Network pharmacology: curing causal mechanisms instead of treating symptoms

Current diseases are defined by a phenotype rather than by a disease mechanism. Thus, we hardly understand any disease mechanistically and treat symptoms chronically with low precision.When a mechanism is described, it often involves single targets (e.g., rare, typically monogenic diseases).In the case of complex diseases, the current 'one disease–one target–one drug' dogma will hardly yield any result when in fact, their causes are small signaling networks.Signaling pathways are currently defined by highly curated mind maps capturing our current understanding of (patho)biology. However, many pathophysiologically relevant signaling mechanisms are likely unknown and can be revealed by unbiased de novo interactome modules.These knowledge gaps will be overcome by systems and network medicine, redefining what we call disease, how we diagnose it, and how we cure, not treat, it. For complex diseases, most drugs are highly ineffective, and the success rate of drug discovery is in constant decline. While low quality, reproducibility issues, and translational irrelevance of most basic and preclinical research have contributed to this, the current organ-centricity of medicine and the 'one disease–one target–one drug' dogma obstruct innovation in the most profound manner. Systems and network medicine and their therapeutic arm, network pharmacology, revolutionize how we define, diagnose, treat, and, ideally, cure diseases. Descriptive disease phenotypes are replaced by endotypes defined by causal, multitarget signaling modules that also explain respective comorbidities. Precise and effective therapeutic intervention is achieved by synergistic multicompound network pharmacology and drug repurposing, obviating the need for drug discovery and speeding up clinical translation. For complex diseases, most drugs are highly ineffective, and the success rate of drug discovery is in constant decline. While low quality, reproducibility issues, and translational irrelevance of most basic and preclinical research have contributed to this, the current organ-centricity of medicine and the 'one disease–one target–one drug' dogma obstruct innovation in the most profound manner. Systems and network medicine and their therapeutic arm, network pharmacology, revolutionize how we define, diagnose, treat, and, ideally, cure diseases. Descriptive disease phenotypes are replaced by endotypes defined by causal, multitarget signaling modules that also explain respective comorbidities. Precise and effective therapeutic intervention is achieved by synergistic multicompound network pharmacology and drug repurposing, obviating the need for drug discovery and speeding up clinical translation. For several drugs already on the market, population-based studies fail to show patient-relevant benefits [1.Wieseler B. et al.New drugs: where did we go wrong and what can we do better?.BMJ. 2019; 366l4340Crossref PubMed Scopus (54) Google Scholar]. In fact, the ten highest-grossing drugs in the USA fail to improve the conditions for most patients, leading to high numbers needed to treat (NNT) [2.Schork N.J. Personalized medicine: time for one-person trials.Nature. 2015; 520: 609-611Crossref PubMed Scopus (586) Google Scholar]. In high-risk patients, the NNTs are smaller, but the problem persists [3.Root A.A. Smeeth L. NNTs and NNHs: handle with care.Br. J. Gen. Pract. 2017; 67: 133Crossref PubMed Scopus (0) Google Scholar]. Thus, a move from chronically treating symptoms towards a more precise and ideally curative therapy, effective for almost every patient, is of utmost importance. Since the 1950s, we have observed a constant decline in our efficacy to translate biomedical research into successful drug discovery, coined as Eroom's law [4.Scannell J.W. et al.Diagnosing the decline in pharmaceutical R&D efficiency.Nat. Rev. Drug Discov. 2012; 11: 191-200Crossref PubMed Scopus (1105) Google Scholar, 5.Loscalzo J. Personalized cardiovascular medicine and drug development: time for a new paradigm.Circulation. 2012; 125: 638-645Crossref PubMed Scopus (0) Google Scholar, 6.Nosengo N. Can you teach old drugs new tricks?.Nature. 2016; 534: 314-316Crossref PubMed Scopus (261) Google Scholar]. Overcoming this requires entirely new approaches to medicine and the acknowledgment of at least two key factors contributing to this innovation roadblock. One factor is the irreproducibility of preclinical and basic research [7.Prinz F. et al.Believe it or not: how much can we rely on published data on potential drug targets?.Nat. Rev. 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Moreover, our preclinical animal models of disease can often only mimic these symptoms, without any evidence that the mechanism causing the symptoms in the animal model matches the human disease [10.Dornas W.C. Silva M.E. Animal models for the study of arterial hypertension.J. Biosci. 2011; 36: 731-737Crossref PubMed Scopus (92) Google Scholar, 11.Segal-Lieberman G. Rosenthal T. Animal models in obesity and hypertension.Curr. Hypertens. Rep. 2013; 15: 190-195Crossref PubMed Scopus (9) Google Scholar, 12.Fluri F. et al.Animal models of ischemic stroke and their application in clinical research.Drug Des. Devel. Ther. 2015; 9: 3445-3454PubMed Google Scholar, 13.Shanks N. et al.Are animal models predictive for humans?.Philos. Ethics Humanit. Med. 2009; 4: 2Crossref PubMed Scopus (375) Google Scholar, 14.O'Collins V.E. et al.1,026 experimental treatments in acute stroke.Ann. 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Nevertheless, although most patients at risk are successfully treated with antihypertensives, they will still experience these adverse outcomes. Thus, our current treatment options for complex diseases are neither curative nor precise and require chronic treatment [16.Ogden L.G. et al.Long-term absolute benefit of lowering blood pressure in hypertensive patients according to the JNC VI risk stratification.Hypertension. 2000; 35: 539-543Crossref PubMed Google Scholar]. Noteworthy exceptions to these limitations and shortcomings are, again, rare diseases, where a precise, typically monogenetic, mechanism is known. The fundamental and conceptual breakthrough to redefine diseases is to move from symptom and organ to mechanism and cause, as conceptually shown in the network of all human diseases, the diseasome (see Glossary) (Figure 1) [17.Goh K.-I. et al.The human disease network.Proc. Natl. Acad. Sci. U. S. A. 2007; 104: 8685-8690Crossref PubMed Scopus (2308) Google Scholar,18.Goh K.-I. Choi I.-G. Exploring the human diseasome: the human disease network.Brief. Funct. Genomics. 2012; 11: 533-542Crossref PubMed Scopus (0) Google Scholar]. In the first version, diseases were linked by joint risk genes in a scale-free network and clustered by several shared risk genes. These clusters of diseases thus hinted towards a common causal mechanism [17.Goh K.-I. et al.The human disease network.Proc. Natl. Acad. Sci. U. S. A. 2007; 104: 8685-8690Crossref PubMed Scopus (2308) Google Scholar]. Later, other multiscale disease networks were formed based on shared symptoms, drugs, or comorbidities [19.Menche J. et al.Uncovering disease-disease relationships through the incomplete interactome.Science. 2015; 3471257601Crossref PubMed Scopus (674) Google Scholar]. Interestingly, most disease clusters contain disease phenotypes of different organs, which substantiates the notion that organ- and symptom-based disease classifications are obsolete and rather obstruct innovation. Thus, these phenotypes are no longer considered the disease definitions but rather the symptoms of their underlying common causal molecular mechanisms. Once elucidated, these mechanisms will become the new disease definitions, the endotypes. These endotypes are constructed from associated risk, driver genes, proteins, and drug targets to form a de novo disease signaling network or disease module [19.Menche J. et al.Uncovering disease-disease relationships through the incomplete interactome.Science. 2015; 3471257601Crossref PubMed Scopus (674) Google Scholar]. One disease phenotype or symptom may be caused by different mechanisms that may be acting together (Figure 2). The validity of these disease modules is essential for precision medicine because they represent new targets for both: (i) diagnostic strategies for patients-at-risk identification and subsequent mechanistic stratification, and (ii) therapeutic strategies to modulate the disease module by network pharmacology. Once all current disease phenotypes are fully endotyped and mechanistically understood, they will segregate into several distinct molecular disease mechanisms and endotypes [20.Vallance P. An audience with Patrick Vallance.Nat. Rev. Drug Discov. 2010; 9: 834Crossref PubMed Scopus (0) Google Scholar]. Consequently, many common or complex disease phenotypes will split up into several rarer and less complex endotypes. Unlike in monogenetic rare diseases, endotypes are caused by a signaling network's dysregulation rather than a single protein [19.Menche J. et al.Uncovering disease-disease relationships through the incomplete interactome.Science. 2015; 3471257601Crossref PubMed Scopus (674) Google Scholar]. Given the redundancy and resilience of signaling networks [21.Gao J. et al.Universal resilience patterns in complex networks.Nature. 2016; 536: 238Crossref PubMed Scopus (3) Google Scholar], the current practice of modulating a single target per disease explains why the 'one disease–one target–one drug' approach has been insufficient. Even combination therapy with drugs targeting single, mechanistically unrelated, and noncausal proteins is no exception to this. Instead, concerted network modulation with multiple mechanistically related drugs will be much more effective [22.Cheng F. et al.Network-based prediction of drug combinations.Nat. Commun. 2019; 10: 1197Crossref PubMed Scopus (166) Google Scholar]. Defining these signaling modules is not trivial, despite the availability of extensive literature and highly curated signaling pathway databases such as Kyoto Encyclopedia of Genes and Genomes (KEGG)i [23.Kanehisa M. Goto S. KEGG: Kyoto encyclopedia of genes and genomes.Nucleic Acids Res. 2000; 28: 27-30Crossref PubMed Google Scholar] or WikiPathwaysii [24.Slenter D.N. et al.WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research.Nucleic Acids Res. 2018; 46: D661-D667Crossref PubMed Scopus (341) Google Scholar] (see Outstanding questions). These databases are primarily collections of manually curated pathway maps that represent our current knowledge of molecular interactions. Importantly, they fail to reflect that biological pathways are not isolated but are connected in different functional contexts. Moreover, curated pathways imply that all its components are in direct contact, which is not the case. Instead, signaling elements such as cAMP and calcium are typically distributed in different parts over several subcellular compartments. Indeed, recent developments in cAMP signaling have highlighted the existence of nanodomains, although still from a canonical signaling pathway point of view [25.Bock A. et al.Optical mapping of cAMP signaling at the nanometer scale.Cell. 2020; 182: 1519-1530Abstract Full Text Full Text PDF PubMed Scopus (34) Google Scholar,26.Omar M.H. Scott J.D. AKAP signaling islands: venues for precision pharmacology.Trends Pharmacol. Sci. 2020; 41: 933-946Abstract Full Text Full Text PDF PubMed Scopus (13) Google Scholar]. Moreover, these signaling elements also interact with different pathways (e.g., the cAMP–cGMP crosstalk) and form hybrid domains composed of elements from distinct signaling principles. Nevertheless, subcellular compartmentalization and even their transition over time matter in defining disease modules [19.Menche J. et al.Uncovering disease-disease relationships through the incomplete interactome.Science. 2015; 3471257601Crossref PubMed Scopus (674) Google Scholar]. Thus, for pharmaco-therapeutic purposes, not only the present concept of disease but also of cellular signaling must be revised. Classical, canonical, or curated pathways are close to meaningless if we want to define disease modules. Leveraging the power of networks in the context of complex diseases requires conceptually novel experimental and, above all, computational approaches that have been uncommon to pharmacology. To construct de novo disease modules, we need to discern between methods using existing molecular interaction networks, such as, for instance, protein–protein interaction (PPI) or gene-regulatory networks, and methods that infer context-specific networks directly from disease-specific data. Such networks can be dissected using community detection or network module identification methods. Recently, the DREAM challenge has demonstrated that such methods are generally suited to discover disease modules [27.Choobdar S. et al.Assessment of network module identification across complex diseases.Nat. Methods. 2019; 16: 843-852Crossref PubMed Scopus (59) Google Scholar]. Alternatively, de novo network enrichment is a popular strategy in which omics data such as gene expression or single-nucleotide variants are projected onto a network for extracting disease modules enriched with genes or proteins for some physiologically relevant measure, such as differential gene expression or high somatic mutation load [28.Batra R. et al.On the performance of de novo pathway enrichment.NPJ Syst. Biol. Appl. 2017; 3: 6Crossref PubMed Scopus (28) Google Scholar]. Although these methods hold great promise for disease module detection, context-specific networks are urgently needed to improve their performance [29.Lazareva O. et al.On the limits of active module identification.Brief. Bioinform. 2021; 22bbab066Crossref PubMed Scopus (0) Google Scholar]. Network inference methods use bulk or single-cell transcriptomics together with other omics data to determine associations between genes, typically using a (partial) correlation, (conditional) mutual information, or machine learning approaches [30.Mochida K. et al.Statistical and machine learning approaches to predict gene regulatory networks from transcriptome datasets.Front. Plant Sci. 2018; 9: 1770Crossref PubMed Scopus (1) Google Scholar]. The inferred networks offer insights into disturbed gene regulation within signaling pathways in diseases and lead to the identification of putative drug targets and experimentally testable hypotheses. In the context of complex age-related diseases, experimental approaches need to focus on the study of homeostasis processes and the identification of key ubiquitous signaling proteins that are sensitive to protein activity or abundance changes. Manipulation of protein abundance at those 'tipping points' may steer networks to a more physiologically effective state and slow disease emergence [31.Vinayagam A. et al.Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets.Proc. Natl. Acad. Sci. U. S. A. 2016; 113: 4976-4981Crossref PubMed Scopus (0) Google Scholar]. For instance, computational models assisted in determining large-scale network behavior in complex retinal degeneration [32.Luthert P.J. et al.Opportunities and challenges of whole-cell and -tissue simulations of the outer retina in health and disease.Ann. Rev. Biomed. Data Sci. 2018; 1: 131-152Crossref Google Scholar]. Notably, disease modules require clinical proof-of-concept and pharmacological validation. Networks provide a broader selection of pharmacologically relevant targets. If a preferred target is not druggable, a neighboring target protein may compensate for this. Moreover, with 4196 approved drugs (of which 2700 are small molecule drugs; DrugBankiii [33.Wishart D.S. et al.DrugBank: a comprehensive resource for in silico drug discovery and exploration.Nucleic Acids Res. 2006; 34: D668-D672Crossref PubMed Google Scholar]), it is quite likely that at least one drug is already available for any given causal disease module, obviating the need for time-consuming drug discovery and development. Based on the PISCESiv dataset, registered drugs bind with high affinity to conserved binding pockets of, on average, 39 proteins [34.Wang G. Dunbrack R.L. PISCES: a protein sequence culling server.Bioinformatics. 2003; 19: 1589-1591Crossref PubMed Scopus (1192) Google Scholar,35.Chartier M. et al.Large-scale detection of drug off-targets: hypotheses for drug repurposing and understanding side-effects.BMC Pharmacol. Toxicol. 2017; 18: 18Crossref PubMed Scopus (23) Google Scholar]. Thus, small-molecule drugs are highly promiscuous and can even be repurposed from one to many other target proteins with similar binding sites. Repurposing registered drugs with a known safety profile may be so powerful that it may rapidly address therapeutic needs in many different causal disease modules and outcompete classical drug discovery. Thus, we may already have almost all the drugs we need [36.Kellenberger E. et al.sc-PDB: an annotated database of druggable binding sites from the Protein Data Bank.J. Chem. Inf. Model. 2006; 46: 717-727Crossref PubMed Scopus (0) Google Scholar,37.Sperandio O. et al.Rationalizing the chemical space of protein-protein interaction inhibitors.Drug Discov. Today. 2010; 15: 220-229Crossref PubMed Scopus (153) Google Scholar]. Rather than relying on serendipitous drug repurposing or high-throughput screening of small compounds to identify candidates, computational approaches leverage molecular networks and known drug–target interactions. Such methods first need to identify suitable drug targets that lie in one or several disease modules. Here, prior knowledge of a disease can be incorporated to guide the search (i.e., in the form of seed nodes) [38.Elbatreek M.H. et al.NOX5-induced uncoupling of endothelial NO synthase is a causal mechanism and theragnostic target of an age-related hypertension endotype.PLoS Biol. 2020; 18e3000885Crossref PubMed Scopus (5) Google Scholar]. Subsequently, drugs targeting the disease module can be extracted. For example, the web application CoVexv integrates drug–target interaction and PPI data to facilitate drug target discovery as well as the search for repurposable drug candidates for severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1) and SARS-CoV-2 using known virus–host PPIs, as well as transcriptomics data [39.Sadegh S. et al.Exploring the SARS-CoV-2 virus-host-drug interactome for drug repurposing.Nat. Commun. 2020; 11: 3518Crossref PubMed Scopus (63) Google Scholar]. An advantage of identifying disease modules is that multiple actionable drug targets can often be identified and leveraged for the development of network pharmacology therapy [40.Aguirre-Plans J. et al.GUILDify v2.0: a tool to identify molecular networks underlying human diseases, their comorbidities and their druggable targets.J. Mol. Biol. 2019; 431: 2477-2484Crossref PubMed Scopus (11) Google Scholar]. Network pharmacology approaches use two or more drugs acting mechanistically on the same causal signaling disease module, thus targeting key network proteins in a synergistic manner (Figure 3). This allows network pharmacology-based treatments to substantially lower the dose of each drug as compared with monotherapy and still achieve the same or even a more significant therapeutic effect while reducing: (i) side effects of each individual drug, and (ii) possible unwanted drug–drug interactions [41.Casas A.I. et al.From single drug targets to synergistic network pharmacology in ischemic stroke.Proc. Natl. Acad. Sci. U. S. A. 2019; 116: 7129-7136Crossref PubMed Scopus (0) Google Scholar, 42.Hopkins A.L. Network pharmacology: the next paradigm in drug discovery.Nat. Chem. Biol. 2008; 4: 682-690Crossref PubMed Scopus (2127) Google Scholar, 43.News in brief.Nat. Rev. Drug Discov. 2012; 11 (589–589)Google Scholar]. Notably, the concept of network pharmacology must not be confused with combination therapy (Figure 3), where drugs acting symptomatically on unrelated targets are combined, but none of them acts on a causal disease mechanism. Such combination therapies are, at best, additive and will not show any pharmacological synergy. Moreover, drug combinations can easily get out of control when polypharmacy results in four or more drugs being prescribed with unwanted drug–drug interactions and side effects [44.Hajjar E.R. et al.Polypharmacy in elderly patients.Am. J. Geriatr. Pharmacother. 2007; 5: 345-351Abstract Full Text PDF PubMed Scopus (779) Google Scholar]. In complex diseases that harbor robust biological networks, such as cancer, single target intervention has been proved ineffective and insufficient [45.Turke A.B. et al.Preexistence and clonal selection of MET amplification in EGFR mutant NSCLC.Cancer Cell. 2010; 17: 77-88Abstract Full Text Full Text PDF PubMed Scopus (818) Google Scholar,46.Maeda H. Khatami M. Analyses of repeated failures in cancer therapy for solid tumors: poor tumor-selective drug delivery, low therapeutic efficacy and unsustainable costs.Clin. Transl. 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Biomarkers thus become a critical diagnostic tool in disease identification, indicating a biological state and resulting in precision medicine [48.Laifenfeld D. et al.Early patient stratification and predictive biomarkers in drug discovery and development: a case study of ulcerative colitis anti-TNF therapy.Adv. Exp. Med. Biol. 2012; 736: 645-653Crossref PubMed Scopus (16) Google Scholar, 49.Carrigan P. Krahn T. Impact of biomarkers on personalized medicine.Handb. Exp. Pharmacol. 2016; 232: 285-311Crossref PubMed Scopus (6) Google Scholar, 50.Liu R. et al.Early diagnosis of complex diseases by molecular biomarkers, network biomarkers, and dynamical network biomarkers.Med. Res. Rev. 2014; 34: 455-478Crossref PubMed Scopus (164) Google Scholar]. Currently, biomarkers are mainly used as correlative surrogates or omics-based indicators. Less frequently do current biomarkers represent validated risk factors. 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