FDA approved drugs as potential Ebola treatments

Inhibition of in vitro Ebola infection by anti-parasitic quinoline derivatives

There continues to be no approved drugs for the treatment of Ebola virus disease (EVD). Despite a number of candidate drugs showing limited efficacy in vitro and/or in non-human primate studies, EVD continues to plaque certain areas of Africa without any efficacious treatments yet available. Recently, we have been exploring the potential for anti-malarial drugs to inhibit an in vitro model of Ebola Zaire replication using a transcription-competent virus-like particle (trVLP) assay. We examined the efficacy of chloroquine, amodiaquine and 36 novel anti-parasite quinoline derivatives at inhibiting Ebola virus replication. Drug efficacy was tested by trVLP assay and toxicity by MTT assay. Both chloroquine and amodiaquine were effective for inhibition of Ebola virus replication without significant toxicity. The half-maximal inhibitory concentration (IC ₅₀) of chloroquine and amodiaquine to inhibit Ebola virus replication were IC _{50, Chl} = 3.95 µM and IC _{50, Amo} = 1.45 µM, respectively. Additionally, three novel quinoline derivatives were identified as having inhibitory activity and low toxicity for Ebola trVLP replication, with 2NH2Q being the most promising derivative, with an IC ₅₀ of 4.66 µM. Quinoline compounds offer many advantages for disease treatment in tropical climates as they are cheap to produce, easy to synthesize and chemically stable. In this report, we have demonstrated the potential of anti-parasite quinolines for further investigation for use in EVD.

Déjà vu: Stimulating open drug discovery for SARS-CoV-2

In the past decade we have seen two major Ebola virus outbreaks in Africa, the Zika virus in Brazil and the Americas and the current pandemic of coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). There is a strong sense of déjà vu because there are still no effective treatments. In the COVID-19 pandemic, despite being a new virus, there are already drugs suggested as active in in vitro assays that are being repurposed in clinical trials. Promising SARS-CoV-2 viral targets and computational approaches are described and discussed. Here, we propose, based on open antiviral drug discovery approaches for previous outbreaks, that there could still be gaps in our approach to drug discovery.

Contemporary Anti-Ebola Drug Discovery Approaches and Platforms

The Ebola virus has a grave potential to destabilize civil society as we know it. The past few deadly Ebola outbreaks were unprecedented in size: The 2014-15 Ebola West Africa outbreak saw the virus spread from the epicenter through to Guinea, Sierra Leone, Nigeria, Congo, and Liberia. The 2014-15 Ebola West Africa outbreak was associated with almost 30,000 suspected or confirmed cases and over 11,000 documented deaths. The more recent 2018 outbreak in the Democratic Republic of Congo has so far resulted in 216 suspected or confirmed cases and 139 deaths. There is a general acceptance within the World Health Organization (WHO) and the Ebola outbreak response community that future outbreaks will become increasingly more frequent and more likely to involve intercontinental transmission. The magnitude of the recent outbreaks demonstrated in dramatic fashion the shortcomings of our mass casualty disease response capabilities and lack of therapeutic modalities for supporting Ebola outbreak prevention and control. Currently, there are no approved drugs although vaccines for human Ebola virus infection are in the trial phases and some potential treatments have been field tested most recently in the Congo Ebola outbreak. Treatment is limited to pain management and supportive care to counter dehydration and lack of oxygen. This underscores the critical need for effective antiviral drugs that specifically target this deadly disease. This review examines the current approaches for the discovery of anti-Ebola small molecule or biological therapeutics, their viral targets, mode of action, and contemporary platforms, which collectively form the backbone of the anti-Ebola drug discovery pipeline.

Efficacy of Tilorone Dihydrochloride against Ebola Virus Infection

Tilorone dihydrochloride (tilorone) is a small-molecule, orally bioavailable drug that is used clinically as an antiviral outside the United States. A machine-learning model trained on anti-Ebola virus (EBOV) screening data previously identified tilorone as a potent in vitro EBOV inhibitor, making it a candidate for the treatment of Ebola virus disease (EVD). In the present study, a series of in vitro ADMET (absorption, distribution, metabolism, excretion, toxicity) assays demonstrated the drug has excellent solubility, high Caco-2 permeability, was not a P-glycoprotein substrate, and had no inhibitory activity against five human CYP450 enzymes (3A4, 2D6, 2C19, 2C9, and 1A2). Tilorone was shown to have 52% human plasma protein binding with excellent plasma stability and a mouse liver microsome half-life of 48 min. Dose range-finding studies in mice demonstrated a maximum tolerated single dose of 100 mg/kg of body weight. A pharmacokinetics study in mice at 2- and 10-mg/kg dose levels showed that the drug is rapidly absorbed, has dose-dependent increases in maximum concentration of unbound drug in plasma and areas under the concentration-time curve, and has a half-life of approximately 18 h in both males and females, although the exposure was ∼2.5-fold higher in male mice. Tilorone doses of 25 and 50 mg/kg proved efficacious in protecting 90% of mice from a lethal challenge with mouse-adapted with once-daily intraperitoneal (i.p.) dosing for 8 days. A subsequent study showed that 30 mg/kg/day of tilorone given i.p. starting 2 or 24 h postchallenge and continuing through day 7 postinfection was fully protective, indicating promising activity for the treatment of EVD.

The new alchemy: Online networking, data sharing and research activity distribution tools for scientists

There is an abundance of free online tools accessible to scientists and others that can be used for online networking, data sharing and measuring research impact. Despite this, few scientists know how these tools can be used or fail to take advantage of using them as an integrated pipeline to raise awareness of their research outputs. In this article, the authors describe their experiences with these tools and how they can make best use of them to make their scientific research generally more accessible, extending its reach beyond their own direct networks, and communicating their ideas to new audiences. These efforts have the potential to drive science by sparking new collaborations and interdisciplinary research projects that may lead to future publications, funding and commercial opportunities. The intent of this article is to: describe some of these freely accessible networking tools and affiliated products; demonstrate from our own experiences how they can be utilized effectively; and, inspire their adoption by new users for the benefit of science.

Machine learning models identify molecules active against the Ebola virus in vitro

The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

Machine learning models identify molecules active against the Ebola virus in vitro

The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC ₅₀ values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

Machine learning models identify molecules active against the Ebola virus in vitro

The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC ₅₀ values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

In silico analysis suggests repurposing of ibuprofen for prevention and treatment of EBOLA virus disease

The large 2014/2015 Ebola virus outbreak in West Africa points out the urgent need to develop new preventive and therapeutic approaches that are effective against Ebola viruses and  can be rapidly utilized. Recently, a simple theoretical criterion for the virtual screening of molecular libraries for candidate inhibitors of Ebola virus infection was proposed. Using this method the ‘drug space’ was screened and 267 approved and 382 experimental drugs as candidates for treatment of the Ebola virus disease (EVD) have been selected. Detailed analysis of these drugs revealed the non-steroidal anti-inflammatory drug ibuprofen as an inexpensive, widely accessible and minimally toxic candidate for prevention and treatment of EVD. Furthermore, the molecular mechanism underlying this possible protective effect of ibuprofen against EVD is suggested in this article.

Finding small molecules for the 'next Ebola'

The current Ebola virus epidemic may provide some suggestions of how we can better prepare for the next pathogen outbreak. We propose several cost effective steps that could be taken that would impact the discovery and use of small molecule therapeutics including: 1. text mine the literature, 2. patent assignees and/or inventors should openly declare their relevant filings, 3. reagents and assays could be commoditized, 4. using manual curation to enhance database links, 5. engage database and curation teams, 6. consider open science approaches, 7. adapt the "box" model for shareable reference compounds, and 8. involve the physician's perspective.

Finding small molecules for the 'next Ebola'

The current Ebola virus epidemic may provide some suggestions of how we can better prepare for the next pathogen outbreak. We propose several cost effective steps that could be taken that would impact the discovery and use of small molecule therapeutics including: 1. text mine the literature, 2. patent assignees and/or inventors should openly declare their relevant filings, 3. reagents and assays could be commoditized, 4. using manual curation to enhance database links, 5. engage database and curation teams, 6. consider open science approaches, 7. adapt the "box" model for shareable reference compounds, and 8. involve the physician's perspective.