ABSTRACT
Recent studies have revealed that plasmodial enoyl-ACP reductase (pfENR, FabI), one of the crucial enzymes in the plasmodial type II fatty acid synthesis II (FAS II) pathway, is a promising target for liver stage malaria infections. Hence, pfENR inhibitors have the potential to be used as causal malarial prophylactic agents. In this study, we report the design, synthesis, structural characterization and evaluation of a new class of pfENR inhibitors. The search for inhibitors began with a virtual screen of the iResearch database by molecular docking. Hits obtained from the virtual screen were ranked according to their Glide score. One hit was selected as a lead and modified to improve its binding to pfENR; from this, a series of phenylamino acetic acid benzylidene hydrazides were designed and synthesized. These molecules were thoroughly characterized by IR, (1) H, (13) C, (15) N, 2D-NMR (COSY, NOESY, (1) H-(13) C, (1) H-(15) N HSQC and HMBC), and X-ray diffraction. NMR studies revealed the existence of conformational/configurational isomers around the amide and imine functionalities. The major species in DMSO solution is the E, E form, which is in dynamic equilibrium with the Z, E isomer. In the solid state, the molecule has a completely extended conformation and forms helical structures that are stabilized by strong hydrogen bond interactions, forming a helical structure stabilized by N-H O interactions, a feature unique to this class of compounds. Furthermore, detailed investigation of the NMR spectra indicated the presence of a minor impurity in most compounds. The structure of this impurity was deduced as an imidazoline-4-one derivative based on (1) H-(13) C and (1) H-(15) H HMBC spectra and was confirmed from the NOESY spectra. The molecules were screened for in vitro activity against recombinant pfENR enzyme by a spectrophotometric assay. Four molecules, viz. 17, 7, 10, and 12 were found to be active at 7, 8, 10, and 12 µm concentration, respectively, showing promising pfENR inhibitory potential. A classification model was derived based on a binary QSAR approach termed recursive partitioning (RP) to highlight structural characteristics that could be tuned to improve activity.