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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Expert Rev Clin Pharmacol. Author manuscript; available in PMC 2014 January 1.
Published in final edited form as:
PMCID: PMC3809828

Polypharmacology: drug discovery for the future


In recent years even with remarkable scientific advancements and significant increase of global R&D spending, drugs are frequently withdrawn from markets. This is primarily due to their side-effects or toxicities. Drug molecules often interact with multiple targets, coined as polypharmacology, and the unintended drug-target interactions could cause side-effects. Polypharmacology remains to be one of the major challenges in drug development, and it opens novel avenues to rationally design next generation of more effective but less toxic therapeutic agents. This review outlines the latest progress and challenges in polypharmacology studies.

Keywords: Polypharmacology, Off-target, Side-effects, Drug repurposing, Drug-target network, Multi-targeting design


Drug discovery and development is a complex and expensive process [1,2]. Due to the exponential growth of molecular data and fast advancement in technologies, the efforts of drug discovery have been tremendously amplified [3-6]. The philosophy of drug design has been transformed from “one drug one target” to “one drug multiple targets”, coined as polypharmacology [7-12]. Polypharmacology is emerging as the next paradigm of drug discovery [7,13-18]. Polypharmacological phenomena includes: (a) single drug acting on multiple targets of a unique disease pathway, or (b) single drug acting on multiple targets pertaining to multiple disease pathways. In addition, polypharmacology for complex diseases is likely to employ multiple drugs acting on distinct targets, that are part of a networks regulating various physiological responses [7,19]. The Polypharmacological approaches aim to discover the unknown off targets for the existing drugs (also known as drug repurposing) [15,20,21]. The approach needs the systematic integration of the data derived from different disciplines including computational modeling, synthetic chemistry, in vitro/in vivo pharmacological testing, and clinical studies[22,23].

Although not designed on purpose, numerous drugs are known for their multi-targeting activities. One such example is Aspirin, often used as an analgesic to relieve minor pains or as an antipyretic to reduce fever [24]; also acts as an anti-inflammatory medication to treat rheumatoid arthritis [25], pericarditis[26], and Kawasaki diseases[27]. Additionally, it has been used in the prevention of transient ischemic attacks [28], strokes, heart attacks[29], pregnancy loss [30], and even cancer[31]. Another example is Sildenafil (Viagra), a phosphodiesterase (PDE) inhibitor. It was initially developed for hypertension and ischemic heart disease. However, it is now more frequently used to treat erectile dysfunction[32]. Certainly, kinase inhibitors are the elephant in the room regarding polypharmacology research. As a target class, kinases have received considerable attention over the last 20 years. There are currently 14 kinase inhibitors on the market for cancer treatment, with many more in clinical development [33,34]. Most of these cancer therapeutics inhibit more than one kinases, although they maintain reasonable selectivity over the serine/threonine and phosphoinositide (PI) kinase classes [34-38]. On the other hand, polypharmacology can be considered a double-edged sword and cause clinical problems when it is not fully understood. Australia's Therapeutic Goods Administration (TGA, the Australian equivalent of the FDA) cancelled the registration of Lumiracoxib (a COX-2 selective inhibitor/non-steroidal anti-inflammatory drug) in Australia due to concerns that it may cause liver failure. Later Merck voluntarily withdrew Rofecoxib from the market because of the increased risk of heart attack and stroke associated with the long-term, high-dosage use. Staurosporine, a potent protein kinase C inhibitor, is also known to interact with many other kinases (Figure 1) and this excluded its use in clinical practice.

Figure 1
The kinase targets of staurosporine.

Current Technologies and Progress

Polypharmacology studies are critical in drug discovery and development. However, the exhaustive coverage of all targets by experimental methods is still an industry-wide challenge. Apparently the most competent approach would be to utilize the -omics (proteomics, cheminformatics, etc.) technologies [39,40]. The enormous molecular data generated in the post-genomic era has significantly accelerated the polypharmacology research. Systems biology approaches integrated with pharmacology are being frequently used to identify new off-targets [16,17,23,41]. There are a large number of public and private molecular databases available and are continuously growing in both size and number; some of them are listed in Table 1. They integrate diverse information of molecular pathways, crystal structures, binding experiments, side-effects, and drug targets. There are also number of other small molecule databases including: ZINC [42], PubChem [43], Ligand Expo [44], KEGG DRUG [45], etc., with enormous information about their disease relevance, chemical properties, and biological activities. These databases can be used not only to predict the protein targets of a small molecule, but also to obtain insights of designing polypharmacological agents in a prospective manner.

Table 1
Some of the representative databases for polypharmacology studies.

With the increasing availability of the above databases, various methods have been applied to predict molecular polypharmacology. Recently Keiser and colleagues used the similarity ensemble approach (SEA) [54,55] in a large scale to predict the activity of 656 marketed drugs on 73 unintended ‘side-effect’ targets, and confirmed half of the predictions with the IC50 activity values ranging from 1nM to 30μM [55]. Among these new associations was the finding that the abdominal pain side-effect of chlorotrianisene was due to its newly discovered inhibition of cyclooxygenase-1 (COX-1). Oprea et al. also reported results from their text mining of 7,684 approved drug and mapped the “adverse reactions” of 988 unique drugs onto 174 side-effects[15,20]. They were then clustered with principal component analysis into a self-organizing map and integrated it into a Cytoscape network. They expected that this type of data could streamline drug repurposing [20]. Barabasi et al. used a polypharmacology approach to build a bipartite graph composed of US Food and Drug Administration-approved drugs and proteins linked by drug-target binary associations [56]. Zhang et al. demonstrated that polypharmacology can not only speed the wide identification of drug targets but also find new applications for the existing drugs [57]. Additionally Apsel et al. reported systematic discovery of molecules that potently inhibit both tyrosine kinases and phosphatidylinositol-3-OH kinases [9]. Crystal structures revealed that the dual selectivity of these molecules is controlled by a hydrophobic pocket conserved in both enzyme classes and accessible through a rotatable bond in the drug skeleton [9]. In another polypharmacology prediction study, Cheng et al. proposed two different weighted network-based inference methods for chemical-protein interaction prediction [58]. Bork and colleagues used phenotypic side-effect similarities to infer whether two drugs share a target. They applied to 746 marketed drugs with a network of 1,018 side-effect followed up with experimental validations, and found that 11 out of 13 implied drug-target interactions reveal inhibition constant equal or less than 10um [59]. Mattingly and coworkers with the aid of text mining tools developed CTD [51] (comparative toxicogenomics database) which includes curated data describing cross-species chemical–gene/protein interactions and chemical– and gene–disease associations. The database is intended to provide insights into complex chemical–gene and protein interaction networks.

Structure-based methods including inverse docking [60-62] are also widely used to predict protein targets of small molecules. A panel of tractable targets involved in a disease network are screened against the approved drug molecules with docking. The targets with available 3D structure are normally used. The top ranked targets (excluding the original known targets) can be treated as the lead off-targets for further testing. For instance, Zou and coworkers [60] used inverse docking approach to identify the potential direct target oxidosqualene cyclase (part of the cholesterol synthetic pathway) of PRIMA-1 (known for its ability to restore mutant p53 tumor suppressor functions). Experimentally they have shown that both PRIMA-1 and Ro 48-8071 (a known potent OSC inhibitor) could significantly reduce the viability of breast cancer cells relative to normal mammary cells. Similarly Tawa et al [63] tested 3D-shape/chemical similarity analysis program ROCS (Rapid Overlay of Chemical Structures) to generate off-target profiles of drugs from DrugBank[24] and KEGG[45]. A systems pharmacology approach was used by Dar and co-workers to indentify polypharmacological agents with an optimal balance of activity and less toxicity against the kinases Ret, Raf, Src, Tor and S6K [23]. These examples demonstrate the importance of this highly promising field, and it is clear that developing multi-target therapeutics agents represents an important avenue to advance drug discovery. Some of the methodologies are summarized in Table 2.

Table 2
Broad classification of polypharmacological methods.

Recently, our group, among others [10,13,70,71], have embarked on the development of novel methods to explore the ligand-target interactions using molecular networks [70,72]. These approaches are to deduce polypharmacological relations among drugs and the protein crystal structures reported in PDB [73]. The drug molecules were obtained from the available public databases such as DrugBank[24], and the corresponding target information was obtained from PDB [73]. After data curation with our in-house programs, databases were constructed for both drugs and targets, and networks were created to evaluate their relationships. The structural similarity among the drug molecules that are likely to bind to the same targets has also been evaluated using JChem software [74]. Using our method, the polypharmacology network created for a query compound Raltitrexed (brand name Tomudex; an anti-metabolite and inhibitor of thymidylate synthase and folylpolyglutamate synthase) is depicted in Figure 2. The complexity of the network is reduced by hiding the targets and only showing the ligand molecules connected to Raltitrexed in three levels. The network shows multiple clusters of compounds that have close relationships with each other based on their shared targets. Ralititrexed is connected to multiple molecules such as Floxuridine and Leucovorin which share common target (thymidylate synthase) in the first level of network, meaning they might have similar biological activities or they bind to similar targets. The network is further extended to more levels by ligand-target pairing.

Figure 2
Graphical depiction of the drug network for Raltitrexed, derived based on their shared targets. The complexity of the network is reduced by hiding the targets and only showing the drug molecules that are directly or in directly related to Raltitrexed ...

Challenges for Polypharmacology

Despite their evident growth, polypharmacological approaches are attributed with several challenges. The major limitation is that we only partially understand the pathways/mechanism of many diseases at the molecular levels. It is exceedingly difficult to derive the full polypharmacological networks without the complete data. Additionally more accurate mining techniques and mapping methodologies are needed to analyze the complex data. On the other hand, understanding the convoluted associations is also a challenging task after the complex networks are constructed [75]. Other methods also face various issues. For instance, inverse docking approaches suffer from several drawbacks including difficulty of addressing target flexibility and meager performance of scoring algorithms [61,76]; In text mining it is still difficult to constantly access/update the enormous information provided by different and non-synchronized databases [68]. Addressing these issues in respective areas will certainly help to improve polypharmacology studies.

Expert commentary

Currently it is acknowledged that polypharmacology goes well beyond target families. The fact is that the toxicities or targets of many phenotypes drugs are either largely unknown or insufficiently understood in most cases. If we can use known drug properties to understand the underlying mechanisms, it will shift the paradigm of chemogenomic studies and drug development. However, it is too costly to conduct these studies experimentally. Therefore we need more predictive algorithms as well as increased integration of available data. The synergy among different disciplines could help the polypharmacology studies for the drug discovery community. There have been several promising attempts [9,20,55,59], and various methods [10,14,58,71,72,77] and databases [24,46,47,50,78] have been developed. Despite the existing challenges in the area, polypharmacology approaches are showing their promise and will certainly have the potential to transform our next-generation drug discovery and development.

Five-year view

Polypharmacology can be employed to identify new drug off-targets. This is particularly important for prediction of possible adverse effects of for new drugs in development [55,59]. On the other hand, it can also be used for drug repurposing where new indications of existing drugs/agents can be identified [20,79-83]. This will significantly improve the current stalling drug discovery engine. As a matter of fact, NIH and FDA have launched such programs to identify new uses for existing agents developed by pharmas[84]. Therefore, during the next five or more years, we will see wide collaborations between academia and pharmaceutical sector for repositioning of existing therapeutic agents. Additionally continuous growth in the computational methods for polypharmacology prediction will be witnessed along with their widespread applications in drug discovery. We expect that the unexpected, potentially disastrous adverse properties of pipeline products will be modeled during their early development. Down the road in the next few years, more sophisticated and comprehensive polypharmacology approaches will boom and the rational design of more potent but less toxic multi-targeting agents may occur, although it is still extremely difficult at the current stage.

Key issues

  • The concept of polypharmacology involves the interaction of drug molecules with multiple targets, which may interfere with a single or multiple disease pathways.
  • The polypharmacological studies could uncover new off-targets for the existing drugs.
  • The approach could provide us with the explanation for the drug side-effects and disastrous toxicities.
  • Polypharmacology can be used for drug repurposing by finding new indications (or new therapeutic targets) for existing drugs.
  • Computational approaches for polypharmacology modeling will witness rapid growth and wide application in drug discovery.
  • There exists various (a) similarity based, (b) network based, and (c) structure based approaches to uncover polypharmacological relationships.
  • High level data curation/integration and methodology development from various drug discovery disciplines would be needed for accurate prediction of polypharmacology prediction and rational design of multi-targeting agents.
  • Various challenges still exist for polypharmacology modeling, and rational design of multi-targeting agents is extremely complex.


We specially thank John Morrow for proof-reading the manuscripts. This work was partially supported by the University Cancer Foundation via the Institutional Research Grant program, NCI SPORE P50CA140388 and 5P50CA127001, and NIH 5R01CA138702-03. The authors declare no competing financial interests.


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