ºÝºÝߣshows by User: WeiChunChou1 / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: WeiChunChou1 / Tue, 17 May 2016 07:56:32 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: WeiChunChou1 Computational Toxicity: Stochastic PBPK modeling /slideshow/computational-toxicity-stochastic-pbpk-modeling/62087625 oralpresentation0505-160517075632
Chronic exposure to inorganic arsenic (iAs) in the human population is associated with various internal cancers and other adverse outcomes. The purpose of this study was to estimate a population-scale exposure risk attributable to iAs consumptions by linking a stochastic physiological based pharmacokinetic (PBPK) model and biomonitoring data of iAs in urine. The urinary As concentrations were obtained from a total of 1,043 subjects living in an industrial area of Taiwan. The results showed that study subjects had an iAs exposure risk of 27% (daily iAs intake for 27% study subjects exceeded the WHO recommended value, 2.1 μg iAs day-1 kg-1 body wt). Moreover, drinking water and cooked rice contributed to the iAs exposure risk of 10% and 41%, respectively. The predicted risks in the current study were 4.82%, 27.21%, 34.69%, and 64.17%, respectively, among the mid-range of Mexico, Taiwan (this study), Korea and Bangladesh reported in literature. In conclusion, we developed a population-scale based risk model that covered the broad range of iAS exposure by integrating stochastic PBPK modeling and reverse dosimetry that generates probabilistic distribution of As intake corresponding to urinary As measured from the cohort study. The model can also be updated as new urinary As information becomes available.]]>
Chronic exposure to inorganic arsenic (iAs) in the human population is associated with various internal cancers and other adverse outcomes. The purpose of this study was to estimate a population-scale exposure risk attributable to iAs consumptions by linking a stochastic physiological based pharmacokinetic (PBPK) model and biomonitoring data of iAs in urine. The urinary As concentrations were obtained from a total of 1,043 subjects living in an industrial area of Taiwan. The results showed that study subjects had an iAs exposure risk of 27% (daily iAs intake for 27% study subjects exceeded the WHO recommended value, 2.1 μg iAs day-1 kg-1 body wt). Moreover, drinking water and cooked rice contributed to the iAs exposure risk of 10% and 41%, respectively. The predicted risks in the current study were 4.82%, 27.21%, 34.69%, and 64.17%, respectively, among the mid-range of Mexico, Taiwan (this study), Korea and Bangladesh reported in literature. In conclusion, we developed a population-scale based risk model that covered the broad range of iAS exposure by integrating stochastic PBPK modeling and reverse dosimetry that generates probabilistic distribution of As intake corresponding to urinary As measured from the cohort study. The model can also be updated as new urinary As information becomes available.]]> Tue, 17 May 2016 07:56:32 GMT /slideshow/computational-toxicity-stochastic-pbpk-modeling/62087625 WeiChunChou1@slideshare.net(WeiChunChou1) Computational Toxicity: Stochastic PBPK modeling WeiChunChou1 Chronic exposure to inorganic arsenic (iAs) in the human population is associated with various internal cancers and other adverse outcomes. The purpose of this study was to estimate a population-scale exposure risk attributable to iAs consumptions by linking a stochastic physiological based pharmacokinetic (PBPK) model and biomonitoring data of iAs in urine. The urinary As concentrations were obtained from a total of 1,043 subjects living in an industrial area of Taiwan. The results showed that study subjects had an iAs exposure risk of 27% (daily iAs intake for 27% study subjects exceeded the WHO recommended value, 2.1 μg iAs day-1 kg-1 body wt). Moreover, drinking water and cooked rice contributed to the iAs exposure risk of 10% and 41%, respectively. The predicted risks in the current study were 4.82%, 27.21%, 34.69%, and 64.17%, respectively, among the mid-range of Mexico, Taiwan (this study), Korea and Bangladesh reported in literature. In conclusion, we developed a population-scale based risk model that covered the broad range of iAS exposure by integrating stochastic PBPK modeling and reverse dosimetry that generates probabilistic distribution of As intake corresponding to urinary As measured from the cohort study. The model can also be updated as new urinary As information becomes available. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/oralpresentation0505-160517075632-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Chronic exposure to inorganic arsenic (iAs) in the human population is associated with various internal cancers and other adverse outcomes. The purpose of this study was to estimate a population-scale exposure risk attributable to iAs consumptions by linking a stochastic physiological based pharmacokinetic (PBPK) model and biomonitoring data of iAs in urine. The urinary As concentrations were obtained from a total of 1,043 subjects living in an industrial area of Taiwan. The results showed that study subjects had an iAs exposure risk of 27% (daily iAs intake for 27% study subjects exceeded the WHO recommended value, 2.1 μg iAs day-1 kg-1 body wt). Moreover, drinking water and cooked rice contributed to the iAs exposure risk of 10% and 41%, respectively. The predicted risks in the current study were 4.82%, 27.21%, 34.69%, and 64.17%, respectively, among the mid-range of Mexico, Taiwan (this study), Korea and Bangladesh reported in literature. In conclusion, we developed a population-scale based risk model that covered the broad range of iAS exposure by integrating stochastic PBPK modeling and reverse dosimetry that generates probabilistic distribution of As intake corresponding to urinary As measured from the cohort study. The model can also be updated as new urinary As information becomes available.
Computational Toxicity: Stochastic PBPK modeling from Wei-Chun Chou
]]> 277 7 https://cdn.slidesharecdn.com/ss_thumbnails/oralpresentation0505-160517075632-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-WeiChunChou1-48x48.jpg?cb=1712797058 A major emphasis in my research has long interested in the development and application of computational models, such as pharmacokinetic models, physiologically-based pharmacokinetic (PBPK) models, and QSAR models to characterize human health risks of exposure to chemicals. I have applied these models to continue work on evaluating the epidemiological observed chemical-human health effects associations on the basis of human variability. I also have expertise in bioinformatics. I have developed a gene-expression network, providing a powerful genetic biomarkers discovery platform to understand cancer biology. During my postdoctoral work at NTU, I developed and applied MCMC-based PBPK model...