From single drug targets to synergistic network pharmacology in ischemic stroke

Abstract

Drug discovery faces an efficacy crisis to which ineffective mainly single-target and symptom-based rather than mechanistic approaches have contributed. We here explore a mechanism-based disease definition for network pharmacology. Beginning with a primary causal target, we extend this to a second using guilt-by-association analysis. We then validate our prediction and explore synergy using both cellular in vitro and mouse in vivo models. As a disease model we chose ischemic stroke, one of the highest unmet medical need indications in medicine, and reactive oxygen species forming NADPH oxidase type 4 (Nox4) as a primary causal therapeutic target. For network analysis, we use classical protein-protein interactions but also metabolite-dependent interactions. Based on this protein-metabolite network, we conduct a gene ontology-based semantic similarity ranking to find suitable synergistic cotargets for network pharmacology. We identify the nitric oxide synthase (Nos1 to 3) gene family as the closest target to Nox4 Indeed, when combining a NOS and a NOX inhibitor at subthreshold concentrations, we observe pharmacological synergy as evidenced by reduced cell death, reduced infarct size, stabilized blood-brain barrier, reduced reoxygenation-induced leakage, and preserved neuromotor function, all in a supraadditive manner. Thus, protein-metabolite network analysis, for example guilt by association, can predict and pair synergistic mechanistic disease targets for systems medicine-driven network pharmacology. Such approaches may in the future reduce the risk of failure in single-target and symptom-based drug discovery and therapy.

Keywords: NOX4; network analysis; network pharmacology; stroke.

Conflict of interest statement

Conflict of interest statement: H.H.H.W.S. is a cofounder of a biotech company, Vasopharm, engaged in the development of small-molecule NOS inhibitors, currently in stage III clinical development. However, H.H.H.W.S. has no operative role in the company and holds less than 1% of shares.

Copyright © 2019 the Author(s). Published by PNAS.

Figures

Fig. 1.

Computational workflow for target prioritization via network pharmacology. The computational target prioritization pipeline consists of three interdependent modules. The blue module extracts the metabolites interacting with the protein NOX4 from the Human Metabolome Database, performs curation of the metabolites, extracts the proteins interacting with them, and filters them based on the availability of drugs from the Therapeutic Target Database (TTD). The gray module uses the Integrated Interaction Database (IID) to extract protein–protein interactions of the proteins yielded by the blue module and constructs a network out of them. The green module calculates gene ontology-based semantic similarity scores of the output of the blue module compared with NOX4 using molecular function (MF) annotations, ranks the proteins based on their similarity scores, and excludes proteins with less than one molecular function. The output of the green module is used to annotate the network with the top four proteins.

Fig. 2.

Integrated NOX4-extended multilayer network of biomolecular interactions used for candidate extraction and the involved protein semantic similarity ranking. (A) The full network constructed using the primary protein, NOX4 (orange node), connected to its direct metabolic interactors (red nodes), which have been linked to the proteins (blue nodes) interacting with them. We also show all protein–protein and metabolite–protein interactions (gray edges). (B) The semantic similarity ranking based on molecular functions (MF SemSim) of proteins with the top four similar proteins is highlighted. (C) The simplified network with only the primary protein, and the top four similar proteins and metabolites shown individually, while the rest of the proteins are grouped as modules and their interactions are merged.

Fig. 3.

In vitro cotarget validation and drug identification of NOX4 and NOS inhibitors as a combinatory treatment. (A) Organotypic hippocampal cultures prepared from hippocampal slices were cultured for 4 d and subsequently subjected to 15 min of OGD period followed by 24-h treatment. Samples for gene expression detection were collected at 0, 2, 4, 8, 12, and 24 h post-OGD. (B) NOX4 expression was up-regulated at 4 and 8 h in comparison with the beginning of the ischemia period (*P < 0.05, ***P < 0.001; n = 3). (C) Inducible NOS (NOS2; square) was up-regulated only in the first 2 h post-OGD, while neuronal NOS (NOS1; circle) was up-regulated in the final 12 to 24 h after the OGD period. Similarly, endothelial NOS (NOS3; triangle) was also significantly up-regulated at 8, 12, and 24 h post-OGD (*P < 0.05, **P < 0.01; n = 4). Gene expression was normalized using β-actin as housekeeping gene. (D) Cell death was significantly reduced in OHCs treated with GKT136901 (0.01 µM) and L-NAME (0.3 µM) in combination (**P < 0.01; n = 8; green slashed bar) in comparison with control slices (#P < 0.05 with respect to basal; n = 8; gray bar). Individual treatments show no effect. (E) ROS formation was also significantly decreased in OHCs treated with the combination of GKT136901 (0.01 µM) and L-NAME (0.3 µM) in comparison with nontreated slices. Again, individual treatments show no effect on cell death. #P < 0.05 compared with basal conditions (gray bar; n = 5); **P < 0.01 with respect to nontreated slices (gray bar; n = 5). (F) Combinatory treatment of GKT136901 and L-NAME increases cell viability in human brain microvascular endothelial cells subjected to hypoxia/reoxygenation (Re-Ox). ##P < 0.01 with respect to basal conditions (n = 4; gray bar); *P < 0.05 with respect to nontreated cells (n = 4; green slashed bar). (G) Cell permeability was assessed by measuring Evans blue fluorescence post-OGD. Evans blue diffusion was significantly reduced in cells treated with GKT136901 (0.01 μM) and L-NAME (0.3 μM) in combination (#P < 0.05; n = 4; gray bar) in comparison with nontreated cells (*P < 0.05; n = 4; green slashed bar). Error bars are mean ± SD.

Fig. 4.

In vivo validation of network pharmacology for clinical translation. (A) Twenty-four hours after tMCAO infarct size was reduced in mice treated with GKT136901 (10 mg/kg) and L-NAME (3 mg/kg) in combination 1 h (**P < 0.01; n = 6) and 3 h poststroke (*P < 0.05; n = 5), while individual treatment showed no effect in reduction of infarct size. Infarct volume was also significantly reduced in aged animals treated with the combination (GKT+L-NAME) 1 h poststroke (**P < 0.01; n = 5). Similarly, combinatory treatment decreased infarct volume after permanent occlusion of the MCA in adult mice (*P < 0.05; n = 5). (B) With respect to the neurological outcome of the combinatory treatment in surviving mice, neurological outcome (Bederson score) was improved in the adult mice treated 1 h poststroke (*P < 0.05; n = 9), 3 h poststroke (*P < 0.05; n = 5), and the aged model (*P < 0.05; n = 4). (C) Likewise, the elevated body swing test indicated a significant increase for the right swing number/total swing number ratio in adult mice treated 1 h PO (*P < 0.05; n = 9) but not in the other groups. (D) Significantly improved motor outcome was detected after four-limb hanging test in all groups: 1 h PO (*P < 0.05; n = 9), 3 h PO (*P < 0.05; n = 5), and aged animals (*P < 0.05; n = 4). (E) Blood–brain barrier integrity assessed by Evans blue extravasation was preserved in treated animals compared with nontreated mice at day 1 after 1 h of tMCAO (*P < 0.05; n = 4). (F) Treated mice showed decreased ROS formation compared with their respective nontreated animals (*P < 0.05; n = 4). (G) N-Tyr–positive cells were significantly reduced with the combinatory therapy compared with nontreated mice. (*P < 0.05; n = 4). Error bars are mean ± SD.