Evidence map›Paper›PMID 30800581›Full record

Article3 Biotech2019

An oncoinformatics study to predict the inhibitory potential of recent FDA-approved anti-cancer drugs against human Polo-like kinase 1 enzyme: a step towards dual-target cancer medication.

Syed Mohd Danish Rizvi, Abdulaziz Arif A Alshammari, Waleed Abdullah Almawkaa, Abo Bakr F Ahmed, Ahmed Katamesh, Ahmed Alafnan, Tariq J Almutairi, Rakan F Alshammari

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in 3 Biotech, 2019. 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
0.5field-weighted citation impact, top 31% 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. Article
  2. Article
  3. Review
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

8 authors at 1 institution in 1 country.

Syed Mohd Danish Rizvi *1Department of Pharmacology and Toxicology, College of Pharmacy, University of Hail, Hail, Saudi Arabia.ORCID 0000-0002-3106-4707
Abdulaziz Arif A Alshammari *2College of Pharmacy, University of Hail, Hail, Saudi Arabia.
Waleed Abdullah Almawkaa2College of Pharmacy, University of Hail, Hail, Saudi Arabia.
Abo Bakr F Ahmed3Department of Pharmaceutics, College of Pharmacy, University of Hail, Hail, Saudi Arabia.
Ahmed Katamesh3Department of Pharmaceutics, College of Pharmacy, University of Hail, Hail, Saudi Arabia.
Ahmed Alafnan1Department of Pharmacology and Toxicology, College of Pharmacy, University of Hail, Hail, Saudi Arabia.
Tariq J Almutairi2College of Pharmacy, University of Hail, Hail, Saudi Arabia.
Rakan F Alshammari2College of Pharmacy, University of Hail, Hail, Saudi Arabia.
University of Ha'il · SA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer prevalence has increased at an alarming rate worldwide. Complexity, resistance mechanism and multiple compensatory survival pathways of cancer cells have abated the response of currently available cancer medications. Therefore, multi-target agents rather than single target might provide a better solution to these cancer therapy issues. In the present study, anti-PLK1 (Polo-like kinase 1) potential of the eight FDA-approved (2017) anti-cancer drugs have been explored using molecular docking approach. Out of all the tested drugs, brigatinib, niraparib and ribociclib showed better binding affinity towards the 'kinase domain' of PLK1. The Gibbs free binding energy (Δ

Indexed as

Anticancer drugsDual therapyMolecular dockingMulti-target agentsPolo-like kinase 1

Identifiers

PMID30800581
PMCPMC6368907
OpenAlexW2914747113

What OpenQuestion holds

Textmetadata
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.