InhibitorsRatsReceptor, IGF Type 1TOR Serine-Threonine KinasesAdenosine TriphosphateCardiotoxinsIGF1R protein, humanPhosphatidylinositol 3-KinasesProtein Kinase InhibitorsReceptor, IGF Type 1TOR Serine-Threonine Kinasescardiotoxicitycell deathpharmacology

Identifiers

PMID31617439
PMCPMC6898841
OpenAlexW2981014184

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.