In-silico molecular modelling studies of some camphor imine based compounds as anti-influenza A (H1N1) pdm09 virus agents

Abstract

The advent of influenza A (H1N1) drug-resistant strains led to the search quest for more potent inhibitors of the influenza A virus, especially in this devastating COVID-19 pandemic era. Hence, the present research utilized some molecular modelling strategies to unveil new camphor imine-based compounds as anti-influenza A (H1N1) pdm09 agents. The 2D-QSAR results revealed GFA-MLR (R²_train = 0.9158, Q²=0.8475) and GFA-ANN (R²_train = 0.9264, Q²=0.9238) models for the anti-influenza A (H1N1) pdm09 activity prediction which have passed the QSAR model acceptability thresholds. The results from the 3D-QSAR studies also revealed CoMFA (R²_train =0.977, Q²=0.509) and CoMSIA_S (R²_train =0.976, Q²=0.527) models for activity predictions. Based on the notable information derived from the 2D-QSAR, 3D-QSAR, and docking analysis, ten (10) new camphor imine-based compounds (22a-22j) were designed using the most active compound 22 as the template. Furthermore, the high predicted activity and binding scores of compound 22j were further justified by the high reactive sites shown in the electrostatic potential maps and other quantum chemical calculations. The MD simulation of 22j in the active site of the influenza hemagglutinin (HA) receptor confirmed the dynamic stability of the complex. Moreover, the appraisals of drug-likeness and ADMET properties of the proposed compounds showed zero violation of Lipinski’s criteria with good pharmacokinetic profiles. Hence, the outcomes in this work recommend further in-depth in vivo and in-vitro investigations to validate these theoretical findings.

Communicated by Ramaswamy H. Sarma

Keywords:

• Camphor imines
• docking
• activity
• anti-influenza A (H1N1) pdm09
• hemagglutinin

Disclosure statement

No potential conflict of interest was reported by the authors.

Ethical approval

The section is not applicable to this research work

Authors contribution

MA actualized the methods, results analysis, and drafted the paper. AU give directives and supervised the research work. GAS and PAM also give directives, supervision, and technical advice throughout the work. MTI contributed in data analysis, AC performed the MD simulation and VKG analysed the MD simulation results.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.