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Biophysical Analysis of Potential Inhibitors of SARS-CoV-2 Cell Recognition and Their Effect on Viral Dynamics in Different Cell Types: A Computational Prediction from In Vitro Experimental Data

González-Paz et al., ACS Omega, doi:10.1021/acsomega.3c06968
Feb 2024  
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In Silico study showing that nafamostat, camostat, chloroquine, hydroxychloroquine, telmisartan, and captopril may be beneficial for COVID-19 by inhibiting SARS-CoV-2 cell entry and replication in multiple cell types expressing ACE2 and TMPRSS2 receptors. Authors used mathematical models to predict the susceptibility of different cell lines to SARS-CoV-2 infection based on their expression of various viral entry receptors. The CaLu3 cell line was predicted to be most susceptible, potentially due to high expression of CD147 and Cathepsin-L. Molecular docking simulations found that nafamostat and camostat had the most favorable binding affinities and stability when interacting with the SARS-CoV-2 spike protein receptor-binding domain (RBD) compared to other tested compounds. Chloroquine and hydroxychloroquine were predicted to have around 80% and 72% antiviral efficacy, respectively, possibly by blocking the interaction between the spike protein and ACE2 receptor. Telmisartan and captopril, both related to the renin-angiotensin system, were predicted to have 56-59% antiviral efficacy, potentially by reducing ACE2 expression and making cells less susceptible to infection. The study predicts that these compounds could significantly reduce viral infectivity and replication in Vero E6, HEK293, HeLa, and CaLu3 cells, with nafamostat being the most effective overall.
González-Paz et al., 14 Feb 2024, peer-reviewed, 8 authors.
In Silico studies are an important part of preclinical research, however results may be very different in vivo.
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Biophysical Analysis of Potential Inhibitors of SARS-CoV-2 Cell Recognition and Their Effect on Viral Dynamics in Different Cell Types: A Computational Prediction from In Vitro Experimental Data
Lenin González-Paz, María Carla Lossada, María Laura Hurtado-León, Joan Vera-Villalobos, José L Paz, Yovani Marrero-Ponce, Felix Martinez-Rios, Ysaías. J Alvarado
ACS Omega, doi:10.1021/acsomega.3c06968
Recent reports have suggested that the susceptibility of cells to SARS-CoV-2 infection can be influenced by various proteins that potentially act as receptors for the virus. To investigate this further, we conducted simulations of viral dynamics using different cellular systems (Vero E6, HeLa, HEK293, and CaLu3) in the presence and absence of drugs (anthelmintic, ARBs, anticoagulant, serine protease inhibitor, antimalarials, and NSAID) that have been shown to impact cellular recognition by the spike protein based on experimental data. Our simulations revealed that the susceptibility of the simulated cell systems to SARS-CoV-2 infection was similar across all tested systems. Notably, CaLu3 cells exhibited the highest susceptibility to SARS-CoV-2 infection, potentially due to the presence of receptors other than ACE2, which may account for a significant portion of the observed susceptibility. Throughout the study, all tested compounds showed thermodynamically favorable and stable binding to the spike protein. Among the tested compounds, the anticoagulant nafamostat demonstrated the most favorable characteristics in terms of thermodynamics, kinetics, theoretical antiviral activity, and potential safety (toxicity) in relation to SARS-CoV-2 spike proteinmediated infections in the tested cell lines. This study provides mathematical and bioinformatic models that can aid in the identification of optimal cell lines for compound evaluation and detection, particularly in studies focused on repurposed drugs and their mechanisms of action. It is important to note that these observations should be experimentally validated, and this research is expected to inspire future quantitative experiments.
Notes The authors declare no competing financial interest. ■ ACKNOWLEDGMENTS This research was supported by regular funds of the Venezuelan Institute for Scientific Research (IVIC). J.L.P. thanks the Vicerrectorado de Investigación y Postgrado of the Universidad Nacional Mayor de San Marcos for the computational resources used.
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