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Design and tests of prospective property predictions for novel antimalarial 2-aminopropylaminoquinolones.

Robert D ClarkDenise N MorrisGary ChinigoMichael S LawlessJacques PrudhommeKarine Le RochMaria José LafuenteSantiago Ferrer-BazagaFrancisco Javier GamoRobert GadwoodWalter S Woltosz
Published in: Journal of computer-aided molecular design (2020)
There is a pressing need to improve the efficiency of drug development, and nowhere is that need more clear than in the case of neglected diseases like malaria. The peculiarities of pyrimidine metabolism in Plasmodium species make inhibition of dihydroorotate dehydrogenase (DHODH) an attractive target for antimalarial drug design. By applying a pair of complementary quantitative structure-activity relationships derived for inhibition of a truncated, soluble form of the enzyme from Plasmodium falciparum (s-PfDHODH) to data from a large-scale phenotypic screen against cultured parasites, we were able to identify a class of antimalarial leads that inhibit the enzyme and abolish parasite growth in blood culture. Novel analogs extending that class were designed and synthesized with a goal of improving potency as well as the general pharmacokinetic and toxicological profiles. Their synthesis also represented an opportunity to prospectively validate our in silico property predictions. The seven analogs synthesized exhibited physicochemical properties in good agreement with prediction, and five of them were more active against P. falciparum growing in blood culture than any of the compounds in the published lead series. The particular analogs prepared did not inhibit s-PfDHODH in vitro, but advanced biological assays indicated that other examples from the class did inhibit intact PfDHODH bound to the mitochondrial membrane. The new analogs, however, killed the parasites by acting through some other, unidentified mechanism 24-48 h before PfDHODH inhibition would be expected to do so.
Keyphrases
  • plasmodium falciparum
  • molecular docking
  • high throughput
  • endothelial cells
  • emergency department
  • big data
  • oxide nanoparticles
  • high resolution
  • systematic review
  • machine learning
  • drug induced