New targets for antiinfective drug development

The initial impact of wide-scale pathogen genome sequencing has been to allow conventional charts of biochemical pathways to be annotated with gene names. Saturation mutagenesis tools have provided information on genes that are essential in particular growth media and, in some cases, under infection conditions. Systems biology aims to convert this static and informationally sparse framework into a dynamic network of nodes and fluxes. Quantitative models will highlight bottlenecks and nodes that are crucial for microbial viability and will distinguish between those at which a small or a large reduction in activity would be required for significant biological impact (see Chapter 7). The ability to input different types of data will allow models to be customised using information from genotypic data and from in vivo expression profiling to optimise for selection of targets that are appropriate in the context of existing drug resistance or in the context of phenotypic drug tolerance associated with latent tuberculosis and treatment of biofilm infections, for example. It can be anticipated that a systems biology framework will allow a rational approach to identification of synergistic drug combinations that will result in more rapid action and perhaps reduction in the evolution of resistance. Genetic experiments have shown that combining mutations which independently have no detectable impact on survival can result in 'synthetic lethality' [69, 70]. Similarly, it may be possible to identify drug combinations which result in a novel enhanced lethality by hitting two or more independent targets.

Systems biology may also help us in understanding infection processes in more detail. An illustrative outlook on what may be to come in the future is provided by a recent study by Uetz et al. [71] who studied interactions among human proteins and herpes-virus proteins. If or when the enormous experimental problems can be overcome - there is as yet no reliable experimental technique which allows us to test for transient or weak interactions - then such studies give much more detailed insights into infection biology at the molecular level with a distinct focus on the physical interaction per se. If we are willing to speculate for a moment then such approaches harbour a host of exciting possibilities waiting to be explored: we may for example be able to study why different species have different susceptibilities to different infectious agents - Simian Immunodeficiency Virus (SIV) and HIV are good examples for the subtle impact of cross-species effects - or we may study whether the molecular interactions between P. falciparum and their human hosts and fly vectors, respectively, can be exploited for clinical purposes.

As models evolve, they will integrate increasingly diverse sources of data. This could include information from structural biology and functional biochemistry that relate to the 'drugability' of targets. Pathogen-host systems biology comes with an additional component as infectious disease biology can only really be understood in an ecological and evolutionary framework: pathogens compete for a potentially limited host population, while hosts in turn mount an immune response against pathogens and may even develop suitable strategies against pathogens. There are a host of beautiful examples of apparent host-pathogen co-evolutionary dynamics (for example between lizards and some species of Plasmodium) [72]. In addition we must consider the interaction between the host and the drug (see Chapter 9); host metabolism or modification of the drug will also influence the way it interacts with its target and the system as a whole. Every effect we study at the molecular or cellular levels may lead to complicated (and long-term) feedback processes at the population level. Thus host-pathogen systems biology has to be even more immodest than other branches of the fledgling discipline of systems biology: it encompasses all levels from molecules all the way up to epidemiological dynamics at the eco-system level.

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