The hope of the rapid translation of ‘genes to drugs’ has floundered on the reality that disease biology is complex, and that drug development must be driven by insights into biological responses. Historically, the scientific approach to drug discovery has been reductionist, focusing on one component of a system at a time. The information gained from studying a single gene or protein is then applied to an entire biological system, be it a tissue, organ or organism. This approach is often too simplistic, as biological systems are more than the sum of their individual parts. The result of ignoring the interactions of the various components and pathways at the systems biology level is an increased likelihood of missing other important biological responses. Further, not all biological systems can be expected to have the same responses to the same activation (perturbation) due to genetic diversity. Development of new therapeutic products requires a biological system understanding of the human genome and the underlying elements and stages of diseases specific to genetically different patient populations. Hence, systems biology is key to the development of personalized medicine; and for this reason the approach should be adopted for drug discovery at all phases of drug development, from petri dish to clinical trials. Additionally, the biological knowledge gained through this approach will ultimately lead to improved disease diagnostics and disease stage therapeutic management.
Systems biology studies biological systems by systematically perturbing them (biologically, genetically, or chemically); monitoring the gene, protein, and informational pathway responses; integrating these data; and ultimately, formulating mathematical models that describe the structure of the system and its responses to individual perturbations. The controlled perturbations generate unique biosignatures (time-course response profiles) of the system leading to improved understanding and more comprehensive hypotheses of the underlying response mechanisms. By incorporating these unique biosignatures into existing genome, proteome, and metabolome databases, it is possible to model, mine, and experimentally test these hypotheses.
Biosignature analysis and modeling is an important new approach for drug discovery and development because of its ability to integrate a vast amount of relevant biological data into the process. It can identify where a network should be perturbed to achieve a desired effect, provide insight into a drug’s function, evaluate a drug’s likely efficacy and toxicity, and act as a screen for combinatorial perturbations. This model can also assess responses in different patient groups and create diagnostics for the development of personalized medicines.
Systems biology analysis requires sophisticated bioinformatics software to find and analyze patterns in diverse forms of data producing an integrated view of specific diseases.