Identification of potent inhibitors of SARS-CoV-2 infection by combined pharmacological evaluation and cellular network prioritization

Abstract

Pharmacologically active compounds with known biological targets were evaluated for inhibition of SARS-CoV-2 infection in cell and tissue models to help identify potent classes of active small molecules and to better understand host-virus interactions. We evaluated 6,710 clinical and preclinical compounds targeting 2,183 host proteins by immunocytofluorescence-based screening to identify SARS-CoV-2 infection inhibitors. Computationally integrating relationships between small molecule structure, dose-response antiviral activity, host target, and cell interactome produced cellular networks important for infection. This analysis revealed 389 small molecules with micromolar to low nanomolar activities, representing >12 scaffold classes and 813 host targets. Representatives were evaluated for mechanism of action in stable and primary human cell models with SARS-CoV-2 variants and MERS-CoV. One promising candidate, obatoclax, significantly reduced SARS-CoV-2 viral lung load in mice. Ultimately, this work establishes a rigorous approach for future pharmacological and computational identification of host factor dependencies and treatments for viral diseases.

Keywords: Bioinformatics; Pharmacoinformatics; Pharmacology; Virology.

Graphical abstract

Figure 1

HTS screen for inhibitors of SARS-CoV-2 (A) Composition of the Drug Repurposing Hub library based on ATC classifications for compounds (4,277 of 6710). (B) Workflow of immunofluorescence-based infection focus assay developed for the screening of SARS-CoV-2 inhibitors and secondary mechanistic assays performed on active compounds. (C) Dynamic range of the HTS assay evaluated using 5-μM E64d (Aloxistatin) versus 2% DMSO as vehicle. Upper panel: examples of microscope images of infected cells stained with SARS-CoV-2 N-specific antibody and cell nuclei stained with Hoechst 33342 for DMSO or E64d-treated cells. Scale bar is shown on left upper image and is 50 μm. Lower panel: outcomes for 190 replicate wells for each treatment are shown. The assay gave a Z-factor of 0.6 in 384 well plates and, thus, was suitable for HTS. (D) Examples of concentration response curve classes seen in the screen and cell images with N protein staining on the left and cell nuclei on the right. The indicated compounds show no effect (solcitinib), strong activity with no cell loss (E64d), weak reduction in infectivity and no cell loss (rimcazole), and reduction in infection that parallels cell loss that is likely owing to cytotoxicity (golgicide). Scale is the same as for images in part C.

Figure 2

Computational analysis of primary screen outcomes (A) Chart showing the active compounds by ATC classifiers for strongly active, weakly active, and cytotoxic compounds. The fraction of compounds in each category is shown as a percentage of all compounds in the library. (B) Pairwise comparison of active compounds based on computed structural similarity using Morgan fingerprints and Jaccard analysis. (C) 3D representation of structural similarity of active molecules based on the reduced dimensionality of the molecular bit vectors. Localized clusters of major structural classes are indicated by red circles and are approximate locations in the plot. Refer to Figure S3 for an interactive version with higher detail. (D) Relationship of enriched protein drug targets (red circles) or unenriched targets (pink circles) with active compounds (blue circles) for compounds with annotated targets in the DRH database. (E) Separation heatmap between the targets of different activity classes within the human protein–protein interaction network. Negative network separation values reflect overlapping neighborhoods. (F) Proximity Z-score between the drug targets of each category and the SARS-CoV-2 protein-binding host factors in the human protein-protein interaction network. Drugs with treatment activity have Z-scores close to zero, much lower than inactive drugs, indicating they hit targets in the network proximity of SARS-CoV-2-binding proteins.

Figure 3

Concentration response curves for active compounds and the measurement of small molecule effect on mRNA production from the virus subgenomic promoter by FISH assay in Huh7 cells (A) Examples of response curves are shown for the indicated active compounds using treatment concentrations ranging from 4 μM down to 0.2 nM. Each concentration was repeated in triplicate and average and standard deviations shown and normalized to vehicle (DMSO)-treated controls. (B) Virus mRNA production was measured by in situ oligonucleotide-based detection. Examples of images are shown for the indicated compounds. Cells were stained for production of virus mRNA encoding the N protein by the smiFISH method using virus gene-specific oligonucleotides and a Cy5-labeled oligonucleotide that bound to a shared complementary region on each (red). Cell nuclei were stained with Hoechst 33342 (blue). The scale bar in the top left image is 100 μm.

Figure 4

Effect of compounds on infection by virus glycoprotein pseudotyped viruses A549 cells expressing recombinant ACE2 protein were pretreated with each indicated compound at the EC₉₀ concentration from assays using a wild-type virus on VeroE6 cells. Lentivirus pseudotypes, encoding firefly luciferase, as a marker of infection, and bearing the SARS-CoV-2 or VSV glycoproteins (VSV-G) were used to transduce cells at 20 ng of p24 capsid equivalent per infection. Transduction efficiency was measured by the luciferase activity as relative light units (RLU) and normalized to DMSO-treated controls. Measurements used three replicates with the average and SD shown. Multiple t-tests compensating for false discovery (Q = 2) were used to identify significant differences (p < 0.05) between the VSV-G and SARS-CoV-2 pseudotype infection efficiencies indicated by ∗.

Figure 5

Inhibition of infection in primary human cell model and efficacy testing in the K18 ACE2 mouse model of disease (A) Primary intestinal epithelial cell cultures were challenged with SARS-CoV-2 and fixed after three days in formalin. Cells were stained for N protein using a specific antibody (red) and cell nuclei using Hoechst 33342 (blue) and examples of active compounds shown (left panel). Infection efficiency was measured by the total fluorescence relative to DMSO-treated controls and normalized to the cell nuclei signal (right panel). Each test was performed in triplicate with average and standard deviations shown. The scale bar in the top left image is 100 μm. (B) Male and female mice were challenged by the intranasal route with SARS-CoV-2 and starting 6 h after a daily treatment with obatoclax at 3 mg/kg. Virus load in the lungs (FFU/g tissue) was measured four days post-challenge. For each mouse tissue sample, two measurements were made and averaged. Two treated female mice gave virus loads below and at the limit of detection for the assay, respectively. Each was plotted at 50% LOD. (C) Beta and delta virus strains were tested for the susceptibility to small molecule treatments tested at the indicated doses (close to EC90) seen in the Washington strain using A549-ACE2 cells. Each was performed in triplicate and averages with standard deviations are shown.