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 (Kiexp) for each training set pKiexp = , R2 = 0.97. The predictive power of the QSAR model was validated with 3D-QSAR pharmacophore generation (PH4): pKiexp = 0.707×pKipred − 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 pKipre up to 73 times lower than that of HDA1 (pKiexp = 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.
Keywords:
Drug Design, Molecular Modelling, ADMET, QSAR Model, Pharmacophore Model, Complexation ModelDOI
https://doi.org/10.25004/IJPSDR.2023.150317References
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