Machine Learning Models and Pathway Genome Data Base for <i>Trypanosoma cruzi</i> Drug Discovery

<div><p>Background</p><p>Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite <i>Trypanosoma cruzi</i>. The current clinical and preclinical pipeline for <i>T</i>. <i>cruzi</i> is extremely sparse and lacks drug target diversity.</p><p>Methodology/Principal Findings</p><p>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 <i>T</i>. <i>cruzi</i> 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 <i>T</i>. <i>cruzi</i> metabolic pathways. This information was used to help us identify compounds and their potential targets. We have constructed a Pathway Genome Data Base for <i>T</i>. <i>cruzi</i>. In addition, we have developed Bayesian machine learning models that were used to virtually screen libraries of compounds. Ninety-seven compounds were selected for <i>in vitro</i> testing, and 11 of these were found to have EC₅₀ < 10μM. We progressed five compounds to an <i>in vivo</i> mouse efficacy model of Chagas disease and validated that the machine learning model could identify <i>in vitro</i> 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 <i>T</i>. <i>cruzi</i>.</p><p>Conclusions/ Significance</p><p>We have demonstrated how combining chemoinformatics and bioinformatics for <i>T</i>. <i>cruzi</i> drug discovery can bring interesting <i>in vivo</i> active molecules to light that may have been overlooked. The approach we have taken is broadly applicable to other NTDs.</p></div

An example showing the CDD Vault for this collaboration, illustrating how the structures and biology data can be securely shared.

<p>An example showing the CDD Vault for this collaboration, illustrating how the structures and biology data can be securely shared.</p

<i>In vivo</i> efficacy of test compounds (50mg/kg b.i.d.) in a 4-day mouse model of infection by transgenic <i>T</i>.<i>cruzi</i> Brazil luc strain35 expressing firefly luciferase.

<p><i>In vivo</i> efficacy of test compounds (50mg/kg b.i.d.) in a 4-day mouse model of infection by transgenic <i>T</i>.<i>cruzi</i> Brazil luc strain35 expressing firefly luciferase.</p

A typical metabolic cellular overview of TCruCyc provided by the Pathway Tools web server.

<p>This view of the TCruCyc PGDB shows the (almost entirely) inferred set of metabolic pathways from gene sequence data. Canonical pathways such as “Amino Acids Biosynthesis”, “Amino Acids Degradation”, “Nucleosides and Nucleotides Biosynthesis”, “Fatty Acids and Lipids Biosynthesis” and “Respiration” are partially inferred as well as a large set of single reaction steps (right side) that Pathway Tools could integrate into larger pathways. This is an expected level of derivable connectivity that would be available from annotated genome and proteome sequence data. We expect that a significant number of unassigned protein functions can be assigned by extending Pathway Tools with (high threshold) automated sequence similarity analysis that is currently done via manual curation.</p

Leave-out cross validation data for <i>T</i>.<i>cruzi</i> Bayesian models.

<p>Leave-out cross validation data for <i>T</i>.<i>cruzi</i> Bayesian models.</p

<i>In vitro</i> and <i>in vivo</i> data for compounds selected in this study.

<p>* Used hydroxymethylnitrofurazone for <i>in vivo</i> study (nitrofural pro-drug)</p><p><i>In vitro</i> and <i>in vivo</i> data for compounds selected in this study.</p