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

Abstract

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.

Tilorone: a Broad-Spectrum Antiviral Invented in the USA and Commercialized in Russia and beyond

“…This model predicted Ebola inhibitory activity for tilorone, which was then tested using an in vitro anti-Ebola assay for activity. Tilorone gave a 50% effective concentration (EC 50 ) in this assay of 230 nM (Table II), making it one of the most potent small-molecule inhibitors of EBOV reported at the time (31,35,36). After a series of toxicity and pharmacokinetic studies, the compound was tested in a mouse model of EBOV infection where it was associated with 90-100% survival in a mouse EBOV efficacy study at three different doses.…”

Perspectives towards antiviral drug discovery against Ebola virus

“…However, the refinement of the model often does not meet the desired level of accuracy . A number of potential in silico studies have been documented over last few years which include homology modeling of unresolved EBOV polymerase and docking‐based virtual screening of compounds that have potential to bind with important residues lining the binding pocket of EBOV VP40, VP24, VP30, and VP35 …”

Repurposing Approved Drugs as Inhibitors of Kv7.1 and Nav1.8 to Treat Pitt Hopkins Syndrome

“…We have previously performed drug repurposing (36) using machine learning methods to identify FDA and EMA approved drugs for Ebola (37) and Chagas disease (38). Most recently we have been actively constructing Bayesian models for absorption, distribution, metabolism and excretion (ADME) properties such as aqueous solubility, mouse liver microsomal stability, and Caco-2 cell permeability (30), complementing earlier ADME machine learning work (39)(40)(41)(42)(43)(44)(45)(46)(47).…”