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1.
ACS Infect Dis ; 8(6): 1147-1160, 2022 06 10.
Article in English | MEDLINE | ID: covidwho-1860283

ABSTRACT

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.


Subject(s)
COVID-19 , Hemorrhagic Fever, Ebola , Animals , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , COVID-19/drug therapy , Hemorrhagic Fever, Ebola/drug therapy , Mice , Naphthyridines , SARS-CoV-2
2.
J Chem Inf Model ; 61(9): 4224-4235, 2021 09 27.
Article in English | MEDLINE | ID: covidwho-1356531

ABSTRACT

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.


Subject(s)
COVID-19 , SARS-CoV-2 , Bayes Theorem , Humans , Machine Learning , Molecular Docking Simulation
3.
Molecules ; 26(16)2021 Aug 12.
Article in English | MEDLINE | ID: covidwho-1355016

ABSTRACT

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.


Subject(s)
Crotalid Venoms/chemistry , Dimerization , Papain/antagonists & inhibitors , Peptides/chemistry , Peptides/pharmacology , SARS-CoV-2/enzymology , Antiviral Agents/chemistry , Antiviral Agents/metabolism , Antiviral Agents/pharmacology , Molecular Docking Simulation , Papain/chemistry , Papain/metabolism , Peptides/metabolism , Protease Inhibitors/chemistry , Protease Inhibitors/metabolism , Protease Inhibitors/pharmacology , Protein Conformation , SARS-CoV-2/drug effects
4.
J Chem Inf Model ; 61(6): 2641-2647, 2021 06 28.
Article in English | MEDLINE | ID: covidwho-1241784

ABSTRACT

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.


Subject(s)
COVID-19 , Drug Discovery , Algorithms , Computing Methodologies , Humans , Machine Learning , Quantum Theory , SARS-CoV-2 , Support Vector Machine
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