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PLoS Negl Trop Dis. 2015 Jun 26;9(6):e0003878. doi: 10.1371/journal.pntd.0003878. eCollection 2015.

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

Author information

1
Collaborative Drug Discovery, Burlingame, California, United States of America; Collaborations in Chemistry, Fuquay-Varina, North Carolina, United States of America.
2
Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, California, United States of America.
3
SRI International, Menlo Park, California, United States of America.
4
Chemistry, Engineering & Medicine for Human Health (ChEM-H), Stanford, California, United States of America.
5
Collaborative Drug Discovery, Burlingame, California, United States of America.
6
Department of Pathology, University of California, San Francisco, San Francisco, California, United States of America.
7
Small Molecule Discovery Center and Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, United States of America.

Abstract

BACKGROUND:

Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite Trypanosoma cruzi. The current clinical and preclinical pipeline for T. cruzi is extremely sparse and lacks drug target diversity.

METHODOLOGY/PRINCIPAL FINDINGS:

In the present study we developed a computational approach that utilized data from several public whole-cell, phenotypic high throughput screens that have been completed for T. cruzi by the Broad Institute, including a single screen of over 300,000 molecules in the search for chemical probes as part of the NIH Molecular Libraries program. We have also compiled and curated relevant biological and chemical compound screening data including (i) compounds and biological activity data from the literature, (ii) high throughput screening datasets, and (iii) predicted metabolites of T. cruzi metabolic pathways. This information was used to help us identify compounds and their potential targets. We have constructed a Pathway Genome Data Base for T. cruzi. In addition, we have developed Bayesian machine learning models that were used to virtually screen libraries of compounds. Ninety-seven compounds were selected for in vitro testing, and 11 of these were found to have EC50 < 10 μM. We progressed five compounds to an in vivo mouse efficacy model of Chagas disease and validated that the machine learning model could identify in vitro active compounds not in the training set, as well as known positive controls. The antimalarial pyronaridine possessed 85.2% efficacy in the acute Chagas mouse model. We have also proposed potential targets (for future verification) for this compound based on structural similarity to known compounds with targets in T. cruzi.

CONCLUSIONS/ SIGNIFICANCE:

We have demonstrated how combining chemoinformatics and bioinformatics for T. cruzi drug discovery can bring interesting in vivo active molecules to light that may have been overlooked. The approach we have taken is broadly applicable to other NTDs.

PMID:
26114876
PMCID:
PMC4482694
DOI:
10.1371/journal.pntd.0003878
[Indexed for MEDLINE]
Free PMC Article

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