A network-based approach to quantifying the impact of biologically active substances.

Fe at u re s P E R S P E C T IV E Society increasingly demands close scrutiny of the potential health risks of long-term exposure to biologically active substances, such as therapeutic drugs or environmental toxins. Such risks are typically assessed a posteriori through clinical epidemiology studies. However, disease might take decades to manifest, at a point where changes in therapeutic regime, life style or exposure would not prevent disease onset. Moreover, disease risk as assessed correlatively in epidemiological studies is not intended to elucidate the mechanisms that link perturbations in molecular signaling to disease and, thus, provides fewer options for intervention. Here, we propose that network-based approaches to pharmacology are a valuable way to not only quantify biological network perturbations caused by active substances, but also identify mechanisms and biomarkers modulated in response to exposure and related to disease onset. We also discuss progress towards a generalizable approach for a mechanistic biological impact assessment. Novel computational methods that derive the quantitative biological impact [defined as a biological impact factor (BIF)] from underlying system-wide data using defined causal biological (i.e. molecular) network models as the substrate for data analysis are currently under