We developed a physiologically based pharmacokinetic model of PCB 153 in women, and predict its transfer via lactation to infants. The model is the first human, population-scale lactational model for PCB 153. Data in the literature provided estimates for model development and for performance assessment. Physiological parameters were taken from a cohort in Taiwan and from reference values in the literature. We estimated partition coefficients based on chemical structure and the lipid content in various body tissues. Using exposure data in Japan, we predicted acquired body burden of PCB 153 at an average childbearing age of 25 years and compare predictions to measurements from studies in multiple countries. Forward-model predictions agree well with human biomonitoring measurements, as represented by summary statistics and uncertainty estimates. The modelsuccessfully describes the range of possible PCB 153 dispositions in maternal milk,suggesting a promising option for back estimating doses for various populations. One example of reverse dosimetry modeling was attempted using our PBPK model for possible exposure scenarios in Canadian Inuits who had the highest level of PCB 153 in their milk in the world.

10abayesian inference10abody burden10aEnvironmental Chemistry, Exposure and Risk Group10ahuman milk biomonitoring10aindoor environment department10alactational transfer10apcb 15310aphysiologically-based pharmacokinetic modeling10apollutant fate and transport modeling10apoly-chlorinated biphenyls10areverse dosimetry1 aRedding, Laurel, E.1 aSohn, Michael, D.1 aMcKone, Thomas, E.1 aWang, Shu-Li1 aHsieh, Dennis, P.H.1 aYang, Raymond, S. H. uhttps://ses.lbl.gov/publications/population-physiologically-based02408nas a2200241 4500008004100000022001400041245007200055210006900127260001200196300000900208490000700217520168500224653002201909653002201931653001601953653002801969653001301997100002502010700002302035700002402058700001402082856007002096 2001 eng d a1436-324000aInfluential input classification in probabilistic multimedia models0 aInfluential input classification in probabilistic multimedia mod c03/2001 a1-170 v153 aMonte Carlo analysis is a statistical simulation method that is often used to assess and quantify the outcome variance in complex environmental fate and effects models. Total outcome variance of these models is a function of (1) the variance (uncertainty and/or variability) associated with each model input and (2) the sensitivity of the model outcome to changes in the inputs. To propagate variance through a model using Monte Carlo techniques, each variable must be assigned a probability distribution. The validity of these distributions directly influences the accuracy and reliability of the model outcome. To efficiently allocate resources for constructing distributions one should first identify the most influential set of variables in the model. Although existing sensitivity and uncertainty analysis methods can provide a relative ranking of the importance of model inputs, they fail to identify the minimum set of stochastic inputs necessary to sufficiently characterize the outcome variance. In this paper, we describe and demonstrate a novel sensitivity/uncertainty analysis method for assessing the importance of each variable in a multimedia environmental fate model. Our analyses show that for a given scenario, a relatively small number of input variables influence the central tendency of the model and an even smaller set determines the spread of the outcome distribution. For each input, the level of influence depends on the scenario under consideration. This information is useful for developing site specific models and improving our understanding of the processes that have the greatest influence on the variance in outcomes from multimedia models.

10aError propagation10amodel development10aMonte Carlo10amultimedia mass balance10avariance1 aMaddalena, Randy, L.1 aMcKone, Thomas, E.1 aHsieh, Dennis, P.H.1 aGeng, Shu uhttps://ses.lbl.gov/publications/influential-input-classification01644nas a2200157 4500008004100000245008300041210006900124260001200193300001200205490000700217520112300224100002201347700002301369700002401392856007001416 1996 eng d00aThe Use of the Molecular Connectivity Index for Estimating Biotransfer Factors0 aUse of the Molecular Connectivity Index for Estimating Biotransf c02/1996 a984-9890 v303 aBiotransfer factors (BTFs) represent the ratio of the concentration of a chemical found in animal tissues such as beef or milk to the animal's daily intake of that chemical. Using currently available citations for BTFs in meat and milk, the use of molecular connectivity indices (MCIs) as a quantitative structureâˆ’activity relationship (QSAR) for predicting the BTFs for organic chemicals is evaluated. Based on a statistical evaluation of correlation, residual error, and cross validation, this evaluation reveals that the MCI provides both higher reliability and a fast and cost-effective method for predicting the potential biotransfer of a chemical from environmental media into food. When compared to the use of *K*_{ow} as a predictor of BTFs, the analysis here indicates that MCI can substantially increase the reliability with which BTFs can be estimated.