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(http://www.niehs.nih.gov//portfolio/index.cfm?do=portfolio.grantdetail&&grant_number=R42ES032642&format=word)
| Principal Investigator: Shao, Kan | |
|---|---|
| Institute Receiving Award | Ks And Associates, Llc |
| Location | Bloomington, IN |
| Grant Number | R42ES032642 |
| Funding Organization | National Institute of Environmental Health Sciences |
| Award Funding Period | 15 Aug 2018 to 30 Jun 2024 |
| DESCRIPTION (provided by applicant): | PROJECT SUMMARY Chemical risk assessment is widely applied in industries and regulatory agencies as an important tool to evaluate chemical toxicity in support of chemical registration, safety evaluation, and exposure limitation development. One of the most notable improvements in dose-response assessment - a required quantitative step in risk assessment - is the development of benchmark dose (BMD) methodology to better utilize toxicological information to facilitate toxicity evaluation of chemicals. Although the BMD method has been advocated by the US Environmental Protection Agency (EPA) and European Food Safety Authority (EFSA) for its scientific advantages (such as less dependency on the design of experiments and more plausible interpretation on uncertainty) for years, the employment of the method in practical risk assessment has been significantly hindered by a few important limitations, one of which is the lack of a reliable modeling system to support consistent practice of BMD modeling across different sectors. Therefore, based on the Bayesian benchmark dose modeling system (BBMD) prototype successfully built in Phase I of the STTR project, the objective of Phase II is to further the development of the BBMD system to meet more diverse needs in dose- response assessment and to enlarge the user base of the system as an essential component for commercialization. The rational is that, given relatively limited practical implementation of BMD modeling for dose-response assessment in industry and some government agencies, demonstrating and improving the utility of the BMD method rather than sophisticating the methodology are more appropriate at the current stage to enhance the acceptance of BMD method and then create business opportunities for the company. To accomplish this objective, three specific aims will be pursued: (1) develop a Bayesian BMD modeling approach with software for typical epidemiological dose-response data; (2) develop a Bayesian BMD modeling approach with software for high-throughput dose-response data; (3) upgrade the BBMD to a data computation and management system to perform, store, and distribute BMD analyses approved by a panel of experts. The success of the project will fill multiple gaps that hamper the large-scale adoption of BMD methodology in industry and government. Meanwhile, Dream Tech will increase the influence of the BBMD system and build up user base through an array of channels to commercialize the dose-response modeling platform and services in support of chemical risk assessment. |
| Science Code(s)/Area of Science(s) |
Primary: 75 - Computational Biology/Computational Methods for Exposure Assessment Secondary: 03 - Carcinogenesis/Cell Transformation |
| Publications | See publications associated with this Grant. |
| Program Officer | Daniel Shaughnessy |