Systems biology is a continually evolving field, and one that holds great promise for drug discovery and development. The ability to process and contextualize large data sets positions systems biology as a foundational element in the emerging big-data era of biomedical science. A complete, data-driven in silico view of what constitutes a disease at the molecular level could highlight the causative components of disease mechanisms, give insight into the systemic effects of a drug candidate, and give developers crucial information for making go/no-go decisions.
In 2008, Merck published two studies describing genetic susceptibility to obesity that involves changes in entire networks of genes rather than changes in just a few genes. The research also demonstrated the use of genomic techniques to understand complex changes that underlie common diseases.
Systems biology can also bring greater personalization to healthcare. Personalized medicine can only work, however, if a disease’s causative molecular alterations can be identified and counteracted. Recently published studies continue to shed light on the heterogeneity of cancer mutations, for example, and reinforce that a more thorough understanding of the molecular landscape is necessary to guide more personalized treatment.
Understanding the complete effects of a drug or drug combination in a given cell system or disease state can provide developers with the opportunity to fine-tune new candidates to maximize efficacy and reduce toxicity, or highlight new indications for existing drugs based on shared pathways or targets.