Systems Pharmacology


Table 1:Comparison of sub-systems in humans. Analysis of the distance (in metres) between individual constituents of each sub-system as well as the average timescale that interactions and reactions occur


SUB-SYSTEM TYPE (HUMAN)


Molecular Pathway/Network Cell Tissue Organism DISTANCE (METRES) ~2x10-8 - ~5x10-10 ~1x10-6 - ~3x10-8 ~1x10-4 - ~8x10-6 ~2x10-2 - ~9x10-4 ~6x10-1 - ~(1-2)


AVERAGE TIMESCALE (SECONDS)


10-6 102 104 105 106 - 108


variety of molecular entities and processes that include, but are not restricted to, single nucleotide polymorphisms (SNPs), alternative genesplicing and protein isoforms (eg cytochrome P-450 super family) and epigenetic phenomena. Our basic understanding of these processes have led to the creation of simple semantic descriptors which define such differences and include concepts such as gender, age differentiation (child versus adult) and race. However, such coarse descriptions do not provide adequate insight into the significant and subtle differences that separate us at the molecular level, given that ALL humans are 99.9% genetically the same at the DNA level18. Finally, the temporal effect on complexity and


variability is an even poorer-understood process. Paradoxically, age is the most obvious manifesta- tion of physical change in the individual. We can all recognise the phenotypical differences between infants versus a young girl/boy versus an elderly woman/man. Also it is ‘well-known’ that we lose bone density, shrink and our metabolism slows down. However, our understanding of individual or population changes at the molecular and cellu- lar levels is still in its infancy. In the past, our understanding of this staggering, dynamic com- plexity and variability has been myopic and limit- ed. Hence, how can we produce safe and effica- cious therapeutic drugs for individual patients18?


Systems biology The emergence of systems biology (also known as pathway, network or integrative biology) was pre- dicted on an attempt to address and embrace human complexity and variability in human metabolism, physiology and pathobiology. The development of systems biology is still in its nascent ascendency. In its first generational incarnation (1940s-50s), a systems approach to biology was


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predicated on theoretical considerations of complex systems analyses. Second generation systems biolo- gy (late 1990s-early 2000s) has its roots in high throughput analytical omic measurements, bioin- formatics, bioengineering, computational sciences and mathematics. It is an attempt to establish a more integrated and hierarchical paradigm that facilitates the creation of new biological pathways and networks at the molecular and cellular level15. This provides a framework for understanding the holistic system of genetic, genomic, transcriptomic, protein, metabolite and cellular events that are in constant flux and interdependent. In order to facil- itate such efforts, two distinct approaches have evolved, namely computational modelling-based systems biology and data-driven systems biology. The former relies primarily on computational mod- elling and simulation tools. While there has been some confusion in the past about terminology it is also now referred to ‘bottom-up’ systems biology. The latter approach predominantly utilises analyti- cal datasets that are mined in a discovery manner for new knowledge using a variety of bioinformat- ics and knowledge assembly tools and is now cate- gorised as ‘top-down’ systems biology17.


Implementation of Systems biology in DDD We opined back in 2004 that systems biology could “…provide a new dynamic to invigorate pharma- ceutical companies… predicated on a more com- plete understanding of problems associated with the DDD process”15. A consensus emerged that systems biology had the potential to impact the entire DDD process by identifying biological sub- systems and how they interact to produce complex molecular, pathway/network, cellular, tissue and organism behaviour23,24. Such claims appear extraordinary when you consider the complexity and variability of both individuals as well as differ-


Drug Discovery World Winter 2018/19


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