Network-targeted combination therapy: a new concept in cancer treatment

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Drug Discovery Today: Therapeutic Strategies Editors-in-Chief Raymond Baker – formerly University of Southampton, UK and Merck Sharp & Dohme, UK Eliot Ohlstein – GlaxoSmithKline, USA DRUG DISCOVERY

TODAY THERAPEUTIC

STRATEGIES

Cancer

Network-targeted combination therapy: a new concept in cancer treatment Robyn P. Araujo1,*, Connemara Doran1, Lance A. Liotta1, Emanuel F. Petricoin2 1

FDA-NCI Clinical Proteomics Program, Laboratory of Pathology, Center for Cancer Research, NCI/NIH, Bethesda, MD 20892, USA FDA-NCI Clinical Proteomics Program, Office of Cell Therapy and Gene Therapy, Center for Biologic Evaluation and Research, Food and Drug Administration, Bethesda, MD 20892, USA 2

Toxicity is a major concern for anti-neoplastic drugs, with much of the existing pharmacopoeia being characterized by a very narrow therapeutic index. ‘Network-targeted’ combination therapy is a promising new concept in cancer therapy, whereby therapeutic index might be improved by targeting multiple nodes in a cell’s signaling network, rather than a single node. Here, we examine the potential of this novel approach, illustrating how therapeutic benefit could be achieved with smaller doses of the necessary agents. Introduction Strategies for new drug development have changed dramatically over recent years as the role of the functional dysregulation of protein interactions as the underlying cause of disease is increasingly understood [1–3]. A major contributor to specific protein–protein interactions that underpin signaling networks is protein phosphorylation, and disease-associated aberrant protein kinase activity drives derangement in these proteinsignaling networks. An expanding body of experimental and clinical literature attests to the promise of combination therapies which combine conventional treatments such as radiotherapy and chemotherapy with small molecule inhibitors that target-activated kinase activity and protein–protein interactions in specific dysregulated pathways [4–12].

*Corresponding author: (R.P. Araujo) [email protected] 1740-6773/$ Published by Elsevier Ltd.

DOI: 10.1016/j.ddstr.2004.11.004

Section Editors: Lance Liotta – National Institutes of Health, Bethesda, MD, USA Neil Gibson – OSI Pharmaceuticals, NY, USA The era of Molecular Medicine is upon us as new classes of targeted therapeutics usher in promise of increased efficacy. This promise is based on the understanding and elucidation of the proteomic molecular network, or cellular ‘circuitry’, which is driven by post-translational modifications involving protein phosphorylation. Mathematical modeling of these networks will provide information about how the circuitry is linked together, and how it could be more effectively targeted. One key early insight that emanates from these modeling efforts is the observation that inhibiting activity of multiple ‘nodes’ within the network can provide increased efficacy with potentially lower doses of each drug. Thus, combinatorial therapeutic strategies, which target specific points in the signaling circuitry and are tailored to the patient’s specific profile, might represent the next exciting phase of Molecular Medicine.

Nevertheless, combination therapies that target multiple interconnected nodes of a cell-signaling network represent a largely unexplored avenue in cancer treatment [13,14]. Since these ‘network-targeted’ combination therapies may produce therapeutic benefit with significantly smaller doses of the necessary agents, drugs that were previously considered too toxic at their therapeutically-effective doses might now play a valuable part in the treatment of disease. Thus, this new concept may spawn an enormous new repertoire of molecular-targeted therapeutics for clinical evaluation. www.drugdiscoverytoday.com

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How network-targeted combination therapy works A simple theoretical model of a protein network To demonstrate the underlying principles of network-targeted combination therapy, we consider a simple model of a cell-signaling pathway (Fig. 1), either with (Fig. 1b and c) or without (Fig. 1a) feedback control. Although unable to exhibit the complex responses of an extensive and highly-interrelated molecular network with feedback control and crosstalk between different pathways, the simplicity of this model renders it a powerful and instructive vehicle for studying the roles of various perturbations to individual nodes in the network, and their influence on downstream signals and the overall cellular response. Indeed, the network presented in Fig. 1 might be considered to represent a kinase cascade, such as the mitogen-activated protein kinase cascades, which are widely involved in eukaryotic signal transduction [15]. The network under consideration consists of an extracellular biochemical stimulus, or ligand (S), that binds to a free receptor (R) on the cell membrane and, in addition, several downstream proteins that may be activated by phosphoryla-

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tion. In the language of network theory, the phosphorylated proteins are the ‘nodes’ while the reactions linking the nodes are the ‘edges’ [16,17]. Once the receptor becomes phosphorylated following ligation, it phosphorylates the first protein in the pathway (P1), which in turn, phosphorylates the next protein in the pathway (P2). In this way, each phosphorylated protein in the cascade phosphorylates the next protein downstream. Proteins may also be dephosphorylated by phosphatases, giving rise to a reverse reaction between a phosphorylated protein and the preceding (upstream) protein. Thus, a phospho-event is transient. Moreover, any given phosphorylated protein may promote (Fig. 1b) or inhibit (Fig. 1c) the phosphorylation of other upstream or downstream proteins. Such feedback loops are known to constitute an important part of the cell’s regulatory machinery [18]. Here, we model the network interactions by simple Mass Action Kinetics (Box 1). The resulting system of ordinary differential equations predicts the temporal variations in the concentrations of each of the phosphorylated signaling proteins in the pathway. Furthermore, the administration of small molecule inhibitors as a treatment is modeled by replicating what such an inhibitor achieves in a cancer cell: decreasing or blocking the transfer of a biochemical signal. Specifically, a kinase inhibitor can be modeled as a reduction in the forward rate constant of a reaction, with decreasing values of the rate constant corresponding to higher doses of the drug. In other words, a drug inhibits a node by reducing the forward rate constant of the preceding edge.

The benefits of targeting multiple nodes rather than a single node The differential equations outlined in Box 1 can be solved simultaneously using a Runge–Kutta scheme (for example) to simulate the administration of kinase inhibitors to various nodes either individually or in different combinations. To elucidate the effects of these different perturbations to the network, the temporal evolution of the most downstream protein activation ([P6]) will be considered in each case, and regarded as ‘the signal’. A careful examination of the model predictions (Fig. 2 and Table 1) highlights several important principles germane to the development of new treatment regimens. A network incorporating the single positive-feedback loop (F1) is considered in the first two points below, with the other possible feedback loops considered in the final point. Targeting a number of serially linked nodes in a pathway is Figure 1. A simple model of a six-node cell-signaling network (a) with no feedback control, (b) with positive feedback control, and (c) with negative feedback control. S represents the extracellular stimulus and R the receptor, whereas P1 through P6 denote the six nodes of the network. Forward reaction rate constants are denoted by ki (i = 1 . . . 7), with reverse reaction rate constants denoted by k_j (j = 1 . . . 6). Positive feedback loop rate constants are denoted by Fm (m = 1 . . . 6), whereas negative feedback loop rate constants are denoted by F_n (n = 1 . . . 6).

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conducive to greater overall signal attenuation compared with targeting a single node in isolation

Fig. 2 considers the changes in signal resulting from inhibiting nodes by 50% each – a dose of IC50 – in several different combinations. It is remarkable to observe how insignificant a change in signal results from targeting the first node alone (Fig. 2b), in comparison with the untreated network (Fig. 2a).

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Box 1. Differential equations and kinetic parameters for the simple network model (A) Equations for pathway without feedback (Fig. 1a) d½R ¼ k1 ½S½R þ k1 ½P1 dt

(1)

d½P1 ¼ k1 ½S½R  k1 ½P1  k2 ½P1 þ k2 ½P2 dt

(2)

d½P2 ¼ k2 ½P1  k2 ½P2  k3 ½P2 þ k3 ½P3 dt

(3)

d½P3 ¼ k3 ½P2  k3 ½P3  k4 ½P3 þ k4 ½P4 dt

(4)

d½P4 ¼ k4 ½P3  k4 ½P4  k5 ½P4 þ k5 ½P5 dt

(5)

d½P5 ¼ k5 ½P4  k5 ½P5  k6 ½P5 þ k6 ½P6 dt

(6)

Nevertheless, as successive nodes are incorporated into the treatment strategy (Fig. 2c–f), the signal is progressively reduced, with an 83% reduction in peak signal magnitude when a dose of IC50 is administered to all five nodes (P1–P5). Targeting a number of serially-linked nodes in a pathway

d½P6 ¼ k6 ½P5  k6 ½P6  k7 ½P6 (7) dt (B) Equations for pathways with positive feedback (Fig. 1b) The system of equations for pathways with positive feedback loops is similar to the system in (A), with only the two nodes involved in the feedback loop being altered. For example, in the pathway with positive feedback loop F6 (from P5 to P1), Eqs. (2) and (6) become: d½P1 ¼ k1 ½S½R  k1 ½P1  k2 ½P1 þ k2 ½P2 þ f6 ½P5 dt

(2+)

d½P5 ¼ k5 ½P4  k5 ½P5  k6 ½P5 þ k6 ½P6  f6 ½P5 (6+) dt (C) Equations for pathways with negative feedback (Fig. 1c) The system of equations for pathways with negative feedback loops is similar to the system in (A), with only the two nodes involved in the feedback loop being altered. For example, in the pathway with negative feedback loop F2 (from P4 to P2), Eqs. (3) and (5) become: d½P2 ¼ k2 ½P1  k2 ½P2  k3 ½P2 þ k3 ½P3  f2 ½P2 dt

(3-)

d½P4 ¼ k4 ½P3  k4 ½P4  k5 ½P4 þ k5 ½P5  f2 ½P4 (5-) dt (D) Uninhibited rate constants (E) Initial conditions ki = 1 [S] = 58t [R](0) = 5 kj = 0.5 fi = 1 [Pj](0) = 0 fj = 1 (j = 1 . . . 6) (i = 1 . . . 7, j = 1 . . . 6)

enables downstream signals to be reduced with smaller doses of the necessary drugs

Having established that the targeting of additional serially connected nodes results in enhanced signal attenuation, the question naturally arises as to how significantly drug doses may be reduced to achieve a desired reduction in signal. Table 1 lists the inhibitor doses that result in a 90% signal reduction, emphasizing the fact that a drug must be extremely effective if only applied to one node. Nevertheless, some nodes seem to be more effective targets than others, with a dose of IC94.7 at node P4 having a similar effect as a dose of IC99.6 at node P1. Although this effect might appear subtle, its greatest utility is realized when multiple nodes are targeted simultaneously. Table 2 illustrates that in a two-node combination therapy, doses ranging from IC82 to IC86 are able to produce a 90% signal attenuation when node P4 is included in the combination, in contrast to doses from IC93 to IC96 when node P1 is one of the target nodes. Similarly, in a threenode combination therapy, doses ranging from IC73 to IC77 produce the desired signal attenuation when node P4 is one of the target nodes, in contrast to doses from IC80 to IC87 when node P1 is included in the combination. As more nodes are added to the treatment strategy, the required drug doses are progressively reduced, with doses of IC63 and IC59 corresponding to five- and six-node combinations, respectively, in a treatment which produces 90% signal attenuation (Table 1). The topology of the network has important implications for the optimal choice of target nodes

Clearly, it is useful to be able to identify which nodes in a network represent the most effective therapeutic targets. An examination of each of the six positive-feedback loops illu-

Table 1. Inhibition required at each node to achieve a 90% reduction in downstream signal magnitude Node

Required inhibition (%)

Nodes

Required inhibition (%)

P1

99.6

–

–

P2

96.6

P1, P2

93

P3

96.2

P1, P2, P3

83

P4

94.7

P1, P2, P3, P4

74

P5

96.4

P1, P2, P3, P4, P5

63

P6

97.6

P1, P2, P3, P4, P5, P6

59

For a six-node network with positive feedback loop F1. P1, first protein in signaling pathway; P2, second protein in signaling pathway; and so on. www.drugdiscoverytoday.com

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Figure 2. Temporal evolution of the biochemical signal for a six-node model with positive feedback loop F1: (a) no inhibition; (b) P1 inhibited by 50%; (c) P1 and P2 each inhibited by 50%; (d) P1, P2 and P3 each inhibited by 50%; (e) P1, P2, P3 and P4 each inhibited by 50%; (f) all nodes inhibited by 50%.

Table 2. Inhibition required for multiple-node combinations Nodes

Required inhibition (%)

Nodes

Required inhibition (%)

Nodes

Required inhibition (%)

Nodes

Required inhibition (%)

P1, P2

93

P4, P2

83

P1, P2, P3

83

P4, P2, P3

73

P1, P3

93

P4, P3

82

P1, P2, P4

80

P4, P2, P5

73

P1, P6

96

P4, P6

86

P1, P2, P6

87

P4, P2, P6

77

P1, first protein in signaling pathway; P2, second protein in signaling pathway; and so on.

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strated in Fig. 1b (results not shown) reveals that inhibition of the node immediately downstream of a feedback ‘output node’ (i.e. the node that enhances the activation of another upstream node) is always the most effective node to target. For example, in a network with feedback loop F1, P4 is the most effective target node (as discussed earlier), whereas a network with feedback loop F2 is best treated with an inhibitor that targets P5 (Fig. 1b). In the case of negative-feedback loops, both the feedback output node and the feedback input node (i.e. the node whose activation is enhanced by a downstream node) seem to be more effective targets than other nodes in the network. Thus, the topology of the network – its functional structure and the architecture of its control modules – has important implications for the most judicious choice of target nodes in a program of network-targeted combination therapy. Importantly, although the role of feedback control in the adaptive behavior of living cells has been studied extensively by both theoreticians and experimentalists [15,18–21], a mechanistic understanding of the role of feedback loops in the study of network target inhibition is currently lacking. Although the simple mathematical model presented here has yielded some preliminary insights, further theoretical investigation is required to elucidate this phenomenon further.

A theoretical consideration of network-targeted combination therapy in a real signaling network Having gleaned some important insights into the effects of various regimens of network-targeted combination therapy on the attenuation of biochemical signals, and how these insights might be exploited in devising a treatment program, we now consider how these principles might affect a real signaling network. Fig. 3 depicts a limited subset of the EGFR signaling network, incorporating three coupled cycles of interactions between the phosphotyrosine residues of the EGF receptor and the cytoplasmic proteins Grb2, Shc and PLCg. A mathematical model of this simple kinetic scheme was first proposed by Kholodenko et al. [22] and later modified by Araujo et al. [14] to simulate the administration of small molecule kinase inhibitors to various parts of the network. Thus, in contrast to the simple network considered earlier, this EGFR network incorporates three parallel pathways which, although ostensibly independent of one another, are weakly interconnected via cross-talk (note reaction 23 in Fig. 3) and feedback (note reactions 7, 11, 15, 18 and 20 in Fig. 3). Moreover, the network comprises three phosphorylation reactions that may be targeted with kinase inhibitors – the autophosphorylation of the receptor (reaction 3 in Fig. 3), along with the two downstream phosphorylation reactions 6 and 14. Furthermore, the biochemical signals of interest here are the concentrations of the most downstream complex in

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each pathway – R–PLP in the PLCg cycle, R–Sh–G–S in the Shc cycle and R–G–S in the Grb2 cycle – in addition to the concentration of the phosphorylated receptor, RP. The interested reader is referred to work of Kholodenko et al. [22] and Araujo et al. [14] for the details of the associated modeling equations, and the choice of rate constants.

The benefits of targeting serially connected nodes rather than nodes in parallel The temporal evolution of each of the four biochemical signals is presented in Fig. 4, for each of three different treatment regimens – a monotherapy that inhibits receptor autophosphorylation (node 3), and two programs of combination therapy that target the receptor autophosphorylation along with an additional downstream node (nodes 3 and 6, or nodes 3 and 14). In each case, the uninhibited profile is shown, along with the profiles for 50 and 90% inhibition of the relevant node(s). These signal profiles point to two important results. First, although an inhibitor might have a profound effect at the target node, it may exert a much more subtle influence on biochemical signals further downstream. It is intriguing to observe that the profiles for R–Sh–G–S and R–G–S in Fig. 4 are very insensitive to an inhibitor dose of IC50 at node 3, despite the appreciable reduction in receptor phosphorylation (RP). Nevertheless, a much larger dose of IC90 at node 3 is able to exert a noticeable, albeit modest, influence on downstream signals, corroborating the finding suggested by the simpler model discussed earlier that a drug must be extremely effective if only applied to one node. Second, target nodes must be serially linked for the greatest inhibitory effect to be realized. As illustrated in Fig. 3, node 3 is upstream of all biochemical signals under consideration, whereas nodes 6 and 14 occur in the PLCg and Shc cycles, respectively. There are no target nodes in the Grb2 cycle. For this reason, a combination therapy that targets nodes 3 and 6 results in a marked attenuation of the R-PLP signal, being the most downstream signal in the PLCg cycle. However, this treatment strategy seems to have an imperceptible effect on signals in the other two cycles, in comparison with the monotherapy targeting node 3. Similarly, a combination therapy that targets nodes 3 and 14 causes a significant diminution of the R–Sh–G–S signal – the most downstream signal in the Shc cycle – while having an imperceptible effect on other signals in the network, in comparison with the monotherapy. Thus, this study suggests that a given biochemical signal is reduced most effectively by inhibiting a combination of serially-connected upstream nodes.

A comparison with other treatment strategies Over recent years, cancer treatment strategies have evolved from systemic, non-specific, high-dose chemotherapies to www.drugdiscoverytoday.com

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Figure 3. Kinetic scheme for EGFR signaling (adapted from Kholodenko et al. [22]). As shown, phosphorylation reactions (ATP ! ADP) occur on the edges (reactions) leading to nodes 3, 6 and 14, whereas dephosphorylation occurs in reactions 8 and 16. EGF binds to the extracellular domain of the monomeric EGFR (denoted R) to form a receptor–ligand complex (Ra), which subsequently dimerizes (R2) and is activated by autophosphorylation of tyrosine residues (RP). The subset of the Grb2 cycle considered here is initiated by the binding of Grb2 (denoted Grb) to a receptor phosphotyrosine to yield the complex R–G. The binding of the Son of Sevenless homolog protein, SOS, to R–G then produces the ternary complex R–G–S, which subsequently dissociates into Grb2 and SOS. In the Shc cycle, Shc binds to the phosphorylated receptor to form the complex R–Sh, whose subsequent phosphorylation yields R–ShP, which might then dissociated to yield the phosphorylated receptor, RP, and phosphorylated Shc (ShP). Alternatively, Grb2 might bind to the R–ShP complex to produce the ternary complex R–Sh–G, which dissociates to produce RP and the complex Sh–G. SOS might also bind to R–Sh–G to produce the four protein complex R–Sh–G–S, which might also be formed by the association of R–ShP and G–S, thereby interacting with the Grb2 cycle. Sh–P is dephosphorylated by phosphatases to release Shc. Thus, both the Shc and Grb cycles involve the activation of SOS, whose downstream target is the membrane-bound Ras protein. PLCg-mediated signaling is initiated by the binding of PLCg to produce the complex R–PL, which is then phosphorylated, yielding R–PLP, before dissociating into phosphorylated phospholipase Cg (PLCgP) and RP. PLCgP might then be dephosphorylated by phosphatases or translocate to cytoskeletal or membrane structures to produce PLCgP–I.

various new treatment modalities including moleculartargeted therapies and multi-modality combination therapies (Table 3). Whereas conventional chemotherapeutic approaches often give rise to high rates of initial remission, 430

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high recurrence rates are often observed. In addition, the narrow therapeutic index associated with these agents might result in severe and life-threatening toxicity in some individuals and poor anti-tumor effects in others [23].

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Figure 4. Temporal evolution of various biochemical signals in the EGFR network. Each row illustrates the behavior of a particular node in response to each of three therapies: Column1 depicts a monotherapy which inhibits receptor autophosphorylation; Column 2 depicts a two-node combination therapy which inhibits nodes 3 and 6; Column 3 depicts a two-node combination therapy which inhibits nodes 3 and 14. In each case, the uppermost curve represents the uninhibited profile [no drug(s) administered], the curve of intermediate peak magnitude reflects 50% inhibition of the relevant node(s), whereas the curve of smallest peak magnitude reflects 90% inhibition of the relevant node(s). As shown, signals downstream of serially connected target nodes diminish considerably in response to a two-node combination therapy, in comparison with a monotherapy targeting receptor phosphorylation alone.

The recent revolution in molecular methods and the concomitant development of small molecule inhibitors that target specific dysregulated signaling pathways has spawned various new treatment regimens. Although these moleculartargeted agents may be able to prolong or stabilize the progression of tumors with minimal systemic toxicities [24], anti-tumor responses may be limited to a small subset of patients. For example, c-erbB1 receptor mutations in lung cancer correlate with the clinical response to Gefitinib, an EGF receptor (EGFR) kinase inhibitor [25,26]. Moreover, mutational analysis of specific kinases (e.g. c-kit, PDGFr) in gastrointestinal stromal tumors [27–29], and B-Raf kinase in colorectal cancer [30], all indicate that effective response to a molecularly-targeted therapy will stratify according to the specificity of the derangements within the signaling network itself.

Some investigators maintain that agents which block specific signal transduction pathways are unlikely to replace standard chemotherapeutic approaches as ‘single-agent first-line therapeutics’ despite their demonstrated cytostatic potential, arguing that their greatest utility and efficacy are realized in combination with conventional therapies [7]. Indeed, an expanding body of experimental and clinical literature attests to the promising additive and synergistic anti-tumor effects of small molecule tyrosine kinase inhibitors when used in conjunction with radiation and chemotherapy. Although these multi-modality combination therapies may enable smaller doses of the required agents to be administered, with an attendant decrease in systemic toxicities, it is important to recognize that treatment strategies incorporating multiple interacting drugs necessitate clinical trials of complex design [31]. www.drugdiscoverytoday.com

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Table 3. A comparison of different therapy regimens Therapeutic strategy

Pros

Cons

Chemotherapy, alone or as a combination, tailored to disease category

High rates of initial remission

High toxicity; non-specific

Well known and predictable side-effects

High rates of recurrence

Molecular-targeted therapy, tailored to disease category

Rational and simple

Only a small subset of patients respond

Drug target can be measured

Almost all patents eventually develop resistance and recur

Multi-modality combination therapy, tailored to disease category

Potential for lower doses and higher efficacy

Complex design of clinical trials Uncertain availability of new inhibitors Toxicity of non-specific agents still a problem

Network-targeted combination therapy

Potentially much lower doses and much higher efficacy Drug target can be measured

Lack of availability of multiple inhibitor drugs Complex design of clinical trials Potential problems with drug interactions Might not be very effective without knowledge of the deranged pathways in a particular tumor

Individualized network-targeted combination therapy

Greater effectiveness for patient because particular signaling defects is mapped so that the therapy is tailored to the patient

Difficult to publish studies because every patient will have different signaling defects and a different treatment

Known toxicities

Conclusions Although the availability of targeted inhibitors that possess appropriate selectivity and acceptable toxicity profiles is currently quite limited, as science progresses and we are armed with an ever-expanding armamentarium of selective targeted inhibitors, the ability to rationally combine these specific inhibitors as a cocktail will become a reality. Here we have discussed the new concept of network-targeted combination therapy where multiple interconnected nodes of a cell-signaling network are targeted with inhibitors. Simple mathematical models of the kinetics of intracellular signaling networks suggest that targeting several serially-linked nodes in a pathway is conducive to greater attenuation of biochemical signals compared with targeting a single node in isolation. In addition, the topology of the network can have a dramatic effect on the degree of synergistic attenuation from combination therapies and has important implications for rational selection of the appropriate target set. Since lower doses of the necessary drugs may be able to elicit the desired anti-tumor response as a consequence, drugs that were previously considered too toxic at their therapeutically-effective doses might soon play a valuable role in the treatment of disease. The interdependency seen within molecular networks could have a dramatic impact on broadening the number of targeted agents that could be used in the clinic today if they could be used in a rational combination. It is important, however, to reflect on the various difficulties that must be overcome before network-targeted combination therapy can be implemented successfully. In common with multi-modality combination therapies, undesirable and 432

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Difficult statistical validation of results Potential problems with drug interactions

unforeseen or idiosyncratic drug–drug interactions may prevail, and the design of clinical trials may be complex for network-targeted combination therapies. In addition, the efficacy of the treatment will be limited by the suitability of the chosen agents to the deranged pathways in a particular tumor. The field of molecular medicine is exploding because new proteomic and metabonomic technologies that can synergize with ongoing genomic and transcriptional profiling are exploited for tailoring therapy to an individual’s molecular portrait. Molecular networks of cellular signaling cascades, whose derangements underpin most human diseases, are clearly within the domain of proteomics. The cellular circuitry is driven by post-translational modifications such as protein phosphorylation, and cannot be understood effectively by transcriptional profiling alone. We propose here, supported by mathematical modeling, that a rational and specific targeting of multiple nodes within the cellular circuitry using combinations of agents could have a dramatic impact on efficacy and treatment over the current strategies employed at the bedside. The ultimate goal of network-targeted combination therapy is an individualized approach that tailors a therapeutic regime to a tumor’s molecular portrait.

References 1 Veit, M. (2004) New strategies for drug development. Berl. Munch. Tierarztl. Wochenschr. 117, 276–287 2 Marchini, S. et al. (2004) New molecules and strategies in the field of anticancer agents. Curr. Med. Chem. Anti-Canc. Agents 4, 247–262

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3 Cohen, P. (2002) Protein kinases – the major drug targets of the twenty-first century? Nat. Rev. Drug Discov. 1, 309–315 4 Awada, A. et al. (2004) New anticancer agents and therapeutic strategies in development for solid cancers: a clinical perspective. Expert Rev. Anticancer Ther. 4, 53–60 5 Shankar, S. and Srivastava, R.K. (2004) Enhancement of therapeutic potential of TRAIL by cancer chemotherapy and irradiation: mechanisms and clinical implications. Drug Resist. Updat. 7, 139–156 6 Harari, P.M. and Huang, S. (2004) Combining EGFR inhibitors with radiation or chemotherapy: will preclinical studies predict clinical results? Int. J. Radiat. Oncol. Biol. Phys. 58, 976–983 7 Noonberg, S.B. and Benz, C.C. (2000) Tyrosine kinase inhibitor targeted to the epidermal growth factor receptor subfamily: role as anticancer agents. Drugs 59, 753–767 8 Raben, D. et al. (2002) ZD1839, a selective epidermal growth factor receptor tyrosine kinase inhibitor, alone and in combination with radiation and chemotherapy as a new therapeutic strategy in non-small cell lung cancer. Semin. Oncol. 29 (1 Suppl. 4), 37–46 9 Koizumi, F. et al. (2004) Synergistic interaction between the EGFR tyrosine kinase inhibitor Gefitinib (‘‘Iressa’’) and the DNA Topoisomerase I inhibitor CPT-11 (Irinotecan) in human colorectal cancer cells. Int. J. Cancer 108, 464–472 10 Langer, C.J. (2004) Emerging role of epidermal growth factor receptor inhibition in therapy for advanced malignancy: focus on NSCLC. Int. J. Radiat. Oncol. Biol. Phys. 58, 991–1002 11 Tortora, G. et al. (2001) Oral administration of a novel taxane, an antisense oligonucleotide targeting protein kinase A, and the epidermal growth factor receptor inhibitor Iressa causes cooperative antitumor and antiangiogenic activity. Clin. Cancer Res. 7, 4156–4163 12 Ranson, M. (2002) ZD1839 (IressaTM): for more than just non-small cell lung cancer. Oncologist 7 (Suppl. 4), 16–24 13 Dasmahapatra, G.P. et al. (2004) In vitro combination treatment with perifosine and UCN-01 demonstrates synergism against prostate (PC-3) and lung (A549) epithelial adenocarcinoma cell lines. Clin. Cancer Res. 10, 5242–5252 14 Araujo, R.P. et al. A mathematical model of combination therapy using the EGFR signaling network. Biosystems (in press) 15 Kholodenko, B.N. (2000) Negative feedback and ultrasensitivity can bring about oscillations in the mitogen-activated protein kinase cascades. Eur. J. Biochem. 267, 1583–1588

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16 Spirin, V. and Mirny, L.A. (2003) Protein complexes and functional modules in molecular networks. Proc. Natl. Acad. Sci. USA 100, 12123–12128 17 Herrmann, C. et al. (2003) Connectivity distribution of spatial networks. Phys. Rev. E 68 (2 Pt. 2), 1–6 18 Tyson, J.J. et al. (2003) Sniffers, buzzers, toggles and blinkers: dynamics or regulatory and signaling pathways in the cell. Curr. Opin. Cell Biol. 15, 221–231 19 Huang, C.Y. and Ferrell, J.E. (1996) Ultrasenstivity in the mitogenactivated protein kinase cascade. Proc. Natl. Acad. Sci. USA 93, 10078– 10083 20 Bluthgen, N. and Herzel, H. (2003) How robust are switches in intracellular signaling cascades? J. Theor. Biol. 225, 293–300 21 Bar-Or, R.L. et al. (2000) Generation of oscillations by the p53–Mdm2 feedback loop: a theoretical and experimental study. Proc. Natl. Acad. Sci. USA 97, 11250–11255 22 Kholodenko, B.N. et al. (1999) Quantification of short term signaling by the epidermal growth factor receptor. J. Biol. Chem. 274, 30169–30181 23 Felici, A. et al. (2002) Dosing strategies for anticancer drugs: the good, the bad and the body-surface area. Eur. J. Cancer 38, 1677–1684 24 Vulfovich, M. and Saba, N. (2004) Molecular biological design of novel antineoplastic therapies. Expert Opin. Investig. Drugs 13, 577–607 25 Paez, J.G. et al. (2004) EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304, 1497–1500 26 Lynch, T.J. et al. (2004) Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N. Engl. J. Med. 350, 2129–2139 27 Heinrich, M.C. et al. (2003) Kinase mutations and imatinib response in patients with metastatic gastrointestinal stromal tumor. J. Clin. Oncol. 21, 4342–4349 28 Duensing, A. et al. (2004) Mechanisms of oncogenic KIT signal transduction in primary gastrointestinal stromal tumors (GISTs). Oncogene 23, 3999–4006 29 Heinrich, M.C. et al. (2003) PDGFRA activating mutations in gastrointestinal stromal tumors. Science 299, 708–710 30 Davies, H. et al. (2002) Mutations of the BRAF gene in human cancer. Nature 417, 949–954 31 Artega, C.L. and Baselga, J. (2003) Clinical trial design and end points for epidermal growth factor receptor-targeted therapies: implications for drug development and practice. Clin. Cancer Res. 9, 1579–1589

www.drugdiscoverytoday.com

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