Exploiting complexity and the robustness of network architecture for drug discovery

The issue of complexity stands at the center of contemporary drug discovery and development. The central problem in drug development today is attrition of drug candidates identified by the modern molecular target- based discovery approach, due to two related features of complex metabolic networks: their fundamentally unpredictable response to targeted interventions and their robustness (tendency to maintain stable function in the face of internal or external perturbations). Complexity and adaptations are, therefore, generally seen as obstacles to drug discovery. Here, the converse proposition is presented-that the complexity and adaptive responses of highly interconnected metabolic networks can be exploited for therapeutic discovery. Unanticipated connectivity relationships may result in off-target changes in metabolic fluxes, leading to unexpected therapeutic actions of agents. Exploiting this approach requires that fully assembled living systems (in vivo models) be studied and that informative in vivo biomarkers of the activity of biochemical pathways responsible for disease be available. These biomarkers should be sensitive, predictive of functional endpoints, and have high enough throughput for efficient screening of large numbers of agents. To the extent that such biomarkers unambiguously reflect the activity of pathways that mediate disease or therapeutic response (i.e., are authentic), their utility will be increased. Examples are presented of pathway-based screening of approved drugs for unexpected actions. Results support the principle that agents that have one action typically have many actions, including unanticipated actions, reflecting connectivity relationships of complex networks. Pathway-based screening in vivo represents an alternative to the high attrition of the molecular target-based discovery paradigm.