From Single Drug Targets to Synergistic Network Pharmacology in Ischemic Stroke
Total Page：16
File Type：pdf, Size：1020Kb
From single drug targets to synergistic network pharmacology in ischemic stroke Ana I. Casasa,1, Ahmed A. Hassana, Simon J. Larsenb, Vanessa Gomez-Rangelc, Mahmoud Elbatreeka, Pamela W. M. Kleikersa, Emre Guneya,d, Javier Egeae, Manuela G. Lópezc, Jan Baumbachf, and Harald H. H. W. Schmidta,1 aDepartment of Pharmacology and Personalised Medicine, Maastricht Center for Systems Biology, Faculty of Health, Medicine and Life Sciences, Maastricht University, 6229 ER Maastricht, The Netherlands; bComputational Biology Lab, Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark; cInstituto Teofilo Hernando, Departamento de Farmacología, Facultad de Medicina, Universidad Autónoma de Madrid, 28029 Madrid, Spain; dResearch Programme on Biomedical Informatics, The Hospital del Mar Medical Research Institute and Pompeu Fabra University, 08003 Barcelona, Spain; eServicio de Farmacología Clínica, Instituto de Investigación Sanitaria–Hospital Universitario de la Princesa, 28006 Madrid, Spain; and fChair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Munich, Germany Edited by Solomon H. Snyder, The Johns Hopkins University School of Medicine, Baltimore, MD, and approved February 15, 2019 (received for review December 6, 2018) Drug discovery faces an efficacy crisis to which ineffective mainly (iii) predicts targets instead of drugs, (iv) is validated through single-target and symptom-based rather than mechanistic ap- experiment, and (v) is readily applicable by a broad range of proaches have contributed. We here explore a mechanism-based biomedical scientists. In fact, our approach can be established as disease definition for network pharmacology. Beginning with a a powerful tool and therefore implemented in novel, complex, primary causal target, we extend this to a second using guilt-by- and frequently unexpected indications where already-marketed association analysis. We then validate our prediction and explore drugs can be repurposed, leading to new therapies. Our strategy synergy using both cellular in vitro and mouse in vivo models. As a disease model we chose ischemic stroke, one of the highest unmet amends the limitations of previous approaches, for example, medical need indications in medicine, and reactive oxygen species simple pairwise combination of drugs as opposed to targeting forming NADPH oxidase type 4 (Nox4) as a primary causal thera- networks (10, 11), or combining drugs, which may have different peutic target. For network analysis, we use classical protein–pro- off-target effects, rather than drug targets (10–12). Furthermore, tein interactions but also metabolite-dependent interactions. most proposed computational methods have not been validated PHARMACOLOGY Based on this protein–metabolite network, we conduct a gene experimentally for de novo predictions (10–14). Moreover, most ontology-based semantic similarity ranking to find suitable syner- of these methods rely on drug similarity signatures extracted gistic cotargets for network pharmacology. We identify the nitric from chemical structures, targets, and side effect profiles, in- Nos1 3 oxidesynthase( to ) gene family as the closest target to troducing a potential bias toward the pharmacological classes Nox4. Indeed, when combining a NOS and a NOX inhibitor at currently represented in knowledge bases (15) and limiting their subthreshold concentrations, we observe pharmacological syn- ergy as evidenced by reduced cell death, reduced infarct size, applicability to de novo candidate discovery (16). stabilized blood–brain barrier, reduced reoxygenation-induced leakage, and preserved neuromotor function, all in a supraaddi- Significance tive manner. Thus, protein–metabolite network analysis, for ex- ample guilt by association, can predict and pair synergistic Current one drug–one target–one disease approaches in drug mechanistic disease targets for systems medicine-driven network discovery have become increasingly inefficient. Network pharmacology. Such approaches may in the future reduce the risk pharmacology defines disease mechanisms as networks best of failure in single-target and symptom-based drug discovery targeted by multiple, synergistic drugs. Using the high unmet and therapy. medical need indication stroke, we here develop an integrative in silico approach based on a primary target, NADPH oxidase network pharmacology | stroke | NOX4 | network analysis type 4, to identify a mechanistically related cotarget, NO syn- thase, for network pharmacology. Indeed, we validate both in n drug discovery, a “one disease–one target–one drug” ap- vivo and in vitro, including humans, that both NOX4 and NOS Iproach is common practice, primarily to simplify compound inhibition is highly synergistic, leading to a significant re- screening, reduce unwanted side effects, and simplify registration duction of infarct volume, direct neuroprotection, and blood– (1). This approach, however, oversimplifies disease mechanisms, brain-barrier stabilization. This systems medicine approach which are in fact complex subnetworks within the interactome (2, provides a ground plan to decrease current failure in the field 3). Moreover, disease definitions are mostly symptom- rather by being implemented in other complex indications. than mechanism-based, and hence the therapeutics are likewise. Author contributions: A.I.C., M.G.L., J.B., and H.H.H.W.S. designed research; A.I.C., A.A.H., Not surprisingly, drug discovery has thus become increasingly S.J.L., V.G.-R., and M.E. performed research; J.E., M.G.L., and J.B. contributed new re- inefficient (4). Conversely, systems medicine and network agents/analytic tools; A.I.C., A.A.H., S.J.L., P.W.M.K., E.G., and J.B. analyzed data; and pharmacology define diseases according to causal mechanisms A.I.C., A.A.H., S.J.L., E.G., J.E., M.G.L., J.B., and H.H.H.W.S. wrote the paper. (5, 6). Moreover, network pharmacology aims to further enhance Conflict of interest statement: H.H.H.W.S. is a cofounder of a biotech company, Vaso- this by targeting not only a single component within such a pharm, 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 network but by combining drugs within these networks with the holds less than 1% of shares. aim of achieving synergy and dose reduction (7). However, most This article is a PNAS Direct Submission. network databases are curated (8); the de novo identification of This open access article is distributed under Creative Commons Attribution-NonCommercial- such networks is only in its beginning. De novo network en- NoDerivatives License 4.0 (CC BY-NC-ND). richment from a single primary validated target toward at least 1To whom correspondence may be addressed. Email: [email protected] one secondary target holds high promise for systems medicine or [email protected]. (9) but is currently not possible. This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. To address this challenge, we designed an approach that (i)is 1073/pnas.1820799116/-/DCSupplemental. integrative, (ii) is based on the network pharmacology paradigm, Published online March 20, 2019. www.pnas.org/cgi/doi/10.1073/pnas.1820799116 PNAS | April 2, 2019 | vol. 116 | no. 14 | 7129–7136 Downloaded by guest on September 24, 2021 We therefore here develop a simple and integrative in silico target in stroke, NOX4 (17), as our primary target protein and approach to pair an existing validated primary causal therapeutic seed node (Fig. 1). In module 1, we expanded from this seed node target with a synergistic cotarget within a network pharmacology to obtain a network of candidate targets and related metabolites, strategy. We importantly validate our prediction both in vitro resulting in five metabolites which were extended to their inter- and in vivo, including a suitable in vitro human model. As a actors, yielding 537 proteins. The main product of Nox4 (20), disease model for this we chose ischemic stroke, a multifactorial hydrogen peroxide (H2O2), and the substrate, oxygen (O2), were high unmet medical need indication for which no neuro- manually added due to their absence from the Human Metab- protective therapy is currently available. As a mechanistic start- olome Database (HMDB), and a curation request was sent to the ing point (node), we selected the reactive oxygen-forming database. As a filtering step for narrowing down the interactions enzyme NADPH oxidase type 4 (NOX4), a preclinically highly search space, drug–target interactions were used, resulting in validated target directly involved in neurotoxicity and poststroke 166 potential druggable target proteins. In module 2, a protein– blood–brain barrier dysfunction (17, 18). protein interaction network was constructed based on the pre- viously obtained druggable target proteins (Fig. 1). Subsequently, Results networks from modules 1 and 2 were combined to obtain a two- Guilt-by-Association Analysis and Network Construction. To identify layered network determining the closest interaction partners of synergistic and mechanistically related cotargets for NOX4, we our primary target by means of guilt by association (21). Hence, employed a guilt-by-association analysis on a multilayered mo- several levels of connectedness to NOX4 were observed, via direct lecular interaction network. Since many signaling events are protein