Computational Modeling of Chloroquine Analogues for Development of Drugs Against Novel Coronavirus (nCoV)

Abstract

The pandemic of coronavirus disease 2019, known as COVID-19, has challenged the global health; unfortunately still we do not have any specific therapeutic agents to treat this disease. The existing drugs are used only for symptomatic relief; among them chloroquine and its analogues have shown apparent and promising effectiveness in the treatment of COVID-19-related pneumonia. Due to unknown etiology of SARS-CoV-2, the mode of action of chloroquine and its derivatives has not been clear, but based on its different positive analysis results, on March 28, 2020, the U.S. Food and Drug Administration (FDA) allowed chloroquine phosphate and hydroxychloroquine sulfate to treat hospitalized COVID-19 patients. Globally, various researchers are continuously working on these compounds to improve their efficacy and affinity to SAR-CoV-2 targets and also trying to design new chloroquine derivatives with better response. In the present time, computational modeling approaches (homology modeling, molecular docking, molecular dynamic simulation, quantitative structure-activity relationship or QSAR, pharmacophore, etc.) are being proved very helpful for the easy identification of novel inhibitors against different SARS-CoV-2 targets. Chloroquine and its analogues are being explored by various researchers for repurposing of these drugs against SARS-CoV-2 by implementing different computational methodologies. In this chapter, we have presented recently published reports on the in silico modeling of chloroquine analogues for the design and identification of novel drugs against SARS-CoV-2. The chapter also provides the reader with a general idea about a successful computational drug discovery research in this particular area of applications.