Evidence map›Paper›PMID 27073973›Full record

ReviewExpert opinion on drug discovery2016

Modern approaches to accelerate discovery of new antischistosomal drugs.

Bruno Junior Neves, Eugene Muratov, Renato Beilner Machado, Carolina Horta Andrade, Pedro Vitor Lemos Cravo

Abstract readReview
In one paragraph

Review in Expert opinion on drug discovery, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Discovery of novel repurposed anthelminthics againstCurrent research in parasitology & vector-borne diseases · 2026
    Article
  2. Review
  3. Article
  4. Review
  5. 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

5 authors.

Bruno Junior Nevesa LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia , Universidade Federal de Goiás , Goiânia , Brazil.
Eugene Muratovb Laboratory for Molecular Modeling, Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , NC , USA.
Renato Beilner Machadoc GenoBio - Laboratory of Genomics and Biotechnology, Instituto de Patologia Tropical e Saúde Pública , Universidade Federal de Goiás , Goiânia , Brazil.
Carolina Horta Andradea LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia , Universidade Federal de Goiás , Goiânia , Brazil.
Pedro Vitor Lemos Cravoc GenoBio - Laboratory of Genomics and Biotechnology, Instituto de Patologia Tropical e Saúde Pública , Universidade Federal de Goiás , Goiânia , Brazil.

Funding

PREDICTIVE QSAR MODELINGR01GM066940 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI TROPSHA, ALEXANDER · 2003 to 2011
$3.0M
Chembench: the Integrated Web Portal to Accelerate Cheminformatics and Chemical GR01GM096967 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI POZEFSKY, DIANE, TROPSHA, ALEXANDER · 2012 to 2015
$1.1M
NIGMS NIH HHS R01 GM066940NIGMS NIH HHS R01 GM096967
6 · The paper itself

Abstract

introductionThe almost exclusive use of only praziquantel for the treatment of schistosomiasis has raised concerns about the possible emergence of drug-resistant schistosomes. Consequently, there is an urgent need for new antischistosomal drugs. The identification of leads and the generation of high quality data are crucial steps in the early stages of schistosome drug discovery projects. AREAS COVERED: Herein, the authors focus on the current developments in antischistosomal lead discovery, specifically referring to the use of automated in vitro target-based and whole-organism screens and virtual screening of chemical databases. They highlight the strengths and pitfalls of each of the above-mentioned approaches, and suggest possible roadmaps towards the integration of several strategies, which may contribute for optimizing research outputs and led to more successful and cost-effective drug discovery endeavors. EXPERT OPINION: Increasing partnerships and access to funding for drug discovery have strengthened the battle against schistosomiasis in recent years. However, the authors believe this battle also includes innovative strategies to overcome scientific challenges. In this context, significant advances of in vitro screening as well as computer-aided drug discovery have contributed to increase the success rate and reduce the costs of drug discovery campaigns. Although some of these approaches were already used in current antischistosomal lead discovery pipelines, the integration of these strategies in a solid workflow should allow the production of new treatments for schistosomiasis in the near future.

Indexed as

Drug DesignAnimalsComputer-Aided DesignDatabases, ChemicalDrug DiscoveryDrug ResistanceHumansSchistosomiasisSchistosomicidesSchistosomicidesautomated assayscomputer-aided molecular designdata qualitydrug discoveryperspectivesSchistosomiasisvirtual screening

Identifiers

PMID27073973
PMCPMC6534417

What OpenQuestion holds

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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.