Systems network pharmaco-toxicology in the study of herbal medicines

Abstract

Analytic "‘omic” techniques and systems biology driven bioinformatics have increasingly been a game changer in the study of herbal drugs, thanks to the simultaneous detection of entire molecular families in a given biological system, and the ability to collect, classify, network, and visualize a large number of analytical data through bioinformatics. The genomics area has been at the vanguard of this evolution. Other "‘omic” techniques, such as proteomics and metabonomics, are providing a fast-growing body of data both on biological targets and on phytocomplexes and their interactions. This has favored a more global view of biological processes, describing how perturbations can influence the steady state of a large number of the components of the system and their relations, changing the system as a whole. It is thus apparent that biological responses induced by phytocomplexes represent the net output of changes in the properties of a very large number of molecules, all acting in an interdependent fashion to form a highly connected network. "‘Omic” techniques and systems biology are applied in herbal medicine at various levels, and provide novel strategies that can be exploited both for herbal drug research and medical use, in applications ranging from drug quality control to patient stratification. Network pharmaco-toxicology represents one of the most important applications of this new approach. Building up networks of molecular interactions between phytocomplex components and pharmaco-toxicological processes can provide a powerful predictive tool in herbal medicine. There is an increasing number of Web-based systems biology platforms, continuously fed with "‘omics” data, providing a view of the complete biological system modulated by a given drug that can be used for predictive pharmacology and toxicology. Systems toxicology promises to be the best context for providing a mechanistic understanding of toxicological effects, thus allowing the prediction of responses to phytochemicals.