Systems approaches to polypharmacology and drug discovery

Abstract

Systems biology uses experimental and computational approaches to characterize large sample populations systematically, process large datasets, examine and analyze regulatory networks, and model reactions to determine how components are joined to form functional systems. Systems biology technologies, data and knowledge are particularly useful in understanding disease processes and drug actions. An important area of integration between systems biology and drug discovery is the concept of polypharmacology: the treatment of diseases by modulating more than one target. Polypharmacology for complex diseases is likely to involve multiple drugs acting on distinct targets that are part of a network regulating physiological responses. This review discusses the current state of the systems-level understanding of diseases and both the therapeutic and adverse mechanisms of drug actions. Drug-target networks can be used to identify multiple targets and to determine suitable combinations of drug targets or drugs. Thus, the discovery of new drug therapies for complex diseases may be greatly aided by systems biology.

Figure 1. Biological signaling networks and network representation of genetic mutations in disease

(A) Biological signaling molecules and the interactions of these molecules are represented in biological signaling networks as nodes and edges, respectively. (B) In a network representation, genetic mutations associated with disease are either node removals or edgetic mutations. Node removals are truncated gene products and edgetic mutations disrupt the interaction between two nodes. For example, two different disorders, the ectrodactyly–ectodermal dysplasia–clefting (EEC) and ankyloblepharon-ectodermal dysplasia-clefting (AEC; Hay-Wells) syndromes are associated with two different edgetic mutations within one signaling motif that contains the interactions between the transcription factor TP63 (tumor protein 63) and DNA, and between TP63 and sterile α motif (SAM)-domain interacting proteins [15].

Figure 2. The analysis of drugs and drug targets

Approved, non-illicit, non-nutraceutical drugs and drug targets were extracted from DrugBank 2.0, [18,19] on March 23, 2010. The number of unique drug targets was 764, and the total number of drugs was 1366. The number of drugs for each target (A) and the number of targets for each drug (B) are shown. A total of 5126 drugs and 1894 drug targets listed on the Therapeutic Target Database (TTD) [20,21] on March 23, 2010 were categorized by drug type (C) and drug target type (D), according to whether the drugs were approved, in clinical trials or in the preclinical phase [21].

Figure 3. Selected examples of therapeutic polypharmacology

(A) Resistance to β-lactam antibiotics (eg, amoxicillin) is caused by degradation of the drugs by bacterial β-lactamase. Inhibitors of β-lactamase (eg, clavulanate) prevent the degradation of the antibiotics, thereby increasing the effectiveness of these drugs. (B) Notch mutations are associated with cancer. Several drugs have been designed that target γ-secretase, the enzyme upstream of Notch; however, resistance to these γ-secretase inhibitors (GSIs) can be caused by mutations in the MYC gene. A combination of drugs that target both Notch and Myc (through inhibition of CDK4) has been demonstrated to reverse Myc-induced GSI resistance and effectively treat cancer. (C) The anticancer agent sorafenib is an example of a drug with multiple targets (eg, Raf and PDGFR/VEGFR) that is used to treat a single disease (ie, renal or liver cancer).

Figure 4. The significance therapeutic polypharmacology in designing combination therapy

(A) Many types of cancer involve mutations in, or aberrant expression of, Ras, Raf, PI3K or receptor tyrosine kinases (RTKs), such as EGFR, HER2, PDGFR and FGFR. Cell proliferation and survival require both the MAPK and PI3K pathways to be active. Compensatory mechanisms cause a limited or null response to monotherapies targeted at different nodes in the RTK signaling network, such as the reduction in the p70S6K/RTK negative feedback caused by either MEK (B) or PI3K inhibition (C). (D) A combination of therapies can overcome these compensatory mechanisms and could effectively cause disease regression.

Figure 5. A phylogenetic tree of the kinases coded for within the human genome

Kinases are grouped and classified primarily by the sequence of the catalytic domains. Different groups are color-coded according to this classification [63,74].