Interpreting PCB levels in breast milk using a physiologically based pharmacokinetic model to reconstruct the dynamic exposure of Italian women

Systematic review of physiologically based kinetic lactation models for transfer of xenobiotic compounds to milk

Lactational elimination has been described mathematically for nearly 50 years. Over 40 published articles, containing >50 physiologically based kinetic (PBK) lactation models were included in the systematic review. These PBK models described the lactational elimination of xenobiotic compounds in humans, rats, mice, and dairy cows and goats. A total of 78 compounds have been modelled, ranging from industrial chemicals, pesticides, to pain medication, antibiotics, and caffeine. Few models included several species or compounds, and models were thus generally not translational or generic. Three dairy cow models mechanistically described the intramammary disposition of pharmaceuticals after intramammary administration, including volume changes caused by milking, while empirically describing the remaining pharmacokinetics. The remaining models were semi- or whole body PBK models, describing long-term exposure of environmental pollutants, or short-term exposure of pharmaceuticals. The absolute majority described the disposition to the mammary gland or milk with perfusion limited compartments, but permeability limited models were available as well. With long-term exposure, models often included changes in milk volume and/or consumption by the offspring, and changes in body weight of offspring. Periodic emptying of the mammary gland, as with feeding or milking, was sparsely applied. Rodent models used similar physiological parameters, while values of physiological parameters applied in human models could range widely. When milk composition was included in the models, it most often included the fat content. The review gives an extensive overview of the applied functions and modelling strategies of PBK lactation models.

Physiologically-based toxicokinetic model for the prediction of perchlorate distribution and its application

Perchlorate is a stable and readily transportable thyroid hormone disruptor, and prevalent exposure to perchlorate through food and drinking water has raised public concern about its health effects. The physiologically based toxicokinetic (PBTK) model as a dose prediction method is effective to predict the toxicant exposure dose of an organism and helps quantitatively assess the dose-dependent relationship with toxic effects. The current study aimed to establish a multi-compartment PBTK model based on updated time-course datasets of single oral exposure to perchlorate in rats. With adjustment of the kinetic parameters, the model fitted well the toxicokinetic characteristics of perchlorate in urine, blood, and thyroid from our experiments and the literature, and the coefficient of determination (R²) between the fitting values and the experimental data in regression analysis was greater than 0.91, indicating the robustness of the current model. The results of sensitivity analysis and daily repeated exposure simulations together confirmed its effective renal clearance. According to the distribution characteristic of perchlorate, a correlation study of internal and external exposure was conducted using urinary perchlorate as a bioassay indicator. The developed multi-compartment model for perchlorate updates important toxicokinetic data and kinetic parameters, providing analytical and modeling tools for deriving total exposure levels in the short term.

A Generic Pharmacokinetic Model for Quantifying Mother-to-Offspring Transfer of Lipophilic Persistent Environmental Chemicals

Lipophilic persistent environmental chemicals (LPECs) can accumulate in a woman's body and transfer to her developing child across the placenta and via breast milk. To assess health risks associated with developmental exposures to LPECs, we developed a pharmacokinetic (PK) model that quantifies mother-to-offspring transfer of LPECs during pregnancy and lactation and facilitates internal dosimetry calculations for offspring. We parameterized the model for mice, rats, and humans using time-varying functions for body mass and milk consumption rates. The only required substance-specific parameter is the elimination half-life of the LPEC in the animal species of interest. We used the model to estimate whole-body concentrations in mothers and offspring following maternal exposures to hexachlorobenzene (HCB) and 2,2',4,4',5,5'-hexachlorobiphenyl (PCB 153) and compared these with measured concentrations from animal studies. We also compared estimated concentrations for humans to those generated using a previously published human LPEC PK model. Finally, we compared human equivalent doses (HEDs) calculated using our model and an allometric scaling method. Estimated and observed whole-body concentrations of HCB and PCB 153 in offspring followed similar trends and differed by less than 60%. Simulations of human exposure yielded concentration estimates comparable to those generated using the previously published model, with concentrations in offspring differing by less than 12%. HEDs calculated using our PK model were about 2 orders of magnitude lower than those generated using allometric scaling. Our PK model can be used to calculate internal dose metrics for offspring and corresponding HEDs and thus informs assessment of developmental toxicity risks associated with LPECs.

PBPK Modeling to Simulate the Fate of Compounds in Living Organisms

Pharmacokinetics study the fate of xenobiotics in a living organism. Physiologically based pharmacokinetic (PBPK) models provide realistic descriptions of xenobiotics' absorption, distribution, metabolism, and excretion processes. They model the body as a set of homogeneous compartments representing organs, and their parameters refer to anatomical, physiological, biochemical, and physicochemical entities. They offer a quantitative mechanistic framework to understand and simulate the time-course of the concentration of a substance in various organs and body fluids. These models are well suited for performing extrapolations inherent to toxicology and pharmacology (e.g., between species or doses) and for integrating data obtained from various sources (e.g., in vitro or in vivo experiments, structure-activity models). In this chapter, we describe the practical development and basic use of a PBPK model from model building to model simulations, through implementation with an easily accessible free software.

The role of epidemiology studies in human health risk assessment of polychlorinated biphenyls

Polychlorinated biphenyls (PCBs) are a public health concern given evidence that they persist and accumulate in the environment and can cause toxic effects in animals and humans. However, evaluating adverse effects of PCBs in epidemiologic studies is complicated by the characteristics of PCB exposure. PCBs exist as mixtures in the environment; the mixture changes over time due to degradation, and given physicochemical differences between specific PCB congeners, the mixture that an individual is exposed to (via food, air, or other sources) is likely different from that which can be measured in biological tissues. This is particularly problematic when evaluating toxicity of shorter-lived congeners that may not be measurable by the time biological samples are collected. We review these and other issues that arise when evaluating epidemiologic studies of PCBs and discuss how epidemiology data can still be used to inform both hazard identification and dose-response evaluation.

Estimated postnatal p,p'-DDT and p,p'-DDE levels and body mass index at 42 months of age in a longitudinal study of Japanese children

BACKGROUND

Children are exposed to p,p'-dichlorodiphenyltrichloroethane (p,p'-DDT) and p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE) through placental and lactational transfer. Some studies have suggested that early-life exposure to these compounds could lead to increased body mass index (BMI) during childhood. Our aim was to assess whether children's exposure during the first 2 years of life is associated with BMI z-score in Japanese children at 42 months of age.

METHODS

We used data from a birth cohort (n = 290) of the Tohoku Study of Child Development. p,p'-DDT and p,p'-DDE levels were measured in breast milk samples collected 1 month after birth, and levels in children were estimated using a toxicokinetic model for three exposure periods (0-6 months, 6-12 months, 12-24 months). Associations between exposure estimates and BMI z-score at 42 months of age were assessed using multivariate linear regression models.

RESULTS

We found no significant association between levels of p,p'-DDT measured in breast milk or estimated in children and BMI z-score. However, we observed associations between estimated p,p'-DDE levels in girls during all postnatal exposure periods and BMI z-score; for each log increase in the estimated p,p'-DDE levels, BMI z-score increased by 0.23 (C.I. 95%: 0.01, 0.45) for the 0-6 months exposure period, 0.26 (C.I. 95%: 0.06, 0.47) for the 6-12 months exposure period, and 0.24 (C.I. 95%: 0.05, 0.43) for the 12-24 months exposure period.

CONCLUSION

In this study of Japanese children, estimated postnatal p,p'-DDE levels were associated with increased BMI z-score at 42 months of age, mostly in girls. These results are in line with previous studies supporting that early-life exposure to p,p'-DDE may be associated with higher BMI during childhood.

Dynamics of PCB exposure in the past 50 years and recent high concentrations in human breast milk: Analysis of influencing factors using a physiologically based pharmacokinetic model

In this study we reconstruct the long-term exposure of Czech mothers to polychlorinated biphenyls (PCBs) and determine the causes of high contamination of breast milk by indicator PCBs (iPCBs). A data set containing information from more than 1000 primiparous women from the Czech Republic was used, including iPCB concentrations in breast milk, individual physiology and living characteristics. The time series of PCB intakes for the whole period from the beginning of PCB production in 1958 until 2011 were reconstructed. We estimated the individual lifetime exposure of mothers for all iPCBs, i.e. congeners 28, 52, 101, 118, 138, 153 and 180, using a physiologically based pharmacokinetic (PBPK) model. Various model scenarios were investigated to determine the influence of physiology, age at delivery, past dietary exposure, and food composition on concentrations in breast milk for all iPCBs. The highest contributions to the presence of iPCBs in breast milk were observed for food composition. The main factor determining the concentration of higher-chlorinated PCBs (138, 153 and 180) was past exposure. The most important parameter for identification of children's postnatal exposure through breast milk was the time-span from the maximum of the exposure peak to the birth of the child. The current concentrations of iPCBs in breast milk in the Czech population are still high because the maximum of the exposure peak occurred more than 10 years later than in other European countries and was very broad, e.g. covered more than 10 years.

Prediction of maternal and foetal exposures to perfluoroalkyl compounds in a Spanish birth cohort using toxicokinetic modelling

Prenatal exposures to perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) have been associated with child health outcomes, but many of these associations remain poorly characterized. The aim of this work was to provide new indicators of foetal exposure for the Spanish INMA birth cohort. First, a pregnancy and lactation physiologically based pharmacokinetic (PBPK) model was calibrated in a population framework to provide quantitative estimates for the PFOA and PFOS placental transfers in humans. The estimated distributions indicated that PFOA crosses the placental barrier at a rate three times higher than PFOS and shows a higher variability between mothers. The PBPK model was then used to back-calculate the time-varying daily intakes of the INMA mothers corrected for their individual history from a spot maternal concentration. We showed the importance of accounting for the mothers' history as different dietary intakes can result in similar measured concentrations at one time point. Finally, the foetal exposure was simulated in target organs over pregnancy using the PBPK model and the estimated maternal intakes. We showed that the pattern of PFOA and PFOS exposures varies greatly among the foetuses. About a third has levels of either one compound always higher than the levels of the other compound. The other two thirds showed different ranking of PFOA and PFOS in terms of concentrations in the target organs. Our simulated foetal exposures bring additional information to the measured maternal spot concentrations and can help to better characterize the prenatal exposure in target organs during windows of susceptibility.

Modeling Pharmacokinetics

Pharmacokinetics is the study of the fate of xenobiotics in a living organism. Physiologically based pharmacokinetic (PBPK) models provide realistic descriptions of xenobiotics' absorption, distribution, metabolism, and excretion processes. They model the body as a set of homogeneous compartments representing organs, and their parameters refer to anatomical, physiological, biochemical, and physicochemical entities. They offer a quantitative mechanistic framework to understand and simulate the time-course of the concentration of a substance in various organs and body fluids. These models are well suited for performing extrapolations inherent to toxicology and pharmacology (e.g., between species or doses) and for integrating data obtained from various sources (e.g., in vitro or in vivo experiments, structure-activity models). In this chapter, we describe the practical development and basic use of a PBPK model from model building to model simulations, through implementation with an easily accessible free software.

Development and Application of a Probabilistic Risk-Benefit Assessment Model for Infant Feeding Integrating Microbiological, Nutritional, and Chemical Components

A probabilistic and interdisciplinary risk-benefit assessment (RBA) model integrating microbiological, nutritional, and chemical components was developed for infant milk, with the objective of predicting the health impact of different scenarios of consumption. Infant feeding is a particular concern of interest in RBA as breast milk and powder infant formula have both been associated with risks and benefits related to chemicals, bacteria, and nutrients, hence the model considers these three facets. Cronobacter sakazakii, dioxin-like polychlorinated biphenyls (dl-PCB), and docosahexaenoic acid (DHA) were three risk/benefit factors selected as key issues in microbiology, chemistry, and nutrition, respectively. The present model was probabilistic with variability and uncertainty separated using a second-order Monte Carlo simulation process. In this study, advantages and limitations of undertaking probabilistic and interdisciplinary RBA are discussed. In particular, the probabilistic technique was found to be powerful in dealing with missing data and to translate assumptions into quantitative inputs while taking uncertainty into account. In addition, separation of variability and uncertainty strengthened the interpretation of the model outputs by enabling better consideration and distinction of natural heterogeneity from lack of knowledge. Interdisciplinary RBA is necessary to give more structured conclusions and avoid contradictory messages to policymakers and also to consumers, leading to more decisive food recommendations. This assessment provides a conceptual development of the RBA methodology and is a robust basis on which to build upon.

Use of Physiologically Based Kinetic Modeling-Based Reverse Dosimetry to Predict in Vivo Toxicity from in Vitro Data

The development of reliable nonanimal based testing strategies, such as in vitro bioassays, is the holy grail in current human safety testing of chemicals. However, the use of in vitro toxicity data in risk assessment is not straightforward. One of the main issues is that concentration-response curves from in vitro models need to be converted to in vivo dose-response curves. These dose-response curves are needed in toxicological risk assessment to obtain a point of departure to determine safe exposure levels for humans. Recent scientific developments enable this translation of in vitro concentration-response curves to in vivo dose-response curves using physiologically based kinetic (PBK) modeling-based reverse dosimetry. The present review provides an overview of the examples available in the literature on the prediction of in vivo toxicity using PBK modeling-based reverse dosimetry of in vitro toxicity data, showing that proofs-of-principle are available for toxicity end points ranging from developmental toxicity, nephrotoxicity, hepatotoxicity, and neurotoxicity to DNA adduct formation. This review also discusses the promises and pitfalls, and the future perspectives of the approach. Since proofs-of-principle available so far have been provided for the prediction of toxicity in experimental animals, future research should focus on the use of in vitro toxicity data obtained in human models to predict the human situation using human PBK models. This would facilitate human- instead of experimental animal-based approaches in risk assessment.

Modelling the exposure to chemicals for risk assessment: a comprehensive library of multimedia and PBPK models for integration, prediction, uncertainty and sensitivity analysis - the MERLIN-Expo tool

MERLIN-Expo is a library of models that was developed in the frame of the FP7 EU project 4FUN in order to provide an integrated assessment tool for state-of-the-art exposure assessment for environment, biota and humans, allowing the detection of scientific uncertainties at each step of the exposure process. This paper describes the main features of the MERLIN-Expo tool. The main challenges in exposure modelling that MERLIN-Expo has tackled are: (i) the integration of multimedia (MM) models simulating the fate of chemicals in environmental media, and of physiologically based pharmacokinetic (PBPK) models simulating the fate of chemicals in human body. MERLIN-Expo thus allows the determination of internal effective chemical concentrations; (ii) the incorporation of a set of functionalities for uncertainty/sensitivity analysis, from screening to variance-based approaches. The availability of such tools for uncertainty and sensitivity analysis aimed to facilitate the incorporation of such issues in future decision making; (iii) the integration of human and wildlife biota targets with common fate modelling in the environment. MERLIN-Expo is composed of a library of fate models dedicated to non biological receptor media (surface waters, soils, outdoor air), biological media of concern for humans (several cultivated crops, mammals, milk, fish), as well as wildlife biota (primary producers in rivers, invertebrates, fish) and humans. These models can be linked together to create flexible scenarios relevant for both human and wildlife biota exposure. Standardized documentation for each model and training material were prepared to support an accurate use of the tool by end-users. One of the objectives of the 4FUN project was also to increase the confidence in the applicability of the MERLIN-Expo tool through targeted realistic case studies. In particular, we aimed at demonstrating the feasibility of building complex realistic exposure scenarios and the accuracy of the modelling predictions through a comparison with actual measurements.

Perspectives for integrating human and environmental exposure assessments

Integrated Risk Assessment (IRA) has been defined by the EU FP7 HEROIC Coordination action as "the mutual exploitation of Environmental Risk Assessment for Human Health Risk Assessment and vice versa in order to coherently and more efficiently characterize an overall risk to humans and the environment for better informing the risk analysis process" (Wilks et al., 2015). Since exposure assessment and hazard characterization are the pillars of risk assessment, integrating Environmental Exposure assessment (EEA) and Human Exposure assessment (HEA) is a major component of an IRA framework. EEA and HEA typically pursue different targets, protection goals and timeframe. However, human and wildlife species also share the same environment and they similarly inhale air and ingest water and food through often similar overlapping pathways of exposure. Fate models used in EEA and HEA to predict the chemicals distribution among physical and biological media are essentially based on common properties of chemicals, and internal concentration estimations are largely based on inter-species (i.e. biota-to-human) extrapolations. Also, both EEA and HEA are challenged by increasing scientific complexity and resources constraints. Altogether, these points create the need for a better exploitation of all currently existing data, experimental approaches and modeling tools and it is assumed that a more integrated approach of both EEA and HEA may be part of the solution. Based on the outcome of an Expert Workshop on Extrapolations in Integrated Exposure Assessment organized by the HEROIC project in January 2014, this paper identifies perspectives and recommendations to better harmonize and extrapolate exposure assessment data, models and methods between Human Health and Environmental Risk Assessments to support the further development and promotion of the concept of IRA. Ultimately, these recommendations may feed into guidance showing when and how to apply IRA in the regulatory decision-making process for chemicals.

Chaotic time series prediction for prenatal exposure to polychlorinated biphenyls in umbilical cord blood using the least squares SEATR model

Chaotic time series prediction based on nonlinear systems showed a superior performance in prediction field. We studied prenatal exposure to polychlorinated biphenyls (PCBs) by chaotic time series prediction using the least squares self-exciting threshold autoregressive (SEATR) model in umbilical cord blood in an electronic waste (e-waste) contaminated area. The specific prediction steps basing on the proposal methods for prenatal PCB exposure were put forward, and the proposed scheme's validity was further verified by numerical simulation experiments. Experiment results show: 1) seven kinds of PCB congeners negatively correlate with five different indices for birth status: newborn weight, height, gestational age, Apgar score and anogenital distance; 2) prenatal PCB exposed group at greater risks compared to the reference group; 3) PCBs increasingly accumulated with time in newborns; and 4) the possibility of newborns suffering from related diseases in the future was greater. The desirable numerical simulation experiments results demonstrated the feasibility of applying mathematical model in the environmental toxicology field.

The human early-life exposome (HELIX): project rationale and design

BACKGROUND

Developmental periods in early life may be particularly vulnerable to impacts of environmental exposures. Human research on this topic has generally focused on single exposure-health effect relationships. The "exposome" concept encompasses the totality of exposures from conception onward, complementing the genome.

OBJECTIVES

The Human Early-Life Exposome (HELIX) project is a new collaborative research project that aims to implement novel exposure assessment and biomarker methods to characterize early-life exposure to multiple environmental factors and associate these with omics biomarkers and child health outcomes, thus characterizing the "early-life exposome." Here we describe the general design of the project.

METHODS

In six existing birth cohort studies in Europe, HELIX will estimate prenatal and postnatal exposure to a broad range of chemical and physical exposures. Exposure models will be developed for the full cohorts totaling 32,000 mother-child pairs, and biomarkers will be measured in a subset of 1,200 mother-child pairs. Nested repeat-sampling panel studies (n = 150) will collect data on biomarker variability, use smartphones to assess mobility and physical activity, and perform personal exposure monitoring. Omics techniques will determine molecular profiles (metabolome, proteome, transcriptome, epigenome) associated with exposures. Statistical methods for multiple exposures will provide exposure-response estimates for fetal and child growth, obesity, neurodevelopment, and respiratory outcomes. A health impact assessment exercise will evaluate risks and benefits of combined exposures.

CONCLUSIONS

HELIX is one of the first attempts to describe the early-life exposome of European populations and unravel its relation to omics markers and health in childhood. As proof of concept, it will form an important first step toward the life-course exposome.