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 (Nos1to3) gene family as the closest target toNox4. 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.

Integrating Phenotypic Search and Phosphoproteomic Profiling of Active Kinases for Optimization of Drug Mixtures for RCC Treatment

“…Our data are in line with these observations as our s-FSC-selected ODC show very little overlap in kinase targets. Major challenges however remain as to how exactly define a diseasome [ 44 , 45 , 46 , 47 ]. Here we coupled pTyr-based phosphoproteomics to INKA analysis as most drugs chosen for the phenotypic screen were affecting tyrosine kinases.…”

“…Global phosphoproteomics capturing pSer and pThr events as well as gene and protein expression levels, (epi)genetic events and/or metabolic changes could undoubtedly be incorporated as well for a more complete molecular understanding. Multi-omics integration of such data into a comprehensive cancer network could be useful for selection of drug combinations by pointing to relevant targetable hubs [ 44 , 45 , 46 , 47 ]. However, we observed different drug interactions in our panel of cell lines despite similarities in molecular profiles, suggesting that phenotype driven selection of drug interactions and synergistic effects with s-FSC takes into account cellular mechanisms that may not be obvious from static, i.e., baseline (untreated), cellular profiles alone.…”

Exploring gene–gene interaction in family‐based data with an unsupervised machine learning method: EPISFA

“…Similarly, in the LAA subtype, the interaction between FER rs10447248 and NOS1 rs2139733 increased the risk of ischemic stroke risk. NOS1 is actively involved in the inflammatory pathway where the adiponectin level associated with FER variants is also closely involved (Akinyemi et al, 2018; Casas et al, 2019; Huang, Jin, & Yang, 2018; Qi et al, 2011). Ischemic stroke has been reported to be associated with inflammation in disease etiology (Gairolla, Kler, Modi, & Khurana, 2017), the interaction identified between FER and NOS1 might enrich evidence of the genetic understanding of ischemic stroke etiology.…”

Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine

“…It is state-of-the-art to rely on highly curated signaling pathway databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) or WikiPathways, a collection of manually drawn pathway maps representing our apparent knowledge on molecular interactions, reactions and relation networks, or review articles. KEGG, however, shows 29 cyclic guanosine monophosphate (GMP) and 12 reactive oxygen pathways, none of which is comprehensive and all of which fail to cover a recently discovered functional and molecular link between the two, 21 uniting both, in fact, to one network. Moreover, subcellular compartmentalization and transition over time also matter in defining disease modules, 18,21 contributing to further deviation from static pathway concepts.…”