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Fragment-based identification of multi-target ligands by self-organizing map alignment

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In the recent years the prevalent paradigm in drug discovery of „one drug – one target – one disease“, following the assumption that highly selective ligands would avoid unwandted side effects caused by binding to seconday non-theratpeutic targets, got reconsidered. The results of post-genomic and network biology showed that proteins rarely act in isolated systems but rather as a part of a highly connected network [1]. It was further shown that the efficacy of several approved drugs is traced back to the fact that they act on multiple targets [2]. Therefore inhibiting a single target of such a network might not lead to the desired therapeutic effect. These findings lead to a shift towards polypharmacology [3] and the rational design of selective multi-target drugs, which have often improved efficacy [4]. But the design of multi target drugs is still a great challenge in regard of a sufficient activity on each target as well as an adequate pharmacokinetic profile [5]. Early design strategies tried to link the pharmacophors of known inhibitors, however these methods often lead to high molecular weight and low ligand efficiency.

We present a new approach based on self-organizing maps [3, 6] (SOM) for the identification of multi-target fragments. We describe a workflow that initially identifies multi-target relevant substructures with a combination of maximum common substructure search and the alignment of multiple SOMs. Furthermore, these substructures are trained together with a fragment library on additional SOMs to find new multi-target fragments, validated by saturation transfer difference (STD)-NMR and biochemical assay systems. We used our approach for the identification of new dual-acting inhibitors of 5-Lipoxygenase (5-LO) and soluble Epoxide Hydrolase (sEH), both enzymes located in the arachidonic acid cascade and involved in inflammatory processes, pain and cadiovascular diseases.

References

  1. 1.

    Jeong HM, Barabási A-L, Oltvai ZN: . Nature. 2001, 411: 41-42. 10.1038/35075138.

  2. 2.

    Yildirim Ma, Goh K-I, Cusick ME, Barabási A-L, Vidal M: . Nature Biotechnology. 2007, 25: 1119-1126. 10.1038/nbt1338.

  3. 3.

    Achenbach J, Tiikkainen P, Franke L, Proschak E: Future medicinal chemistry. 2011, 3: 961-968.

  4. 4.

    Morphy R, Rankovic Z: . J Med Chem. 2005, 48: 6523-6543. 10.1021/jm058225d.

  5. 5.

    Morphy R, Kay C, Rankovic Z: . Drug discovery today. 2004, 9: 641-651. 10.1016/S1359-6446(04)03163-0.

  6. 6.

    Kohonen T: Biological Cybernetics. 1982, 43: 59-69.

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Correspondence to Janosch Achenbach.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Achenbach, J., Klingler, F., Hahn, S. et al. Fragment-based identification of multi-target ligands by self-organizing map alignment. J Cheminform 4, P57 (2012) doi:10.1186/1758-2946-4-S1-P57

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Keywords

  • Epoxide Hydrolase
  • Arachidonic Acid Cascade
  • Ligand Efficiency
  • Saturation Transfer Difference
  • Soluble Epoxide Hydrolase