A Novel Multispecies Toxicokinetic Modeling Approach in Support of Chemical Risk Assessment.
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ABSTRACT:
SUBMITTER: Mangold-Doring A
PROVIDER: S-EPMC9066611 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundHumans are routinely and concurrently exposed to multiple toxic chemicals, including various metals and organics, often at levels that can cause adverse and potentially synergistic effects. However, toxicokinetic modeling studies of exposures to these chemicals are typically performed on a single chemical basis. Furthermore, the attributes of available models for individual chemicals are commonly estimated specifically for the compound studied. As a result, the available models usually have parameters and even structures that are not consistent or compatible across the range of chemicals of concern. This fact precludes the systematic consideration of synergistic effects, and may also lead to inconsistencies in calculations of co-occurring exposures and corresponding risks. There is a need, therefore, for a consistent modeling framework that would allow the systematic study of cumulative risks from complex mixtures of contaminants.MethodsA Generalized Toxicokinetic Modeling system for Mixtures (GTMM) was developed and evaluated with case studies. The GTMM is physiologically-based and uses a consistent, chemical-independent physiological description for integrating widely varying toxicokinetic models. It is modular and can be directly "mapped" to individual toxicokinetic models, while maintaining physiological consistency across different chemicals. Interaction effects of complex mixtures can be directly incorporated into the GTMM.ConclusionsThe application of GTMM to different individual metals and metal compounds showed that it explains available observational data as well as replicates the results from models that have been optimized for individual chemicals. The GTMM also made it feasible to model toxicokinetics of complex, interacting mixtures of multiple metals and nonmetals in humans, based on available literature information. The GTMM provides a central component in the development of a "source-to-dose-to-effect" framework for modeling population health risks from environmental contaminants. As new data become available on interactions of multiple chemicals, the GTMM can be iteratively parameterized to improve mechanistic understanding of human health risks from exposures to complex mixtures of chemicals.
| S-EPMC2903511 | biostudies-literature
Project description:Temporally resolved environmental risk assessment of fluctuating concentrations of micropollutants is presented. We separated the prediction of toxicity over time from the extrapolation from one to many species and from acute to sublethal effects. A toxicokinetic-toxicodynamic (TKTD) model predicted toxicity caused by fluctuating concentrations of diazinon, measured by time-resolved sampling over 108 days from three locations in a stream network, representing urban, agricultural and mixed land use. We calculated extrapolation factors to quantify variation in toxicity among species and effect types based on available toxicity data, while correcting for different test durations with the TKTD model. Sampling from the distribution of extrapolation factors and prediction of time-resolved toxicity with the TKTD model facilitated subsequent calculation of the risk of undesired toxic events. Approximately one-fifth of aquatic organisms were at risk and fluctuating concentrations were more toxic than their averages. Contribution of urban and agricultural sources of diazinon to the overall risk varied. Thus using fixed concentrations as water quality criteria appears overly simplistic because it ignores the temporal dimension of toxicity. However, the improved prediction of toxicity for fluctuating concentrations may be small compared to uncertainty due to limited diversity of toxicity data to base the extrapolation factors on.
| S-EPMC3213766 | biostudies-literature
Project description:The toxicokinetic (TK) parameters fraction of the chemical unbound to plasma proteins and metabolic clearance are critical for relating exposure and internal dose when building in vitro-based risk assessment models. However, experimental toxicokinetic studies have only been carried out on limited chemicals of environmental interest (~1000 chemicals with TK data relative to tens of thousands of chemicals of interest). This work evaluated the utility of chemical structure information to predict TK parameters in silico; development of cluster-based read-across and quantitative structure-activity relationship models of fraction unbound or fub (regression) and intrinsic clearance or Clint (classification and regression) using a dataset of 1487 chemicals; utilization of predicted TK parameters to estimate uncertainty in steady-state plasma concentration (Css); and subsequent in vitro-in vivo extrapolation analyses to derive bioactivity-exposure ratio (BER) plot to compare human oral equivalent doses and exposure predictions using androgen and estrogen receptor activity data for 233 chemicals as an example dataset. The results demonstrate that fub is structurally more predictable than Clint. The model with the highest observed performance for fub had an external test set RMSE/σ=0.62 and R2=0.61, for Clint classification had an external test set accuracy = 65.9%, and for intrinsic clearance regression had an external test set RMSE/σ=0.90 and R2=0.20. This relatively low performance is in part due to the large uncertainty in the underlying Clint data. We show that Css is relatively insensitive to uncertainty in Clint. The models were benchmarked against the ADMET Predictor software. Finally, the BER analysis allowed identification of 14 out of 136 chemicals for further risk assessment demonstrating the utility of these models in aiding risk-based chemical prioritization.
| S-EPMC8193769 | biostudies-literature
Project description:Scientifically sound, risk-informed evaluation of chemicals is essential to protecting public health. Systematically leveraging information from exposure, toxicology, and epidemiology studies can provide a holistic understanding of how real-world exposure to chemicals may impact the health of populations, including sensitive and vulnerable individuals and life-stages. Increasingly, public health policy makers are employing toxicokinetic (TK) modeling tools to integrate these data streams and predict potential human health impact. Development of a suite of tools for predicting internal exposure, including physiologically-based toxicokinetic (PBTK) models, is being driven by needs to address large numbers of data-poor chemicals efficiently, translate bioactivity, and mechanistic information from new in vitro test systems, and integrate multiple lines of evidence to enable scientifically sound, risk-informed decisions. New modeling approaches are being designed "fit for purpose" to inform specific decision contexts, with applications ranging from rapid screening of hundreds of chemicals, to improved prediction of risks during sensitive stages of development. New data are being generated experimentally and computationally to support these models. Progress to meet the demand for internal exposure and PBTK modeling tools will require transparent publication of models and data to build credibility in results, as well as opportunities to partner with decision makers to evaluate and build confidence in use of these for improved decisions that promote safe use of chemicals.
| S-EPMC6760598 | biostudies-literature
Project description:Providing reliable environmental quality standards (EQSs) is a challenging issue in environmental risk assessment (ERA). These EQSs are derived from toxicity endpoints estimated from dose-response models to identify and characterize the environmental hazard of chemical compounds released by human activities. These toxicity endpoints include the classical x% effect/lethal concentrations at a specific time t (EC/LC(x, t)) and the new multiplication factors applied to environmental exposure profiles leading to x% effect reduction at a specific time t (MF(x, t), or denoted LP(x, t) by the EFSA). However, classical dose-response models used to estimate toxicity endpoints have some weaknesses, such as their dependency on observation time points, which are likely to differ between species (e.g., experiment duration). Furthermore, real-world exposure profiles are rarely constant over time, which makes the use of classical dose-response models difficult and may prevent the derivation of MF(x, t). When dealing with survival or immobility toxicity test data, these issues can be overcome with the use of the general unified threshold model of survival (GUTS), a toxicokinetic-toxicodynamic (TKTD) model that provides an explicit framework to analyse both time- and concentration-dependent data sets as well as obtain a mechanistic derivation of EC/LC(x, t) and MF(x, t) regardless of x and at any time t of interest. In ERA, the assessment of a risk is inherently built upon probability distributions, such that the next critical step is to characterize the uncertainties of toxicity endpoints and, consequently, those of EQSs. With this perspective, we investigated the use of a Bayesian framework to obtain the uncertainties from the calibration process and to propagate them to model predictions, including LC(x, t) and MF(x, t) derivations. We also explored the mathematical properties of LC(x, t) and MF(x, t) as well as the impact of different experimental designs to provide some recommendations for a robust derivation of toxicity endpoints leading to reliable EQSs: avoid computing LC(x, t) and MF(x, t) for extreme x values (0 or 100%), where uncertainty is maximal; compute MF(x, t) after a long period of time to take depuration time into account and test survival under pulses with different periods of time between them.
| S-EPMC6685980 | biostudies-literature
Project description:BackgroundOne of the main challenges of modern risk assessment is to account for combined exposure to the multitude of various substances present in food and the environment.ObjectiveThe present work proposes a methodological approach to perform chemical risk assessment of contaminant mixtures across regulatory silos regarding an extensive range of substances and to do so when comprehensive relevant data concerning the specific effects and modes of action of the mixture components are not available.MethodsWe developed a complete step-by-step approach using statistical methods to prioritize substances involved in combined exposure, and we used a component-based approach to cumulate the risk using dose additivity. The most relevant toxicological end point and the associated reference point were selected from the literature to construct a toxicological threshold for each substance.DiscussionBy applying the proposed method to contaminants in breast milk, we observed that among the 19 substances comprising the selected mixture, ∑DDT, ∑PCBi, and arsenic were main joint contributors to the risk of neurodevelopmental and thyroid effects for infants. In addition, ∑PCCD/F contributed to the thyroid effect and ∑aldrin-dieldrin to the neurodevelopmental effect. Our case study on contaminants in breast milk demonstrated the importance of crossing regulatory silos when studying mixtures and the importance of identifying risk drivers to regulate the risk related to environmental contamination. Applying this method to another set of data, such as human biomonitoring or in ecotoxicology, will reinforce its relevance for risk assessment. https://doi.org/10.1289/EHP8262.
| S-EPMC8893236 | biostudies-literature
Project description:Humans are continuously exposed to chemicals with suspected or proven endocrine disrupting chemicals (EDCs). Risk management of EDCs presents a major unmet challenge because the available data for adverse health effects are generated by examining one compound at a time, whereas real-life exposures are to mixtures of chemicals. In this work, we integrate epidemiological and experimental evidence toward a whole mixture strategy for risk assessment. To illustrate, we conduct the following four steps in a case study: (1) identification of single EDCs ("bad actors")-measured in prenatal blood/urine in the SELMA study-that are associated with a shorter anogenital distance (AGD) in baby boys; (2) definition and construction of a "typical" mixture consisting of the "bad actors" identified in Step 1; (3) experimentally testing this mixture in an in vivo animal model to estimate a dose-response relationship and determine a point of departure (i.e., reference dose [RfD]) associated with an adverse health outcome; and (4) use a statistical measure of "sufficient similarity" to compare the experimental RfD (from Step 3) to the exposure measured in the human population and generate a "similar mixture risk indicator" (SMRI). The objective of this exercise is to generate a proof of concept for the systematic integration of epidemiological and experimental evidence with mixture risk assessment strategies. Using a whole mixture approach, we could find a higher rate of pregnant women under risk (13%) when comparing with the data from more traditional models of additivity (3%), or a compound-by-compound strategy (1.6%).
| S-EPMC6973107 | biostudies-literature
Project description:OBJECTIVES:Bedside ultrasound techniques have the unique ability to produce instantaneous, dynamic images, and have demonstrated widespread utility in both emergency and critical care settings. The aim of this article is to introduce a novel application of this imaging modality by utilizing an ultrasound based mathematical model to assess respiratory function. With validation, the proposed models have the potential to predict pulmonary function in patients who cannot adequately participate in standard spirometric techniques (inability to form tight seal with mouthpiece, etc.). METHODS:Ultrasound was used to measure diaphragm thickness (Tdi) in a small population of healthy, adult males at various points of the respiratory cycle. Each measurement corresponded to a generated negative inspiratory force (NIF), determined by a handheld meter. The data was analyzed using mixed models to produce two representative mathematical models. RESULTS:Two mathematical models represented the relationship between Tdi and NIFmax, or maximum inspiratory pressure (MIP), both of which were statistically significant with p-values <0.005: 1. log(NIF) = -1.32+4.02×log(Tdi); and 2. NIF = -8.19+(2.55 × Tdi)+(1.79×(Tdi2)). CONCLUSIONS:With validation, these models intend to provide a method of estimating MIP, by way of diaphragm ultrasound measurements, thereby allowing evaluation of respiratory function in patients who may be unable to reliably participate in standard spirometric tests.
| S-EPMC7067453 | biostudies-literature
Project description:Current formulations of combined oral contraceptives (COC) containing ethinylestradiol (EE) have ?35 ?g due to increased risks of cardiovascular diseases (CVD) with higher doses of EE. Low-dose formulations however, have resulted in increased incidences of breakthrough bleeding and contraceptive failure, particularly when coadministered with inducers of cytochrome P450 enzymes (CYP). The developed physiologically based pharmacokinetic model quantitatively predicted the effect of CYP3A4 inhibition and induction on the pharmacokinetics of EE. The predicted Cmax and AUC ratios when coadministered with voriconazole, fluconazole, rifampicin, and carbamazepine were within 1.25 of the observed data. Based on published clinical data, an AUCss value of 1,000 pg/ml.h was selected as the threshold for breakthrough bleeding. Prospective application of the model in simulations of different doses of EE (20 ?g, 35 ?g, and 50 ?g) identified percentages of the population at risk of breakthrough bleeding alone and with varying degrees of CYP modulation.
| S-EPMC6282492 | biostudies-literature
Project description:Individuals are exposed to a large number of diverse environmental chemicals simultaneously and the evaluation of multiple chemical exposures is important for identifying cancer risk factors. The measurement of a large number of chemicals (the exposome) in epidemiologic studies is allowing for a more comprehensive assessment of cancer risk factors than was done in earlier studies that focused on only a few chemicals. Empirical evidence from epidemiologic studies shows that chemicals from different chemical classes have different magnitudes and directions of association with cancers. Given increasing data availability, there is a need for the development and assessment of statistical methods to model environmental cancer risk that considers a large number of diverse chemicals with different effects for different chemical classes. The method of grouped weighted quantile sum (GWQS) regression allows for multiple groups of chemicals to be considered in the model such that different magnitudes and directions of associations are possible for each group of chemicals. In this paper, we assessed the ability of GWQS regression to estimate exposure effects for multiple chemical groups and correctly identify important chemicals in each group using a simulation study. We compared the performance of GWQS regression with WQS regression, the least absolute shrinkage and selection operator (lasso), and the group lasso in estimating exposure effects and identifying important chemicals. The simulation study results demonstrate that GWQS is an effective method for modeling exposure to multiple groups of chemicals and compares favorably with other methods used in mixture analysis. As an application, we used GWQS regression in the California Childhood Leukemia Study (CCLS), a population-based case-control study of childhood leukemia in California to estimate exposure effects for many chemical classes while also adjusting for demographic factors. The CCLS analysis found evidence of a positive association between exposure to the herbicide dacthal and an increased risk of childhood leukemia.
| S-EPMC7827322 | biostudies-literature