Computer-Aided Discovery and Characterization of Novel Ebola Virus Inhibitors.

The Ebola virus (EBOV) causes severe human infection that lacks effective treatment. A recent screen identified a series of compounds that block EBOV-like particle entry into human cells. Using data from this screen, quantitative structure-activity relationship models were built and employed for virtual screening of a ∼17 million compound library. Experimental testing of 102 hits yielded 14 compounds with IC50 values under 10 μM, including several sub-micromolar inhibitors, and more than 10-fold selectivity against host cytotoxicity. These confirmed hits include FDA-approved drugs and clinical candidates with non-antiviral indications, as well as compounds with novel scaffolds and no previously known bioactivity. Five selected hits inhibited BSL-4 live-EBOV infection in a dose-dependent manner, including vindesine (0.34 μM). Additional studies of these novel anti-EBOV compounds revealed their mechanisms of action, including the inhibition of NPC1 protein, cathepsin B/L, and lysosomal function. Compounds identified in this study are among the most potent and well-characterized anti-EBOV inhibitors reported to date.

[1]  Yanli Wang,et al.  PubChem BioAssay: 2014 update , 2013, Nucleic Acids Res..

[2]  Alexander Tropsha,et al.  Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research , 2010, J. Chem. Inf. Model..

[3]  R. Lenk,et al.  Post-exposure efficacy of oral T-705 (Favipiravir) against inhalational Ebola virus infection in a mouse model. , 2014, Antiviral research.

[4]  Kathryn L. Schornberg,et al.  FDA-Approved Selective Estrogen Receptor Modulators Inhibit Ebola Virus Infection , 2013, Science Translational Medicine.

[5]  William A. Lee,et al.  Therapeutic efficacy of the small molecule GS-5734 against Ebola virus in rhesus monkeys , 2016, Nature.

[6]  Alexander Golbraikh,et al.  Data Set Modelability by QSAR , 2014, J. Chem. Inf. Model..

[7]  J. Dye,et al.  Niemann-Pick C1 Is Essential for Ebolavirus Replication and Pathogenesis In Vivo , 2015, mBio.

[8]  Eugene N Muratov,et al.  Per aspera ad astra: application of Simplex QSAR approach in antiviral research. , 2010, Future medicinal chemistry.

[9]  S. Ekins,et al.  Efficacy of Tilorone Dihydrochloride against Ebola Virus Infection , 2017, Antimicrobial Agents and Chemotherapy.

[10]  Jürgen Bajorath,et al.  How Frequently Are Pan-Assay Interference Compounds Active? Large-Scale Analysis of Screening Data Reveals Diverse Activity Profiles, Low Global Hit Frequency, and Many Consistently Inactive Compounds. , 2017, Journal of medicinal chemistry.

[11]  John J. Irwin,et al.  ZINC 15 – Ligand Discovery for Everyone , 2015, J. Chem. Inf. Model..

[12]  G. Olinger,et al.  The lipid moiety of brincidofovir is required for in vitro antiviral activity against Ebola virus. , 2016, Antiviral research.

[13]  Paul Shinn,et al.  Identification of 53 compounds that block Ebola virus-like particle entry via a repurposing screen of approved drugs , 2014, Emerging Microbes & Infections.

[14]  B. Garner,et al.  Attenuation of the lysosomal death pathway by lysosomal cholesterol accumulation , 2010, Alzheimer's & Dementia.

[15]  Gang Fu,et al.  PubChem Substance and Compound databases , 2015, Nucleic Acids Res..

[16]  Sean Ekins,et al.  Machine learning models identify molecules active against the Ebola virus in vitro , 2015, F1000Research.

[17]  P. Nordlund,et al.  Monitoring Drug Target Engagement in Cells and Tissues Using the Cellular Thermal Shift Assay , 2013, Science.

[18]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[19]  Marc C. Nicklaus,et al.  A New Approach to Radial Basis Function Approximation and Its Application to QSAR , 2014, J. Chem. Inf. Model..

[20]  Gianluca Pegoraro,et al.  Protection against filovirus diseases by a novel broad-spectrum nucleoside analogue BCX4430 , 2014, Nature.

[21]  A. García-Sastre,et al.  An enzymatic virus-like particle assay for sensitive detection of virus entry. , 2010, Journal of virological methods.

[22]  Maria A Miteva,et al.  Pan-assay interference compounds (PAINS) that may not be too painful for chemical biology projects. , 2017, Drug discovery today.

[23]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. , 2001, Advanced drug delivery reviews.

[24]  J. Dye,et al.  Ebola virus entry requires the cholesterol transporter Niemann-Pick C1 , 2011, Nature.

[25]  Christopher P Austin,et al.  δ-Tocopherol Reduces Lipid Accumulation in Niemann-Pick Type C1 and Wolman Cholesterol Storage Disorders* , 2012, The Journal of Biological Chemistry.

[26]  P. Shinn,et al.  Synergistic drug combination effectively blocks Ebola virus infection , 2017, Antiviral research.

[27]  I. Tetko,et al.  ISIDA - Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors , 2008 .

[28]  Y. Sakurai,et al.  Two-pore channels control Ebola virus host cell entry and are drug targets for disease treatment , 2015, Science.

[29]  G. Gao,et al.  Selective inhibition of Ebola entry with selective estrogen receptor modulators by disrupting the endolysosomal calcium , 2017, Scientific Reports.

[30]  E. Muratov,et al.  Investigation of anticancer activity of macrocyclic Schiff bases by means of 4D-QSAR based on simplex representation of molecular structure , 2005, SAR and QSAR in environmental research.

[31]  Darren R. Flower,et al.  On the Properties of Bit String-Based Measures of Chemical Similarity , 1998, J. Chem. Inf. Comput. Sci..

[32]  E. Abel,et al.  Lipids, lysosomes, and autophagy , 2016, Journal of Lipid Research.

[33]  J. Baell,et al.  New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. , 2010, Journal of medicinal chemistry.

[34]  Alexey V Zakharov,et al.  Quantitative prediction of antitarget interaction profiles for chemical compounds. , 2012, Chemical research in toxicology.

[35]  A. Green Ebola outbreak in the DR Congo , 2017, The Lancet.

[36]  Alexander Tropsha,et al.  Phantom PAINS: Problems with the Utility of Alerts for Pan-Assay INterference CompoundS , 2017, J. Chem. Inf. Model..

[37]  J. Dye,et al.  Ebola virus entry requires the host‐programmed recognition of an intracellular receptor , 2012, The EMBO journal.

[38]  Stephen J. Capuzzi,et al.  QSAR Modeling of Tox21 Challenge Stress Response and Nuclear Receptor Signaling Toxicity Assays , 2016, Front. Environ. Sci..

[39]  D. Stuart,et al.  Target Identification and Mode of Action of Four Chemically Divergent Drugs against Ebolavirus Infection , 2017, Journal of medicinal chemistry.

[40]  Alexander Tropsha,et al.  Chembench: a cheminformatics workbench , 2010, Bioinform..

[41]  Victor Kuzmin,et al.  Hierarchical QSAR technology based on the Simplex representation of molecular structure , 2008, J. Comput. Aided Mol. Des..

[42]  E. Burd Ebola Virus: a Clear and Present Danger , 2014, Journal of Clinical Microbiology.

[43]  S. Whelan,et al.  Endosomal Proteolysis of the Ebola Virus Glycoprotein Is Necessary for Infection , 2005, Science.

[44]  T. Brummelkamp,et al.  Emerging intracellular receptors for hemorrhagic fever viruses. , 2015, Trends in microbiology.

[45]  Patrick R. Griffin,et al.  PubChem promiscuity: a web resource for gathering compound promiscuity data from PubChem , 2012, Bioinform..

[46]  Ruili Huang,et al.  A Grid Algorithm for High Throughput Fitting of Dose-Response Curve Data , 2010, Current chemical genomics.

[47]  Christopher P Austin,et al.  Quantitative analyses of aggregation, autofluorescence, and reactivity artifacts in a screen for inhibitors of a thiol protease. , 2010, Journal of medicinal chemistry.

[48]  Alexander Tropsha,et al.  Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation , 2016, J. Chem. Inf. Model..

[49]  A. McMahon,et al.  Hedgehog Signaling: From Basic Biology to Cancer Therapy. , 2017, Cell chemical biology.

[50]  Karina Martínez Mayorga Database fingerprint (DFP): an approach to represent molecular databases , 2017 .

[51]  Alexander Tropsha,et al.  Curation of chemogenomics data. , 2015, Nature chemical biology.

[52]  Alexander Tropsha,et al.  Best Practices for QSAR Model Development, Validation, and Exploitation , 2010, Molecular informatics.

[53]  Claire Marie Filone,et al.  Small molecule inhibitors reveal Niemann-Pick C1 is essential for ebolavirus infection , 2011, Nature.

[54]  Alexander Tropsha,et al.  Chembench: A Publicly Accessible, Integrated Cheminformatics Portal , 2017, J. Chem. Inf. Model..