Pyronaridine Protects Against SARS-CoV-2 in Mouse (preprint)

There are currently relatively few small-molecule antiviral drugs that are either approved or emergency approved for use against SARS-CoV-2. One of these is remdesivir, which was originally repurposed from its use against Ebola and functions by causing early RNA chain termination. We used this as justification to evaluate three molecules we had previously identified computationally with antiviral activity against Ebola and Marburg. Out of these we previously identified pyronaridine, which inhibited the SARS-CoV-2 replication in A549-ACE2 cells. Herein, 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. Furthermore, the pyronaridine treated group reduced lung pathology, which was also associated with significant reduction in the levels of pro-inflammatory cytokines/chemokine and cell infiltration. Notably, pyronaridine inhibited the viral PLpro activity in vitro (IC50 of 1.8 {micro}M) without any effect on Mpro, indicating a possible molecular mechanism involved in its ability to inhibit SARS-CoV-2 replication. Interestingly, pyronaridine also selectively inhibits the host kinase CAMK1 (IC50 of 2.4 {micro}M). 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. One sentence summaryThere is currently intense interest in discovering small molecules with direct antiviral activity against the severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2). Pyronaridine, an antiviral drug with in vitro activity against Ebola, Marburg and SARS-CoV-2 has now statistically significantly reduced the viral load in mice along with IL-6, TNF-, and IFN-{beta} ultimately demonstrating a protective effect against lung damage by infection to provide a new potential treatment for testing clinically.

Repurposing the Ebola and Marburg Virus Inhibitors Tilorone, Quinacrine and Pyronaridine: In vitro Activity Against SARS-CoV-2 and Potential Mechanisms (preprint)

SARS-CoV-2 is a newly identified virus that has resulted in over 1.3 M deaths globally and over 59 M cases globally to date. 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 virus 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 virus would seem 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 virus in vitro in HeLa cells and of mouse adapted Ebola virus in mouse 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 have also tested them in a pseudovirus assay and used microscale thermophoresis to test the binding of these molecules to the spike protein. They bind to spike RBD protein with Kd values of 339 nM and 647 nM, respectively. Human Cmax for pyronaridine and quinacrine is greater than the IC50 hence justifying in vivo evaluation. We also provide novel insights into their mechanism which is likely lysosomotropic.

Quantum Machine Learning for Drug Discovery (preprint)

The growing public and private datasets focused on small molecules screened against biological targets or whole organisms 1 provides a wealth of drug discovery relevant data. Increasingly this is used to create machine learning models which can be used for enabling target-based design 2-4, predict on- or off-target effects and create scoring functions 5,6. 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 datasets and 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, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on QC. Here we show how to achieve compression with datasets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands (whole cell screening datasets for plague and M. tuberculosis) with SVM and data re-uploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This illustrates a quantum advantage for drug discovery to build upon in future.

Machine Learning Models Identify Inhibitors of SARS-CoV-2 (preprint)

With the ongoing SARS-CoV-2 pandemic there is an urgent need for the discovery of a treatment for the coronavirus disease (COVID-19). Drug repurposing is one of the most rapid strategies for addressing this need and numerous compounds have been selected for in vitro testing by several groups already. 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, CPI1062 and CPI1155 showed antiviral activity in HeLa-ACE2 cell-based assays and represent potential repurposing opportunities for COVID-19. This approach can be greatly expanded to exhaustively virtually screen available molecules with predicted activity against this virus as well as a prioritization tool for SARS-CoV-2 antiviral drug discovery programs. The very latest model for SARS-CoV-2 is available at www.assaycentral.org.Competing Interest StatementSE is CEO and owner of Collaborations Pharmaceuticals, Inc. DHF, KMZ, TRL, AP are employees of Collaborations Pharmaceuticals, Inc.View Full Text