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

Ekins S, de Siqueira-Neto JL, McCall LI, Sarker M, Yadav M, Ponder EL, Kallel EA, Kellar D, Chen S, Arkin M, Bunin BA, McKerrow JH, Talcott C - PLoS Negl Trop Dis (2015)

Bottom Line: Ninety-seven compounds were selected for in vitro testing, and 11 of these were found to have EC50 < 10 μM.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.

View Article: PubMed Central - PubMed

Affiliation: Collaborative Drug Discovery, Burlingame, California, United States of America; Collaborations in Chemistry, Fuquay-Varina, North Carolina, 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.

No MeSH data available.


Related in: MedlinePlus

In vivo efficacy of test compounds (50mg/kg b.i.d.) in a 4-day mouse model of infection by transgenic T.cruzi Brazil luc strain35 expressing firefly luciferase.
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pntd.0003878.g002: In vivo efficacy of test compounds (50mg/kg b.i.d.) in a 4-day mouse model of infection by transgenic T.cruzi Brazil luc strain35 expressing firefly luciferase.

Mentions: To assess in vivo efficacy of test compounds, a 4-day treatment mouse model of infection by transgenic T.cruzi Brazil luc strain35 expressing firefly luciferase was used [83] which enabled the activity in the mouse to be visually measured (S5 Fig). All compounds were dosed at 50mg/kg bid. Benznidazole was used as a positive control and showed 100% efficacy alongside furazolidone (Fig 2 and Table 2). Hydroxymethylnitrofurazone is a prodrug of nitrofural (which had in vitro activity) and is an additional known active compound against Chagas Disease, with an efficacy of 78.5%. We chose the prodrug form to reduce the toxicity of nitrofural in the mouse model [84]. Pyronaridine showed 85.2% efficacy while verapamil showed 55.1% and tetrandrine 43.6%, respectively. Apart from tetrandrine, these are statistically significant (Fig 2 and Table 2).


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

Ekins S, de Siqueira-Neto JL, McCall LI, Sarker M, Yadav M, Ponder EL, Kallel EA, Kellar D, Chen S, Arkin M, Bunin BA, McKerrow JH, Talcott C - PLoS Negl Trop Dis (2015)

In vivo efficacy of test compounds (50mg/kg b.i.d.) in a 4-day mouse model of infection by transgenic T.cruzi Brazil luc strain35 expressing firefly luciferase.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4482694&req=5

pntd.0003878.g002: In vivo efficacy of test compounds (50mg/kg b.i.d.) in a 4-day mouse model of infection by transgenic T.cruzi Brazil luc strain35 expressing firefly luciferase.
Mentions: To assess in vivo efficacy of test compounds, a 4-day treatment mouse model of infection by transgenic T.cruzi Brazil luc strain35 expressing firefly luciferase was used [83] which enabled the activity in the mouse to be visually measured (S5 Fig). All compounds were dosed at 50mg/kg bid. Benznidazole was used as a positive control and showed 100% efficacy alongside furazolidone (Fig 2 and Table 2). Hydroxymethylnitrofurazone is a prodrug of nitrofural (which had in vitro activity) and is an additional known active compound against Chagas Disease, with an efficacy of 78.5%. We chose the prodrug form to reduce the toxicity of nitrofural in the mouse model [84]. Pyronaridine showed 85.2% efficacy while verapamil showed 55.1% and tetrandrine 43.6%, respectively. Apart from tetrandrine, these are statistically significant (Fig 2 and Table 2).

Bottom Line: Ninety-seven compounds were selected for in vitro testing, and 11 of these were found to have EC50 < 10 μM.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.

View Article: PubMed Central - PubMed

Affiliation: Collaborative Drug Discovery, Burlingame, California, United States of America; Collaborations in Chemistry, Fuquay-Varina, North Carolina, 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.

No MeSH data available.


Related in: MedlinePlus