Evidence map›Paper›PMID 25489176›Full record

ArticleBioinformation2014

Molecular docking studies shows tivozanib and lapatinib as potential inhibitors of EML4-ALK translocation mediated fusion protein in non small cell lung cancer.

Vijayalakshmi Ramshankar, Subha Yegnaswamy, Kumarasamy P, Krishnamurthy Arvind

Open access · hybridAbstract read
In one paragraph

Article in Bioinformation, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 40% 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

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

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 2 institutions in 1 country.

Vijayalakshmi RamshankarDepartment of Preventive Oncology, Cancer Institute (WIA), Adyar, Chennai.
Subha YegnaswamyDepartment of Bioinformatics, Madras Veterinary College, Vepery, Chennai.
Kumarasamy PDepartment of Bioinformatics, Madras Veterinary College, Vepery, Chennai.
Krishnamurthy ArvindDepartment of Surgical Oncology, Cancer Institute (WIA), Adyar, Chennai.
Cancer Institute (WIA) · INUniversity of Madras · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identification of activating mutations in non-small cell lung cancers (NSCLC) has been a focus in recent years. This led to successful evidence of using tyrosine kinase inhibitors (TKIs) over the standard platinum doublet based chemotherapy as the first line treatment in the metastatic setting.The rearrangements of fusion protein EML4-ALK in NSCLC lead to the use of crizotinib for this class of tumors. Preclinical and Phase 1 clinical studies show that ceritinib is more effective against both crizotinib sensitive and resistant tumors. Although robust responses to crizotinib are observed in NSCLC harboring ALK mutations, majority of tumors eventually become resistant, posing a major challenge in treatment course. Thus, there is a need for the identification and development of second-generation of ALK inhibitors. Computer aided molecular docking data show Tivozanib and Lapatinib bind EML4-ALK with high score. Tivozanib is in clinical trials for renal cell cancer and Lapatinib is a known dual tyrosine kinase inhibitor effective in breast cancer patients with HER2 over-expression. Additional data on these compounds for use in EML4-ALK positive NSCLC will provide evidence for use in patients treated with crizotinib. Data shows the importance of computer aided molecular docking in developing candidates with improved activity for further consideration in vitro and in vivo validation.

Identifiers

PMID25489176
PMCPMC4248349
OpenAlexW1964977209

What OpenQuestion holds

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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.