Discovery of PLpro and Mpro Inhibitors for SARS-CoV-2.

There are very few small-molecule antivirals for SARS-CoV-2 that are either currently approved (or emergency authorized) in the US or globally, including remdesivir, molnupiravir, and paxlovid. The increasing number of SARS-CoV-2 variants that have appeared since the outbreak began over three years ago raises the need for continual development of updated vaccines and orally available antivirals in order to fully protect or treat the population. The viral main protease (Mpro) and the papain-like protease (PLpro) are key for viral replication; therefore, they represent valuable targets for antiviral therapy. We herein describe an in vitro screen performed using the 2560 compounds from the Microsource Spectrum library against Mpro and PLpro in an attempt to identify additional small-molecule hits that could be repurposed for SARS-CoV-2. We subsequently identified 2 hits for Mpro and 8 hits for PLpro. One of these hits was the quaternary ammonium compound cetylpyridinium chloride with dual activity (IC50 = 2.72 ± 0.09 µM for PLpro and IC50 = 7.25 ± 0.15 µM for Mpro). A second inhibitor of PLpro was the selective estrogen receptor modulator raloxifene (IC50 = 3.28 ± 0.29 µM for PLpro and IC50 = 42.8 ± 6.7 µM for Mpro). We additionally tested several kinase inhibitors and identified olmutinib (IC50 = 0.54 ± 0.04 µM), bosutinib (IC50 = 4.23 ± 0.28 µM), crizotinib (IC50 = 3.81 ± 0.04 µM), and dacominitinib (IC50 = IC50 3.33 ± 0.06 µM) as PLpro inhibitors for the first time. In some cases, these molecules have also been tested by others for antiviral activity for this virus, or we have used Calu-3 cells infected with SARS-CoV-2. The results suggest that approved drugs can be identified with promising activity against these proteases, and in several cases we or others have validated their antiviral activity. The additional identification of known kinase inhibitors as molecules targeting PLpro may provide new repurposing opportunities or starting points for chemical optimization.

First crystal structures of 1-deoxy-D-xylulose 5-phosphate synthase (DXPS) from Mycobacterium tuberculosis indicate a distinct mechanism of intermediate stabilization.

The development of drug resistance by Mycobacterium tuberculosis and other pathogenic bacteria emphasizes the need for new antibiotics. Unlike animals, most bacteria synthesize isoprenoid precursors through the MEP pathway. 1-Deoxy-D-xylulose 5-phosphate synthase (DXPS) catalyzes the first reaction of the MEP pathway and is an attractive target for the development of new antibiotics. We report here the successful use of a loop truncation to crystallize and solve the first DXPS structures of a pathogen, namely M. tuberculosis (MtDXPS). The main difference found to other DXPS structures is in the active site where a highly coordinated water was found, showing a new mechanism for the enamine-intermediate stabilization. Unlike other DXPS structures, a "fork-like" motif could be identified in the enamine structure, using a different residue for the interaction with the cofactor, potentially leading to a decrease in the stability of the intermediate. In addition, electron density suggesting a phosphate group could be found close to the active site, provides new evidence for the D-GAP binding site. These results provide the opportunity to improve or develop new inhibitors specific for MtDXPS through structure-based drug design.

Pyronaridine Protects against SARS-CoV-2 Infection in Mouse.

There are currently relatively few small-molecule antiviral drugs that are either approved or emergency-approved for use against severe acute respiratory coronavirus 2 (SARS-CoV-2). One of these is remdesivir, which was originally repurposed from its use against Ebola. We evaluated three molecules we had previously identified computationally with antiviral activity against Ebola and Marburg and identified pyronaridine, which inhibited the SARS-CoV-2 replication in A549-ACE2 cells. The in vivo efficacy of pyronaridine has now been assessed in a K18-hACE transgenic mouse model of COVID-19. Pyronaridine treatment demonstrated a statistically significant reduction of viral load in the lungs of SARS-CoV-2-infected mice, reducing lung pathology, which was also associated with significant reduction in the levels of pro-inflammatory cytokines/chemokine and cell infiltration. Pyronaridine inhibited the viral PLpro activity in vitro (IC50 of 1.8 µM) without any effect on Mpro, indicating a possible molecular mechanism involved in its ability to inhibit SARS-CoV-2 replication. We have also generated several pyronaridine analogs to assist in understanding the structure activity relationship for PLpro inhibition. Our results indicate that pyronaridine is a potential therapeutic candidate for COVID-19.

High-throughput Antiviral Assays to Screen for Inhibitors of Zika Virus Replication.

Antiviral drug discovery requires the development of reliable biochemical and cellular assays that can be performed in high-throughput screening (HTS) formats. The flavivirus non-structural (NS) proteins are thought to co-translationally assemble on the endoplasmic reticulum (ER) membranes, forming the replication complex (RC). The NS3 and NS5 are the most studied enzymes of the RC and constitute the main targets for drug development due to their crucial roles in viral genome replication. NS3 protease domain, which requires NS2B as its cofactor, is responsible for the cleavage of the immature viral polyprotein into the mature NS proteins, whereas NS5 RdRp domain is responsible for the RNA replication. Herein, we describe in detail the protocols used in replicon-based screenings and enzymatic assays to test large compound libraries for inhibitors of the Zika virus (ZIKV) replication. Replicons are self-replicating subgenomic systems expressed in mammalian cells, in which the viral structural genes are replaced by a reporter gene. The inhibitory effects of compounds on viral RNA replication can be easily evaluated by measuring the reduction in the reporter protein activity. The replicon-based screenings were performed using a BHK-21 ZIKV replicon cell line expressing Renilla luciferase as a reporter gene. To characterize the specific targets of identified compounds, we established in-vitro fluorescence-based assays for recombinantly expressed NS3 protease and NS5 RdRp. The proteolytic activity of the viral protease was measured by using the fluorogenic peptide substrate Bz-nKRR-AMC, while the NS5 RdRp elongation activity was directly detected by the increase of the fluorescent signal of SYBR Green I during RNA elongation, using the synthetic biotinylated self-priming template 3'UTR-U30 (5'-biotin-U30-ACUGGAGAUCGAUCUCCAGU-3').

Machine Learning Models Identify Inhibitors of SARS-CoV-2.

With the rapidly evolving SARS-CoV-2 variants of concern, there is an urgent need for the discovery of further treatments for the coronavirus disease (COVID-19). Drug repurposing is one of the most rapid strategies for addressing this need, and numerous compounds have already been selected for in vitro testing by several groups. These have led to a growing database of molecules with in vitro activity against the virus. Machine learning models can assist drug discovery through prediction of the best compounds based on previously published data. Herein, we have implemented several machine learning methods to develop predictive models from recent SARS-CoV-2 in vitro inhibition data and used them to prioritize additional FDA-approved compounds for in vitro testing selected from our in-house compound library. From the compounds predicted with a Bayesian machine learning model, lumefantrine, an antimalarial was selected for testing and showed limited antiviral activity in cell-based assays while demonstrating binding (Kd 259 nM) to the spike protein using microscale thermophoresis. Several other compounds which we prioritized have since been tested by others and were also found to be active in vitro. This combined machine learning and in vitro testing approach can be expanded to virtually screen available molecules with predicted activity against SARS-CoV-2 reference WIV04 strain and circulating variants of concern. In the process of this work, we have created multiple iterations of machine learning models that can be used as a prioritization tool for SARS-CoV-2 antiviral drug discovery programs. The very latest model for SARS-CoV-2 with over 500 compounds is now freely available at www.assaycentral.org.

Non-Toxic Dimeric Peptides Derived from the Bothropstoxin-I Are Potent SARS-CoV-2 and Papain-like Protease Inhibitors.

The COVID-19 outbreak has rapidly spread on a global scale, affecting the economy and public health systems throughout the world. In recent years, peptide-based therapeutics have been widely studied and developed to treat infectious diseases, including viral infections. Herein, the antiviral effects of the lysine linked dimer des-Cys11, Lys12,Lys13-(pBthTX-I)2K ((pBthTX-I)2K)) and derivatives against SARS-CoV-2 are reported. The lead peptide (pBthTX-I)2K and derivatives showed attractive inhibitory activities against SARS-CoV-2 (EC50 = 28-65 µM) and mostly low cytotoxic effect (CC50 > 100 µM). To shed light on the mechanism of action underlying the peptides' antiviral activity, the Main Protease (Mpro) and Papain-Like protease (PLpro) inhibitory activities of the peptides were assessed. The synthetic peptides showed PLpro inhibition potencies (IC50s = 1.0-3.5 µM) and binding affinities (Kd = 0.9-7 µM) at the low micromolar range but poor inhibitory activity against Mpro (IC50 > 10 µM). The modeled binding mode of a representative peptide of the series indicated that the compound blocked the entry of the PLpro substrate toward the protease catalytic cleft. Our findings indicated that non-toxic dimeric peptides derived from the Bothropstoxin-I have attractive cellular and enzymatic inhibitory activities, thereby suggesting that they are promising prototypes for the discovery and development of new drugs against SARS-CoV-2 infection.

Development of Machine Learning Models and the Discovery of a New Antiviral Compound against Yellow Fever Virus.

Yellow fever (YF) is an acute viral hemorrhagic disease transmitted by infected mosquitoes. Large epidemics of YF occur when the virus is introduced into heavily populated areas with high mosquito density and low vaccination coverage. The lack of a specific small molecule drug treatment against YF as well as for homologous infections, such as zika and dengue, highlights the importance of these flaviviruses as a public health concern. With the advancement in computer hardware and bioactivity data availability, new tools based on machine learning methods have been introduced into drug discovery, as a means to utilize the growing high throughput screening (HTS) data generated to reduce costs and increase the speed of drug development. The use of predictive machine learning models using previously published data from HTS campaigns or data available in public databases, can enable the selection of compounds with desirable bioactivity and absorption, distribution, metabolism, and excretion profiles. In this study, we have collated cell-based assay data for yellow fever virus from the literature and public databases. The data were used to build predictive models with several machine learning methods that could prioritize compounds for in vitro testing. Five molecules were prioritized and tested in vitro from which we have identified a new pyrazolesulfonamide derivative with EC50 3.2 µM and CC50 24 µM, which represents a new scaffold suitable for hit-to-lead optimization that can expand the available drug discovery candidates for YF.

Quantum Machine Learning Algorithms for Drug Discovery Applications.

The growing quantity of public and private data sets focused on small molecules screened against biological targets or whole organisms provides a wealth of drug discovery relevant data. This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large data sets with thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations. In the case of cheminformatics, which is widely used in drug discovery, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on a QC. Here, we show how to achieve compression with data sets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands of molecules (whole cell screening data sets for plague and M. tuberculosis) with SVM and the data reuploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This study illustrates the steps needed in order to be "quantum computer ready" in order to apply quantum computing to drug discovery and to provide the foundation on which to build this field.