Ligand-based autotaxin pharmacophore models reflect structure-based docking results.

The autotaxin (ATX) enzyme exhibits lysophospholipase D activity responsible for the conversion of lysophosphatidyl choline to lysophosphatidic acid (LPA). ATX and LPA have been linked to the initiation of atherosclerosis, cancer invasiveness, and neuropathic pain. ATX inhibition therefore offers currently unexploited therapeutic potential, and substantial interest in the development of ATX inhibitors is evident in the recent literature. Here we report the performance-based comparison of ligand-based pharmacophores developed on the basis of different combinations of ATX inhibitors in the training sets against an extensive database of compounds tested for ATX inhibitory activity, as well as with docking results of the actives against a recently reported ATX crystal structure. In general, pharmacophore models show better ability to select active ATX inhibitors binding in a common location when the ligand-based superposition shows a good match to the superposition of actives based on docking results. Two pharmacophore models developed on the basis of competitive inhibitors in combination with the single inhibitor crystallized to date in the active site of ATX were able to identify actives at rates over 40%, a substantial improvement over the <10% representation of active site-directed actives in the test set database.