The current treatments against Leishmania parasites present high toxicity and multiple side effects, which makes the control and elimination of leishmaniasis challenging. Natural products constitute an interesting and diverse chemical space for the identification of new antileishmanial drugs. To identify new drug options, an in-house database of 360 kauranes (tetracyclic diterpenes) was generated, and a combined ligand- and structure-based virtual screening (VS) approach was performed to select potential inhibitors of Leishmania major (Lm) pteridine reductase I (PTR1). The best-ranked kauranes were employed to verify the validity of the VS approach through LmPTR1 enzyme inhibition assay. The half-maximal inhibitory concentration (IC50) values of selected bioactive compounds were examined using the random forest (RF) model (i.e., 2β-hydroxy-menth-6-en-5β-yl ent-kaurenoate (135) and 3α-cinnamoyloxy-ent-kaur-16-en-19-oic acid (302)) were below 10 μM. A compound similar to 302, 3α-p-coumaroyloxy-ent-kaur-16-en-19-oic acid (302a), was also synthesized and showed the highest activity against LmPTR1. Finally, molecular docking calculations and molecular dynamics simulations were performed for the VS-selected, most-active kauranes within the active sites of PTR1 hybrid models, generated from three Leishmania species that are known to cause cutaneous leishmaniasis in the new world (i.e., L. braziliensis, L. panamensis, and L. amazonensis) to explore the targeting potential of these kauranes to other species-dependent variants of this enzyme.
Keywords: Asteraceae; Leishmania; kauranes; machine learning; molecular docking; natural products; virtual screening.