Therapeutic candidates for the Zika virus identified by a high-throughput screen for Zika protease inhibitors

Abstract

When Zika virus emerged as a public health emergency there were no drugs or vaccines approved for its prevention or treatment. We used a high-throughput screen for Zika virus protease inhibitors to identify several inhibitors of Zika virus infection. We expressed the NS2B-NS3 Zika virus protease and conducted a biochemical screen for small-molecule inhibitors. A quantitative structure-activity relationship model was employed to virtually screen ∼138,000 compounds, which increased the identification of active compounds, while decreasing screening time and resources. Candidate inhibitors were validated in several viral infection assays. Small molecules with favorable clinical profiles, especially the five-lipoxygenase-activating protein inhibitor, MK-591, inhibited the Zika virus protease and infection in neural stem cells. Members of the tetracycline family of antibiotics were more potent inhibitors of Zika virus infection than the protease, suggesting they may have multiple mechanisms of action. The most potent tetracycline, methacycline, reduced the amount of Zika virus present in the brain and the severity of Zika virus-induced motor deficits in an immunocompetent mouse model. As Food and Drug Administration-approved drugs, the tetracyclines could be quickly translated to the clinic. The compounds identified through our screening paradigm have the potential to be used as prophylactics for patients traveling to endemic regions or for the treatment of the neurological complications of Zika virus infection.

Keywords: Zika virus; encephalitis; flavivirus; high-throughput screening; serine protease.

Fig. 1.

Identification of Zika virus inhibitor workflow. Three screening strategies were used to identify Zika virus inhibitors. Strategy 1: Hits from the Pilot protease screen were directly tested in NSCs. Strategy 2: Hits from the qHTS, prior art library, and virtual screen using QSAR screens were tested in Vero cells, then those compounds that were active were tested in NSCs. Strategy 3: Hits from the qHTS, prior art library screen, and QSAR were directly tested in NSCs. (A) Inhibition of the Zika virus NS2B-NS3 protease. The number of compounds tested in the primary screen is indicated, followed by the number of active compounds in each assay, and finally the number of compounds that were selected from these assays (based on potency and confirmation). (B) Inhibition of Zika virus in Vero cells using Zika virus-RLuc, Zika virus-mCherry, and qRT-PCR. The number of active compounds (and the number of compounds tested) are indicated. (C) Inhibition of Zika virus in NSCs. The number of compounds that confirmed in each NSC infection assay and names of our top candidate inhibitors identified through each strategy are indicated. Of the 11 tetracyclines tested, 4 inhibited Zika virus without toxicity (ICC: immunocytochemistry); 1 was chosen to confirm by qRT-PCR. Of the 18 Vero cell assay inhibitors, 5 inhibited Zika virus without toxicity; 1 was chosen to confirm by qRT-PCR. Of the 50 additional biochemical inhibitors selected, 6 inhibited Zika virus without toxicity; 1 was chosen to confirm by qRT-PCR.

Fig. 2.

Inhibition of Zika virus infection and Zika virus protease. (A and B) Drugs were preincubated for 1 h with NSCs prior to Zika virus infection (French Polynesian strain). At 48- or 72-hpi, supernatant was collected, and cells were fixed with 4% PFA. Percent inhibition is relative to DMSO-treated, Zika virus-infected cells. (A) Immunostaining with anti-Flavivirus envelope 4G2 antibody was used to quantify the rate of infection, which was normalized to the total number of cells (Hoechst nuclear stain). Drug-induced toxicity was observed at 20 µM of MK591 and JNJ404 and 10 and 20 µM of D942. Data represent mean ± SD from three independent experiments. (B) qRT-PCR for Zika virus RNA was used to quantify the rate of infection in cell-free supernatant. (C and D) Inhibition of the linked Zika virus protease. Each experiment was performed in quadruplicates. Data represent mean ± SEM of three independent experiments. (C) MK-591 and JNJ-404 (30 nM to 100 µM) inhibit Zika virus protease; Bz-Nle-KRR-AMC (Bz: benzoyl; AMC: 7-amino-4-methylcoumarin) was used as a substrate. (D) Tetracyclines (300 nM to 1 mM) weakly inhibit Zika virus protease; Ac-VKTGKR-AMC was used as a substrate. (E) NSCs were treated with a combination of 5 µM of methacycline and either 5 µM of MK-591 or 5 µM of JNJ-404 followed by infection with Zika virus. Infection was monitored by immunostaining. Data represent mean ± SD from three independent experiments. Significance was calculated using an unpaired, two-tailed t test: ****P < 0.0001; *P < 0.05. (F–K) Kinetic experiments with the linked Zika virus protease (Ac-VKTGKR-AMC [Ac: acetyl] or Bz-Nle-KRR-AMC); values were normalized to no-enzyme and no-inhibitor controls. (F) Methacycline-inhibited, (G) MK-591-inhibited, and (H) JNJ-404-inhibited enzyme velocity could not be restored by additional substrate. The data were best fit to the noncompetitive inhibition model. Jump dilution analysis indicated (I) methacycline (velocity ∼85%), (J) MK-591 (velocity ∼81%), and (K) JNJ-404 (velocity ∼85%) are reversible inhibitors with a fast dissociation rate (velocity is closer to 90% of the uninhibited enzyme than 10%). Data represents mean ± SD of triplicate samples.

Fig. 3.

Chemical structures and IC₅₀ (μM) values for selected hits in the linked Zika virus protease (Protease) and Zika virus infection assay in NSCs. (A) FLAP inhibitors, (B) γ-secretase modulators, (C) tetracyclines, (D) AMPK activator, (E) farnesoid X receptor agonists, (F) liver X receptor agonist, (G) retinoic acid analog, (H) mineralocorticoid receptor antagonist, (I) compound from QSAR screen. An asterisk (*) indicates IC₅₀ values are a single data point from qHTS. All other values are an average of at least three independent experiments from the confirmation testing of commercial compounds. Protease and NSC IC₅₀ values were calculated in GraphPad Prism using the equation: Log(inhibitor) vs. normalized response–variable slope.

Fig. 4.

Methacycline reduces Zika virus (ZIKV) infection in a mouse model. (A) Overview of in vivo protocol. Mice were treated with methacycline daily from birth, infected with Zika virus at postnatal day 1 (PND1), and killed at PND12. (B) Zika virus-infected mice gain weight at a slower rate. Methacycline treatment improved (C) trunk control, (D and E) muscle strength, and (F) balance. Methacycline reduced (G) Zika virus RNA levels, visualized using a Zika virus-specific probe that targets the 219–5443 fragment of the consensus sequence and quantified by qRT-PCR (SI Appendix, Fig. S8A), and Zika virus protein levels, visualized using (H) chromogenic immunohistochemistry and (I) fluorescent immunohistochemistry images and quantified with MetaMorph software. (J) Methacycline prevented Purkinjie cell loss. (K) Methacycline decreased astrocyte activation (GFAP) and increased neuronal cell staining (NeuN). Quantified with MetaMorph software (SI Appendix, Fig. S8 B–D). All data were analyzed with GraphPad Prism. Differences between means were assessed by one-way ANOVA followed by post hoc tests. ****P < 0.0001; ***P < 0.001; **P < 0.01; *P < 0.05. The mean is displayed, and all error bars represent SE. We analyzed 10 mock, 7 mock and methacycline, 11 Zika, and 11 Zika and methacycline animals.