Evidence map›Paper›PMID 18483767›Full record

ArticleJournal of computer-aided molecular design2008

Bond-based linear indices in QSAR: computational discovery of novel anti-trichomonal compounds.

Yovani Marrero-Ponce, Alfredo Meneses-Marcel, Oscar M Rivera-Borroto, Ramón García-Domenech, Jesus Vicente De Julián-Ortiz, Alina Montero, José Antonio Escario, Alicia Gómez Barrio, David Montero Pereira, Juan José Nogal and 4 more

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Article in Journal of computer-aided molecular design, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
7.0field-weighted citation impact, top 3% 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

4 citing papers in PubMed, 32 citations in OpenAlex.

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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

14 authors at 4 institutions in 3 countries.

Yovani Marrero-PonceFaculty of Chemistry-Pharmacy, Unit of Computer-Aided Molecular Biosilico Discovery and Bioinformatic Research (CAMD-BIR Unit), Central University of Las Villas, Santa Clara, 54830 Villa Clara, Cuba. ymarrero77@yahoo.es
Alfredo Meneses-Marcel
Oscar M Rivera-Borroto
Ramón García-Domenech
Jesus Vicente De Julián-Ortiz
Alina Montero
José Antonio Escario
Alicia Gómez Barrio
David Montero Pereira
Juan José Nogal
Ricardo Grau
Francisco Torrens
Christian Vogel
Vicente J Arán
Universidad Central "Marta Abreu" de las Villas (UCLV) · CUUniversitat de València · ESInstituto de Química Médica · ESUniversity of Rostock · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Trichomonas vaginalis (Tv) is the causative agent of the most common, non-viral, sexually transmitted disease in women and men worldwide. Since 1959, metronidazole (MTZ) has been the drug of choice in the systemic treatment of trichomoniasis. However, resistance to MTZ in some patients and the great cost associated with the development of new trichomonacidals make necessary the development of computational methods that shorten the drug discovery pipeline. Toward this end, bond-based linear indices, new TOMOCOMD-CARDD molecular descriptors, and linear discriminant analysis were used to discover novel trichomonacidal chemicals. The obtained models, using non-stochastic and stochastic indices, are able to classify correctly 89.01% (87.50%) and 82.42% (84.38%) of the chemicals in the training (test) sets, respectively. These results validate the models for their use in the ligand-based virtual screening. In addition, they show large Matthews' correlation coefficients (C) of 0.78 (0.71) and 0.65 (0.65) for the training (test) sets, correspondingly. The result of predictions on the 10% full-out cross-validation test also evidences the robustness of the obtained models. Later, both models are applied to the virtual screening of 12 compounds already proved against Tv. As a result, they correctly classify 10 out of 12 (83.33%) and 9 out of 12 (75.00%) of the chemicals, respectively; which is the most important criterion for validating the models. Besides, these classification functions are applied to a library of seven chemicals in order to find novel antitrichomonal agents. These compounds are synthesized and tested for in vitro activity against Tv. As a result, experimental observations approached to theoretical predictions, since it was obtained a correct classification of 85.71% (6 out of 7) of the chemicals. Moreover, out of the seven compounds that are screened, synthesized and biologically assayed, six compounds (VA7-34, VA7-35, VA7-37, VA7-38, VA7-68, VA7-70) show pronounced cytocidal activity at the concentration of 100 mug/ml at 24 h (48 h) within the range of 98.66%-100% (99.40%-100%), while only two molecules (chemicals VA7-37 and VA7-38) show high cytocidal activity at the concentration of 10 mug/ml at 24 h (48 h): 98.38% (94.23%) and 97.59% (98.10%), correspondingly. The LDA-assisted QSAR models presented here could significantly reduce the number of synthesized and tested compounds and could increase the chance of finding new chemical entities with anti-trichomonal activity.

Indexed as

Drug DesignQuantitative Structure-Activity RelationshipAlgorithmsAnimalsAntitrichomonal AgentsCell ProliferationCell SurvivalChemical PhenomenaChemistry, PhysicalComputational BiologyDiscriminant AnalysisLinear ModelsMetronidazoleMolecular StructureQuinoxalinesSoftwareAntitrichomonal AgentsMetronidazoleQuinoxalines

Identifiers

PMID18483767
OpenAlexW2017374774

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.