Evidence map›Paper›PMID 28699538›Full record

ArticleCurrent pharmaceutical design2018

Virtual Screening and Statistical Analysis in the Design of New Caffeine Analogues Molecules with Potential Epithelial Anticancer Activity.

Josivan da Silva Costa, Karina da Silva Lopes Costa, Josiane Viana Cruz, Ryan da Silva Ramos, Luciane Barros Silva, Davi Do Socorro Barros Brasil, Carlos Henrique Tomich de Paula da Silva, Cleydson Breno Rodrigues Dos Santos, Williams Jorge da Cruz Macedo

Abstract read
In one paragraph

Article in Current pharmaceutical design, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

  1. Article
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  4. Pharmaceuticals (Basel, Switzerland) · 2025
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  6. Journal of tropical medicine · 2025
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  13. Identification of Potential NewInternational journal of molecular sciences · 2022
    Article
  14. Article
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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

9 authors.

Josivan da Silva CostaPostgraduate Program in Biotechnology and Biodiversity-Network BIONORTE, Federal University of the Para, Belem, Brazil.
Karina da Silva Lopes CostaLaboratory of Modeling and Computational Chemistry, Federal University of Amapa, Department of Biological Sciences. Rod. Juscelino Kubitschek, Km 02, s/n, Jardim Marco Zero, 68902-280 Macapa-AP, Brazil.
Josiane Viana CruzLaboratory of Modeling and Computational Chemistry, Federal University of Amapa, Department of Biological Sciences. Rod. Juscelino Kubitschek, Km 02, s/n, Jardim Marco Zero, 68902-280 Macapa-AP, Brazil.
Ryan da Silva RamosLaboratory of Modeling and Computational Chemistry, Federal University of Amapa, Department of Biological Sciences. Rod. Juscelino Kubitschek, Km 02, s/n, Jardim Marco Zero, 68902-280 Macapa-AP, Brazil.
Luciane Barros SilvaLaboratory of Modeling and Computational Chemistry, Federal University of Amapa, Department of Biological Sciences. Rod. Juscelino Kubitschek, Km 02, s/n, Jardim Marco Zero, 68902-280 Macapa-AP, Brazil.
Davi Do Socorro Barros BrasilInstitute of Technology, Federal University of Para, Av. Augusto Correa, 01, Belem, Para 66075-900, Brazil.
Carlos Henrique Tomich de Paula da SilvaComputational Laboratory of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences of Ribeirao Preto, University of Sao Paulo, Ribeirao Preto, Sao Paulo, Brazil.
Cleydson Breno Rodrigues Dos SantosLaboratory of Modeling and Computational Chemistry, Federal University of Amapa, Department of Biological Sciences. Rod. Juscelino Kubitschek, Km 02, s/n, Jardim Marco Zero, 68902-280 Macapa-AP, Brazil.
Williams Jorge da Cruz MacedoLaboratory of Molecular Modeling and Simulation System, Federal Rural University of Amazonia, Rua Joao Pessoa, 121, Campus Capanema-Centro, Capanema, Para 68700-030, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

About 132 thousand cases of melanoma (more severe type of skin cancer) were registered in 2014 according to the World Health Organization. This type of cancer significantly affects the quality of life of individuals. Caffeine has shown potential inhibitory effect against epithelial cancer. In this study, it was proposed to obtain new caffeine-based molecules with potential epithelial anticancer activity. For this, a training set of 21 molecules was used for pharmacophore perception procedures. Multiple linear regression analyses were used to propose mono-, bi-, tri-, and tetra-parametric models applied in the prediction of the activity. The generated pharmacophore was used to select 350 molecules available at the ZINCpharmer server, followed by reduction to 24 molecules, after selection using the Tanimoto index, yielding 10 molecules after final selection by predicted activity values > 1.5229. These ten molecules had better pharmacokinetic properties than the other ones used as reference and within the clinically significant limits. Only two molecules show minor hits of toxicity and were submitted to molecular docking procedures, showing BFE (binding free energy) values lower than the reference values. Statistical analyses indicated strong negative correlations between BFE and pharmacophoric properties (high influence on BFE lowering) and practically null correlation between BFE and BBB. The two most promising molecules can be indicated as candidates for further in vitro and in vivo analyzes.

Indexed as

Drug DesignAnimalsAntineoplastic AgentsCaffeineCarcinoma, Ovarian EpithelialDrug Evaluation, PreclinicalFemaleHumansMolecular Docking SimulationOvarian NeoplasmsRatsStatistics as TopicAntineoplastic AgentsCaffeinecaffeineChk1Epithelial cancerMolecular modelingmultiple linear regression.Pharmacokinetic and toxicological properties

Identifiers

PMID28699538
PMCPMC5944109

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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.