In 2014, the outbreak of the Ebola virus (EBOV) in West Africa highlighted the need for broad-spectrum antiviral drugs for this and other emerging viruses. Several groups had previously performed high throughput screens in 2013 and identified FDA approved drugs (amodiaquine, chloroquine, clomiphene and toremifene) with in vitro growth inhibitory activities against EBOV. We used these compounds to create a computational pharmacophore to identify additional compounds to test in vitro. In addition, data from a published large scale high throughput screen performed by SRI International and Texas Biomedical Research Institute was used to create machine learning models and then subsequently used to score clinical compounds for testing. We have published on how these combined methods identified 3 compounds for testing which were ultimately found to be nM in vitro. One of these compounds is an antimalarial approved in Europe called pyronaridine. We propose to characterize the ADME and PK properties of this compound prior to determining its efficacy in a mouse model of the Ebola virus infection. Therefore the Aims of this R21 proposal will fill some of the gaps inherent in the published data on pyronaridine so far:
Aim 1. Perform preclinical in vitro characterization of pyronaridine.
Aim 2. Formulate pyronaridine and perform PK studies in mouse.
Aim 3. In vitro characterization of pyronaridine against multiple EBOV strains and in vivo efficacy in the mouse model of Ebola virus infection. The results of these aims will determine go/no go criteria for pursuing larger animal studies in non-human primates prior to clinical studies. In the light of a recent paper in the New England Journal of Medicine showing a clinical observation that EBOV patients treated with artesunate-amodiaquine had a 31% higher survival rate than those treated with artemether- lumefantrine 2, there will be considerable interest in evaluating antimalarials against Ebola. Our proposal to consider testing the efficacy in the mouse EBOV model using pyronaridine (which is used as artesunate- pyronaridine (Pyramax) and would be readily accessible in the clinic), presents a rapid approach to leverage the aforementioned clinical observations with a more potent compound. Pyronaridine also has additional benefits of tolerability which may be important in this patient population.

Public Health Relevance

Preliminary clinical data showed that Ebola virus (EBOV) patients treated with the antimalarials artesunate- amodiaquine had a higher survival rate than those treated with artemether-lumefantrine, in agreement with the in vitro EC50 for amodiaquine EC50 of 2.6M. The antimalarial pyronaridine, a structural analog of amodiaquine, was identified by a computational repurposing strategy and further shown to have an EC50 of 420 nM against EBOV in vitro. We now propose to fully characterize this compound using standard preclinical ADME assays prior to mouse pharmacokinetic analysis, determine broad-spectrum applicability against multiple EBOV strains and ultimately in vivo efficacy testing in the mouse Ebola virus model prior to testing in a non-human primate model. Our aim is to show whether Pyronaridine is a viable clinical candidate to treat patients infected with EBOV.

National Institute of Health (NIH)
National Center for Advancing Translational Sciences (NCATS)
Exploratory/Developmental Grants (R21)
Project #
Application #
Study Section
Special Emphasis Panel (ZTR1-DPI-2 (01))
Program Officer
Austin, Bobbie Ann
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Collaborations Pharmaceuticals, Inc.
Fuquay Varina
United States
Zip Code
Baker, Nancy C; Ekins, Sean; Williams, Antony J et al. (2018) A bibliometric review of drug repurposing. Drug Discov Today 23:661-672
Ekins, Sean; Lingerfelt, Mary A; Comer, Jason E et al. (2018) Efficacy of Tilorone Dihydrochloride against Ebola Virus Infection. Antimicrob Agents Chemother 62:
Korotcov, Alexandru; Tkachenko, Valery; Russo, Daniel P et al. (2017) Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets. Mol Pharm 14:4462-4475