Translational Systems Pharmacology‐Based Predictive Assessment of Drug‐Induced Cardiomyopathy

Figures

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  Figure 1. Workflow of predictive modeling. We built datasets using gene expression data and we compared two piplelines to predict clinical drug‐induced cardiomyopathy and extract features that best predict such toxicity. Running the Gene Expression Data at hand through a linear regression model with elastic net regularization or constructing signaling networks from the data before modeling using an integer linear programming (ILP) formulation. DToxS, Drug Toxicity Signature Generation Center.

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  Figure 2. Plots of elastic net regularization results. (a and b) Show selection of the alpha parameter in the elastic net regularization by minimizing the leave‐one‐out cross validation (LOOCV) mean squared error to extract the features (genes) that best predict clinical incidence of cardiomyopathy. (c and d) Show the number of variables kept in the model, with a vertical line showing the optimal number for maximization of accuracy. a and c refer to the results of analyzing gene expression data only, whereas b and d correspond to the results of analyzing drugs' signaling networks obtained from integer linear programming formulation analysis. Each of the plotted lines in c and d corresponds to a variable (for example, a specific gene's expression) and shows how its coefficient changes with the log lambda parameter of elastic net. The vertical line shows the optimal number of parameters kept and their coefficients for maximization of accuracy.

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  Figure 3. Receiver operating characteristic (ROC) curves. (a) ROC curve from modeling differentially expressed genes (DEGs) using elastic net (EN) and (b) ROC curve from modeling by subjecting these DEGs to integer linear programming (ILP) to construct their individual drugs' signaling networks and then subject these networks to EN. PSCCM, human cardiomyocytes.

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  Figure 4. Interactions among the top 15 gene/protein predictors. Interactions among the top 15 genes/proteins selected by our model to best predict cardiomyopathy using cardiomyocytes data are depicted as a network using the STITCH website for visualization. Small nodes correspond to protein of unknown 3D structure and large nodes to known or predicted. Edges represent protein‐protein associations and the intensity of the line is proportional to the confidence score of each association. The confidence score is calculated by combining the probabilities from all evidence channels and is corrected for random observation probability.