Systems Modeling in Developmental Toxicity
Published Online: 15 SEP 2011
Copyright © 2009 John Wiley & Sons, Ltd. All rights reserved.
General, Applied and Systems Toxicology
How to Cite
Knudsen, T. B. and DeWoskin, R. S. 2011. Systems Modeling in Developmental Toxicity. General, Applied and Systems Toxicology.
- Published Online: 15 SEP 2011
High-throughput or high-content studies are now providing a rich source of data that can be applied to in vitro profiling of chemical compounds for biological activity and potential in vivo toxicity. EPA's ToxCast™ project, and the broader Tox21 consortium, as well as other projects worldwide, are providing high-throughput and high-content screening data (HTS-HCS) focusing on the in vitro targets and cellular bioactivity profiles for thousands of chemical compounds in commerce or entering the environment. A goal of chemical profiling is to rapidly identify and efficiently classify signatures of biological activity that are potentially diagnostic of in vivo toxicities using automated technologies. Predictive modeling of developmental toxicity faces several challenges: correlating in vitro concentration–response with internal dose–response kinetics; understanding how in vitro bioactivity profiles extrapolate from one cell-type or technology platform to another; and linking individual targets of in vitro bioactivity to complex signatures associated with pathways of in vivo toxicity. Toxicity in the intact organism is an expression of complex and interwoven events that follow from cellular perturbations. As such, multicellular computer models known as ‘virtual tissues’ that recapitulate developmental events can provide a technology platform to simulate non-linear behaviors of dynamical systems and to model perturbations. A virtual embryo, for example, might be envisaged as a toolbox of computational (in silico) models that execute morphogenetic programs to simulate developmental toxicity.
Keywords: computational toxicology; high-throughput; screening; developmental toxicity; chemical profiling; toxicity pathways; predictive models; risk assessment; mechanistic models; systems biology; virtual tissues