Merging methods, measurements and models to estimate metabolism rates in fish and select mammal species

摘要

Chemical concentrations in humans and ecological receptors are required for the exposure and risk assessment of thousands of chemicals; however, there are few or no measured concentration data available for the vast majority of chemicals. In the absence of measured concentrations, models are often used to predict exposures and concentrations in receptors. A key parameter required to calculate concentrations is the chemical half-life in a receptor. Half-life data are also required for reconstructing exposures and interpreting biomonitoring data, i.e., relating external exposure estimates (e.g., intake rates in mg/kg/d) with internal concentrations (blood, tissues) and biomarkers of exposure. For hydrophobic, low volatility chemicals the chemical half-life is largely determined by the biotransformation (metabolism) rate constant. Chemical biotransformation rates are also required for in vitro to in vivo extrapolation and reverse toxicokinetics. Despite the fundamental value of biotransformation rate information, relatively few measured in vivo data are available compared to the thousands of commercial chemicals requiring evaluation. The objectives of this research are to compile, evaluate and compare existing in vivo, in vitro and in silico data streams for estimating biotransformation rates for organic chemicals in fish and select mammalian species. The literature and existing publicly available databases of in vitro (S9, hepatocytes, microsomal assays) and in vivo biotransformation rate estimates in mammals (humans and rodents) and fish are collected and evaluated. In vitro to in vivo extrapolation models are developed and applied to the in vitro data to obtain estimates of hepatic clearance, and as applicable, whole body biotransformation rate (clearance, or half-life) estimates. In vitro biotransformation rate estimates and in silico predictions from existing screening-level quantitative structure-activity relationships (QSARs) are compared to in vivo biotransformation rate estimates. The data compilation includes: (1) whole body biotransformation rate constant estimates for approximately 940 chemicals in humans and 700 organic chemicals in fish and (2) in vitro biotransformation rate constants measured for 8,000 chemicals in humans and 130 chemicals in fish. Key uncertainties and challenges comparing the datasets are described and a strategy to address data gaps and uncertainty for estimating biotransformation rates is discussed.