Integrated analysis of drug-induced gene expression profiles predicts novel hERG inhibitors

Abstract

Growing evidence suggests that drugs interact with diverse molecular targets mediating both therapeutic and toxic effects. Prediction of these complex interactions from chemical structures alone remains challenging, as compounds with different structures may possess similar toxicity profiles. In contrast, predictions based on systems-level measurements of drug effect may reveal pharmacologic similarities not evident from structure or known therapeutic indications. Here we utilized drug-induced transcriptional responses in the Connectivity Map (CMap) to discover such similarities among diverse antagonists of the human ether-à-go-go related (hERG) potassium channel, a common target of promiscuous inhibition by small molecules. Analysis of transcriptional profiles generated in three independent cell lines revealed clusters enriched for hERG inhibitors annotated using a database of experimental measurements (hERGcentral) and clinical indications. As a validation, we experimentally identified novel hERG inhibitors among the unannotated drugs in these enriched clusters, suggesting transcriptional responses may serve as predictive surrogates of cardiotoxicity complementing existing functional assays.

Figure 1. Pipeline for construction and analysis of drug transcriptional response network.

Raw microarray data for drugs profiled in three cancer cell lines in the Connectivity Map (left) are normalized and clustered using affinity propagation (top center) based on similarities in drug-induced gene expression profiles (nodes) to yield clusters with a characteristic “exemplar” (highlighted by red) representing the expression profile shared by cluster members. The resulting clusters (middle center) are annotated for experimental and clinical evidence of hERG inhibition (bottom center), and enrichment analysis conducted to find clusters with a statistically significant fraction of hERG inhibitors. Unannotated compounds in these enriched clusters (top right) are then experimentally assessed for hERG inhibition in a high-throughput electrophysiology assay (middle right) to yield potency values (bottom right).

Figure 2. Network analysis of drug-induced gene expression profiles.

(A) Drug-induced gene expression profiles tested in MCF7 (breast cancer) cells (nodes) are linked by shared expression patterns to a cluster exemplar (line width proportional to Pearson correlation) representing their characteristic response. Clusters enriched for literature or experimentally annotated hERG inhibitors are outlined in red. (B) Drug induced gene expression profiles generated from MCF7, PC3 (prostate cancer), and HL60 (leukemia) cell lines are clustered as in (A), with cell of origin indicated by node shape.

Figure 3. Expression and structural similarity of hERG inhibitor-enriched clusters.

(A) Chemical similarity (Tanimoto coefficient = TC) computed from FCFP_6 circular fingerprints versus expression similarity (Pearson coefficient = PC) computed from drug-induced transcriptional response for selected hERG inhibitor-enriched clusters for MCF7 (top) PC3 (middle) and HL60 (bottom). Cluster in drug expression networks are highlighted, with example compounds outlined in black in inset (left column). Chemical structures are illustrated with corresponding chemical and expression similarity values. (B) Distribution of pairwise expression response similarities within hERG inhibitor-enriched clusters and between drugs in enriched and non-enriched clusters from Figure 2B . (C) As (B), comparing distribution of chemical similarities.

Figure 4. Experimental validation of novel hERG inhibitors.

(A) (Left) Exemplars of hERG inhibitor enriched clusters from Figure 2B converge at the MCF7-derived Astemizole cluster (red arrows, inset), which contains six unannotated drugs (black highlights in inset) (Right). Chemical structures of the six unannotated drugs in the highlighted cluster. (B) Dose response curves for hERG inhibition measured for four unannotated drugs using the Ionworks automated patch clamp system (n = 4, mean +/- s.e.m. for each data point).

Figure 5. Mechanistic hypotheses for hERG-inhibition correlated gene expression signatures.

(A) Schematic of drug-induced gene expression response directly controlled by blockade of potassium conductance by the hERG channel. (B) Parallel direct (straight repression line) or indirect (bent repression line) modulation of hERG and alternative molecular targets on the cell membrane (blue) or in the cytoplasm (red) may lead to convergent transcriptional responses. (C) Perfect confounding, in which drugs simultaneously inhibit channel function and independently modulate downstream transcriptional response through alternative molecular targets.