Reclassification of the compounds CPA, TAA, and WY.

<p>(<b>A</b>) Points indicate treatment groups which were originally represented by a vector containing the fold-changes of all signature genes and then transformed to its two principal components. Here, the informative genes for discriminating GC vs. NGC after 3 days of repeated dosing were used. Groups of male animals are drawn as squares and female ones as circles. The fill color of the points indicates the compound class. Polygons indicate the convex hulls of clusters corresponding to either male or female mice treated with a certain class of compounds. (<b>B</b>) Similar plot as in (A), but the multi-gene signature for GC vs. NGC classification after 14 days was used here. (<b>C</b>) The heatmap provides a graphical representation of the fold-changes of 15 selected signature genes from the 14-day signature for GC vs. NGC classification. Rows correspond to genes and columns to treatment groups. Upregulated genes are colored in red and downregulated ones in green. The colorbar on top indicates the corresponding compound classes. (<b>D</b>) Heatmaps showing confidence of predictions made by diverse C vs. NC classifiers for male (M) and female (F) mice treated with CPA, TAA and WY for 3 days (left heatmap) or 14 days (right heatmap). (<b>E</b>) Similar illustration as in (D) showing prediction outcomes of GC vs. NGC classifiers.</p

Overview of compounds used for training and evaluation of classifiers.

<p>The table lists all compounds along with their carcinogenicity class and CAS Registry Number. Corn oil (CO) or 0.1% carboxymethyl cellulose (CMC) was used as vehicle. Doses were selected for each compound based on the tumorigenic dose rate 50 (TD₅₀), long-term animal studies leading to liver cancer known from the literature or initial dose range-finding studies. After a dosing period of 3 or 14 days the mouse livers were subjected to gene expression analysis using an Affymetrix platform. Two treatment groups, each comprising 5–6 male and female mice, respectively, were examined for each compound, dose and point of time. Time matched control groups were included for both vehicles. Each treatment group has a unique group ID which is composed of the compound short name, the group number and the sex (Female or Male).</p

Accuracy and stability of signatures for compound classification.

<p>(<b>A</b>) The line plots depict the mean performance of C vs. NC classification after 3 days of repeated dosing, which was achieved based on gene sets extracted with different feature selection methods. Each curve corresponds to a feature selection method and the performance was assessed depending on the number of genes selected as informative features. The prediction accuracy was assessed on the samples left out from 25 random subsamplings of the dataset (bootstraps), each containing 90% of the data, and measured in terms of area under the ROC curve. The inset bar plot depicts the ROC scores averaged across bootstraps and signature sizes. (<b>B</b>) Performance of C vs. NC classification after 14 days of treatment illustrated as in (A). (<b>C</b>) The correspondence of the extracted C vs. NC gene sets across 25 bootstraps was assessed based on the Kuncheva stability index (KI) for each of the 4 employed feature selection methods. The KI was then for each method plotted against the number of selected signature genes. (<b>D</b>) Robustness of signatures for C vs. NC classification after 14 days of treatment illustrated as in (C). (<b>E, F</b>) Prediction accuracy achieved with signatures for GC vs. NGC classification after (E) 3 days and (F) 14 days of repeated dosing, respectively, depicted as in (A). (<b>G, H</b>) Similar illustration as in (C) showing robustness of signatures for GC vs. NGC classification after (G) 3 days and (H) 14 days of administration, respectively.</p

Performance comparison with signatures known from the literature.

<p>(<b>A</b>) The grouped bar plots depict the area under the ROC curves obtained for novel and known signatures for the separation of C and NC after 3 days of treatment. For this purpose, the performance of diverse classifiers was evaluated by a 3-fold cross-validation. Each bar corresponds to a certain classifier (see legend) and each group of bars refers to a certain signature. The horizontal dashed line indicates the performance that would have been achieved by random guessing. The adjacent Venn diagrams illustrate informative genes common between signatures. (<b>B</b>) Same plots as in (A), but for C vs. NC classification after 14 days of repeated dosing. (<b>C</b>) Mean ROC scores and signature overlaps for GC vs. NGC classification after 3 days of treatment. (<b>D</b>) Same plots as in (C), but for GC vs. NGC classification after 14 days of repeated dosing.</p

Classification results obtained for different signatures.

<p>(<b>A</b>) The four heatmaps show the predictions resulting from diverse binary classifiers for the discrimination of C from NC after 3 days (left two heatmaps) or 14 days (right two heatmaps) of repeated dosing. For each dosing time two heatmaps are depicted which correspond to male and female mice, respectively. The rows correspond to different classifiers and the columns to different treatment groups. The continuous prediction scores, returned from the classifiers, were transformed to confidence scores between 0 and 1, which provide an estimate of the probability of class C. The colorbar on top shows the true class annotation. The black vertical lines separate the test samples from the three folds of cross-validation. (<b>B</b>) Heatmaps illustrating the classification outcome of diverse predictors to distinguish GC from NGC based on characteristic gene expression profiles observed in male and female mouse liver samples after 3 or 14 days of administration. KNN, K-Nearest Neighbor; SVM, Support Vector Machine; PAM, Prediction Analysis for Microarrays.</p

Qualification of LSP1-2111 as a Brain Penetrant Group III Metabotropic Glutamate Receptor Orthosteric Agonist

LSP1-2111 is a group III metabotropic glutamate receptor agonist with preference toward the mGlu4 receptor subtype. This compound has been extensively used as a tool to explore the pharmacology of mGlu4 receptor activation in preclinical animal behavioral models. However, the blood–brain barrier penetration of this amino acid derivative has never been studied. We report studies on the central nervous system (CNS) disposition of LSP1-2111 using quantitative microdialysis in rat. Significant unbound concentrations of the drug relative to its <i>in vitro</i> binding affinity and functional potency were established in extracellular fluid (ECF). These findings support the use of LSP1-2111 to study the CNS pharmacology of mGlu4 receptor activation through orthosteric agonist mechanisms