Computer-Assisted Design of Hydroxamic Acid Derivatives Inhibitors of M1 Metallo Aminopeptidase of Plasmodium falciparum with Favorable Pharmacokinetic Profile
Niaré Adama
Laboratory of Fundamental and Applied Physics, University of Abobo Adjamé (Now Nangui Abrogoua), Côte d’Ivoire.
Hermann N’Guessan
Laboratory of Fundamental and Applied Physics, University of Abobo Adjamé (Now Nangui Abrogoua), Côte d’Ivoire.
Brice Dali
Laboratory of Fundamental and Applied Physics, University of Abobo Adjamé (Now Nangui Abrogoua), Côte d’Ivoire.
Eugene Megnassan *
Laboratory of Fundamental and Applied Physics, University of Abobo Adjamé (Now Nangui Abrogoua), Côte d’Ivoire, Laboratory of Structural and Theoretical Organic Chemistry, University of Cocody (Now Felix Houphouët Boigny), Côte d’Ivoire and ICTP-UNESCO, QLS, Strada Costiera 11, I 34151 Trieste, Italy.
*Author to whom correspondence should be addressed.
Abstract
We virtually design here new subnanomolar range antimalarial, inhibitors of plasmodium falciparum M1 Aminopeptidase (PfA-M1), by means of structure-based molecular design. We developed the complexation QSAR models from Hydroxamic Acid derivatives (AHO). A linear correlation was established between the computed Gibbs free energies of binding (GFE: ∆∆Gcom) and observed enzyme inhibition constants (Kiexp) for each training set pKiexp = −0.063×∆∆Gcom+ 8.003, R2 = 0.92. The predictive power of the QSAR model was validated with 3D-QSAR pharmacophore generation (PH4): pKiexp = 1.0289×pKipred − 0.155, R2 = 0.90. We then conducted a study on catalytic residues to exploit the different interactions (enzyme: inhibitor). Structural information from the models guided us in designing of a virtual combinatorial library (VCL) of more than 44 thousands AHOs. The PH4 screening retained 51 new and potent AHOs with predicted inhibitory potencies pKipre up to 13 times lower than that of AHO1 (pKiexp = 700 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, QSAR model, pharmacophore model, complexation model, molecular modeling, ADMET