The responsible development of beneficial manufactured nanomaterials (MNMs) requires a thorough understanding of their potential adverse environmental and human health impacts. Accomplishing the above requires approaches of predicting the biological response of various receptors when exposed to MNMs, along with understanding of the fate and transport of MNMs and their range of likely exposure concentrations. In addition, it is imperative to develop flexible data management infrastructure that will provide researchers, industry and regulatory agencies with access to raw and summarized data to enable the development of mechanistic and data-driven models of the biological response of MNMs so as to mitigate potential adverse outcomes and guide the design of safe MNMs. Accordingly, a unique online (web-based) nanoinformatics platform was developed hosting state-of-the-art data processing tools and models to enable evaluation of the potential environmental impact of ENMs. Recent models added to nanoinfo.org include those based on Bayesian Networks that served to: (a) conduct comprehensive meta-analysis of the body of published evidence, (b) assess the relevant descriptors to correlating toxicity, and (c) allow for rapid interrogation of curated data. The above approach, which is particularly suitable for decision analysts, was demonstrated for quantum dots, gold nanoparticles with various surface modifications and metal oxide MNMs. In addition to the above, new explorations of association rules among various biological responses, as a result of exposure to MNMs, are now feasible and have been recently demonstrated for zebrafish-MNMs toxicity studies. The above developments, along with integration of toxicity data with exposure estimations (using multimedia fate and transport models), can then serve to rank various MNMs with respect to their potential environmental impact as demonstrated in the present work.

Nanoinformatics platform for environmental impact assessment of manufactured nanomaterials

254th National Meeting (2017)

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