Evidence map›Paper›PMID 22669532›Full record

ArticleJournal of molecular modeling2012

In silico evaluation of TERT inhibition by anticancer drugs.

Porika Mahendar, Kalam Sirisha, Umasankar Kulandaivelu, Prakhya Laxmi Jaya Shankar, Tippani Radhika, Abbagani Sadanandam

Abstract read
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In one paragraph

Article in Journal of molecular modeling, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 37% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
  2. 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

6 authors at 1 institution in 1 country.

Porika MahendarDepartment of Biotechnology, Kakatiya University, Warangal 506009, Andhra Pradesh, India.
Kalam Sirisha
Umasankar Kulandaivelu
Prakhya Laxmi Jaya Shankar
Tippani Radhika
Abbagani Sadanandam
Kakatiya University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The activation of telomerase represents an early step in carcinogenesis. Increased telomerase expression in malignant tumors suggests that telomerase inactivation may represent a potential chemotherapeutic target. In this work, existing anticancer drugs were docked against telomerase reverse transcriptase (TERT) using a Lamarckian genetic algorithm (LGA). Autodock's scoring function was applied to each of the molecules in order to identify the inhibitor with the strongest pharmacological action. The structural insights provided by this study regarding binding poses and possible interactions, free energies of binding, and drug scores aided in the identification of potential inhibitory compounds. The ranks of the various ligands investigated were based on the final docked energy values. Among nine selected compounds, vindesine, temsirolimus, and cyclosporine were found to be more potent TERT inhibitors than the standard inhibitor, curcumin.

Indexed as

Computational BiologyAnimalsAntineoplastic AgentsEnzyme InhibitorsLigandsMolecular Docking SimulationTelomeraseTriboliumAntineoplastic AgentsEnzyme InhibitorsLigandsTelomerase

Identifiers

PMID22669532
OpenAlexW2065736637

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.