Unveiling the role of network and systems biology in drug discovery
Upcoming SlideShare
Loading in...5
×
 

Like this? Share it with your network

Share

Unveiling the role of network and systems biology in drug discovery

on

  • 1,779 views

 

Statistics

Views

Total Views
1,779
Views on SlideShare
1,779
Embed Views
0

Actions

Likes
2
Downloads
52
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Unveiling the role of network and systems biology in drug discovery Document Transcript

  • 1. Review Unveiling the role of network and systems biology in drug discovery Albert Pujol1,2, Roberto Mosca1, Judith Farres2 and Patrick Aloy1,3 ´ 1 Institute for Research in Biomedicine and Barcelona Supercomputing Center. c/Baldiri i Reixac 10-12, 08028 Barcelona, Spain 2 Anaxomics Biotech. c/Balmes 89, 08008 Barcelona, Spain 3 Institucio Catalana de Recerca i Estudis Avanc ´ ¸ats (ICREA), Pg. Lluıs Companys 23, 08010 Barcelona, Spain ´ Network and systems biology offer a novel way of process permitted pharmaceutical companies to ‘handpick’ approaching drug discovery by developing models that those proteins that they thought would make a good target, consider the global physiological environment of protein and rationally design compounds to interfere with them. As targets, and the effects of modifying them, without a consequence, the initial discovery phases removed the losing the key molecular details. Here we review some targets from their physiological context to study them at recent advances in network and systems biology applied quasi-atomic level, and focused on the optimization of the to human health, and discuss how they can have a big target-compound duet, placing a special emphasis in impact on some of the most interesting areas of drug increasing binding affinity and target selectivity [10]. Thus, discovery. In particular, we claim that network biology the criteria to evaluate the potential of a novel molecule will play a central part in the development of novel shifted from a strict physiological observation of the results polypharmacology strategies to fight complex multifac- obtained with the assayed compounds to a molecular one, torial diseases, where efficacious therapies will need to where the best lead chemicals were those displaying a center on altering entire pathways rather than single strong binding with the target protein and a good specificity proteins. We briefly present new developments in the profile (i.e. binding to only one target). However, induction of two areas where we believe network and system biology a disease state is often the result of an incredibly complex strategies are more likely to have an immediate contri- combination of molecular events [11] and, despite several bution: predictive toxicology and drug repurposing. success stories, the reductionist approach adopted also had striking consequences. For instance, many promising drug Introduction candidates failed the last (and most expensive) clinical Fifty years ago, the first steps in a drug discovery process phases because the action mechanisms of the pathways they were mostly driven by the response to assayed molecules target are incompletely understood or due to an inappropri- observed in animal models, what we would today call ate choice of in-vitro cellular models that proved ineffective ‘advanced pre-clinical tests’. The rating of potential drug at predicting off-target effects [1,12]. Approaches using net- compounds was thus based on their ability to generate the work and systems biology hold the promise to take protein desired detectable changes in the physio-pathological targets back to their physiological context, considering a states of the animals, and little attention was paid to other, much broader systemic perspective of their environment more biochemical, aspects such as binding affinities of the without losing the molecular details. If successful, these compound to its primary targets or its specificity. This interrelated disciplines could represent the next step in drug means that most early ‘go’ or ‘no-go’ decisions on different discovery, fostering the conception of mechanism-based molecules were taken on the basis of their global pharma- drug design. cological properties under physiological conditions [1]. Biological interaction networks have been in the scien- In the early 1980s, the development and broad imple- tific limelight for nearly a decade, but it has been in the mentation of methods to isolate and study individual cells last five years that the concept of network biology, and its and molecules significantly increased our understanding of various applications, has became commonplace in the the individual players taking part in complex biological community [13]. Despite being incomplete and error- processes, placing molecular biology in a privileged position prone, the initial versions of human interactome net- among biological sciences. Recent years have seen the works [3–5] are of sufficient quality to provide useful climax of these component-based approaches with genome information [14]. Indeed, several models and analytical sequencing projects providing nearly complete lists of the measurements borrowed from graph theory have been genes and gene products found in the human body [2], first tried to decipher biological networks, in particular Baye- drafts of connectivity maps between proteins [3–5], gene sian networks, which have given the most promising expression profiles for many different tissues and conditions results (Box 1). Partly because of these analytical tech- [6,7], and initial quantifications of metabolites [8,9]. This big niques, network biology is already making important success experienced by molecular biology also triggered a contributions to biomedical research, and it is clear that deep change of strategy in the drug discovery process: it will play a pivotal role in the future of drug discovery. knowing the molecules involved in a certain pathological For instance, interaction discovery experiments, com- bined with computational analyses, have deciphered Corresponding author: Aloy, P. (patrick.aloy@irbbarcelona.org) the first draft of the human B-lymphocyte interactome 0165-6147/$ – see front matter ß 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.tips.2009.11.006 Available online 1 February 2010 115
  • 2. Review Trends in Pharmacological Sciences Vol.31 No.3 Box 1. Network models for molecular interactions Networks (i.e. graphs of connected nodes) are used in systems  Motif analysis is the identification of small network patterns (or biology to represent the different types of relationships between subgraphs) that are over-represented when compared with a biological entities such as genes, proteins, chemical compounds, and randomized version of the same network. Discrete biological transcription factors. These biological components typically represent processes such as regulatory elements are often composed of such network nodes, and they are connected through edges that illustrate motifs [82,83]. their inter-relationships, from physical or functional associations to metabolic pathways and regulatory networks. Furthermore, recently developed network alignment and com- Different models have been applied to their analysis, for instance, parison tools [84–86] can identify similarities between networks Power Graphs [68] and the Information Flow Model [69]. Bayesian (such as common subgraphs) and have been used to study networks, however, are the most commonly used due to their evolutionary relationships between protein networks of several capacity for expressing causal relationships, and learning from organisms [87]. incomplete datasets while avoiding overfitting problems. In Bayesian networks, network nodes represent random variables (e.g. functional classification of a protein) and the edges conditional probabilities between them. In systems biology, they have been applied to gene expression analysis, cellular networks inference [70], pathway modeling [71], prediction and assessment of the quality of protein– protein interactions [72] and functional annotation of proteins [73]. Another commonly used model are Boolean networks, in which every node can have two states (on/off) representing, respectively, an active or inactive gene. The state of a node is affected by the other nodes (genes) connected to it, allowing representation of complex regulatory systems. Boolean networks, together with Bayesian networks, have also been used to model the dynamical behavior of gene regulatory circuits [74]. Apart from these networks, recent years have seen an ever-growing tendency to combine different types of networks to attempt modeling of more complex types of inference [15,75–77]. Computational measurements for the analysis of molecular interac- tion networks There are several topological aspects of biological networks that have been proved to be useful for inferring functional properties (see [78] for a review). The most common aspects are listed below.  Network statistics and topological features such as node centrality, between-ness, degree distribution of nodes, clustering coefficient, shortest path between nodes and robustness of the network to the random removal of single nodes.  Modularity refers to the identification of sub-networks of inter- connected nodes that might represent molecules physically or functionally linked that work coordinately to achieve a specific function [79–81]. from indirect expression data, which helped to identify modulated by complex genetic loci and environmental deregulated interactions in specific pathological or factors [19]. We anticipate that as the coverage, quality physiological phenotypes, as well as causal lesions in and variety of protein interaction data improve, the several well-studied B-cell malignancies [15]. Network- number of approaches exploiting emerging network biology approaches have also been successfully used in properties will grow. tumor research, and the analysis of the disease-network The definition of systems biology is somehow elusive associated to BRCA1 permitted identification of novel because it means different things to different people, from genes associated with a higher risk of breast cancer, in those who think of it as the development of large-scale the process uncovering a genetic link with centrosome experiments with the aim to understand how the whole is dysfunction [16]. Also, a strategy based on monitoring greater than the sum of its parts to those that consider it a transcriptional responses induced by the perturbation branch of mathematical biology and metabolic modelling. of candidate regulators have permitted derivation of Yet, it is clear that systems biology should include the regulatory networks mediating pathogen responses quantitative component that is missing in network biology in primary mammalian cells [17]. The most exciting [20]. This means that, unlike network biology, we should discovery, however, is that current models are already not only identify the physical or functional relationships sufficiently accurate to allow global properties of the between the different components shaping a biological networks to emerge. A recent study illustrated how func- system, but also measure their concentrations in the stu- tional properties arising directly from the topology of the died physio-pathological cellular states and the kinetic network can be used to identify novel markers for breast parameters governing these interactions. Fortunately, cancer metastasis [18]. Additionally, by integrating co- recent years have seen the development of many tech- expression and genotypic data, it has been possible to niques that can provide quantitative measurements in demonstrate that complex traits such as obesity are high-throughput experiments devoted to unveil the cell emergent properties of molecular networks that are networks underlying biological principles (Box 2). Taken 116
  • 3. Review Trends in Pharmacological Sciences Vol.31 No.3 Box 2. Quantitative techniques for systems biology can have a big impact in some of the most important areas of drug discovery. Several quantitative experimental techniques have been developed in recent years that are applied in approaches using network biology and systems biology to human health. Complex diseases require complex therapeutic Expression-quantitative trait locus (e-QTL) is the composition of approaches classical QTL analyses with gene expression profiling (i.e. by DNA Mathematical systems theory states that ‘the scale and microarrays). It provides information about the expression variation of genetically diverse individuals, and in recent years has been used complexity of the solution should match the scale and to identify networks of genes involved in disease pathogenesis [88]. complexity of the problem’ [25], and biology is no exception. Quantitative mass spectrometry is an analytical technique that In recent years, it has become apparent that many common permits the identification of the chemical composition of com- diseases such as cancer, cardiovascular disease as well as pounds on the basis of the mass-to-charge ratios of charged mental disorders are much more complex than initially particles. Technical advances in mass spectrometry-based proteo- mics has allowed it to be applied to measure changes in protein anticipated because they are often caused by multiple abundance, post-translational modifications and protein–protein molecular abnormalities, rather than being the result of interactions in mutants at the proteome scale [89,90]. a single defect [26,27]. Recent outstanding works in cancer Quantitative surface plasmon resonance (SPR) is an optical tech- genetics have shown that there are many diverse genetic nique based on a biosensor that measures molecular binding events routes that might perturb certain cellular pathways, lead- at a metal surface by detecting changes in the local refractive index. Coupling of SPR with new protein array technologies may allow ing to the origination of, for instance, pancreatic cancer development of high-throughput SPR methods to analyze pathways, [28–30]. Moreover, these studies highlighted that, despite screen drug candidates, and monitor protein–protein interactions the great genetic variation observed among each type of [91,92]. cancer in different patients, they clearly share common Isothermal titration calorimetry (ITC) is a quantitative technique that features at the protein pathway level, which supports the can directly measure the binding affinity (Ka), enthalpy changes (DH), and binding stoichiometry (n) of the interaction between two or more view that cancer is a ‘disease of pathways’. It therefore molecules in solution by measuring the heat released or absorbed seems clear that modulating a single target, even with a during a biomolecular binding event. very efficient drug, is unlikely to yield the desired outcome. Fluorescence techniques benefit from the labeling of bio-molecules A growing perception is that we should increase the level of with fluorophores to identify the variation of their concentration in time during a binding event. Fluorescence detection by confocal micro- complexity of our proposed therapies by changing the way scopy and fluorescence spectroscopy techniques have been success- we think about complex diseases from a gene-centric to a fully applied to determination of dynamic constants for protein binding network-centric view [31]. [93,94]. This novel network perspective of complex diseases Nuclear magnetic resonance (NMR) spectroscopy uses the shift on has many implications in the drug discovery process the resonant frequencies of the nuclei present in a sample to obtain physical, chemical, electronic and structural information about mol- because the entity that needs to be targeted and modu- ecules. It has been shown to be applicable to the quantitative charac- lated must change from single proteins to entire molecu- terization of dynamic binding constants for protein–DNA [95] and lar pathways and cell networks. A detailed interaction protein–protein complexes [96]. map of the studied disease becomes essential to rationally decide which are the optimal points for therapeutic intervention [32] (i.e. the collection of drug targets to together, the data generated by these novel techniques modify ill-functioning cellular routes). Using reliable should move forward systems biology to allow simulation of interactome maps to select these strategic network nodes how the molecules function in coordination to achieve a enables consideration of the robustness of the system, particular outcome and, consequently, confer it the tre- which is not possible in gene-centered approaches mendous power of predicting the result of yet unstudied because they mostly disregard the biological context of perturbations (i.e. the effect of modulating the function of a the target. Biological systems such as disease states are, given protein). Despite the lack of quantitative data, in general, resistant to perturbations and they can main- models based on systems biology already account for a tain their functions through different mechanisms such few success stories in the pharmaceutical and biotechno- as back-up circuits and fail-safe mechanisms (i.e. redun- logical sectors. For instance, inclusion of synthetic path- dancy and diversity) [33]. Therefore, the selection process ways designed from a systemic perspective in yeast have of putative drug targets should also consider their posi- led to a drastic reduction of the production costs of arte- tioning in the network, preferring those enclaves that are misinin, a compound that has proved to be very effective essential to drive the network traffic and, simultaneously against multidrug-resistant strains of the malaria parasite can avoid back-up circuits that could neutralize the drug Plasmodium falciparum [21]. Unfortunately, the detailed effect. Interestingly, recent observations have also shown level of knowledge required to apply systems-biology strat- that the wiring of interaction networks can change from egies to their full potential is available for only very small healthy to diseased states (see [34] for a recent review), and controlled biological systems such as some bacteria and charting such changes would be an excellent guide to [22] or intracellular organelles [23], which virtually pre- suggest potential points of intervention. For instance, cludes direct application of these models to human health. several signaling pathways involved in liver function In the absence of any ‘blockbuster’ drug developed with a show different functional wiring in receptor–nucleus paramount contribution of systems-biology models, it is not downstream routes when comparing normal hepatocytes surprising that the pharmaceutical industry remains scep- with HepG2-transformed cells, an observation that has tical about these novel technologies [24]. In the following already caught the attention of the pharmacuetical sections, we will discuss how network and systems biology industry [35]. 117
  • 4. Review Trends in Pharmacological Sciences Vol.31 No.3 Network-centric therapeutic approaches imply to target like a promising idea and, as described above, has achieved entire pathways rather than single proteins. The final goal several remarkable results. However, there is the percep- of these approaches would not only be to identify drugs that tion among drug development companies that simul- can be prescribed together, but a combination of targets and taneous administration of several compounds would modulators acting on different therapeutic areas that can necessarily imply more undesired off-target effects. For- produce more-than-additive (e.g. synergistic) effects trig- tunately, this is not necessarily the case. Use of several gered by actions converging at a specific pathway site. This drugs with synergistic effects acting on unrelated targets might sound like an impossible quest, but >100 drug syner- might permit considerable reduction of the dose of each gistic cases have been recently reported or are currently individual compound and thus the derived adverse events being commercialized (see [36] for a review). Experimental [41]. Conversely, it has recently been shown that synergis- therapies against breast cancer metastasis represent one of tic drug combinations tend to display a greater selectivity, the few examples where drug combinations have been being more specific to particular cellular phenotypes than rationally designed from the extensive knowledge of the single drugs, thus synergistic toxicity is not expected [42]. pathways involved. In this case, after elucidating that the We anticipate that the considerable efforts currently being epidermal growth factor receptor (EGF-R) ligand eregulin devoted to chart the cell networks related to complex (EREG), the cyclo-oxygenase COX2 and the matrix metal- diseases, in combination with systems-biology method- loproteases 1 and 2 (MMP1, MMP2) were key genes to ologies to identify and validate target combinations and trigger cancer metastasis from the breast to the lungs, cells optimize multiple structure–activity relationships while were treated with the anti-EGFR antibody cetuximab, the maintaining drug-like properties, will permit rapid devel- COX2 inhibitor celecoxib, and the broad-spectrum MMP opment of network-based therapies [43]. inhibitor GM6001, to produce a spectacular reduction of metastatic extravasation [37]. The more the merrier The synergistic drug effects obtained through combi- For almost a century drug discovery was driven by the nation of two or more compounds can be the result of very quest for ‘magic bullets’ that acted by targeting one diverse strategies. This can be from direct interference particular and critical point or step in a disease process, with several points in the same pathway to negative and thus effect cure with few other consequences. Many regulation of network compensatory responses (i.e. back- drugs have been designed rationally in this manner but, up circuits), drug resistance sources, or anti-target and after 20 years, it appears that this single target-based counter-target activities. An example of a synergistic effect approach does not guarantee success and might not be reached through the complementary action on two differ- the best strategy. As well as the impossibility of finding a ent targets of cross-talking pathways is the combination of single intervention point to efficaciously fight complex taxane and gefinitib in anti-cancer therapies (Figure 1). diseases, some specific drugs were directed towards targets Taxane produces an anti-cancer effect by inducing apop- that resulted not essential for the pathophysiology of the tosis and disruption of microtubule dynamics. However, disease, eventually became inactive because of gradually the observed cross-talk between the EGF-R and hypoxia- increasing resistance of cells [44] or generated unpredicted inducible factor-1 alpha (HIF1a) pathways increases the effects on off-target biochemical mechanisms [12] . resistance to apoptosis by upregulating survival. In this Even one of the most recent success stories of rational case, the addition of gefinitib, with its EGF-R tyrosine drug design of a magic bullet appeared to be not as suc- kinase inhibiton activity, offsets the counteractive EGF- cessful as initially thought. The Novartis blockbuster drug R–hypoxia cross-talk in resisting the pro-apoptosis activity imatinib mesylate (gleevec) was designed to act on a single of taxane displaying a strong synergistic effect in breast aberrant protein expressed in cancerous cells, specifically cancer MCF7/ADR cells [38]. killing them while leaving healthy cells unharmed. How- Drug combinations can also display pharmaco-kineti- ever, it was soon discovered that it also bound with sig- cally potentiative or reductive effects such that the thera- nificant affinities to the platelet-derived growth factor peutic activity of one drug is enhanced or reduced by receptor (PDGF-R) and c-kit, which confers it with remark- another drug via regulation of its absorption, distribution, able properties against gastrointestinal stromal tumors, metabolism and excretion (i.e. ADME). A good example of directly linked to malfunctioning of the latter protein [45]. this is the widely used combination of amoxicillin and Today, the emerging picture is that drugs rarely bind clavulanate to treat bacterial infections (particularly in specifically to a single target, challenging the concept of children). Amoxicilin inhibits synthesis in the cell walls of magic bullets, which is not necessarily prejudicial. bacteria. Clavulanate is an inhibitor of b-lactamase, the Indeed, recent analysis of drug and drug-target net- enzyme responsible for amoxicillin destruction. When works show a rich pattern of interactions among drugs administered together, these two drugs produce very and their targets in which drugs acting on single targets potent antibacterial activity because clavulanate main- appear to be the exception [46,47]. Likewise, many proteins tains the levels of amoxicillin in the cell wall by inhibiting are targeted by more than one drug containing distinct its degradation [39]. Finally, a very interesting case of drug chemical structures [48,49]. Consequently, a concept that combinations are coalistic combinations, in which all of the is increasingly gaining acceptance as a way to treat poly- drugs are individually inactive, but become active in com- genic diseases, both from target and drug perspectives, is bination [40]. ‘polypharmacology’ [43]. As discussed above, the analysis Using drug cocktails to target multiple enclaves to of the biological networks associated with a given disease produce synergistic effects against a certain disease sounds can suggest multiple targets to achieve the desired 118
  • 5. Review Trends in Pharmacological Sciences Vol.31 No.3 Figure 1. Synergistic anti-cancer effect produced by the combination of taxane and gefinitib. Taxane is an anti-cancer drug that works by disrupting microtubules through its binding to b-tubulin. It also induces expression of the tumor suppressor gene p53 and CDK inhibitor p21, and downregulates bcl-2, eventually leading to apoptosis. However, it also induces apoptosis resistance through stimulation of the EGF-R pathway. Phosphorylated EGF-R activates PI3K, which subsequently activates AKT1. The latter has a positive role in the activation of the complex between the HIF1-a and HIF1-b (ARNT) transcription factors, forming a complex that binds to a promoter region called HRE which triggers the transcription of survival, a strong inductor of apoptosis resistance by inhibiting caspase activity. Gefinitib, an EGF-R tyrosine kinase inhibitor, can re-establish the anti-cancer effect of taxane and also has pro-apoptotic activity by inducing the expression of the CDK inhibitors p27 and p21, and decreasing the enzyme activity of MMP2 and MMP9, resulting in a potent synergistic effect in breast cancer MCF7/ADR cells. outcome. Conversely, we can also envisage new molecules pathway could trigger a synergistic response, and that specifically synthesized from building blocks that enable their combination can eliminate compensatory reactions, them to bind to multiple targets, although probably with thereby avoiding a drug-resistance denouement [55]. lower affinities (i.e. ‘magic shotguns’ or ‘dirty drugs’). This Although the single-target approach remains the main polypharmacology strategy has been successfully applied strategy in big pharmaceutical companies, some remark- to some central nervous system (CNS) disorders [50], able efforts are being put into the development and mar- Alzheimer’s disease [51], oncogenic mutations [52], and keting of ‘promiscuous’ drugs that can block, for instance, looks very promising for identification of effective antibac- several kinases simultaneously [56]. However, one of the terial drugs [53]. Similarly, multi-target antibodies (in the principal limitations of the rational design of dirty drugs is form of diabodies, triabodies, tetrabodies and recombinant the difficulty in designing reliable assays to screen for the polyclonal antibodies) are also increasingly used in cancer best compounds that can hit multiple targets [57]. A deeper therapy to delay the development of resistance [54]. The understanding of the cellular processes and molecular efficacy of such therapies can be explained by the fact that networks related to the disease at which the drug is aimed drugs targeting different proteins in the disease network or would help in the selection process because it would permit 119
  • 6. Review Trends in Pharmacological Sciences Vol.31 No.3 the identification of all the targets that need to be influ- liver injury that has accuracies approaching 60% and low enced, either positively or negatively, and the design of the rates of false-positives results [35]. Elsewhere, it is now pertinent cellular assays. possible to predict chemical toxicities with increasing accuracy by considering the physical or functional proxi- The (not so) low-hanging fruits mity in a network of the target for a given compound and It is expected that the more contextualized view provided by proteins known to cause some undesired side-effects if network and systems biology will, in the long-term, revolu- their function is affected [60] (Figure 2). Despite these tionize current drug discovery strategies, propelling them advances, most of these toxicology models are qualitative from the classical empiricism to a mechanism-based rational and, to be most valuable, they should incorporate the design of global therapies [58]. However, there are two quantitative components necessary to allow appropriate important areas in drug discovery in which network-based prediction of, for instance, dose-related effects. approaches are likely to make key contributions in the near Partially due to limited success in novel pipeline pro- future: predictive toxicology and drug repurposing. ducts, one area of research that is becoming increasingly Accurate prediction of potential adverse reactions to important in drug development is drug repurposing, i.e. compounds in the early stages of drug development pipe- finding new therapeutic uses for approved drugs. The main lines is one of the major challenges in the pharmaceutical advantage of this approach is that, theoretically, it should industry. Today, evaluation of the safety and toxicology of drastically reduce the risks of drug development because drugs is largely empirical, centered in the chemical proper- the starting point is usually approved compounds with ties of the compounds and very prone to errors. Integrating well-established safety and bioavailability profiles, proven network biology and network chemistry to identify formulation and manufacturing routes, and well-charac- putative secondary targets for a given compound or explore terized pharmacology. In principle, all of these factors potential downstream effects of blocking the action of a key should facilitate repurposed compounds to enter clinical node in the biological network could rapidly provide an phases more rapidly and at a lower cost than novel chemi- alternative to the way drug candidates are assessed. For cal entities [61]. instance, Pfizer has developed an in-vitro testing strategy The rationale behind drug repurposing is based on two based on Boolean models of hepatocyte death–survival concepts discussed in previous sections. First, we have pathways [59] and cell imaging to predict drug-induced seen how it is common for a drug to interact with multiple Figure 2. Network biology applied to predictive toxicology and drug repurposing. The disease-associated networks for diabetes (dark-blue dashed lines) and nausea (light-blue dashed lines) contain several proteins that have been reported to be likely to cause some frequent adverse effects if their normal functioning is affected (red nodes). In addition, the networks contain drug targets annotated to a specific disease (green nodes). Intense research is being carried out to create models that can identify the areas of influence of proteins leading to undesired effects, and to explore how they are related to network connectivity. If successful, these models could help to identify potential drug targets that are likely to trigger severe adverse reactions at early stages of the discovery process, and to rationally design the toxicity tests needed to check the safety of other drug targets under the area of influence of a certain red node. In addition, detailed description of the molecular networks associated with certain diseases can highlight the existence of validated drug targets for a given therapeutic indication in key enclaves of the network describing a different disease, thereby suggesting candidates for drug repurposing (i.e. finding new indications for a target). 120
  • 7. Review Trends in Pharmacological Sciences Vol.31 No.3 protein targets (i.e. dirty drugs). This has triggered one pipelines of big pharmaceutical companies. This can be repurposing approach based on the identification of sec- partially attributed to the lack of robust network models ondary targets for a given drug in a different therapeutic able to optimally balance multiple drug activities on differ- area. One of the first examples of this approximation to find ent targets with the control of undesired off-target effects. new targets for a marketed compound was the repurposing We anticipate that considerable international efforts, such of thalidomine in the 1990s. Thalidomine was initially as the ongoing initiatives to chart disease-related inter- prescribed to treat nausea and insomnia in pregnant action maps [66] and the phenotypic effects of targeting women, and had to be withdrawn because it was found multiple proteins in model organisms with chemical probes to cause severe defects in the fetus. It was subsequently [67], will soon permit refinement of systems-biology models observed that thalidomine also had very pronounced anti- to the point where they can be routinely applied to some of angiogenic and immunomodulatory effects on the tumor the most important areas of drug development. growth factor-alpha (TGF-a) pathway, and was reapproved for the treatment of leprosy [62]. The second scientific Acknowledgements concept that supports drug repurposing is the high con- ´ We thank Teresa Sardon (CRG) for critically reading the manuscript. PA acknowledges the financial support received from the Spanish Ministerio nectivity among apparently unrelated cellular processes. ´n de Educacio y Ciencia through the grant BIO2007-62426 and the This could mean that a given target might be relevant to European Commission under FP7 Grant Agreement 223101 several diseases (Figure 2). A classical example is that of (AntiPathoGN). finasteride, a compound that targets the 5a-reductase, which was originally approved for the treatment of pros- References tate enlargement. The target enzyme that converts testos- 1 Butcher, E.C. (2005) Can cell systems biology rescue drug discovery? Nat. Rev. Drug Discov. 4, 461–467 terone to dihydrotestosterone was found to have a role in 2 Lander, E.S. et al. (2001) Initial sequencing and analysis of the human hair loss in males, so finastride was also approved for the genome. Nature 409, 860–921 prevention of baldness [63]. 3 Rual, J.F. et al. (2005) Towards a proteome-scale map of the human The concept of finding new indications for approved protein-protein interaction network. Nature 437, 1173–1178 drugs can also be applied in a semi-blind approach that 4 Stelzl, U. et al. (2005) A human protein-protein interaction network: a resource for annotating the proteome. Cell 122, 957–968 screens existing compounds against a multitude of targets, 5 Ewing, R.M. et al. (2007) Large-scale mapping of human protein- and identifies possible therapeutic benefits or side-effects protein interactions by mass spectrometry. Mol. Syst. Biol. 3, 89 in an unbiased manner [64]. For instance, by testing 6 Barrett, T. et al. (2007) NCBI GEO: mining tens of millions of thousands of combinations of off-patent drugs in cell-based expression profiles—database and tools update. Nucleic Acids Res. 35, D760–765 assays, it has been found that two drugs with different 7 Parkinson, H. et al. (2007) ArrayExpress—a public database of indications (one antipsycothic and one antiprotozoal) exhi- microarray experiments and gene expression profiles. Nucleic Acids bit unexpected antitumoral activity [65]. Unfortunately, Res 35, D747–750 the design of the screens is mainly based on the availability 8 Pearson, H. (2007) Meet the human metabolome. Nature 446, 8 of in-house cell assays, and we foresee that the inclusion of 9 Wishart, D.S. et al. (2007) HMDB: the Human Metabolome Database. Nucleic Acids Res. 35, D521–526 network information and systems-biology models could 10 Drews, J. (2006) Case histories, magic bullets and the state of drug rapidly improve the repurposing outcome. For instance, discovery. Nat. Rev. Drug Discov. 5, 635–640 identification of protein targets for a given drug that 11 Lindsay, M.A. (2005) Finding new drug targets in the 21st century. occupy a relevant position in a cell network associated Drug Discov. Today 10, 1683–1687 with a certain disease could give valuable information 12 van der Greef, J. and McBurney, R.N. (2005) Innovation: Rescuing drug discovery: in vivo systems pathology and systems pharmacology. Nat. regarding a new therapeutic area in which its activity Rev. Drug Discov. 4, 961–967 could be tested. 13 Russell, R.B. and Aloy, P. (2008) Targeting and tinkering with interaction networks. Nat. Chem. Biol. 4, 666–673 Concluding remarks 14 Venkatesan, K. et al. (2009) An empirical framework for binary The reductionism that has prevailed in drug discovery in interactome mapping. Nat. Methods 6, 83–90 15 Mani, K.M. et al. (2008) A systems biology approach to prediction of the last 20 years has resulted in many promising drug oncogenes and molecular perturbation targets in B-cell lymphomas. candidates failing in the final phases of clinical testing. Mol. Syst. Biol. 4, 169 This has been mainly due to a lack of knowledge about 16 Pujana, M.A. et al. (2007) Network modeling links breast cancer disease pathways at the molecular level, which often led to susceptibility and centrosome dysfunction. Nat. Genet. 39, 1338– 1349 unforeseen adverse effects and unacceptable toxicity pro- 17 Amit, I. et al. (2009) Unbiased reconstruction of a mammalian files. Network biology and systems biology offer a novel transcriptional network mediating pathogen responses. Science 326, way of approaching drug discovery through the develop- 257–263 ment of models that can place protein targets in their 18 Chuang, H.Y. et al. (2007) Network-based classification of breast cancer physiological context, offering a global perspective of their metastasis. Mol. Syst. Biol. 3, 140 19 Chen, Y. et al. (2008) Variations in DNA elucidate molecular networks environment without losing the key molecular details. The that cause disease. Nature 452, 429–435 high interconnectivity observed in disease-associated net- 20 Kitano, H. (2005) International alliances for quantitative modeling in works suggests that, to be most effective, new therapeutic systems biology. Mol. Syst. Biol. 1, 0007 strategies to fight complex diseases should focus on target- 21 Ro, D.K. et al. (2006) Production of the antimalarial drug precursor ing entire cellular pathways rather than single proteins, artemisinic acid in engineered yeast. Nature 440, 940–943 22 Bonneau, R. et al. (2007) A predictive model for transcriptional control either through the use of drug cocktails or promiscuous of physiology in a free living cell. Cell 131, 1354–1365 compounds. However, and despite the growing evidence, 23 Stuart, L.M. et al. (2007) A systems biology analysis of the Drosophila multi-target approaches are still anecdotal in the discovery phagosome. Nature 445, 95–101 121
  • 8. Review Trends in Pharmacological Sciences Vol.31 No.3 24 Abbott, A. (2008) Pharmaceutical futures: a fiendish puzzle. Nature 56 Choong, N.W. et al. (2009) Phase II study of sunitinib malate in head 455, 1164–1167 and neck squamous cell carcinoma. Invest. New Drugs Aug, 4. Ahead of 25 Bar-Yan, Y. (2004) Making Things Work: Solving Complex Problems in print a Complex World, NECSI Knowledge Press 57 Peterson, J.J. and Novick, S.J. (2007) Nonlinear blending: a useful 26 Scriver, C.R. and Waters, P.J. (1999) Monogenic traits are not simple: general concept for the assessment of combination drug synergy. J. lessons from phenylketonuria. Trends Genet. 15, 267–272 Recept. Signal Transduct. Res. 27, 125–146 27 Dipple, K.M. and McCabe, E.R. (2000) Modifier genes convert ‘‘simple’’ 58 Henney, A. and Superti-Furga, G. (2008) A network solution. Nature Mendelian disorders to complex traits. Mol. Genet. Metab. 71, 43–50 455, 730–731 28 Jones, S. et al. (2008) Core signaling pathways in human pancreatic 59 Saez-Rodriguez, J. et al. (2008) Flexible informatics for linking cancers revealed by global genomic analyses. Science 321, 1801–1806 experimental data to mathematical models via DataRail. 29 Parsons, D.W. et al. (2008) An integrated genomic analysis of human Bioinformatics 24, 840–847 glioblastoma multiforme. Science 321, 1807–1812 60 Apic, G. et al. (2005) Illuminating drug discovery with biological 30 Cancer Genome Atlas Research Network (2008) Comprehensive pathways. FEBS Lett. 579, 1872–1877 genomic characterization defines human glioblastoma genes and 61 Boguski, M.S. et al. (2009) Drug discovery. Repurposing with a core pathways. Nature 455, 1061–1068 difference. Science 324, 1394–1395 31 Jones, D. (2008) Pathways to cancer therapy. Nat. Rev. Drug Discov. 7, 62 Cleach, L.L. et al. (1998) Reactions and interactions of some commonly 875–876 used systemic drugs in dermatology. Dermatol. Clin. 16, 421–429 32 Pache, R.A. et al. (2008) Towards a molecular characterisation of 63 Ekman, P. (1999) Finasteride in the treatment of benign prostatic pathological pathways. FEBS Lett. 582, 1259–1265 hypertrophy: an update. New indications for finasteride therapy. 33 Kitano, H. (2004) Biological robustness. Nat. Rev. Genet 5, 826–837 Scand. J. Urol. Nephrol. Suppl. 203, 15–20 34 Zanzoni, A. et al. (2009) A network medicine approach to human 64 O’Connor, K.A. and Roth, B.L. (2005) Finding new tricks for old drugs: disease. FEBS Lett. 583, 1759–1765 an efficient route for public-sector drug discovery. Nat. Rev. Drug 35 Xu, J.J. et al. (2008) Cellular imaging predictions of clinical drug- Discov. 4, 1005–1014 induced liver injury. Toxicol. Sci. 105, 97–105 65 Lee, M.S. et al. (2007) The novel combination of chlorpromazine and 36 Jia, J. et al. (2009) Mechanisms of drug combinations: interaction and pentamidine exerts synergistic antiproliferative effects through dual network perspectives. Nat. Rev. Drug Discov. 8, 111–128 mitotic action. Cancer Res. 67, 11359–11367 37 Gupta, G.P. et al. (2007) Mediators of vascular remodelling co-opted for 66 Charbonnier, S. et al. (2008) The social network of a cell: recent sequential steps in lung metastasis. Nature 446, 765–770 advances in interactome mapping. Biotechnol. Annu. Rev. 14, 1–28 38 Takabatake, D. et al. (2007) Tumor inhibitory effect of gefitinib 67 Lehar, J. et al. (2008) Combination chemical genetics. Nat. Chem. Biol. (ZD1839, Iressa) and taxane combination therapy in EGFR- 4, 674–681 overexpressing breast cancer cell lines (MCF7/ADR, MDA-MB-231). 68 Royer, L. et al. (2008) Unraveling protein networks with power graph Int. J. Cancer 120, 181–188 analysis. PLoS Comput. Biol. 4, e1000108 39 Matsuura, M. et al. (1980) Combined antibacterial activity of 69 Missiuro, P.V. et al. (2009) Information flow analysis of interactome amoxicillin with clavulanic acid against ampicillin-resistant strains. networks. PLoS Comput. Biol. 5, e1000350 Antimicrob. Agents Chemother. 17, 908–911 70 Friedman, N. (2004) Inferring cellular networks using probabilistic 40 Johnson, M.D. et al. (2004) Combination antifungal therapy. graphical models. Science 303, 799–805 Antimicrob. Agents Chemother. 48, 693–715 71 Sachs, K. et al. (2005) Causal protein-signaling networks derived from 41 Zimmermann, G.R. et al. (2007) Multi-target therapeutics: when the multiparameter single-cell data. Science 308, 523–529 whole is greater than the sum of the parts. Drug Discov. Today 12, 34– 72 Jansen, R. et al. (2003) A Bayesian networks approach for predicting 42 protein-protein interactions from genomic data. Science 302, 42 Lehar, J. et al. (2009) Synergistic drug combinations tend to improve 449–453 therapeutically relevant selectivity. Nat. Biotechnol. 27, 659–666 73 Llewellyn, R. and Eisenberg, D.S. (2008) Annotating proteins with 43 Hopkins, A.L. (2008) Network pharmacology: the next paradigm in generalized functional linkages. Proc. Natl. Acad. Sci. U S A 105, drug discovery. Nat. Chem. Biol. 4, 682–690 17700–17705 ´ 44 Schrattenholz, A. and Soskic, V. (2008) What does systems biology 74 Lahdesmaki, H. et al. (2006) Relationships between probabilistic mean for drug development? Curr. Med. Chem. 15, 1520–1528 Boolean networks and dynamic Bayesian networks as models of 45 Giles, F.J. et al. (2009) Class effects of tyrosine kinase inhibitors in the gene regulatory networks. Signal Processing 86, 814–834 treatment of chronic myeloid leukemia. Leukemia 75 Kelley, R. and Ideker, T. (2005) Systematic interpretation of genetic 46 Yildirim, M.A. et al. (2007) Drug-target network. Nat. Biotechnol. 25, interactions using protein networks. Nat. Biotechnol. 23, 561–566 1119–1126 76 Zhu, J. et al. (2008) Integrating large-scale functional genomic data to 47 Paolini, G.V. et al. (2006) Global mapping of pharmacological space. dissect the complexity of yeast regulatory networks. Nat. Genet. 40, Nat. Biotechnol. 24, 805–815 854–861 ´ 48 Gregori-Puigjane, E. and Mestres, J. (2008) A ligand-based approach to 77 Klamt, S. et al. (2009) Hypergraphs and cellular networks. PLoS mining the chemogenomic space of drugs. Comb. Chem. High Comput. Biol. 5, e1000385 Throughput Screen. 11, 669–676 78 Barabasi, A.L. and Oltvai, Z.N. (2004) Network biology: understanding 49 Nacher, J.C. and Schwartz, J-M. (2008) A global view of drug-therapy the cell’s functional organization. Nat. Rev. Genet. 5, 101–113 interactions. BMC Pharmacol. 8, 5 79 Hartwell, L.H. et al. (1999) From molecular to modular cell biology. 50 Roth, B.L. et al. (2004) Magic shotguns versus magic bullets: selectively Nature 402, C47–52 non-selective drugs for mood disorders and schizophrenia. Nat. Rev. 80 Ravasz, E. et al. (2002) Hierarchical organization of modularity in Drug Discov. 3, 353–359 metabolic networks. Science 297, 1551–1555 51 Bolognesi, M.L. et al. (2009) Memoquin: a multi-target-directed ligand 81 Gavin, A.C. et al. (2006) Proteome survey reveals modularity of the as an innovative therapeutic opportunity for Alzheimer’s disease. yeast cell machinery. Nature 440, 631–636 Neurotherapeutics 6, 152–162 82 Shen-Orr, S.S. et al. (2002) Network motifs in the transcriptional 52 Apsel, B. et al. (2008) Targeted polypharmacology: discovery of dual regulation network of Escherichia coli. Nat. Genet. 31, 64–68 inhibitors of tyrosine and phosphoinositide kinases. Nat. Chem. Biol. 4, 83 Milo, R. et al. (2002) Network motifs: simple building blocks of complex 691–699 networks. Science 298, 824–827 53 Rasko, D.A. et al. (2008) Targeting QseC signaling and virulence for 84 Kelley, B.P. et al. (2004) PathBLAST: a tool for alignment of protein antibiotic development. Science 321, 1078–1080 interaction networks. Nucleic Acids Res. 32, W83–88 54 Todorovska, A. et al. (2001) Design and application of diabodies, 85 Kalaev, M. et al. (2008) NetworkBLAST: comparative analysis of triabodies and tetrabodies for cancer targeting. J. Immunol. protein networks. Bioinformatics 24, 594–596 Methods 248, 47–66 86 Singh, R. et al. (2008) Global alignment of multiple protein interaction 55 Kitano, H. (2007) A robustness-based approach to systems-oriented networks with application to functional orthology detection. Proc. Natl. drug design. Nat. Rev. Drug Discov. 6, 202–210 Acad. Sci. U S A 105, 12763–12768 122
  • 9. Review Trends in Pharmacological Sciences Vol.31 No.3 87 Suthram, S. et al. (2005) The Plasmodium protein network diverges 92 Nedelkov, D. (2007) Development of surface plasmon resonance mass from those of other eukaryotes. Nature 438, 108–112 spectrometry array platform. Anal. Chem. 79, 5987–5990 88 Cookson, W. et al. (2009) Mapping complex disease traits with global 93 Sudhaharan, T. et al. (2009) Determination of in vivo dissociation gene expression. Nat. Rev. Genet. 10, 184–194 constant, KD, of Cdc42-effector complexes in live mammalian cells 89 Kumar, C. and Mann, M. (2009) Bioinformatics analysis of mass using single wavelength fluorescence cross-correlation spectroscopy. J. spectrometry-based proteomics data sets. FEBS Lett. 583, 1703–1712 Biol. Chem. 284, 13602–13609 90 Gstaiger, M. and Aebersold, R. (2009) Applying mass spectrometry- 94 Simonetta, M. et al. (2009) The influence of catalysis on mad2 based proteomics to genetics, genomics and network biology. Nat. Rev. activation dynamics. PLoS Biol. 7, e10 Genet. 10, 617–627 95 Iwahara, J. et al. (2006) NMR structural and kinetic characterization of 91 Boozer, C. et al. (2006) Looking towards label-free biomolecular a homeodomain diffusing and hopping on nonspecific DNA. Proc. Natl. interaction analysis in a high-throughput format: a review of new Acad. Sci. U S A 103, 15062–15067 surface plasmon resonance technologies. Curr. Opin. Biotechnol. 17, 96 Sprangers, R. and Kay, L.E. (2007) Quantitative dynamics and binding 400–405 studies of the 20S proteasome by NMR. Nature 445, 618–622 123