Network pharmacology for cancer drug discovery: are we there yet?
2012; Future Science Ltd; Volume: 4; Issue: 8 Linguagem: Inglês
10.4155/fmc.12.44
ISSN1756-8927
Autores Tópico(s)Monoclonal and Polyclonal Antibodies Research
ResumoFuture Medicinal ChemistryVol. 4, No. 8 EditorialFree AccessNetwork pharmacology for cancer drug discovery: are we there yet?This article is corrected byCorrigendumAsfar S AzmiAsfar S AzmiDepartment of Pathology, Wayne State University School of Medicine, Karmanos Cancer Institute, 4100 John R, HWCRC Room 732, Detroit, MI, USA. Published Online:1 Jun 2012https://doi.org/10.4155/fmc.12.44AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInRedditEmail Keywords: bioinformaticscombination cancer drug discoveryde-risking drugsnetwork medicinenetwork pharmacologysynergistic drug pairssystems biologyIn the era of targeted drug discovery, the major thrust has been on designing small-molecule drugs that show high binding affinity and specificity towards a druggable protein of interest [1]. However, cancer is a complex disease arising from alterations in multiple pathways (biological/protein networks) [2] that have evolved to be very robust [3] and designing drugs against single proteins has yet to yield optimal clinical success [4]. As they are fundamental organizing principles of life, the removal of individual components through single protein-targeted drugs from such networks has surprisingly little functional consequence, even in pathological states of cancer. Such robustness is commonly observed with many other types of highly optimized large-scale networks that have a general property to resist change. To have an impact, interventions within a biological network need to be multiple, but highly selective. Most cancer drugs are designed to target proteins having critical functions that are structurally intricate and deeply seated in complex networks. Predicting the functional outcome of interventions, such as those originating from drug treatment, is not as straightforward as originally thought. So-called 'targeted drugs', no matter how specific, are never chaste and usually show promiscuous behavior, targeting multiple ligands at the same time [5] – hence, both disease understanding and drug discovery requires departure from a single protein or pathway-centric approach to a more holistic multi-pathway or network-centric approach [16].Recent advancements in molecular biology and the emergence of integrated technologies, such as systems biology, have allowed rapid assessment of large expression datasets from cancer patients. These strategies have partially aided target-population stratification based on susceptibility towards single or combination treatment regimens [6]. The emergence of network biology has enhanced our knowledge of multi-pathway interactions in cancer and helped to make sense of drug-response signature datasets that collectively decode the complex mechanisms of drug action [7]. These holistic approaches have laid the foundation for emerging concepts of 'network pharmacology', which is solidifying its position in cancer medicine. Unlike earlier reductionist 'one drug, one target' approaches, network pharmacology invokes the idea that a drug engages with multiple targets and, in fact, rarely with a single protein in isolation. Lacking the proper integrated tools, traditional molecular biology pooled these secondary interactions as off-target effects and many were categorized as toxic side effects. Such a narrow view negatively impacted decisions regarding the clinical future of promiscuous drugs. However, network pharmacology has allowed for a deeper understanding of these secondary drug interactions, revealing that promiscuity often engages a synergistic combination of appropriate high-value targets in cancer cells to produce treatment success [8].Networks are amenable to analysis using several branches of mathematics. The most simple way of representing a biological network is through graph points, more commonly termed nodes or vertices, which could be either genes, proteins or even drugs connected by lines representing interactions (called edges). Local and global properties of this map can be evaluated and neighboring substructures or the role of adjacent nodes can be inferred from information in patterns of connections/interactions. This information can be used to identify sets of high-value nodes, some of which may serve as targets for drugs, depending on the model of interest. Drugs generally exert their effects through binding to proteins, thereby modulating their activity. However, bioactive compounds invariably influence more than one protein, either: as a consequence of structural similarities between the intended target and other proteins; through allosteric effects on other proteins; through pleiotropic mechanisms, where an interaction results in multiple downstream effects on other proteins; or through multivalent target binding by different presentations of the active molecule. Many prodrugs convert to active metabolites and these are subject to an exponential range of possible interactions within a target network or distantly unrelated network proteins. In whatever way these polyvalent interactions occur, the end result is often unpredictable efficacy or reduction in toxicity, or both. It is important to know that the more highly specific and the less promiscuous a drug is for a particular cancer target, the more important that target must be in the cancer network for it to have a significant effect.Network pharmacology takes into account all of the aforementioned principles to optimize the efficacy and safety of a candidate drug and their potent combinations. The first component of network pharmacology is the selection of optimal intervention points in the network(s) of relevance to cancer subtype. Having extracted/curated cancer-specific network data, combinatorial network impact algorithms can be applied to calculate the optimal combination of proteins targeting, which should maximally affect the network(s) while inducing minimal or acceptable modulation in healthy cell networks. Such combinatorial impact analyses can be quantified by many different integrated methods to evaluate how much cancer network integrity can be changed by any specific intervention. Since cancer-related networks typically relate directly to systems-level function or dysfunction, incorporating combinatorial impact in drug design is predicted to influence the disease.Many examples in the literature exist where network pharmacology has been applied to cancer drug discovery. The many applications of this technology include identification of weak nodes in global cancer networks [9], predicting drug toxicity [10], drug repurposing [11] and identifying multi-scale mechanisms of drug action [12]. In this direction, we were among the first to demonstrate the application of network pharmacology in the rational design of potent anticancer drug combinations [13]. Recently, other groups have also performed large-scale evaluations on synergistic pairs [14]. Network pharmacology has also been utilized to discover anticancer effects of drugs developed for other disease. For example, in a highly significant study, chemical systems biology demonstrated that pleiotropic effects against cancer cells by Nelfinavir, a potent HIV protease inhibitor, was due to weak inhibition of multi-kinase activity [15]. This example demonstrates the importance of this highly promising field, still in its infancy. Key points in this area that need extensive evaluation include: ▪ Obtaining network insights on optimally synergistic combinations of targets, where optimality relates to holistic network impact and not only to binding affinity towards a single targeted protein;▪ Selection of drugs that intervene upon the function of weak links in the targeted pathway to exert the desired effect through searching chemoproteomic databases for existing drugs with the desired binding and pleiotropic footprints, and use of reverse chemical engineering (i.e., generating novel compounds with predictive multi-targeted footprints);▪ Computation of the potential impact of proposed compounds on normal cellular networks and selection of most optimal drugs yielding maximum efficacy with minimum effects on healthy cells.Drug discovery in the last two decades has pursued a 'black box' approach in which the pharmaceutical industry handpicked targets and focused on developing small molecules with the highest binding affinity and specificity towards the identified protein of interest. Such reductionist single protein/pathway-centric approaches flooded the market with many promising high-affinity drugs and these were rapidly pushed for clinical evaluation. The cost of bringing one cancer drug to the market ranges from US$2 to $4 billion. These staggering numbers cover the cost starting from high-throughput screening to identification of high-affinity compounds, large-scale chemical synthesis of candidate drugs, extensive preclinical evaluations in different models, multispecies pharmacokinetic/toxicity profiling and finally, the most expensive, clinical testing. Although such candidate drugs have strong preclinical data supporting their anticancer potential, they rarely pass the ultimate test at Phase II and III clinical trials. The majority of these drugs either do not translate their efficacy in patients or elicit serious and sometimes fatal adverse events, leading to their withdrawal because of incomplete understanding of experimental evaluation or scientific explanation. Thus, thousands of candidate molecules in the laboratory are reduced to just a couple for clinical use. This consistent failure indicates that the field of drug discovery needs to be totally revamped in its approach for designing newer molecules. Network pharmacology is one such advancement that holds promise to revolutionize our approach towards next-generation cancer drug design and development. Skeptics have argued that cancer is a heterogeneous biological process carrying a number of variables, and drug behavior cannot be predicted by mathematical or integrated network modeling alone. Recently, researchers have appreciated the power of integrated technologies, which has resulted in multidisciplinary interactions between molecular biologist and bioinformaticists, albeit, still with some skepticism. Therefore, proponents of this technology must be proactive in presenting convincing evidence that promotes these emerging tools and rapidly merges them into mainstream cancer drug discovery. These strategies may help revive some hastily discontinued drugs and could also reduce the cost of new drugs entering the cancer pipeline.But are we there yet? Not quite; however, it is predicted that within the next 10 years, newer technological advances plus methodologies better developed to make analysis software economical, easy-to-use and more acceptable to molecular biologists and pharmaceutical researchers will no doubt result in the use of network analyses as a priority tool in drug development. In conclusion, network pharmacology certainly has the potential to change our view of drug design and understanding mechanisms of action and, if used correctly, is predicted to significantly de-risk cancer drug discovery.AcknowledgementsThe author wishes to give sincere thanks to FWJ Beck for proof reading this article. This editorial is an overview of the theme discussed at the Molecular Medicine Tricon Conference 2012, Drug Discovery and Development Channel, De-risking Drug Discovery. A substantial proportion of the content of this article is taken verbatim from the course materials prepared by Dr A V Whitmore, Dr J Wray and Professor M P Young of e-Therapeutics PLC and their contribution is gratefully acknowledged.Financial & competing interests disclosureThe author has no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.No writing assistance was utilized in the production of this manuscript.References1 Swinney DC, Anthony J. How were new medicines discovered? Nat. Rev. Drug. Discov.10(7),507–519 (2011).Crossref, Medline, CAS, Google Scholar2 Pache RA, Aloy P. A novel framework for the comparative analysis of biological networks. PLoS ONE7(2),e31220 (2012).Crossref, Medline, CAS, Google Scholar3 Larhlimi A, Blachon S, Selbig J, Nikoloski Z. Robustness of metabolic networks: a review of existing definitions. Biosystems106(1),1–8 (2011).Crossref, Medline, Google Scholar4 Gerlinger M, Rowan AJ, Horswell S et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl J. Med.366(10),883–892 (2012).Crossref, Medline, CAS, Google Scholar5 Peters JU, Hert J, Bissantz C et al. 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This editorial is an overview of the theme discussed at the Molecular Medicine Tricon Conference 2012, Drug Discovery and Development Channel, De-risking Drug Discovery. A substantial proportion of the content of this article is taken verbatim from the course materials prepared by Dr A V Whitmore, Dr J Wray and Professor M P Young of e-Therapeutics PLC and their contribution is gratefully acknowledged.Financial & competing interests disclosureThe author has no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.No writing assistance was utilized in the production of this manuscript.PDF download
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