Computer-aided Design of New Hydroxamic Acid Derivatives Targeting the Plasmodium falciparum M17 Metallo-aminopeptidase with Favorable Pharmacokinetic Profile

Abstract

Through structure-based molecular design, we virtually design new subnanomolar range antimalarial, inhibitors of Plasmodium falciparum M17 aminopeptidase (PfA-M17). We developed the complexation QSAR models from hydroxamic acid derivatives (HDA). A linear correlation was established between the computed Gibbs free energies of binding (GFE: ΔΔGcom) and observed enzyme inhibition constants (Ki exp) for each training set pKi exp = , R2 = 0.97. The predictive power of the QSAR model was validated with 3D-QSAR pharmacophore generation (PH4): pKi exp = 0.707×pKi pred − 2.5182, R2 = 0.89. We then conducted a study on catalytic residues to exploit the different interactions (enzyme: inhibitor). Structural information from the models guided us in designing a virtual combinatorial library (VCL) of more than 56 thousand HDAs. The PH4 screening retained 48 new and potent HDAs with predicted inhibitory potencies pKi pre up to 73 times lower than that of HDA1 (pKi exp = 2.5 nM). Combining molecular modeling and PH4 in-silico screening of the VCL resulted in the proposed novel potent antimalarial agent candidates with favorable pharmacokinetic profiles.