Evidence map›Paper›PMID 30346968›Full record

ArticlePLoS computational biology2018

Computationally-guided drug repurposing enables the discovery of kinase targets and inhibitors as new schistosomicidal agents.

Sandra Giuliani, Arthur C Silva, Joyce V V B Borba, Pablo I P Ramos, Ross A Paveley, Eugene N Muratov, Carolina Horta Andrade, Nicholas Furnham

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
3.2field-weighted citation impact, top 8% 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

17 citing papers in PubMed, 36 citations in OpenAlex.

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  11. Protein Kinases: Potential Drug Targets AgainstFrontiers in cellular and infection microbiology · 2021
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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

8 authors at 5 institutions in 4 countries.

Sandra GiulianiDepartment of Pathogen Molecular Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom.ORCID 0000-0002-2302-414X
Arthur C SilvaLabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás - UFG, Goiânia, Goiás, Brazil.ORCID 0000-0001-9866-3344
Joyce V V B BorbaLabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás - UFG, Goiânia, Goiás, Brazil.ORCID 0000-000S-7813-2018
Pablo I P RamosInstituto Gonçalo Moniz, Fundação Oswaldo Cruz (FIOCRUZ), Salvador, Bahia, Brazil.ORCID 0000-0002-9075-7861
Ross A PaveleyDepartment of Pathogen Molecular Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Eugene N MuratovLaboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, University of North Carolina, Chapel Hill, North Carolina, United States of America.
Carolina Horta AndradeLabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás - UFG, Goiânia, Goiás, Brazil.ORCID 0000-0003-0101-1492
Nicholas FurnhamDepartment of Pathogen Molecular Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom.ORCID 0000-0002-7532-1269
University of London · GBUniversidade Federal de Goiás · BRFundação Oswaldo Cruz · BRUniversidade Estadual de Campinas (UNICAMP) · BRUniversity of North Carolina at Chapel Hill · US

Funding

Medical Research Council MR/K020420Medical Research Council MR/K020420/1Medical Research Council MR/M026221/1
6 · The paper itself

Abstract

The development of novel therapeutics is urgently required for diseases where existing treatments are failing due to the emergence of resistance. This is particularly pertinent for parasitic infections of the tropics and sub-tropics, referred to collectively as neglected tropical diseases, where the commercial incentives to develop new drugs are weak. One such disease is schistosomiasis, a highly prevalent acute and chronic condition caused by a parasitic helminth infection, with three species of the genus Schistosoma infecting humans. Currently, a single 40-year old drug, praziquantel, is available to treat all infective species, but its use in mass drug administration is leading to signs of drug-resistance emerging. To meet the challenge of developing new therapeutics against this disease, we developed an innovative computational drug repurposing pipeline supported by phenotypic screening. The approach highlighted several protein kinases as interesting new biological targets for schistosomiasis as they play an essential role in many parasite's biological processes. Focusing on this target class, we also report the first elucidation of the kinome of Schistosoma japonicum, as well as updated kinomes of S. mansoni and S. haematobium. In comparison with the human kinome, we explored these kinomes to identify potential targets of existing inhibitors which are unique to Schistosoma species, allowing us to identify novel targets and suggest approved drugs that might inhibit them. These include previously suggested schistosomicidal agents such as bosutinib, dasatinib, and imatinib as well as new inhibitors such as vandetanib, saracatinib, tideglusib, alvocidib, dinaciclib, and 22 newly identified targets such as CHK1, CDC2, WEE, PAKA, MEK1. Additionally, the primary and secondary targets in Schistosoma of those approved drugs are also suggested, allowing for the development of novel therapeutics against this important yet neglected disease.

Indexed as

AnimalsComputational BiologyDatabases, ProteinDrug RepositioningProtein Kinase InhibitorsReproducibility of ResultsSchistosomaSchistosomicidesProtein Kinase InhibitorsSchistosomicides

Identifiers

PMID30346968
PMCPMC6211772
OpenAlexW2897557335

What OpenQuestion holds

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LicenceCC BY
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.