Evidence map›Paper›PMID 25960652›Full record

ArticleInternational journal of nanomedicine2015

Insight into molecular dynamics simulation of BRAF(V600E) and potent novel inhibitors for malignant melanoma.

Hsin-Chieh Tang, Yu-Chian Chen

Abstract read
In one paragraph

Article in International journal of nanomedicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Article
  6. Advances in anti-BRAF therapies for lung cancer.Investigational new drugs · 2021
    Review
  7. Review
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. "Is it time for personalised medicine for Ameloblastoma?": A hypothesis.Journal of oral and maxillofacial pathology : JOMFP
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Hsin-Chieh TangDepartment of Biomedical Informatics, Asia University, Taichung, Taiwan.
Yu-Chian ChenDepartment of Biomedical Informatics, Asia University, Taichung, Taiwan ; Human Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan ; Research Center for Chinese Medicine and Acupuncture, China Medical University Hospital, Taichung, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BRAF inhibitors have changed the standard therapeutic protocol for advanced or metastatic melanoma which harbored notorious BRAF(V600E) single mutation. However, drug resistance to BRAF inhibitors happens just like other cancer treatment. In this study, we constructed the ideal BRAF(V600E)-modeled structure through homology modeling and introduced the method of structure-based docking or virtual screening from the large compound database. Through certain methods of molecular dynamics simulation, we realized that BRAF(V600E) had quite prominent difference of molecular character or structural variation from the wild-type BRAF protein. It might confer the metamorphic character of advanced melanoma for the patients who harbored BRAF(V600E) mutation. By the methods of ligand-based quantitative structure-activity relationship and molecular dynamics simulation, we further recommend that aknadicine and 16beta-hydroxy-19s-vindolinine N-oxide from the traditional Chinese medicine are potent novel inhibitors for the management of malignant melanoma in the future.

Indexed as

Antineoplastic AgentsMelanomaMolecular Dynamics SimulationProtein Kinase InhibitorsProto-Oncogene Proteins B-rafDrug Resistance, NeoplasmHumansAntineoplastic AgentsBRAF protein, humanProtein Kinase InhibitorsProto-Oncogene Proteins B-rafBRAF inhibitordockingligand-basedquantitative structure-activity relationship (QSAR)structure-basedvirtual screening

Identifiers

PMID25960652
PMCPMC4412490

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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.