As a description for the binding of a drug to more than one target, polypharmacology can lead to multiple outcomes, both beneficial and harmful. To consider both outcomes separately, polypharmacology can be divided into two types: therapeutic polypharmacology and adverse polypharmacology (). Therapeutic polypharmacology includes the concept of treating multigenic, complex diseases by targeting multiple targets with one or more drugs, in order to effectively reset the regulatory network processes that are altered in the disease state. Adverse polypharmacology comprises the scenarios in which the ‘off-target’ binding of drugs leads to adverse effects. Such interactions include binding to protein targets other than the therapeutic target and binding to the therapeutic target in non-target tissue.
Comparison of therapeutic and adverse polypharmacology.
The systematic treatment of a single disease at two or more targets (ie, critical nodes) requires a combination of the empirical knowledge of which drugs are effective against each pathophysiology and the knowledge gained from a systems approach to understanding how multiple nodes cooperate in a signaling network to produce the pathophysiology associated with the disease. Ideally, each disease would be treated by therapeutic polypharmacology – the combination of ‘targeted therapies’ that modulate multiple, specific signaling components or interactions that malfunction in a given disease [25
]. The regulatory network surrounding the drug targets and/or disease would be analyzed, accounting for the modulations resulting from the targeted therapy treatment. Combining the building of interaction networks for various diseases with genome-wide analyses of changes, such as SNPs or copy-number variations, will allow a significant expansion of the list of possible drug targets in a physiologically relevant manner. Each gene product associated with disease is not necessarily ‘druggable’; however, an analysis of the druggability of the human genome, according to the structural and functional properties of each protein, supports the view that the current ‘drugome’ can be greatly expanded and diversified [26
]. Combining a network perspective with the empirical knowledge of genetic changes associated with disease, in order to achieve an understanding of the consequences of targeting each node, contrasts with the establishment of drug targets through empirical knowledge, such as the use of trial-and-error approaches in prescribing drugs and drug combinations. For example, given the known genetic amplifications or mutations associated with cancer, there are several protocols that require a receptor expression test before drug use, including the requirement for HER2 expression in breast cancer prior to the prescription of trastuzumab [27
], and EGFR expression in pancreatic cancer for enrollment in a cetuximab combination clinical trial [29
]. However, these diagnostic tests and empirical studies do not offer a mechanistic insight into why drugs function or do not function, or what role a genetic change plays in disease progression. Such information may be obtained through careful analyses of the signaling networks involved. As discussed in subsequent sections, a therapeutic polypharmacology approach can introduce robustness in therapeutic targeting and has the potential to minimize the clinical failure of drugs resulting from lack of effectiveness, through targeting a combination of multiple, critical nodes.
The decision to treat a disease using a combination of drugs with different targets is often derived from insights based on genomic studies. For example, a gene expression or protein signal profiling experiment in cells treated with a drug can indicate which genes or signaling mechanisms are upregulated and interfere with drug action. In addition, GWAS on cohorts of patients with different responses to a drug can indicate which upregulated genes may be causing drug resistance. However, because of the variety of genetic backgrounds in disease, particularly cancer, a given combination of therapies designed to downregulate multiple targets will not be effective for every patient. Before designing combination therapies, analyzing the signaling network involved with a drug action within the context of a patient’s genetic background may be critical to the success of the therapy. Accounting for the effect of the combination of drug effects within a signaling network will be a distinguishing feature between currently used, empirical combination therapy and therapeutic polypharmacology. The following examples demonstrate how components of the signaling network surrounding a drug target can play important roles in drug action, and why possible drug combinations can be effective.
Therapeutic pharmacology in antibiotic therapy
A well-understood example of therapeutic polypharmacology is resistance to β-lactam antibiotics, often caused by the bacterial enzyme β-lactamase. Bacteria produce various isoforms of β-lactamase, which degrades β-lactam antibiotics and renders these drugs inactive at inhibiting bacterial cell wall synthesis (). Combining β-lactamase inhibitors with β-lactam antibiotics overcomes this antibiotic resistance. This combination is now widely used as an effective antibiotic therapy. However, there are multiple mechanisms of antibiotic resistance, which continues to be a problem and has been the focus of many studies (reviewed in [30
Selected examples of therapeutic polypharmacology
Therapeutic polypharmacology for multiple drugs at multiple targets
Studies have demonstrated that certain mutations within a targeted molecule, or at a molecule in another pathway, can render a drug ineffective. In these scenarios, which are common in the treatment of cancer, a therapeutic polypharmacology approach is most likely to succeed. In a recent example, the discovery that Notch mutations are associated with T-cell acute lymphoblastic leukemia (T-ALL) led to the development of inhibitors of this pathway, such as γ-secretase inhibitors (GSIs; γ-secretase is an upstream regulator of Notch), for the treatment of cancer [31
]. A stapled peptide that directly inhibits the transcriptional activity of Notch was recently described [32
]. The enhanced signaling of the Notch pathway that results from Notch mutations leads to aberrant cell survival through anabolic gene expression. GSI treatment typically has a limited success rate because many patients with T-ALL exhibit other mutations that compensate for the inhibition of the γ-secretase pathway (eg, PTEN deletion) [31
] (). This situation illustrates the potential utility of a therapeutic polypharmacology approach to target both Myc and Notch in diseases involving aberrant Notch signaling, which has also been observed in lung adenocarcinoma and other cancers [31
]. In a recent study by Rao et al
, a therapeutic polypharmacology approach was used to examine the properties that render cells sensitive or insensitive to GSI therapy [33
]. The results prompted the design of a combination therapy of a GSI with an inhibitor of CDK4, which acts upstream of Myc (). The therapeutic polypharmacology downregulated the compensatory signal (ie, Myc) through CDK4 inhibition, allowing increased sensitivity to Notch inhibition, thereby producing a therapeutic effect (ie, inhibition of cell growth) [33
Therapeutic polypharmacology for one drug at multiple targets
Therapeutic polypharmacology can also arise from one drug binding multiple targets that contribute to the overall effectiveness of a treatment. For example, sorafenib (Nexavar), a drug used to treat renal and liver cancers, was originally designed to inhibit Raf kinase isoforms, but was also demonstrated to inhibit the PDGF and VEGF receptor tyrosine kinases (RTKs) [34
], both of which play a role in cancer progression. Thus, the ability of sorafenib to demonstrate activity against these receptors in conjunction with anti-Raf activity is advantageous. Sorafenib appears to inhibit cancer cell proliferation by inhibiting Raf, and prevent tumor progression and angiogenesis by inhibiting PDGFR and VEGFR (). In another example, lapatinib (Tykerb), which is used to treat metastatic breast cancer, inhibits ErbB1 and HER2; the upregulation of both of these molecules has been observed in breast cancer, as well as in several other types of cancer [37
Therapeutic polypharmacology in designed combination therapy
Another example of therapeutic polypharmacology is illustrated with the regulatory network surrounding the growth factor RTK in cancer (). Dysregulation of the RTK, MEK and PI3K pathways resulting from various mutations within these pathways have been observed in cancer. These altered signaling events have implications for the effectiveness of targeted therapies; for example, KRAS
mutations are often associated with resistance to EGFR-targeted therapies [38
]. The observed high frequency of KRAS
mutations in cancer has led to the development of inhibitors of Raf and MEK, both of which act downstream of K-Ras [39
] (). BRAF
mutations have also been observed in various cancers, leading to the development of B-Raf inhibitors [41
]. Recent studies in cells containing mutated Ras have also demonstrated that B-Raf inhibitors can lead to enhanced MEK/ERK activation, caused by the inhibitors functioning as C-Raf activators because of the heterodimerization of B-Raf and C-Raf [42
]. Resistance to EGFR therapy can also be coupled with the maintenance of PI3K signaling through a variety of mechanisms, including loss of PTEN (a phosphatase and negative regulator of AKT) activity, or ErbB3 or Met amplification [44
]. The association of PI3K signaling with cancer and resistance to therapies has led to the development of mTOR and PI3K inhibitors [45
The significance therapeutic polypharmacology in designing combination therapy
Each targeted therapy affects the signaling network in a different manner, and downstream inhibitors produce a set of compensatory signals that differs from those involved in upstream inhibition. For example, the mechanisms of compensation in MEK and PI3K/AKT inhibition (downstream inhibition) differ from those of EGFR inhibition (upstream inhibition). MEK inhibition can cause an increase in PI3K/AKT activation () and, conversely, PI3K/AKT inhibition can cause an increase in MAPK activation () [46
]. These paradoxical results are caused by a negative feedback loop in RTK signaling that is mediated by TSC2/p70S6K signaling and controlled by AKT and ERK. Thus, when MEK/ERK or PI3K/AKT is inhibited, the negative feedback loop is partially shut down (), causing signal amplification in the non-inhibited arm of the pathway that could enable cancer progression [46
]. Such mechanisms of resistance are particularly common in cancer progression and underscore the importance of careful analysis of signal flow within the surrounding signaling network of drug targets, in order to understand the mechanisms of resistance and to design therapeutic polypharmacology appropriately to overcome this resistance.