Biological and Medicinal Chemistry

Multitarget, Selective Compound Design Yields Potent Inhibitors of a Kinetoplastid Pteridine Reductase 1



The optimization of compounds with multiple targets is a difficult multidimensional problem in the drug discovery cycle. Here, we present a systematic, multidisciplinary approach to the development of selective anti-parasitic compounds. Computational fragment-based design of novel pteridine derivatives along with iterations of crystallographic structure determination allowed for the derivation of a structure-activity relationship for multitarget inhibition. The approach yielded compounds showing apparent picomolar inhibition of T. brucei pteridine reductase 1 (PTR1), nanomolar inhibition of L. major PTR1, and selective submicromolar inhibition of parasite dihydrofolate reductase (DHFR) versus human DHFR. Moreover, by combining design for polypharmacology with a property-based on-parasite optimization, we found three compounds that exhibited micromolar EC50 values against T. brucei brucei, whilst retaining their target inhibition. Our results provide a basis for the further development of pteridine-based compounds, and we expect our multitarget approach to be generally applicable to the design and optimization of anti-infective agents.

Version notes

Further additions to and clarifications of methodological details for enzyme inhibition and anti-parasitic assays; correction of TbPTR1 (cpds 3b, 3c, 4c) and LmPTR1 IC50 values (1h); addition of selected dose-response curves and compound NMR spectra (Supporting Information)


Thumbnail image of poehner_pteridines_main.pdf

Supplementary material

Thumbnail image of poehner_pteridines_si.pdf
pteridine-manuscript Supporting Information
Supplemental Figures S1-9, Supplemental Tables S1-12, Supplemental experimental procedures and compound characterization, NMR spectra of compounds

Supplementary weblinks

Additional supplementary data on FAIRDOMHub
QikProp prediction results for synthesized and in silico pteridines and corresponding SOP. PAINS filtering results, Python modules for correlating QikProp data with experimental activities and for computing a multiple correlation between target and parasite inhibition. Compound library construction data and SOP, prepared docking receptors (PDB) with SOP, all Glide XP rigid-body docking results as PDB files of the receptor-ligand complexes and SOP as well as selected discussed induced fit docking results and corresponding SOP.