Screening of Clinically Approved and Investigation Drugs as Potential Inhibitors of SARS-CoV-2: A Combined in silico and in vitro Study

Using TransR to enhance drug repurposing knowledge graph for COVID-19 and its complications

MOTIVATION

The COVID-19 pandemic has been spreading globally for four years, yet specific drugs that effectively suppress the virus remain elusive. Furthermore, the emergence of complications associated with COVID-19 presents significant challenges, making the development of therapeutics for COVID-19 and its complications an urgent task. However, traditional drug development processes are time-consuming. Drug repurposing, which involves identifying new therapeutic applications for existing drugs, presents a viable alternative.

RESULT

In this study, we construct a knowledge graph by retrieving information on genes, drugs, and diseases from databases such as DRUGBANK and GNBR. Next, we employ the TransR knowledge representation learning approach to embed entities and relationships into the knowledge graph. Subsequently, we train the knowledge graph using a graph neural network model based on TransR scoring. This trained knowledge graph is then utilized to predict drugs for the treatment of COVID-19 and its complications. Based on experimental results, we have identified 15 drugs out of the top 30 with the highest success rates associated with treating COVID-19 and its complications. Notably, out of these 15 drugs, 10 specifically aimed at treating COVID-19, such as Torcetrapib and Tocopherol, has not been previously identified in the knowledge graph. This finding highlights the potential of our model in aiding healthcare professionals in drug development and research related to this disease.

Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers

The human ether-à-go-go-related gene (hERG) channel plays a crucial role in membrane repolarization. Any disruptions in its function can lead to severe cardiovascular disorders such as long QT syndrome (LQTS), which increases the risk of serious cardiovascular problems such as tachyarrhythmia and sudden cardiac death. Drug-induced LQTS is a significant concern and has resulted in drug withdrawals from the market in the past. The main objective of this study is to pinpoint crucial heteroatoms present in ligands that initiate interactions leading to the effective blocking of the hERG channel. To achieve this aim, ligand-based quantitative structure-activity relationships (QSAR) models were constructed using extensive ligand libraries, considering the heteroatom types and numbers, and their associated hERG channel blockage pIC50 values. Machine learning-assisted QSAR models were developed to analyze the key structural components influencing compound activity. Among the various methods, the KPLS method proved to be the most efficient, allowing the construction of models based on eight distinct fingerprints. The study delved into investigating the influence of heteroatoms on the activity of hERG blockers, revealing their significant role. Furthermore, by quantifying the effect of heteroatom types and numbers on ligand activity at the hERG channel, six compound pairs were selected for molecular docking. Subsequent molecular dynamics simulations and per residue MM/GBSA calculations were performed to comprehensively analyze the interactions of the selected pair compounds.

Immunostimulatory Activity of Cordyceps militaris Fermented with Pediococcus pentosaceus SC11 Isolated from a Salted Small Octopus in Cyclophosphamide-Induced Immunocompromised Mice and Its Inhibitory Activity against SARS-CoV 3CL Protease

In this study, we investigated the immune-enhancing and anti-viral effects of germinated Rhynchosia nulubilis (GRC) fermented with Pediococcus pentosaceus SC11 (GRC-SC11) isolated from a salted small octopus. The cordycepin, β-glucan, and total flavonoid contents increased in GRC after SC11 fermentation. GRC-SC11 inhibits 3CL protease activity in severe acute respiratory syndrome-associated coronavirus (SARS-CoV). GRC-SC11 significantly increased thymus and spleen indices in immunocompromised mice. The rate of splenocyte proliferation was higher in GRC-SC11-treated immunocompromised mice than that in GRC-treated immunocompromised mice in the presence or absence of concanavalin A. In addition, GRC-SC11 increased the phagocytic activity and nitric oxide production in immunocompromised mice. The mRNA expression of interferon-gamma (IFN-γ), interferon-alpha (IFN-α), and interferon-stimulated gene 15 (ISG15) was up-regulated in GRC-SC11 treated RAW 264.7 macrophages, compared to GRC. Our study indicates that GRC-SC11 might be a potential therapeutic agent for immunocompromised patients who are vulnerable to SARS-CoV infection.

Molecular dynamics studies reveal structural and functional features of the SARS-CoV-2 spike protein

The SARS-CoV-2 virus is responsible for the COVID-19 pandemic the world experience since 2019. The protein responsible for the first steps of cell invasion, the spike protein, has probably received the most attention in light of its central role during infection. Computational approaches are among the tools employed by the scientific community in the enormous effort to study this new affliction. One of these methods, namely molecular dynamics (MD), has been used to characterize the function of the spike protein at the atomic level and unveil its structural features from a dynamic perspective. In this review, we focus on these main findings, including spike protein flexibility, rare S protein conformational changes, cryptic epitopes, the role of glycans, drug repurposing, and the effect of spike protein variants.

Bioactive Components of Houttuynia cordata Thunb and Their Potential Mechanisms Against COVID-19 Using Network Pharmacology and Molecular Docking Approaches

We investigated the molecular mechanism by which Houttuynia cordata Thunb (HCT) may intervene in coronavirus disease 2019 (COVID-19) and COVID-19-induced cytokine storms using network pharmacology and molecular docking approaches. Using the Traditional Chinese medicine Systems Pharmacology Database and Analysis Platform (TCMSP), a "component-target-pathway" topology map of HCT for COVID-19 treatment was constructed using Cytoscape. Core target genes were analyzed using the STRING database, and the signal pathway map and biological mechanism of COVID-19 therapy were obtained using cluster profilers. Active components of HCT were docked with severe respiratory syndrome coronavirus 2 (SARS-CoV-2) 3C-like protease (3CL^pro) and RNA-dependent RNA polymerase (RdRp) using AutoDockTools. Data visualization and statistical analysis were conducted using the R program. A molecular dynamic simulation was carried out with the Groningen Machine for Chemical Simulation program. HCT had six active anti-COVID-19 ingredients and 45 molecular targets. Their crucial target proteins for COVID-19 treatment were the RELA (nuclear factor kappa B [NF-κB] p65 subunit), interleukin 6, and mitogen-activated protein kinase 1. In functional enrichment analysis, the potential molecular targets of active components of HCT for COVID-19 treatment belonged to 18 signaling pathways (adjusted P = 2.12E-11). Gene ontology obtained by Kyoto Encyclopedia of Genes and Genome enrichment screening showed that the primary mechanism of COVID-19 treatment was upregulation of protein kinase C followed by downregulations of T cell differentiation and proliferation and NF-κB signaling. Molecular docking showed that the active components of HCT (quercetin and kaempferol) had similar binding affinities for SARS-CoV-2 3CL^pro and SARS-CoV-2 RdRp, primary COVID-19 target proteins as did clinically used drugs. These results were confirmed with molecular dynamics simulation. In conclusion, multiple components of HCT, especially quercetin and kaempferol, have the potential to treat COVID-19 infection and COVID-19-induced cytokine storm by targeting multiple proteins.

Antiviral drug discovery by targeting the SARS-CoV-2 polyprotein processing by inhibition of the main protease

The spread of SARS-CoV-2, the causative agent for COVID-19, has led to a global and deadly pandemic. To date, few drugs have been approved for treating SARS-CoV-2 infections. In this study, a structure-based approach was adopted using the SARS-CoV-2 main protease (M^pro) and a carefully selected dataset of 37,060 compounds comprising M^pro and antiviral protein-specific libraries. The compounds passed two-step docking filtration, starting with standard precision (SP) followed by extra precision (XP) runs. Fourteen compounds with the highest XP docking scores were examined by 20 ns molecular dynamics simulations (MDs). Based on backbone route mean square deviations (RMSD) and molecular mechanics/generalized Born surface area (MM/GBSA) binding energy, four drugs were selected for comprehensive MDs analysis at 100 ns. Results indicated that birinapant, atazanavir, and ritonavir potently bound and stabilized SARS-CoV-2 M^pro structure. Binding energies higher than -102 kcal/mol, RMSD values <0.22 nm, formation of several hydrogen bonds with M^pro, favourable electrostatic contributions, and low radii of gyration were among the estimated factors contributing to the strength of the binding of these three compounds with M^pro. The top two compounds, atazanavir and birinapant, were tested for their ability to prevent SARS-CoV-2 plaque formation. At 10 µM of birinapant concentration, antiviral tests against SARS-CoV-2 demonstrated a 37% reduction of virus multiplication. Antiviral assays demonstrated that birinapant has high anti-SARS-CoV-2 activity in the low micromolar range, with an IC50 value of 18 ± 3.6 µM. Therefore, birinapant is a candidate for further investigation to determine whether it is a feasible therapy option.

The neutralization effect of montelukaston SARS-CoV-2 is shown by multiscale in silicosimulations and combined in vitro studies

Small molecule inhibitors have previously been investigated in different studies as possible therapeutics in the treatment of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). In the current drug repurposing study, we identified the leukotriene (D4) receptor antagonist montelukast as a novel agent that simultaneously targets two important drug targets of SARS-CoV-2. We initially demonstrated the dual inhibition profile of montelukast through multiscale molecular modeling studies. Next, we characterized its effect on both targets by different in vitro experiments including the enzyme (main protease) inhibition-based assay, surface plasmon resonance (SPR) spectroscopy, pseudovirus neutralization on HEK293T/hACE2+TMPRSS2, and virus neutralization assay using xCELLigence MP real-time cell analyzer. Our integrated in silico and in vitro results confirmed the dual potential effect of montelukast both on the main protease enzyme inhibition and virus entry into the host cell (spike/ACE2). The virus neutralization assay results showed that SARS-CoV-2 virus activity was delayed with montelukast for 20 h on the infected cells. The rapid use of new small molecules in the pandemic is very important today. Montelukast, whose pharmacokinetic and pharmacodynamic properties are very well characterized and has been widely used in the treatment of asthma since 1998, should urgently be completed in clinical phase studies and, if its effect is proved in clinical phase studies, it should be used against coronavirus disease 2019 (COVID-19).