Evidence map›Paper›PMID 30605479›Full record

ArticlePloS one2019

The integration of pharmacophore-based 3D QSAR modeling and virtual screening in safety profiling: A case study to identify antagonistic activities against adenosine receptor, A2A, using 1,897 known drugs.

Fan Fan, Dora Toledo Warshaviak, Hisham K Hamadeh, Robert T Dunn

Abstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Inhibition of SARS-CoV-2 NSP-15 by Uridine-5'-Monophosphate Analogues Using QSAR Modelling, Molecular Dynamics Simulations, and Free Energy Landscape.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2024
    Article
  5. Article
  6. Review
  7. Review
  8. Review
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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

4 authors.

Fan FanAmgen Research, Department of Comparative Biology and Safety Sciences, Thousand Oaks, CA, United States of America.ORCID 0000-0001-6068-9493
Dora Toledo WarshaviakSchrodinger Inc., San Diego, CA, United States of America.
Hisham K HamadehAmgen Research, Department of Comparative Biology and Safety Sciences, Thousand Oaks, CA, United States of America.
Robert T DunnAmgen Research, Department of Comparative Biology and Safety Sciences, Thousand Oaks, CA, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Safety pharmacology screening against a wide range of unintended vital targets using in vitro assays is crucial to understand off-target interactions with drug candidates. With the increasing demand for in vitro assays, ligand- and structure-based virtual screening approaches have been evaluated for potential utilization in safety profiling. Although ligand based approaches have been actively applied in retrospective analysis or prospectively within well-defined chemical space during the early discovery stage (i.e., HTS screening and lead optimization), virtual screening is rarely implemented in later stage of drug discovery (i.e., safety). Here we present a case study to evaluate ligand-based 3D QSAR models built based on in vitro antagonistic activity data against adenosine receptor 2A (A2A). The resulting models, obtained from 268 chemically diverse compounds, were used to test a set of 1,897 chemically distinct drugs, simulating the real-world challenge of safety screening when presented with novel chemistry and a limited training set. Due to the unique requirements of safety screening versus discovery screening, the limitations of 3D QSAR methods (i.e., chemotypes, dependence on large training set, and prone to false positives) are less critical than early discovery screen. We demonstrated that 3D QSAR modeling can be effectively applied in safety assessment prior to in vitro assays, even with chemotypes that are drastically different from training compounds. It is also worth noting that our model is able to adequately make the mechanistic distinction between agonists and antagonists, which is important to inform subsequent in vivo studies. Overall, we present an in-depth analysis of the appropriate utilization and interpretation of pharmacophore-based 3D QSAR models for safety screening.

Indexed as

Adenosine A2 Receptor AgonistsAdenosine A2 Receptor AntagonistsDrug DiscoveryFeasibility StudiesHigh-Throughput Screening AssaysModels, MolecularQuantitative Structure-Activity RelationshipReceptor, Adenosine A2ASmall Molecule LibrariesAdenosine A2 Receptor AgonistsAdenosine A2 Receptor AntagonistsADORA2A protein, humanReceptor, Adenosine A2ASmall Molecule Libraries

Identifiers

PMID30605479
PMCPMC6317804

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LicenceCC BY
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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.