Identification of Probucol as a candidate for combination therapy with Metformin for Type 2 diabetes

Abstract

Type 2 Diabetes (T2D) is often managed with metformin as the drug of choice. While it is effective overall, many patients progress to exhibit complications. Strategic drug combinations to tackle this problem would be useful. We constructed a genome-wide protein-protein interaction network capturing a global perspective of perturbations in diabetes by integrating T2D subjects' transcriptomic data. We computed a 'frequently perturbed subnetwork' in T2D that captures common perturbations across tissue types and mapped the possible effects of Metformin onto it. We then identified a set of remaining T2D perturbations and potential drug targets among them, related to oxidative stress and hypercholesterolemia. We then identified Probucol as the potential co-drug for adjunct therapy with Metformin and evaluated the efficacy of the combination in a rat model of diabetes. We find Metformin-Probucol at 5:0.5 mg/kg effective in restoring near-normal serum glucose, lipid, and cholesterol levels.

Fig. 1. Network approach used to identify the potential co-drug candidate for Metformin.

a Drug-target bipartite network of the shortlisted drugs and their targets in the FPS. b Drugs were ranked based on the number of known targets captured by the FPS and further filtered based on literature and mechanism of action. c Sub-bipartite network of Probucol, Metformin, and their targets. Probucol was shortlisted as the potential co-drug, and a sub-bipartite network of Probucol, Metformin, and their targets was generated from the parent bipartite network. Metformin is represented in red, Probucol in yellow, blue nodes represent target genes in the FPS, and the top 5 KEGG pathways (based on p-value (Fisher’s exact test)) in which the genes are enriched are indicated.

Fig. 2. Graphical representation of the biochemical parameters measured in Set II.

Statistical significance was calculated using ANOVA followed by Dunnett’s test (*p-value < 0.05, **p-value < 0.01, ***p-value < 0.001, ****p-value < 0.0001). Mean with S.E.M are plotted. M + P (5:0.5) shows concentrations closest to the normal healthy control for all parameters and hence is chosen as the ideal dosage concentration for the drug combination. See also Supplementary Fig. 1. The significance bars in black indicate statistical significance calculated against normal/diabetic control, while the ones in red indicate statistical significance calculated against metformin/probucol monotherapies.

Fig. 3. Histopathological analysis of pancreatic tissue.

Tissue was obtained from NAD-STZ induced diabetic model (400x; scale bars = 25 µm). All analyses were performed in duplicates (1, 2). The sections studied showed pancreatic lobules separated by connective tissue septa. The center of islet cells consists of Beta-cells (Long-arrow), while the periphery comprises large Alpha-cells (Short-arrow) with intervening vascular spaces. The pancreatic lobules consist largely of the exocrine acini and their intralobular ducts. Most of the lobules show small, round, light-staining islets of Langerhans. a, b Controls – Normal and Diabetic. Some of the beta cells show degenerative changes in the positive control. c, d Individual drug treatment - Metformin and Probucol treated. e Drug combination M + P (5:0.5). f M + P (2.5:0.5). g M + P (2.5:0.25). Dose I [M + P (5:0.5)] restored the condition of cells closest to that of the normal tissue conditions. See also Supplementary Fig. 2.

Fig. 4. Overview of workflow to obtain the Frequently Perturbed Subnetwork (FPS).

Transcriptomic datasets were obtained from the NCBI GEO database. T2D-specific response networks (TopNet) were generated from each dataset using a master human protein-protein interaction network (hPPIn). The topnets from all five datasets were combined, and the nodes which appeared in at least four topnets were retained to obtain a T2D-specific frequently perturbed subnetwork across multiple tissues. Functional clusters of the FPS are marked in different colors, and the top 5 most significantly enriched KEGG pathways are shown in boxes with the same colors. AGE-RAGE signaling in diabetic complications, an oxidative stress-induced pathway, seemed to be the most perturbed pathway captured by the FPS.

Fig. 5. Experimental design.

The experiments were split into two sets with overlapping ranges of drug doses. Animals in Group I and Group II served as the normal and diabetic control, respectively, in both studies. Group III of Set I, II served as metformin monotherapy control, while Group IV of Set II served as the probucol monotherapy control.