FutureTox II: in vitro data and in silico models for predictive toxicology

Abstract

FutureTox II, a Society of Toxicology Contemporary Concepts in Toxicology workshop, was held in January, 2014. The meeting goals were to review and discuss the state of the science in toxicology in the context of implementing the NRC 21st century vision of predicting in vivo responses from in vitro and in silico data, and to define the goals for the future. Presentations and discussions were held on priority concerns such as predicting and modeling of metabolism, cell growth and differentiation, effects on sensitive subpopulations, and integrating data into risk assessment. Emerging trends in technologies such as stem cell-derived human cells, 3D organotypic culture models, mathematical modeling of cellular processes and morphogenesis, adverse outcome pathway development, and high-content imaging of in vivo systems were discussed. Although advances in moving towards an in vitro/in silico based risk assessment paradigm were apparent, knowledge gaps in these areas and limitations of technologies were identified. Specific recommendations were made for future directions and research needs in the areas of hepatotoxicity, cancer prediction, developmental toxicity, and regulatory toxicology.

Keywords: in silico; in vitro; modeling; predictive toxicology; risk assessment.

FIG. 1.

Systems Toxicology draws on multiple disciplines and integrates them across all levels of biological organization to derive a detailed mechanistic understanding of toxicity. This understanding can then be used to predict adverse outcomes, and contribute to risk assessment for all applications of chemicals. Used with permission from Sturla et al., (2014). Artwork by Samantha J. Elmhurst (www.livingart.org.uk).

FIG. 2.

Platform set-up diagram for generation of vascularized cortical tissue from progenitor cells and use in toxicity testing. a, Neural and glial progenitor cells are assembled on a three-dimensional (3D) vascular network formed by endothelial cells, pericytes, and microglia in poly(ethylene glycol) hydrogel to promote the formation of stratified neural epithelium with a vascular network. b, The neural vascular assembly from (a) will be exposed to a training drug set. The gene expression profiles from the training set will be used to establish a drug toxicity prediction model using a machine learning algorithm. c, The model established in (b) can be used to predict the toxicity of an unknown chemical. Used with permission from Hou et al., (2013).

FIG. 3.

Adverse outcome pathway (AOP) for aromatase inhibition (molecular initiating event), resulting in declining population trajectory (adverse outcome). The intermediate steps are used to experimentally verify the link between the molecular initiating event (MIE) and adverse outcome. Courtesy of D. Villeneuve, US EPA.