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ance, environment and many other conditions31. While this information processing machinery effortlessly instructs the body how to respond to heterogenous simultaneous input signals, the plas- ticity of this non-linear complex system creates formidable challenges for data analytics. An illus- tration of the power of this disruptive methodolo- gy is provided in the application of EBA technolo- gy for identifying drug candidates capable of reversing antibiotic resistance of bacteria.


Antibiotic resistance – an imminent health threat Antibiotic resistance emerges naturally, but misuse of antibiotics in humans and animals is accelerat- ing the process. A growing number of infections – such as pneumonia, tuberculosis, gonorrhoea and salmonellosis – are becoming harder to treat as the antibiotics used to treat them become less effective and present a key limitation for treatment of vari- ous life-threatening infections. The World Health Organization (WHO) has compiled a list of antibi- otic resistant bacteria (including Escherichia coli, Klebsiella pneumoniae and Staphylococcus aureus) for addressing this global health problem. Innovative strategies to mitigate the crisis of antimicrobial drug resistance include the identifi- cation of new therapies, chemo-sensitising modu- lators, as wells as approved drugs that can be repurposed for an alternate therapeutic indication (drug repurposing).


Application of EBA platform for discovery of drugs targeting multi-drug resistant bacteria We have used EBA to identify:


Figure 4 Application of EBA in the


identification of mechanisms involved in multi-drug


resistance across bacterial strains


body29. In this context, the term network connec- tivity refers to the transfer of information from one network node to another, and the term network node refers to a connection point or redistribution point (eg, protein, process, function, organ, tissue, cell type, etc) for the propagation of information. Likewise, the term information transfer refers to the relative gain or loss of information experienced at various system levels when a set of input signals (regardless of their nature or origin) change. Directions of information flows in these dynam-


ic network systems depend on topologies of sub- network systems adapting to characteristics of input signals30. Affecting this non-linear regulato- ry scheme are gender differences, age, gene vari-


48


lShared mechanisms of drug resistance to multiple antibiotics across a broad range of bacterial strains. l Mode of action (including biomarkers) that can be used for targeting multi-drug resistance either as reversing agents or as direct-acting antibacterials. l Substances capable of targeting drug resistant bacterial phenotypes.


The first step was to construct a protein interac-


tion network that links the pharmacologies of a broad range of antibiotics to molecular mecha- nisms involved in drug resistance of bacteria listed as prime candidates for the global health crisis by WHO. Figure 4 shows the results of a network overlap analysis involving network comparisons of 2,800 bacteria, most antibiotics and the drug-resis- tant strains listed by WHO. The second step was to partition the primary network (described in Figure 4), consisting of 290


Drug Discovery World Summer 2018


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