Mining Public Domain Data to Develop Selective DYRK1A Inhibitors

Scott H. Henderson (Lead / Corresponding author), Fiona Sorrell, James Bennett, Marcus T. Hanley, Sean Robinson, Iva Hopkins Navratilova, Jonathan M. Elkins, Simon E. Ward (Lead / Corresponding author)

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Abstract

Kinases represent one of the most intensively pursued groups of targets in modern-day drug discovery. Often it is desirable to achieve selective inhibition of the kinase of interest over the remaining ∼500 kinases in the human kinome. This is especially true when inhibitors are intended to be used to study the biology of the target of interest. We present a pipeline of open-source software that analyzes public domain data to repurpose compounds that have been used in previous kinase inhibitor development projects. We define the dual-specificity tyrosine-regulated kinase 1A (DYRK1A) as the kinase of interest, and by addition of a single methyl group to the chosen starting point we remove glycogen synthase kinase β (GSK3β) and cyclin-dependent kinase (CDK) inhibition. Thus, in an efficient manner we repurpose a GSK3β/CDK chemotype to deliver 8b, a highly selective DYRK1A inhibitor.

Original languageEnglish
Pages (from-to)1620-1626
Number of pages7
JournalACS Medicinal Chemistry Letters
Volume11
Issue number8
Early online date30 Jun 2020
DOIs
Publication statusPublished - 13 Aug 2020

Keywords

  • DYRK1A
  • polypharmacology
  • chemoinformatics
  • selectivity

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    Henderson, S. H., Sorrell, F., Bennett, J., Hanley, M. T., Robinson, S., Hopkins Navratilova, I., Elkins, J. M., & Ward, S. E. (2020). Mining Public Domain Data to Develop Selective DYRK1A Inhibitors. ACS Medicinal Chemistry Letters, 11(8), 1620-1626. https://doi.org/10.1021/acsmedchemlett.0c00279