An interdisciplinary group of researchers at the University of North Carolina at Chapel Hill and Massachusetts Institute of Technology was awarded a five-year $2.5M grant from the National Institute of Environmental Health Sciences (NIEHS) to establish a partnership between environmental health scientists,
biological engineers, chem-informaticians, biostatisticians and geneticists. The funding comes from a trans-NIH Bioengineering Research Partnerships Program which is specifically designed to encourage basic, applied, and translational bioengineering research that could make a significant contribution to improving human health.
The team, led by Ivan Rusyn, is comprised of four Lead Investigators who each bring a distinct set of tools and intellectual backgrounds to the project:
- Ivan Rusyn, MD, PhD (PI) - Associate Professor of Environmental Sciences and Engineering (UNC School of Public Health) who is an
environmental health scientist with a focus on liver toxicology and mouse models of toxicity.
- Linda Griffith, PhD - S.E.T.I. Professor of Mechanical and Biological Engineering (Massachusetts Institute of Technology) who is a
world-renowned researcher in the field of liver tissue engineering.
- Alex Tropsha, PhD - Professor and Chair, Division of Medicinal Chemistry and Natural products (UNC School of Pharmacy) who is a
leader in the field of quantitative structure activity/toxicity relationship modeling.
- David Threadgill, PhD - Associate Professor of Genetics (UNC School of Medicine) who is a geneticist and a pioneer for the
applications of mouse genetics into cancer research and toxicology.
This multidisciplinary team will apply an integrative, systems approach to:
(1) Develop a 3D microscale mouse liver tissue bioreactor that can be applied to high-throughput screening of chemicals. This is a design-directed effort to produce a unique tool that can increase throughput of testing while reducing the number of animals;
(2) To build, test and validate Quantitative Structure-Toxicity Relationship (QSTR) models that employ both chemical and biological descriptors of molecular structures and take into account genetic diversity between individuals. This aim is a discovery-driven approach that will produce a computational method for compound-prioritization based on the chemical structure, multi-dimensional toxicity data that includes -omics endpoints, and information on genetic diversity of the population; and
(3) Validate a fiscally sensible in vivo and in vitro toxicity screening paradigm for a class of allylbenzene derivatives by producing knowledge anchored on the genetic variability present within the population. This aim will test the hypothesis that genetic variability among individuals is a major determinant in the toxic effects of chemical hazards and that the genetic basis for susceptibility can be successfully elucidated using a panel of mouse inbred strains.
