CONFORMATIONAL STUDY OF MOLECULES IN A BIOLOGICAL ENVIRONMENT, DESIGN OF INHIBITORS OF HUMAN AMINOPEPTIDASE M1 IMPLICATED IN CANCER THERAPY

Issouf Soro; Hermann N’Guessan; Akoun Abou; Raymond Kre N’Guessan; Eugene Megnassan

doi:10.22270/ujpr.v8i5.1011

Issouf Soro 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.
Akoun Abou Department of Training and Research in Electrical and Electronic Engineering, Research Team: Instrumentation, Image and Spectroscopy, National Polytechnic Institute, Yamoussoukro, Côte d’Ivoire.
Raymond Kre N’Guessan 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 Félix Houphouët Boigny), Côte d’Ivoire. Laboratory of Material Sciences, the Environment and Solar Energy, University Félix Houphouet Boigny, Ivory Coast. Quantitative Life Science, ICTP-UNESCO, Strada Costiera 11, I 34151 Trieste, Italy.

10.22270/ujpr.v8i5.1011

Keywords:

ADMET, complexation model, Drug design, molecular modeling, pharmacophore model, QSAR model

Abstract

Objective: A novel subnanomolar anticancer hydroxamic acid containing drug candidates, inhibitors of human M1 aminopeptidase (APN) a recent validated target and has reached the predicted subnanomolar range of inhibitory potency.

Methods: A quantitative structure activity relationships (QSAR) complexation model has been developed from a compounds of 37 hydroxamic acid derivatives (AHD1-37 as training set, TS) to establish a linear correlation between the calculated relative Gibbs free energies (GFE: ΔΔG_com) of APN-AHDx complex formation and the experimental inhibition potency (K_i^exp). The predictive power of the QSAR model was then validated first with 9 other AHDs not included in the TS and thereafter with the generation of a 3D-QSAR-PH4 pharmacophore (PH4) model to screen the AHD chemical subspace built as a virtual combinatorial library of more than 58,644 AHD analogs). Finally the best PH4 hits were evaluated with the initial QSAR model for predicted potency (K_i^pre) and pharmacokinetic profile.

Results: The QSAR model linear correlation equation: pK_i^exp=-0.1901×∆∆Gcom + 8.2886 , R²=0.94, the subsequent PH4 model linear correlation between experiment and PH4-estimated K_i: pK_i^exp=1.0006× pK_i^pre + 0.0028, R²=0.79 documents the high predictive power of this approach. Finally the screening of the virtual library of AHD analogs yielded 95 orally bioavailable candidates the best reaching a predicted potency (K_i^pre) of 50 pM and displaying favorable pharmacokinetic profile.

Conclusion: The combined use of molecular modeling (QSAR) and in silico PH4-based screening of the hypothetical combinatorial library has resulted in proposed and predicted potent anticancer candidates with a suitable pharmacokinetic profile.

Peer Review History:

Received: 8 August 2023; Revised: 3 September; Accepted: 29 October; Available online: 15 November 2023

Academic Editor: Dr. Nuray Arı, Ankara University, Turkiye, ari@ankara.edu.tr

Received file: Reviewer's Comments:

Average Peer review marks at initial stage: 7.0/10

Average Peer review marks at publication stage: 9.0/10

Reviewers:

Prof. Hassan A.H. Al-Shamahy, Sana'a University, Yemen, shmahe@yemen.net.ye

Prof. Dr. A. Hakan AKTAŞ, Süleyman Demirel University, Faculty of Science and Art, Department of Chemistry, Isparta-Turkey, hakanaktas@sdu.edu.tr