Assessing the Effects of Chemical Mixtures using a Bayesian Network-Relative Risk Model (BN-RRM) Integrating Adverse Outcome…

There are long-standing uncertainties about toxicity of chemical mixtures to populations. Laboratory toxicity tests have confirmed synergistic and antagonistic effects to individuals, but not to populations.We will conduct a regional scale ecological risk assessment by evaluating the effects chemical mixtures to populations with a new Bayesian Network- Relative Risk Model (BN-RRM) incorporating an Adverse Outcome Pathway (AOP). We started applying this new BN-RRM framework in a case study with organophosphate pesticide mixtures (diazinon, chlorpyrifos, and malathion). Acetylcholinesterase inhibition (AChE) was chosen the molecular initiating event and the Puget Sound Chinook salmon (Oncorhynchus tshawytscha) and Coho salmon (Oncorhynchus kisutch) Evolutionary Significant Units (ESU) were chosen as population endpoints. Dose-response equations will be generated from the mixtures, integrated into the new BN-RRM framework and then overall risk will be calculated for the populations. Preliminary results indicate that organophosphate pesticide mixtures act synergistically and impair olfactory function that lead to loss of antipredator, homing and reproductive behavior which lead to changes in population age structure and patch dynamics. Assessing mixtures through this new BN-RRM framework is an innovative method of predicting effects to populations. This research will demonstrate a probabilistic approach to synthesize the effects of mixtures and predict impacts to populations.