Identification and Validation of Targets of Phenotypic High Throughput Screening Hits for Chagas Disease Project Summary Nearly 10 million people in Latin America are infected with the eukaryotic parasite Trypanosoma cruzi, the causative agent of Chagas disease. The World Health Organization (WHO) classifies Chagas disease as a neglected tropical disease, but Chagas disease is gaining recognition as an emerging infection in the United States where an estimated 300,000 people may be infected. Unfortunately, there are no FDA approved treatments for Chagas disease and treatments used outside the U.S. have toxic side effects. New therapeutics for Chagas disease are desperately needed. However, few promising drug candidates have advanced to the clinic and the existing drug development pipeline lacks target diversity. In order to facilitate and catalyze the identification of novel therapeutics for Chagas disease, Collaborative Drug Discovery and SRI propose to develop and validate a new combined computational- systems biology approach that predicts metabolic enzyme targets of phenotypic screening hits. The proposed methodology will be developed and validated for Chagas disease (Phase I) and expanded to develop a prototype research tool to support target prediction and validation for phenotypic screening hits from multiple diseases (Phase II). More specifically, in Phase I CDD and SRI will (i) develop a novel approach that using computational methods to identify parasite metabolites structurally mimicked by high throughput screening (HTS) hits and bioinformatics analyses of metabolic pathways to ultimately predict the target of hits, (ii) apply the approach to HTS hits for Chagas disease compiled from over 300,000 compounds tested against T. cruzi in the literature and public HTS datasets compiled in CDD's public database, and (iii) conduct preliminary experiments to validate predicted target-compound pairs. In Phase II, CDD and SRI will conduct more extensive experimental validation of predictions and apply the drug target prediction methodologies to additional neglected tropical diseases to demonstrate the broader utility of the approach. Ultimately, CDD will develop a software module that automates workflow and facilitates sharing of bioinformatics and chemiformatic data between CDD's software platform and external bioinformatics databases such as the SRI BioCyc database. This module is one of a suite of proposed modules addressing aspects of the drug discovery process that will be integrated and commercialized along with CDD's existing drug discovery software platform.

Public Health Relevance

Nearly 10 million people in Latin America and 300,000 people in the United States are believed to be infected with the eukaryotic parasite Trypanosoma cruzi, the causative agent of Chagas disease;new drugs to treat Chagas disease are desperately needed. In order to identify novel drug targets for Chagas disease and accelerate drug development, Collaborative Drug Discovery (CDD) and SRI propose to develop a computational approach to help scientists predict the drug targets of small molecules that kill the parasite. These methods will eventually be expanded to predict novel drug targets for multiple diseases and serve as the basis for a software module that can be commercialized through CDD's existing drug discovery software.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Small Business Technology Transfer (STTR) Grants - Phase I (R41)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-IDM-U (10))
Program Officer
Rogers, Martin J
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Collaborative Drug Discovery, Inc.
United States
Zip Code
Clark, Alex M; Dole, Krishna; Ekins, Sean (2016) Open Source Bayesian Models. 3. Composite Models for Prediction of Binned Responses. J Chem Inf Model 56:275-85
Clark, Alex M; Dole, Krishna; Coulon-Spektor, Anna et al. (2015) Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets. J Chem Inf Model 55:1231-45
Ekins, Sean; de Siqueira-Neto, Jair Lage; McCall, Laura-Isobel et al. (2015) Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery. PLoS Negl Trop Dis 9:e0003878