Currently, SAR (structure activity relationship) is a widely used tool for filling toxicological data gaps.Guidance for application of SAR has been provided by OECD and ECHA.The internal P&G framework provides both a structure for conducting and documenting SAR assessments using current methods as well as structure for identifying and integrating additional types of data.An integrated framework driven by structural, mechanistic, cheminformatics and pharmacokinetic analyses is presented for SAR based toxicological assessments.The approach involves identifying potential analogs (i.e.source chemicals) based upon their degree of structural,bioactivity, metabolic and physicochemical similarity to the chemical with missing toxicological data (target chemical).Furthermore, the use of cheminformatics in our framework involves the construction of chemical data sets organized by bio-activity data.This approach has resulted in development and incorporation of decision trees based on the biological activity associated with specific chemical structural features.These decision trees facilitate identifying chemicals with either common core structural features or modes of action in common with established toxicants according to precedents in the published literature.In the future, the chemical groupings generated from these decision trees will be useful as a starting point for the development of hypotheses for in vitro testing to elucidate mode of action and ultimately in the development of refined SAR principles.SAR-based read-across assessments include application of chemical, biological and toxicological principles in a systematic manner to maximize appropriate use of all available data.These principles are also used to identify factors that result in uncertainty in the assessments so that any residual uncertainty in the read-across can be evaluated and addressed as appropriate.Finally, the results of SAR-based read-across assessments can be strengthened by experiments to verify assumptions about the source and target chemicals having common metabolism, when data for metabolic transformations of related chemicals are not available.In addition, PBPK modeling can be used to estimate the pharmacokinetics of new ingredients compared to their analogs, which further refines our quantitative estimates of acceptable levels of exposure.