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Combinatorially-generated library of 6-fluoroquinolone analogs as potential novel antitubercular agents: a chemometric and molecular modeling assessment

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Abstract

The virtual combinatorial chemistry approach as a methodology for generating chemical libraries of structurally-similar analogs in a virtual environment was employed for building a general mixed virtual combinatorial library with a total of 53.871 6-FQ structural analogs, introducing the real synthetic pathways of three well known 6-FQ inhibitors. The druggability properties of the generated combinatorial 6-FQs were assessed using an in-house developed drug-likeness filter integrating the Lipinski/Veber rule-sets. The compounds recognized as drug-like were used as an external set for prediction of the biological activity values using a neural-networks (NN) model based on an experimentally-determined set of active 6-FQs. Furthermore, a subset of compounds was extracted from the pool of drug-like 6-FQs, with predicted biological activity, and subsequently used in virtual screening (VS) campaign combining pharmacophore modeling and molecular docking studies. This complex scheme, a powerful combination of chemometric and molecular modeling approaches provided novel QSAR guidelines that could aid in the further lead development of 6-FQs agents.

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Abbreviations

TB:

Tuberculosis

ATP:

Adenosine triphosphate

MIC:

Minimal Inhibitory Concentration

6-FQs:

6-Fluoroquinolones

SAR:

Structure-Activity Relationships

QSAR:

Quantitative Structure-Activity Relationships

CombiChem:

Combinatorial Chemistry

SSS:

Substructure Search

NN:

Neural-Networks

KANN:

Kohonen Artificial Neural Networks

CP ANN:

Counter-Propagation Artificial Neural Networks

GHA:

Global Hypothetical Activity

LBP:

Ligand-Based Pharmacophore

SBP:

Structure-Based Pharmacophore

VS:

Virtual Screening

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Acknowledgments

Authors thank Agency of Research of R. Slovenia (ARRS) for the financial support through the Grants P1-0017 and 1000-07-310016. We are sincerely grateful to Dr. Marjana Novič for valuable insights, discussion and her continuing support of this research.

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Authors and Affiliations

  1. National Institute of Chemistry, Hajdrihova 19, 1001, Ljubljana, Slovenia

    Nikola Minovski, Andrej Perdih & Tom Solmajer

Authors
  1. Nikola Minovski
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  2. Andrej Perdih
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  3. Tom Solmajer
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Corresponding author

Correspondence to Tom Solmajer.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Online resource 1 (part 1)

The general virtual combinatorial library (CombiTot) of total 53.871 6-FQ structural analogs obtained using the virtual combinatorial chemistry approach (available as *.sdf file format). (TXT 83.0 MB)

Online resource 1 (part 2)

(TXT 50.0 MB)

Online resource 2

Chemical structures of the combinatorially-generated, filtered 6-FQ structural analogs marked as drug-like compounds (CombiDL, 1.101 compounds), obtained after the drug-likeness assessment (available as *.sdf file format). (TXT 3.74 MB)

Online resource 3

Chemical structures of the experimentally-determined 6-FQ structural analogs (Assay2, 145compounds), together with their measured biological activity values (MIC) (available as *.sdf file format). (TXT 476 KB)

Online resource 4

Chemical structures of the combinatorially-generated 6-FQ structural analogs (CombiLib, 427 compounds) extracted from the pool of 1.101 drug-like compounds (CombiDL), defining the global hypothetical activity range (GHA) of 0.0 ≤ MIC pred-combi [μg/mL] ≤ 0.1 (available as *.sdf file format). (TXT 1.41 MB)

Online resource 5

The experimental and combinatorial compounds, extracted as most promising 6-FQs (marked as true (T)) after the Boolean-type VS analysis using three-dimensional pharmacophore models (LBP, SBP shared , and SBP merged ). (XLS 205 KB)

Online resource 6

The post-docking first level VS analysis of both the sets (the experimental and the combinatorial one) into the 3K9F binding pocket. The geometrically-suitable compounds (with (T) outcome) are highlighted in green. (XLS 788 KB)

Online Resource 7

The post-docking second level VS analysis of the retained compounds from the first level post-docking analysis (experimental and combinatorial) into the 3K9F binding pocket. The selected compounds with optimal interatomic distances (with (T) outcome) are highlighted in green. (XLS 2.06 MB)

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Minovski, N., Perdih, A. & Solmajer, T. Combinatorially-generated library of 6-fluoroquinolone analogs as potential novel antitubercular agents: a chemometric and molecular modeling assessment. J Mol Model 18, 1735–1753 (2012). https://doi.org/10.1007/s00894-011-1179-0

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  • DOI: https://doi.org/10.1007/s00894-011-1179-0

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