Environmental Decision Analysis for Nanoparticles - Theme 6 (EDA)
Leader: Yoram Cohen
Theme 6 is rooted in the need to establish a rational approach to identify and rank nanomaterials that could be of environmental concern. The approach is based on the premise that the environmental impact of eNMs is governed by exposure to eNMs and their toxicity. Accordingly, this Theme focuses on the development of a decision-based process (DeP) framework and tools for environmental impact analysis (EIA) of eNMs that incorporate both quantitative and qualitative CEIN information regarding the physicochemical properties of eNMs (Theme 1), their environmental release, fate and transport (Theme 4), toxicity (Themes 2, 4 and 5), as well as aspects of risk perception (Theme 7). In order to accomplish the above, Theme 6 investigators have been working toward developing the building blocks of the DeP-EIA approach. In this regard, the rich library of characterized metal and metal oxide nanoparticles (Theme 1) and HTS data of Theme 2 have served as a basis for developing needed robust statistical analysis and machine learning methods and tools for the analysis of large CEIN data sets of eNMs toxicity. These include knowledge extraction from both numerical data (1) and HTS images of zebrafish embryo (5). For example, Theme 6 developed methods and tools have enabled the development of predictive quantitative-structure-activity relationships for nanomaterials (nano-SARs) toxicity, identification of relationships among cell signaling pathway activities (induced by exposure eNMs) considering differing assays and multiple cell lines, correlation of information from in-vitro HTS data with in-vivo response and automated phenotype recognition of whole organism HTS images. In order to arrive to appropriate ranking of potential environmental impact it is also necessary to assess the expected levels of eNM concentrations in the various environmental media. Thus, aided by basic experimental studies of Theme 3, regarding aggregation behavior and surface interactions of eNMs under environmental conditions and their life cycle analysis and by Theme 1 on eNMs characteristics, Theme 6 has been leading the development of fate and transport models suitable for the assessment of the environmental multimedia distribution of nanomaterials.
1. Machine Learning Analysis and Modeling of High Throughput Screening Data for Nanoparticles
2. QSARs of NP Toxicity and Physicochemical Properties
3. Modeling of the Environmental Multimedia Distribution of Nanoparticles
4. Environmental Impact Analysis
Core Activity C - Data Repository and Nano Collaboratory