Effects Of Multiple Chemicals Research Articles

Exploring the associations of serum concentrations of PCBs, PCDDs, and PCDFs with walking speed in the U.S. general population: Beyond standard linear models

Studies have shown that persistent organic pollutants (POPs) can have various health effects. However, little is known about the effects of multiple chemicals with possible common sources of exposure on walking speed, a proxy index reflecting lower limb neuromuscular function and physical function. We simultaneously applied multiple linear and nonlinear statistical models to explore the complex exposure-response relationship between a mixture of 22 selected POPs and walking speed. A total of 14 polychlorinated biphenyls (PCBs), 3 polychlorinated dibenzo-p-dioxins (PCDDs), and 5 polychlorinated dibenzofurans (PCDFs) were measured in the serum of participants in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2002. Walking speed was measured during a physical examination. Linear regression (LR), least absolute shrinkage and selection operator (LASSO), and group LASSO were used to evaluate the linearity of mixtures, while restricted cubic spline (RCS) regression, random forest (RF), and Bayesian kernel machine regression (BKMR) models were used to evaluate the nonlinearity of mixtures. Potential confounders were adjusted in the above models. A total of 436 subjects were included in our final analysis. The results of the LR model did not identify any POP exposure that was significantly associated with walking speed. The LASSO results revealed an inverse association of one PCDD congener and two PCDF congeners with walking speed, while the group LASSO analysis identified PCDFs at the exposure level and at the group level. In the RCS analysis, two PCB congeners presented significant overall associations with walking speed. The PCB congener PCB194 showed statistically significant effects on the outcome (P = 0.01) when a permutation-based RF was used. The BKMR analysis suggested that PCBs and PCDFs (probabilities = 0.887 and 0.909, respectively) are potentially associated with walking speed. Complex statistical models, such as RCS regression, RF and BKMR models, can detect the nonlinear and nonadditive relationships between PCBs and walking speed, while LASSO and group LASSO can identify only the linear relationships between PCDFs and walking speed. Fully considering the influence of collinearity in each method during modelling can increase the comprehensiveness and reliability of conclusions in studies of multiple chemicals.

Application of toxicogenomic analysis to risk assessment of delayed long-term effects of multiple chemicals including endocrine disruptors in human fetuses

Application of toxicogenomic analysis to risk assessment of delayed long-term effects of multiple chemicals including endocrine disruptors in human fetuses

The use of physiologically-based pharmacokinetic/ pharmacodynamic dosimetry models for chemical mixtures

Human exposure to chemicals is rarely, if ever, limited to a single chemical. Therefore, it is essential that we consider multiple chemical effects and interactions in our risk assessment process. However, with the almost infinitely large number of chemical mixtures in the environment, systematic studies of the toxicology of these chemical mixtures with conventional methodologies and approaches are impossible because of the immense resources and unrealistically long durations required. Thus, the development of ‘Predictive and Alternative Toxicology’ is imperative. At Colorado State University (CSU), our research effort is entirely devoted to this challenge. In order to have a reasonable chance to deal with the complex issue of toxicology of chemical mixtures, we believe that the following concepts must be considered: (1) the utilization of computer; (2) the exploitation of mathematical/statistical methodologies; (3) developing very focused, mechanistically based, and short-term toxicology studies; (4) coupling computer/mathematical modeling with mechanistically-based toxicology. Our strategy is therefore the utilization of physiologically-based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling, coupled with very focused, modeldirected toxicology experiments as well as other statistical/mathematical methodologies such as Monte Carlo simulation, isobolographic analysis, and response surface methodology. We believe that ‘Predictive and Alternative Toxicology’ in terms of tissue dosimetry at the pharmacokinetic and pharmacodynamic levels is achievable with simple and complex but chemically defined mixtures. In this presentation, we describe two ongoing research projects as an illustration of our ‘Bottom-Up’ and ‘Top-Down’ approaches for handling the chemical mixtures: (1) PBPK/PD modeling of toxicologie interactions between Kepone and carbon tetrachloride (CC1 4) and the coupling of Monte Carlo simulation for the prediction of acute toxicity; (2) the conceptual development of PBPK/PD modeling for a more complex chemical mixture of seven groundwater contaminants from hazardous waste sites and the consideration of subfractionation of this chemical mixture.

Physiologically based pharmacokinetic/pharmacodynamic modeling of chemical mixtures and possible applications in risk assessment

Human exposure to chemicals, be it environmental or occupational, is rarely, if ever, limited to a single chemical. Therefore, it is essential that we consider multiple chemical effects and interactions in our risk assessment process. However, with the almost infinitely large number of chemical mixtures in the environment, systematic studies of the toxicology of these chemical mixtures with conventional methodologies and approaches are impossible because of the immense resources and unrealistically long durations required. Thus, the development of predictive and alternative toxicology method is imperative. In order to have a reasonable chance to deal with the complex issue of toxicology of chemical mixtures, we believe that the following concepts must be considered: (1) the exploitation of recent advances in computational technology; (2) the utilization of mathematical/statistical modeling; (3) coupling computer modeling with very focused, mechanistically based, and short-term toxicology studies. Our approach is. therefore, the utilization of physiologically based pharmacokinetic/pharmacodynamic (PB-PK/PD) modeling, coupled with very focused, model-directed toxicology experiments as well as other statistical/mathematical modeling such as isobolo- graphic analysis and response surface methodology. Tissue dosimetry at the pharmacokinetic and pharmacodynamic levels is achievable with simple and complex, but chemically defined, mixtures. Our long-term goal is to formulate innovative risk assessment methodologies for chemical mixtures. In this presentation, one of our specific research projects is described: PB-PK/PD modeling of toxicologic interactions between Kepone and carbon tetrachloride (CCl 4) and the coupling of Monte Carlo simulation for the prediction of acute toxicity.