Autotaxin inhibition: development and application of computational tools to identify site-selective lead compounds

Derek D Norman1, Ayolah Ibezim, Whitney E Scott

  • 1Department of Chemistry, The University of Memphis, Memphis, TN 38152, United States.

Insights

Researchers identified novel autotaxin (ATX) inhibitors using computational models. These inhibitors show therapeutic potential for conditions like cancer and neuropathic pain, with selectivity cliffs observed in cytotoxicity assays.

Area of Science:

  • Biochemistry
  • Medicinal Chemistry
  • Pharmacology

Background:

  • Autotaxin (ATX) enzyme activity is linked to diseases such as cancer and neuropathic pain.
  • Inhibiting ATX is a therapeutic strategy to reduce lysophosphatidic acid (LPA) production.

Purpose of the Study:

  • To apply subsite-targeted pharmacophore models to identify novel ATX inhibitors.
  • To evaluate the identified compounds for ATX inhibition and cytotoxicity.
  • To investigate the binding sites of identified inhibitors.

Main Methods:

  • Utilized a screening workflow with docking or binary QSAR as secondary filters.
  • Applied previously developed, subsite-targeted pharmacophore models.
  • Conducted cell-based assays for ATX inhibition and cytotoxicity.
  • Performed substrate-based assays to determine inhibitor binding sites.

Main Results:

  • Identified novel structural types of ATX inhibitors, with four compounds exhibiting sub-micromolar inhibition constants.
  • Observed selectivity cliffs in structure-activity relationships for ATX inhibition versus cytotoxicity.
  • Confirmed that tools targeting allosteric sites were more effective than those targeting the active site.

Conclusions:

  • General cytotoxicity should not be an early filter for ATX inhibitor scaffolds.
  • Computational tools targeting allosteric sites demonstrated better prediction accuracy for binding location.
  • The identified ATX inhibitors represent promising leads for therapeutic development.