Network Pharmacology [chapter]

Uma Chandran, Neelay Mehendale, Saniya Patil, Rathnam Chaguturu, Bhushan Patwardhan
2017 Innovative Approaches in Drug Discovery  
The postgenomic era witnessed a rapid development of computational biology techniques to analyze and explore existing biological data. The key aim of the postgenomic biomedical research was to systematically catalogue all molecules and their interactions within a living cell. It is essential to understand how these molecules and the interactions among them determine the function of this immensely complex machinery, both in isolation and when surrounded by other cells. This led to the emergence
more » ... nd advancement of network biology, which indicates that cellular networks are governed by universal laws and offer a new conceptual framework that could potentially revolutionize our view of biology and disease pathologies in the 21st century ( Barabási and Oltvai, 2004) . During the first decade of the 21st century, several approaches for biological network construction were put forward that used computational methods, and literature mining especially, to understand the relation between disease phenotypes and genotypes. As a consequence, LMMA (literature mining and microarray analysis), a novel approach to reconstructing gene networks by combining literature mining and microarray analysis, was proposed (Li et al., 2006; Huang and Li, 2010) . With this, a global network was first derived using the literatureÀbased, cooccurrence method and then refined using microarray data. The LMMA biological network approach enables researchers to keep themselves up to date with relevant literature on specialized biological topics and to make sense of the relevant large-scale microarray dataset. Also, LMMA serves as a useful tool for constructing specific biological network and experimental design. LMMAÀlike representations enable a systemic recognition for the specific diseases in the context of complex gene interactions and are helpful for studying the regulation of various complex biological, physiological, and pathological systems. The significance of accumulated-data integration was appreciated by pharmacologists, and they began to look beyond the classic lock-and-key concept as a far more intricate picture of drug action became clear in the postgenomic era. The global mapping of pharmacological space uncovered promiscuity, the specific binding of a chemical to more than one target (Paolini et al., 2006) . As there can be multiple keys for a single lock, in the same way, a single key can fit into multiple locks. Similarly, a ligand might interact with many targets and a target may accommodate different types of ligands. This is referred to as "polypharmacology." The concept of network biology was used to integrate data from DrugBank (Re and Valentini, 2013) and OMIM (Hamosh et al., 2005), an online catalog of human genes and Network Pharmacology Chapter | 5 129 genetic disorders to understand the industry trends, the properties of drug targets, and to study how drug targets are related to disease-gene products. In this way, when the first drug-target network was constructed, isolated and bipartite nodes were expected based on the existed one-drug/one-target/onedisease approach. Rather, the authors observed a rich network of polypharmacology interactions between drugs and their targets (Yildirim et al., 2007). An overabundance of "follow-on" drugs that are drugs that target already targeted proteins was observed. This suggested a need to upgrade the singletarget single-drug paradigm, as single-protein single-function relations are limited to accurately describing the reality of cellular processes. Advances in systems biology led to the realization that complex diseases cannot be effectively treated by intervention at single proteins. This made the drug researchers accept the concept of polypharmacology which they previously thought as an undesirable property that needs to be removed or reduced to produce clean drugs acting on single-targets. According to network biology, simultaneous modulation of multiple targets is required for modifying phenotypes. Developing methods to aid polypharmacology can help to improve efficacy and predict unwanted off-target effects. Hopkins (Hopkins, 2007 (Hopkins, , 2008 observed that network biology and polypharmacology can illuminate the understanding of drug action. He introduced the term "network pharmacology." This distinctive new approach to drug discovery can enable the paradigm shift from highly specific magic bulletÀbased drug discovery to multitargeted drug discovery. NP has the potential to provide new treatments to multigenic complex diseases and can lead to the development of e-therapeutics where the ligand formulation can be customized for each complex indication under every disease type. This can be expanded in the future and lead to customized and personalized therapeutics. Integration of network biology and polypharmacology can tackle two major sources of attrition in drug development such as efficacy and toxicity. Also, this integration holds the promise of expanding the current opportunity space for druggable targets. Hopkins proposed NP as the next paradigm in drug discovery. Polypharmacology expands the space in drug discovery approach. Hopkins had suggested three strategies to the designers of multitarget therapies: the first was to prescribe multiple individual medications as a multidrug combination cocktail. Patient compliance and the danger of drugÀdrug interactions would be the expected drawbacks of this method. The second proposition was the development of multicomponent drug formulations. The change in metabolism, bioavailability, and pharmacokinetics of formulation as well as safety would be the major concerns of this approach. The third strategy was to design a single compound with selective polypharmacology. According to Hopkins, the third method is advantageous, as it would ease the dosing studies. Also, the regulatory barriers for the single compound are fewer compared to a formulation. An excellent example of this is metformin,
doi:10.1016/b978-0-12-801814-9.00005-2 fatcat:zhprs4augbcffnnh3iljlkth7i