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

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

Physiochemical and ADME data.

<p>For microsomal stability, verapamil was used as a high-metabolism control (0.24% remaining with NADPH) and warfarin was a low-metabolism control (85% remaining with NADPH). The kinetic solubility limit was the highest concentration with no detectable precipitate. For Caco-2 cell permeability, compounds at a concentration of 10 μM were incubated for 2 h. P_app = apparent permeability coefficient. All compounds showed poor recovery due to either low solubility or non-specific binding. Ranitidine, warfarin and talindol were used as low permeability, high permeability and P-gp efflux, controls respectively.</p

Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for <i>Mycobacterium tuberculosis</i> Drug Discovery

<div><p>Integrated computational approaches for <i>Mycobacterium tuberculosis</i> (<i>Mtb</i>) are useful to identify new molecules that could lead to future tuberculosis (TB) drugs. Our approach uses information derived from the TBCyc pathway and genome database, the Collaborative Drug Discovery TB database combined with 3D pharmacophores and dual event Bayesian models of whole-cell activity and lack of cytotoxicity. We have prioritized a large number of molecules that may act as mimics of substrates and metabolites in the TB metabolome. We computationally searched over 200,000 commercial molecules using 66 pharmacophores based on substrates and metabolites from <i>Mtb</i> and further filtering with Bayesian models. We ultimately tested 110 compounds <i>in vitro</i> that resulted in two compounds of interest, BAS 04912643 and BAS 00623753 (MIC of 2.5 and 5 μg/mL, respectively). These molecules were used as a starting point for hit-to-lead optimization. The most promising class proved to be the quinoxaline di<i>-N</i>-oxides, evidenced by transcriptional profiling to induce mRNA level perturbations most closely resembling known protonophores. One of these, SRI58 exhibited an MIC = 1.25 μg/mL versus <i>Mtb</i> and a CC₅₀ in Vero cells of >40 μg/mL, while featuring fair Caco-2 A-B permeability (2.3 x 10⁻⁶ cm/s), kinetic solubility (125 μM at pH 7.4 in PBS) and mouse metabolic stability (63.6% remaining after 1 h incubation with mouse liver microsomes). Despite demonstration of how a combined bioinformatics/cheminformatics approach afforded a small molecule with promising <i>in vitro</i> profiles, we found that SRI58 did not exhibit quantifiable blood levels in mice.</p></div

Activity of SRI50 against wild type and clinical MDR-TB strains.

<p>Activity of SRI50 against wild type and clinical MDR-TB strains.</p

<i>Mtb</i> transcriptional response to SRI54 as compared to other small molecule antituberculars and environmental stresses.

<p>100 SRI54 most induced and repressed genes (top-bottom) are clustered with responses to other treatments (left-right). The top dendrogram indicates relatedness of the <i>Mtb</i> perturbations based on gene clusters. Red indicates increase, blue indicates decrease and white no change in expression versus DMSO treatment. <i>Amp</i>, ampicillin; <i>EMB</i>, ethambutol; <i>TLM</i>, thiolactomycin; <i>INH</i>, isoniazid; <i>ETH</i>, ethionamide; <i>5-Cl-PZA</i>, 5-chloropyrazinamide, <i>CPZ</i>, chlorpromazine; <i>CCCP</i>, carbonyl cyanide 3-chlorophenylhydrazone; <i>GSNO</i>, S-nitrosoglutathione; <i>DNP</i>, 2,4-dinitrophenol.</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

<i>Mtb</i> growth inhibitory activities of BAS 00623753 and a small set of analogs.

<p>Molecule structures are in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141076#pone.0141076.s001" target="_blank">S1 Data</a>.</p

Synthetic routes to the A) arylamide and B) quinoxaline di-<i>N</i>-oxide families.

<p>Synthetic routes to the A) arylamide and B) quinoxaline di-<i>N</i>-oxide families.</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