SBIR-STTR Award

The objective of this research project is to develop structure-activity models of certain carcinogenesis bioassays. Through these models it will be possible to predict the probability of carcinogenicity of untested compounds. The application will be to those chemicals that have not and probably never will be tested, but are already in current commerce, as well as those being introduced who commerce at the rate of approximately 1,000 per year. Other applications exist at the developmental stage to enhance the safety of laboratory and production personnel, and to minimize the use of animals in research and testing. During Phase I the feasibility of this approach will be demonstrated. Phase II would see a refinement of the approach and its commercialization.The methodology consists chiefly of collecting results of carcinogenesis bioassays, evaluating them critically, and modeling assembled data bases by means of conventional multivariate statistical methodology.The technological innovations expected from this project are the ability to predict to a degree not before possible which chemicals are likely to be carcinogens on the basis of structure-activity equations. The commercial applications are to those compounds in the Toxic Substances Control Act inventory that have not been tested, to chemicals currently being developed, and as a tool in the inventory's software implementation.National Cancer Institute

The long-term goal of this research is to make available to the chemical community methods for the estimation of toxicity endpoints, especially carcinogenicity. Several aims are included in the research under this grant: (1) enhancement of the carcinogenicity database; (2) development of a global carcinogenicity equation; (3) further refinement of the NCI/NTP assay-based carcinogenicity equations for heterogeneous as well as more closely related sets of chemicals; (5) development of organ-specific oncogenicity equations for 10 organ systems or sites; (6) development of equations for carcinogenic potency for heterogeneous and more closely related data sets; and (7) development of carcinogenicity equations for closely related series of chemicals based on NCI/NTP, pharmaceutical, and CFR 21 data.Independent parameters will include physicochemical parameters such as log P. molar refractivity, and molecular area and volume. Other parameters will include molecular connectivity indexes and MOLSTAC~ substructural descriptors. Statistical methodology will be based on discriminant analysis; multiple, particle, and ridge regression; and principal component analysis.National Cancer Institute (NCI)