NSF Nanoscale Science and Engineering Grantees Conference, Dec 3-5, 2008
Grant # : 0830117
UC Center for the Environmental Implications of Nanotechnology
NSF Cooperative Agreement Number EF 0830117
PIs: Andre Nel, Arturo A Keller, Hilary Godwin, Yoram Cohen, Roger Nisbet
University of California
Vision and overarching description of the Center: A recent report1 by the National Research Council (NRC) of the National Academy of Sciences (NAS) set forth a vision of dramatic change in toxicological testing from individual testing to a predictive high-throughput paradigm premised on the established mechanisms and pathways of toxicity. This report was endorsed in a Science policy forum on toxicology2 and is in accordance with the implementation of high-throughput screening efforts by the US EPA3 and the National Toxicology Program.4 The NAS report cites the work at UCLA in the fields of air pollution and engineered nanoparticle (NP) research as an example of how a mechanistic paradigm can be used to build a predictive platform for NP hazards.1,5,6 In addition to the paradigm of oxidant injury through reactive oxygen species (ROS) generation, which is relevant to environmental toxicology, other mechanisms of injury are emerging, allowing high-throughput screening (HTS) approaches to be implemented to make predictions about biocompatible and bio-hazardous nanomaterial (NM) properties.5,7–12 Establishing a predictive science is a timely approach for nanotechnology-based enterprises wishing to avoid the problems faced by the chemical industry, where only a few hundred of the ca. 40,000 industrial chemicals13 have undergone toxicity testing, making it very challenging to control the toxicological impact of chemicals in the environment. Building on this seminal concept, we will conduct predictive toxicological sciencefor NMs through the founding of the UC Center for Environmental Implications of Nanotechnology (UC CEIN) at UC Los Angeles (UCLA) in partnership with UC Santa Barbara (UCSB), UC Davis (UCD), UC Riverside (UCR), Columbia University (New York), University of Texas (El Paso, TX), Nanyang Technological University (NTU, Singapore), the Molecular Foundry at Lawrence Berkeley National Laboratory (LBNL), Lawrence Livermore National Laboratory (LLNL), Sandia National Laboratory (SNL), the University of Bremen (Germany), University College Dublin (UCD, Ireland), and the Universitat Rovira i Virgili (URV, Spain).
The goal of the Center is to develop a broad-based model of predictive toxicology premised on quantitative structure–activity relationships (QSARs) and NM injury mechanisms at the biological level. Our predictive scientific model will consider: (i) the NMs most likely to come into contact with the environment; (ii) their distribution in the environment, as governed by modes of release, physicochemical and transport properties, interactions with biological substrates, and bioaccumulation; (iii) representative ecological life forms serving as early sentinels to monitor the spread and bio-accumulation of hazardous NMs; (iv) biological screening assays allowing QSARs to be developed based on the bio-physicochemical properties of NMs; (v) HTS of a combinatorial NM library; and (vi) a self-learning computational system providing a framework for predictive risk analysis. These research activities will be combined with educational programs informing the public, future generations of scientists, public agencies, and industrial stakeholders of the importance of safe implementation of nanotechnology in the environment. The overall impact will be to reduce uncertainty about the possible consequences of NMs in the environment, while at the same time providing guidelines for their safe design to prevent environmental hazards.
Intellectual Merit: The UC CEIN integrates and advances knowledge from multiple disciplines required tounderstand the complex intersection of nanotechnology with the environment. The Center will unite recognized experts in the fields of engineering, chemistry, physics, materials science, ecology, cell biology, marine biology, bacteriology, particle and chemical toxicology, computer modeling, HTS, and risk prediction to establish the foundation of a new scientific discipline: Environmental Nanotechnology and Nanotoxicology. This team combines resources of several major US research universities and national laboratories—including the California NanoSystems Institute (CNSI), LLNL, SNL, the Center for Nanotechnology in Society (CNS), and the NationalCenter for Ecological Analysis and Synthesis (NCEAS)—with those of international collaborators and industrial partners in the USA, Asia, and Europe. This effort is centered on the creation of a new scientific platform on which diverse disciplines will be integrated into a predictive toxicological science to determine the novel physicochemical properties of NMs and their interactions with ecological life forms at the nano–bio interface. Our predictive toxicological paradigm and QSAR-based analysis of the bio-physicochemical properties of NMs also provides the logical entrée into the knowledge generation, self-learning, and risk predictions that are required for safe implementation of nanotechnology in the environment. Thus, we envisage that predictive toxicological science will grow in step with expansion of the nanotechnology industry, thereby making it possible to act preemptively, rather than retroactively.
Broader Impact: Traditional and current toxicity testing in humans and the natural environment relies mainly on a complex set of whole-animal-based toxicity testing strategies. This approach cannot handle the rapid pace at which nanotechnology-based enterprises are generating new materials and ideas. The UC CEIN will address these challenges of scale by implementing a scientific platform that will be updated as nanotechnology evolves from single- to multitasking and, ultimately, to highly complex hybrid products that might include engineered NMs linked to biological components. In addition, the UC CEIN will train the next generation of nanotechnologists in this science-based, paradigm-shift approach toward determining toxicity in the environment. The UC CEIN’s creation of a comprehensive computational risk model will allow powerful risk predictions to be made for and by the academic community, industry, the public, and regulating agencies. Our partner centers (CNS, CSNI, NCEAS, LLNL, SNL) will be powerful portals for the dissemination and integration of our research findings to the scientific, educational, and industrial communities, both nationally and internationally—as will be our annual International Summit. Our outreach activities will serve to inform both experts and the public at large about the safety issues surrounding nanotechnology and how to safely produce, use, and dispose NMs.
Activities in research, education, and their integration: Our research goal of developing a predictive risk model for NM impact on the environment will be executed through seven IRGs.Todevelop an understanding of theQSARs, IRG 1 will establish a physical library of standard reference NMs representing the major classes of commercial products; it will also use advanced NM design and synthesis methods to develop a combinatorial library that enables our study of the interfacial properties responsible for biocompatible and bio-adverse responses. These NMs will be characterized to determine the physicochemical properties (IRG 1) that are associated with cellular, tissue, and systemic injury in aquatic and terrestrial life forms (IRG 2). These ecological life forms will be chosen to represent a hierarchy of trophic levels in the environment and will be used for assessment of NM uptake, clearance, bioaccumulation, and dose–response relationships (IRG 3). The engineered NPs will be compared with naturally existing congeners to determine their transport, aggregation, stability, and fate in soil, water, and air (IRG 4). We propose to use the key interfacial properties governing interactions at the nano–bio interface (size, surface area, wettability, aggregation, dispersibility, charge) to develop HTS approaches (IRGs 1 & 5) allowing contemporaneous testing of batches of NMs in representative cellular systems (e.g., bacteria, yeasts) for hazard prediction according to a variety of cellular endpoints (oxidant stress response, proliferation, ATP production, mitochondrial dysfunction, apoptosis).1,5,6 To train novel cognitive neural networks for risk prediction, the physicochemical, biological, toxicological, exposure, and dose–response data will ultimately be integrated into a comprehensive environmental multimedia assessment model for NPs and NP-bound toxicants (IRG 6). This computational risk model will interface with the CNS and NCEAS at UCSB to responsibly convey the risks to industry, the public, and regulatory agencies, and to set environmental safety guidelines (IRG 7).
A major goal of the UC CEIN will be to train the next generation of nano-scale scientists, engineers, and regulators to anticipate and mitigate potential future environmental hazards associated with nanotechnology. These educational programs will broaden the knowledge base of the environmental implications of nanotechnology through academic coursework, world-class research, training courses for industrial practitioners, and a journalist–scientist communication program. We will expand representation and access to this knowledge base through an internship program directed at California community colleges serving underrepresented groups. Our partner centers (CNS, CSNI, NCEAS) will be powerful portals for the dissemination and integration of knowledge to the scientific, educational, and industrial communities, both nationally and internationally.)
References
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