Relationships between the genome, proteome, diseaseome and drugome. The number of distinct protein species (about 400,000) comprising the proteome (green circle, scaled down by 25% relative to the other circles), is estimated by taking the approximately 25,000 currently annotated genes (yellow circle) and assuming about four splice variants per gene and about four post-translationally modified proteins per splice variant. The genome, diseaseome and drugome form a Venn diagram. The red circle represents the approximately 1,800 genes known to be involved in various diseases (the diseaseome). Of these, a small fraction (the drugome) is targeted by FDA-approved drugs. Not all drug targets have been characterized as disease genes. In total, proteins encoded by approximately 400 genes (0.1% of the proteome) are targeted by about 1,200 FDA-approved drugs. There are more drugs than protein targets because more than one drug can target the same protein.

The relationship between genome medicine and systems pharmacology. The diagram summarizes various aspects of genome medicine (in blue) and systems pharmacology (in yellow). Overlapping aspects of analyses and practice are in green (intersection of circles). The positioning of the circles indicates the operational classification of 'genome medicine to systems pharmacology' as top-down and 'systems pharmacology to genome medicine' as bottom-up. The key analyses and practices are in the circle for the field that uses them. Approaches and practices that are used in both fields are in the overlapping region. Genome medicine starts with genetic and genomic testing. Experimental data are computationally processed using statistical genetics tools to yield information that is used in personalized medicine for therapeutic-index targeting (such as dosage of warfarin) and combination therapy. Network analysis is a common approach that integrates genome medicine and systems pharmacology. Systems pharmacology starts from cataloguing the characteristics of individual drugs and targets from biochemistry and cell-physiology experiments. Computational methods and genomic and proteomic data together enable the use of this catalog of information to make predictions regarding drug discovery, drug action and adverse events. Such predictions can be experimentally and clinically tested. Approaches common to both genome medicine and systems pharmacology are based on network analyses that underlie systems pathophysiology, whereby the origins of disease are understood in the context of multi-scale systems. Such understanding enables network-based drug screening and whole genome-based predictions of adverse events and drug resistance. Thus, ultimately, therapeutics intervention will be guided by integrating genome medicine and systems pharmacology.

Multi-scale analyses in systems pharmacology. The top half of the figure is a schematic representation of different scales of organization involved in human pathophysiology and systems pharmacology. Clinical indicators and analyses (left) indicate measurements of various types of blood concentrations, blood pressure, stress and so on; these parameters are available in the electronic medical records of patients. From left to right, the scale becomes smaller, or 'zoomed in'. The human body (or organism) can be analyzed at the levels of organs, tissues, cells (represented here together with tissues) or molecules. Drugs are prescribed and taken at the organismal level but exert their effects by interacting with their target at the molecular level (red arrow). The gradient from white to blue corresponds to the various levels of interaction systems: white represents a clinical setting; blue represents a laboratory setting. Studies in systems pharmacology fully span all levels shown here.