Analysis of information flows in interaction networks: implication for drug discovery and pharmacological research.

Frequent failures of experimental medicines in clinical trials question current concepts for predicting drug-effects in the human body. Improving the probability for success in drug discovery requires a better understanding of cause-effect relationships at the organism, organ, tissue, cellular, and molecular levels, each having a different degree of complexity. Despite the longstanding realization that clinical and preclinical drug-effect information needs to be integrated for generating more accurate forecasts of drug-effects, a road map for linking these disparate sources of information currently does not exist. This review focuses on a possible approach for obtaining these relationships by analyzing causes and effects on the basis of the topology of network interaction systems that process information at the cellular and organ system levels.