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Anticoagulants as Potential SARS-CoV-2 Mpro Inhibitors for COVID-19 Patients: In Vitro, Molecular Docking, Molecular
Ayman Abo Elmaaty1, Wagdy M Eldehna2,3, Muhammad Khattab4
1Department of Medicinal Chemistry, Faculty of Pharmacy, Port Said University, Port Said 42526, Egypt.
Abstract:
In this article, 34 anticoagulant drugs were screened in silico against the main protease (Mpro) of SARS-CoV-2 using molecular docking tools. Idraparinux, fondaparinux, eptifibatide, heparin, and ticagrelor demonstrated the highest binding affinities towards SARS-CoV-2 Mpro. A molecular dynamics study at 200 ns was also carried out for the most promising anticoagulants to provide insights into the dynamic and thermodynamic properties of promising compounds. Moreover, a quantum mechanical study was also conducted which helped us to attest to some of the molecular docking and dynamics findings. A biological evaluation (in vitro) of the most promising compounds was also performed by carrying out the MTT cytotoxicity assay and the crystal violet assay in order to assess inhibitory concentration 50 (IC50). It is worth noting that ticagrelor displayed the highest intrinsic potential for the inhibition of SARS-CoV-2 with an IC50 value of 5.60 µM and a safety index of 25.33. In addition, fondaparinux sodium and dabigatran showed promising inhibitory activities with IC50 values of 8.60 and 9.40 µM, respectively, and demonstrated safety indexes of 17.60 and 15.10, respectively. Moreover, the inhibitory potential of the SARS-CoV-2 Mpro enzyme was investigated by utilizing the SARS-CoV-2 Mpro assay and using tipranavir as a reference standard. Interestingly, promising SARS-CoV-2 Mpro inhibitory potential was attained for fondaparinux sodium with an IC50 value of 2.36 µM, surpassing the reference tipranavir (IC50 = 7.38 µM) by more than three-fold. Furthermore, highly eligible SARS-CoV-2 Mpro inhibitory potential was attained for dabigatran with an IC50 value of 10.59 µM. Finally, an SAR was discussed, counting on the findings of both in vitro and in silico approaches.
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