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Updated: Mar 11, 2026

Remote Laboratory Management: Respiratory Virus Diagnostics

Remote Laboratory Management: Respiratory Virus Diagnostics

Published on: April 6, 2019

Combating Ebola with Repurposed Therapeutics Using the CANDO Platform.

Gaurav Chopra¹, Sashank Kaushik^2,3, Peter L Elkin^4,5

¹Department of Chemistry; Purdue Institute for Drug Discovery; Purdue Institute for Inflammation, Immunology, and Infectious Disease; Purdue Institute for Integrative Neuroscience; Purdue Center for Cancer Research, Purdue University, West Lafayette, IN 47907, USA. gchopra@purdue.edu.

Molecules (Basel, Switzerland)

|November 30, 2016

Summary

This summary is machine-generated.

Computational drug discovery for Ebola virus disease (EVD) identified promising FDA-approved candidates. This approach accelerates the search for effective EVD treatments, reducing development time and costs.

Keywords:

candock compound–proteome interaction drug repurposing and discovery multitarget docking

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Area of Science:

Computational drug discovery
Virology
Drug repurposing

Background:

Ebola virus disease (EVD) has a high mortality rate and limited treatment options.
The 2014 West Africa epidemic highlighted the urgent need for rapid therapeutic development against EVD.
Developing novel drugs is time-consuming and resource-intensive, necessitating efficient discovery pathways.

Purpose of the Study:

To identify and rank FDA-approved drug candidates for EVD treatment using a computational approach.
To leverage drug repurposing for rapid therapeutic discovery against Ebola virus strains.
To validate computational findings through comparison with in vitro screening studies.

Main Methods:

Development and application of the Computational Analysis of Novel Drug Opportunities (CANDO) platform.
CANDO platform used to identify drugs targeting all proteins encoded by five Ebola virus strains.
Comparison of CANDO-generated drug candidates with in vitro screening data from existing studies.

Main Results:

The CANDO platform successfully identified and ranked potential drug candidates for EVD.
Overlaps were found between computationally predicted drugs and those validated in vitro.
This integration of virtual and in vitro studies provides a prioritized list of compounds for further testing.

Conclusions:

Integrating computational predictions with in vitro screening accelerates the selection of efficacious EVD therapies.
This approach significantly reduces the time, risk, cost, and resources for developing treatments against future Ebola outbreaks.
The CANDO platform offers a powerful strategy for rapid therapeutic repurposing and discovery in infectious disease outbreaks.