Proximal Pathway Enrichment Analysis for Targeting Comorbid Diseases via Network Endopharmacology

Abstract

The past decades have witnessed a paradigm shift from the traditional drug discovery shaped around the idea of “one target, one disease” to polypharmacology (multiple targets, one disease). Given the lack of clear-cut boundaries across disease (endo)phenotypes and genetic heterogeneity across patients, a natural extension to the current polypharmacology paradigm is to target common biological pathways involved in diseases via endopharmacology (multiple targets, multiple diseases). In this study, we present proximal pathway enrichment analysis (PxEA) for pinpointing drugs that target common disease pathways towards network endopharmacology. PxEA uses the topology information of the network of interactions between disease genes, pathway genes, drug targets and other proteins to rank drugs by their interactome-based proximity to pathways shared across multiple diseases, providing unprecedented drug repurposing opportunities. Using PxEA, we show that many drugs indicated for autoimmune disorders are not necessarily specific to the condition of interest, but rather target the common biological pathways across these diseases. Finally, we provide high scoring drug repurposing candidates that can target common mechanisms involved in type 2 diabetes and Alzheimer’s disease, two conditions that have recently gained attention due to the increased comorbidity among patients.

Keywords: Alzheimer’s disease; autoimmune disorders; comorbidity; drug repurposing; network endopharmacology; proximal pathway enrichment analysis; systems medicine; type 2 diabetes.

Figure 1

Genetic, phenotypic and functional overlap across autoimmune disorders. Disease relationships (links) based on (a) shared genes (gray solid lines), shared symptoms (orange dashed lines) and comorbidity (blue sinusoidal lines); (b) shared pathways (gray solid lines) using common disease and pathway genes, (c) shared pathways (gray solid lines) using the proximity of the pathway genes to the diseases genes in the interactome.

Figure 2

Number of shared pathways across disease pairs that are targeted by the same drug compared to the rest of the pairs. The pathway enrichment is calculated using (a) gene overlap and (b) proximity of genes in the interactome. The number of disease pairs in each group is given in the parenthesis below the group label in the x-axis.

Figure 3

Schematic overview of proximal pathway enrichment analysis (PxEA). PxEA scores a drug with respect to its potential to target the pathways shared between two diseases. For a given drug and two diseases of interest, PxEA first identifies the common pathways between the two diseases and then uses the proximity-based ranking of the pathways (i.e., average distance in the interactome to the nearest pathway gene, normalized with respect to a background distribution of expected scores) to assign a score to the drug and the disease pair.

Figure 4

PxEA scores of drugs used in autoimmune disorders. (a) Disease-disease heatmap, in which for each disease pair, the common pathways proximal to the two diseases are used to run PxEA. Note that the diagonal contains the PxEA scores obtained when the proximal pathways for only that disease are used. The hue of the color scales with the PxEA score. (b) Drug-disease heatmap, in which the PxEA is run using the pathways proximal to the pathways of the disease in the column for the drugs in the rows (25 drugs that are used at least in two diseases). The last two columns show the median and maximum values of the PxEA scores obtained for the drug among all disease pairs the drug is indicated for.

Figure 5

Orlistat from PxEA perspective. The subnetwork shows how the targets of Orlistat are connected to the nearest pathway protein for the pathways shared between T2D and AD. For clarity, only the pathways that are proximal to the drug are shown. Blue rectangles represent pathways, circles represent drug targets (orange) or proteins on the shortest path to the nearest pathway gene (gray). Blue dashed lines denote pathway membership, solid lines are protein interactions. The interactions between the drug and its targets are shown in dashed orange lines and the interactions between the drug targets and their neighbors are highlighted with solid orange lines.

Figure 6

Effect of noise in the pathway data on the random walk and shortest path based proximity calculation. To assess the robustness of the interactome-based proximity in regards to noise in the pathway data, we generated synthetic gold standard pathways containing a certain proportion (k%) of the known disease genes in T2D and AD (see text for details). We compared the proximity between these gold standard pathways and the disease genes to the proximity between the control pathways (random groups of gene in the interactome) and the disease genes. The proximity values using random walk and the shortest path for increasing k values are shown for the control and gold standard pathways.