Identification of small-molecule inhibitors of Trypansoma cruzi replication

Germain, Andrew; Carmody, Leigh C.; Dockendorff, Chris; Galan-Rodriguez, Cristina; Rodrı́guez, Ana; Johnston, Stephen; Bittker, Joshua A.; MacPherson, Lawrence; Dandapani, Sivaraman; Palmer, Michelle; Schreiber, Stuart L.; Muñoz, Benito

doi:10.1016/j.bmcl.2011.09.057

Cited by 12 publications

(20 citation statements)

References 16 publications

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“…tuberculosis genome from the genome of the eukaryotic human host, and the availability of a well-annotated M . tuberculosis genome [ 24 , 34 ]. In contrast, T .…”

Section: Discussionmentioning

confidence: 99%

“…An analysis of the Chagas disease literature was performed resulting in the curation of over 500 molecules with associated target information (when available). The Broad Chagas screening data [ 31 – 34 ] were also collected and both datasets were uploaded into the CDD database (Collaborative Drug Discovery Inc. Burlingame, CA) [ 64 ] from sdf files and mapped to custom protocols [ 65 ]. All public datasets used in model building are available for free public read-only access and mining upon registration in the CDD database [ 66 ].…”

Section: Methodsmentioning

confidence: 99%

“…Several whole-cell, phenotypic high throughput screens have been completed for T . cruzi , including most recently a screen of 1.8 million compounds at GlaxoSmithKline in Spain [ 30 ], another of over 300,000 molecules at the Broad Institute [ 31 – 34 ] and a proprietary screen by the Genomics Institute of the Novartis Research Foundation (GNF) [ 35 ]. Therefore more HTS is leading to new hits [ 31 – 39 ] from academia [ 40 ], industry, and the non-profit sector, primarily with the support of NIAID and the Drugs for Neglected Diseases Initiative (DNDi).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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BackgroundChagas 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 FindingsIn 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 < 10μM. 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/ SignificanceWe 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.

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“…tuberculosis genome from the genome of the eukaryotic human host, and the availability of a well-annotated M . tuberculosis genome [ 24 , 34 ]. In contrast, T .…”

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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“…Recombinant T. cruzi parasites have already been used in a series of in vitro assays designed to detect drug susceptibility mainly of the intracellular amastigote life-stage [26] – [28] , the parasite replicative form in the mammalian host and the preferred parasitic target stage. When screening a molecular library against intracellular amastigotes, parallel host cell toxicity assays on the retrieved hits or “active” compounds are necessary to determine whether the activity is specifically antiparasitic or either totally or partially due to a disruption of host-cell biology [27] , [41] , [42] . The selectivity index is then calculated using the relative IC 50 values of the host cell and the parasite.…”

Section: In Vitro Phenotypic Assays To Identify New Anti– Tmentioning

confidence: 99%

“…It is likely that the reason for such discrepancies resided in the quality of the chemical compounds used for the original HTS [41] . Another HTS of a diversity-oriented synthesis chemical library of 77,312 compounds has been performed [42] . The most attractive confirmed hits in terms of potency, selectivity, and predicted drug-like physicochemical properties between these two HTSs were picked up for further development and the data obtained from them have served as the basis for different initiatives ( Figure 1 ) [43] – [46] .…”

Section: In Vitro Phenotypic Assays To Identify New Anti– Tmentioning

confidence: 99%

High Throughput Screening for Anti–Trypanosoma cruzi Drug Discovery

Alonso-Padilla

Rodrı́guez

2014

PLoS Negl Trop Dis

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The discovery of new therapeutic options against Trypanosoma cruzi, the causative agent of Chagas disease, stands as a fundamental need. Currently, there are only two drugs available to treat this neglected disease, which represents a major public health problem in Latin America. Both available therapies, benznidazole and nifurtimox, have significant toxic side effects and their efficacy against the life-threatening symptomatic chronic stage of the disease is variable. Thus, there is an urgent need for new, improved anti–T. cruzi drugs. With the objective to reliably accelerate the drug discovery process against Chagas disease, several advances have been made in the last few years. Availability of engineered reporter gene expressing parasites triggered the development of phenotypic in vitro assays suitable for high throughput screening (HTS) as well as the establishment of new in vivo protocols that allow faster experimental outcomes. Recently, automated high content microscopy approaches have also been used to identify new parasitic inhibitors. These in vitro and in vivo early drug discovery approaches, which hopefully will contribute to bring better anti–T. cruzi drug entities in the near future, are reviewed here.

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A New Era for Chagas Disease Drug Discovery?

Keenan¹,

Chaplin²

2015

Progress in Medicinal Chemistry

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Identification of small-molecule inhibitors of Trypansoma cruzi replication

Cited by 12 publications

References 16 publications

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

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

High Throughput Screening for Anti–Trypanosoma cruzi Drug Discovery

A New Era for Chagas Disease Drug Discovery?

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