Transporter Inhibition Profile for the Antivirals Tilorone, Quinacrine and Pyronaridine.

Pyronaridine, tilorone and quinacrine are cationic molecules that have in vitro activity against Ebola, SARS-CoV-2 and other viruses. All three molecules have also demonstrated in vivo activity against Ebola in mice, while pyronaridine showed in vivo efficacy against SARS-CoV-2 in mice. We have recently tested these molecules and other antivirals against human organic cation transporters (OCTs) and apical multidrug and toxin extruders (MATEs). Quinacrine was found to be an inhibitor of OCT2, while tilorone and pyronaridine were less potent, and these displayed variability depending on the substrate used. To assess whether any of these three molecules have other potential interactions with additional transporters, we have now screened them at 10 µM against various human efflux and uptake transporters including P-gp, OATP1B3, OAT1, OAT3, MRP1, MRP2, MRP3, BCRP, as well as confirmational testing against OCT1, OCT2, MATE1 and MATE2K. Interestingly, in this study tilorone appears to be a more potent inhibitor of OCT1 and OCT2 than pyronaridine or quinacrine. However, both pyronaridine and quinacrine appear to be more potent inhibitors of MATE1 and MATE2K. None of the three compounds inhibited MRP1, MRP2, MRP3, OAT1, OAT3, P-gp or OATP1B3. Similarly, we previously showed that tilorone and pyronaridine do not inhibit OATP1B1 and have confirmed that quinacrine behaves similarly. In total, these observations suggest that the three compounds only appear to interact with OCTs and MATEs to differing extents, suggesting they may be involved in fewer clinically relevant drug-transporter interactions involving pharmaceutical substrates of the other major transporters tested.

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.

Defending Antiviral Cationic Amphiphilic Drugs That May Cause Drug-Induced Phospholipidosis.

A recent publication in Science has proposed that cationic amphiphilic drugs repurposed for COVID-19 typically use phosholipidosis as their antiviral mechanism of action in cells but will have no in vivo efficacy. On the contrary, our viewpoint, supported by additional experimental data for similar cationic amphiphilic drugs, indicates that many of these molecules have both in vitro and in vivo efficacy with no reported phospholipidosis, and therefore, this class of compounds should not be avoided but further explored, as we continue the search for broad spectrum antivirals.

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.

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.

Repurposing the Ebola and Marburg Virus Inhibitors Tilorone, Quinacrine, and Pyronaridine: In Vitro Activity against SARS-CoV-2 and Potential Mechanisms.

Severe acute respiratory coronavirus 2 (SARS-CoV-2) is a newly identified virus that has resulted in over 2.5 million deaths globally and over 116 million cases globally in March, 2021. Small-molecule inhibitors that reverse disease severity have proven difficult to discover. One of the key approaches that has been widely applied in an effort to speed up the translation of drugs is drug repurposing. A few drugs have shown in vitro activity against Ebola viruses and demonstrated activity against SARS-CoV-2 in vivo. Most notably, the RNA polymerase targeting remdesivir demonstrated activity in vitro and efficacy in the early stage of the disease in humans. Testing other small-molecule drugs that are active against Ebola viruses (EBOVs) would appear a reasonable strategy to evaluate their potential for SARS-CoV-2. We have previously repurposed pyronaridine, tilorone, and quinacrine (from malaria, influenza, and antiprotozoal uses, respectively) as inhibitors of Ebola and Marburg viruses in vitro in HeLa cells and mouse-adapted EBOV in mice in vivo. We have now tested these three drugs in various cell lines (VeroE6, Vero76, Caco-2, Calu-3, A549-ACE2, HUH-7, and monocytes) infected with SARS-CoV-2 as well as other viruses (including MHV and HCoV 229E). The compilation of these results indicated considerable variability in antiviral activity observed across cell lines. We found that tilorone and pyronaridine inhibited the virus replication in A549-ACE2 cells with IC50 values of 180 nM and IC50 198 nM, respectively. We used microscale thermophoresis to test the binding of these molecules to the spike protein, and tilorone and pyronaridine bind to the spike receptor binding domain protein with K d values of 339 and 647 nM, respectively. Human Cmax for pyronaridine and quinacrine is greater than the IC50 observed in A549-ACE2 cells. We also provide novel insights into the mechanism of these compounds which is likely lysosomotropic.

Tilorone: a Broad-Spectrum Antiviral Invented in the USA and Commercialized in Russia and beyond.

For the last 50 years we have known of a broad-spectrum agent tilorone dihydrochloride (Tilorone). This is a small-molecule orally bioavailable drug that was originally discovered in the USA and is currently used clinically as an antiviral in Russia and the Ukraine. Over the years there have been numerous clinical and non-clinical reports of its broad spectrum of antiviral activity. More recently we have identified additional promising antiviral activities against Middle East Respiratory Syndrome, Chikungunya, Ebola and Marburg which highlights that this old drug may have other uses against new viruses. This may in turn inform the types of drugs that we need for virus outbreaks such as for the new coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Tilorone has been long neglected by the west in many respects but it deserves further reassessment in light of current and future needs for broad-spectrum antivirals.