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Article Cite This: ACS Omega 2019, 4, 2353−2361
http://pubs.acs.org/journal/acsodf
Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads Manu Anantpadma,†,#,∇ Thomas Lane,‡,# Kimberley M. Zorn,‡ Mary A. Lingerfelt,‡ Alex M. Clark,§ Joel S. Freundlich,∥ Robert A. Davey,†,∇ Peter B. Madrid,⊥ and Sean Ekins*,‡
ACS Omega 2019.4:2353-2361. Downloaded from pubs.acs.org by 188.68.0.66 on 01/31/19. For personal use only.
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Department of Virology and Immunology, Texas Biomedical Research Institute, 8715 West Military Drive, San Antonio, Texas 78227, United States ‡ Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States § Molecular Materials Informatics, Inc., 1900 St. Jacques #302, Montreal H3J 2S1, Quebec, Canada ∥ Departments of Pharmacology, Physiology, and Neuroscience & Medicine, Center for Emerging and Reemerging Pathogens, Rutgers UniversityNew Jersey Medical School, 185 South Orange Avenue, Newark, New Jersey 07103, United States ⊥ SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025, United States S Supporting Information *
ABSTRACT: We have previously described the first Bayesian machine learning models from FDA-approved drug screens, for identifying compounds active against the Ebola virus (EBOV). These models led to the identification of three active molecules in vitro: tilorone, pyronaridine, and quinacrine. A follow-up study demonstrated that one of these compounds, tilorone, has 100% in vivo efficacy in mice infected with mouse-adapted EBOV at 30 mg/ kg/day intraperitoneal. This suggested that we can learn from the published data on EBOV inhibition and use it to select new compounds for testing that are active in vivo. We used these previously built Bayesian machine learning EBOV models alongside our chemical insights for the selection of 12 molecules, absent from the training set, to test for in vitro EBOV inhibition. Nine molecules were directly selected using the model, and eight of these molecules possessed a promising in vitro activity (EC50 < 15 μM). Three further compounds were selected for an in vitro evaluation because they were antimalarials, and compounds of this class like pyronaridine and quinacrine have previously been shown to inhibit EBOV. We identified the antimalarial drug arterolane (IC50 = 4.53 μM) and the anticancer clinical candidate lucanthone (IC50 = 3.27 μM) as novel compounds that have EBOV inhibitory activity in HeLa cells and generally lack cytotoxicity. This work provides further validation for using machine learning and medicinal chemistry expertize to prioritize compounds for testing in vitro prior to more costly in vivo tests. These studies provide further corroboration of this strategy and suggest that it can likely be applied to other pathogens in the future.
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INTRODUCTION In 2014, the outbreak of Ebola virus (EBOV) in West Africa highlighted the direct need for broad-spectrum antiviral drugs for this and other emerging viruses1,2 and remains currently relevant. EBOV was the causative agent responsible for over 11 3103 deaths in 10 countries, making it one of the deadliest viral pathogens in modern human history based on the percentage fatality.1 Although no drug has been approved for the treatment of EBOV, multiple medium and high-throughput screens (HTS) of large molecular libraries4−10 have identified many small molecules effective against EBOV in vitro11−13 (Table S1), but so far few have advanced to clinical testing. This is still important as we are currently in the midst of an EBOV outbreak in the Congo. To date numerous compounds have been validated in vivo1 (mouse and or nonhuman primate), and two small molecules are in early clinical trials (BCX443014 and favipiravir15). © 2019 American Chemical Society
BCX4430 is an adenosine analog that inhibits RNA transcription, whereas favipiravir is a nucleotide analog that inhibits viral RNA-dependent RNA polymerase. In contrast, several nonsmall molecule interventions have also been brought to clinical trial, including treatments using phosphorodiamidate morpholino oligomers16 (AVI-6002), chimeric mouse−human antibodies,17 and a rVSV-GP vaccine.18 Alternative approaches to find treatments for EBOV include repurposing FDA-approved drugs for novel therapies, and this methodology has several advantages over the traditional de novo approach.13,19,20 These drugs are already well characterized and much is known about their absorption, distribution, metabolism, and excretion (ADME) and toxicity Received: October 25, 2018 Accepted: January 17, 2019 Published: January 30, 2019 2353
DOI: 10.1021/acsomega.8b02948 ACS Omega 2019, 4, 2353−2361
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Figure 1. Assay Central model ROC plots and 5-fold cross-validation statistics for (A) entry and (B) replication Bayesian machine learning models.
described a set of compounds identified by another computational model and then tested in vitro which led to 14 hits with
