Computational Toxicology Research
About the Science
Developmental toxicity refers to adverse effects produced prior to conception or during pregnancy and childhood. EPA's guidelines for developmental toxicity risk assessment are recorded in a 1991 Federal Registry and were updated at a 1998 Scientific Advisory Panel (SAP) workshop.
- EPA's Guidelines for Developmental Toxicity Risk Assessment (Federal Register) (PDF, 83pp, 0.2mb, About PDF)
- EPA's 1998 Scientific Advisory Panel Final Report (PDF, 12pp, 0.3mb, About PDF)
The potential of an environmental chemical to cause adverse effects on a fetus is an important risk assessment consideration. There are limitations to the number of chemicals that can be tested using traditional animal studies and there are uncertainties associated with extrapolating animal testing results to humans. Due to limitations, there are motivations to develop computational tools to increase the number of chemicals that can be tested and quantitatively integrate numerous information sources in developmental risk assessment.
Rationale for computational systems biology
Developmental biology is fundamental to all biological systems. It addresses questions such as what processes determine anatomical structures (morphogenesis) and tissues (differentiation) and the mechanisms through which these processes are controlled by the genome. Teratogenesis refers to the complex processes by which chemicals perturb or subvert these processes to invoke altered developmental phenotypes or adverse pregnancy outcome. Understanding developmental toxicity thus dictates information superimposed across multiple biological scales. Evaluating the potential for developmental defects is an exceedingly complex problem.
Computational systems are needed that can apply this knowledge across scales and dissect the relative contributions of genetic variation, stage vulnerability, dose-response patterns, chemical mechanisms, fetal (epigenetic) programming, and maternal-fetal interactions to developmental defects. A key challenge for computational systems biology is to build useful multi-scale models that can be used to investigate systematically any or all interactions between the complex variables. The goal is to predict ‘lever-points' for toxicity pathways and cellular networks in altered development.
v-Embryo™ is a platform to test the critical effects of environmental chemicals on developmental toxicity pathways that may be encoded as computer simulations of morphogenetic processes that draw from knowledge regarding the flow of molecular regulatory information in rudimentary tissues, the cell-autonomous responses to genetic (programmed) and environmental (induced) signals, and the emergent morphogenetic properties associated with collective cellular behavior in any given system.
Developmental systems biology
v-Embryo™ aligns with recommendations from the National Research Council's (2007) report on "Toxicity Testing in the 21st Century: a vision and strategy" (http://www.nap.edu/catalog/11970.html ), transforming traditional toxicity testing into one focused on detailed mode of action (MOA) and dose response information for human health risk assessment. Engineering the in silico solutions to predict MOA in development will be a problem more challenging than what has been encountered for physical phenomena. This is partly due to the existence of many interrelated molecular interactions (some of which are yet undiscovered) and the additional complication that teratogenesis is a threshold phenomenon.
Understanding teratogenesis at a systems level requires thinking about how toxicity pathways are integrated with the genomic control of conserved cell signaling pathways. This requires substantial investment in bioinformatics and applied mathematics in order to interpret biological responses from the myriad of interrelated data and associative relationships covering the exposure-disease continuum, and presumes reasonable knowledge of biological networks under normal conditions and the behavior of cellular networks during chemical insult and in different model systems.