The global pandemic caused by infections of the new coronavirus (COVID-19) makes it necessary to find possible less toxic and easily accessible therapeutic agents. In this study, we used strategies docking and molecular dynamics to analyze phytochemical compounds against FDA-approved antimalarial drugs recommended for the treatment of COVID-19. The evaluation was performed with the docking scores MolDock Score and Rerank Score calculated by Molegro Molecular. The DockThor server was used to generate the complexes and myPresto for the dynamic studies. Preliminary results suggested that piperine, capsaicin, and curcumin have the best docking scores and that they are capable of promoting structural changes in the viral protease by inducing folding of the enzyme. Curcumin and capsaicin bring the enzyme to a more compact conformational state compared to the native state, compared to chloroquine. Even though, it is unknown if these induced changes in protease are related to any inhibitory effect observed both in vitro and in vivo for any of these compounds. Further studies on the mechanisms of action of these compounds of interest are required, as well as experimental demonstrations. However, these results are interesting because they can serve as a starting point for subsequent experimental or/and in silico studies based on chemical structure-activity relationships taking these small molecules and their possible derivatives.

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The SARS-CoV-2 pandemic has accelerated the study of existing drugs. The mixture of homologs called ivermectin (avermectin-B1a [HB1a] + avermectin-B1b [HB1b]) has shown antiviral activity against SARS-CoV-2 in vitro . However, there are few reports on the behavior of each homolog. We investigated the interaction of each homolog with promising targets of interest associated with SARS-CoV-2 infection from a biophysical and computational-chemistry perspective using docking and molecular dynamics. We observed a differential behavior for each homolog, with an affinity of HB1b for viral structures, and of HB1a for host structures considered. The induced disturbances were differential and influenced by the hydrophobicity of each homolog and of the binding pockets. We present the first comparative analysis of the potential theoretical inhibitory effect of both avermectins on biomolecules associated with COVID-19, and suggest that ivermectin through its homologs, has a multiobjective behavior.

Cellular susceptibility to SARS-CoV-2 infection in the respiratory tract has been associated with the ability of the virus to interact with potential receptors on the host membrane. We have modeled viral dynamics by simulating various cellular systems and artificial conditions, including macromolecular crowding, based on experimental and transcriptomic data to infer parameters associated with viral growth and predict cell susceptibility. We have accomplished this based on the type, number and level of expression of the angiotensin-converting enzyme 2 (ACE2), transmembrane serine 2 (TMPRSS2), basigin2 (CD147), FURIN protease, neuropilin 1 (NRP1) or other less studied candidate receptors such as heat shock protein A5 (HSPA5) and angiotensin II receptor type 2 (AGTR2). In parallel, we studied the effect of simulated artificial environments on the accessibility to said proposed receptors. In addition, viral kinetic behavior dependent on the degree of cellular susceptibility was predicted. The latter was observed to be more influenced by the type of proteins and expression level, than by the number of potential proteins associated with the SARS CoV-2 infection. We predict a greater theoretical propensity to susceptibility in cell lines such as NTERA-2, SCLC-21H, HepG2 and Vero6, and a lower theoretical propensity in lines such as CaLu3, RT4, HEK293, A549 and U-251MG. An important relationship was observed between expression levels, protein diffusivity, and thermodynamically favorable interactions between host proteins and the viral spike, suggesting potential sites of early infection other than the lungs. This research is expected to stimulate future quantitative experiments and promote systematic investigation of the effect of crowding presented here.