Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery

  • Sean Ekins, Jair Lage de Siqueira-Neto, Laura-Isobel McCall, Malabika Sarker, Maneesh Yadav, Elizabeth L. Ponder, E. Adam Kallel, Danielle Kellar, Steven Chen, Michelle Arkin, Barry A. Bunin, James H. McKerrow, Carolyn Talcott
  • PLoS Neglected Tropical Diseases, June 2015, Public Library of Science (PLoS)
  • DOI: 10.1371/journal.pntd.0003878

Identifying new compounds for Chagas disease

What is it about?

In this project we developed a Pathway Genome Databse for T. Cruzi which is publically accessible. We used published data from the Broad Institute to build machine learning models to predict additional compounds. We bough and test ed compounds in vitro. Some of the most active were tested in vivo. Pyronaridine was found to have 85% efficacy in the mouse model of disease.

Why is it important?

There is no FDA approved drug for Chagas Disease and its becoming a major issue in the south of the USA as well as in South America. Pyronaridine is approved in Europe as an antimalarial and may have a faster rout to the clinic.

Perspectives

Dr Sean Ekins
Collaborations in Chemistry

The route to discovery of pyronaridine was a highlight. Our machine learning and experimental approaches were able to do this quite quickly. Thus we could take the same approach with other neglected tropical diseases. Identification of pyronaridine is exciting as we have also shown activity against Ebola virus.

Read Publication

http://dx.doi.org/10.1371/journal.pntd.0003878

The following have contributed to this page: Dr Sean Ekins