Reconstruction of exposure to methylene diphenyl-4,4'-diisocyanate (MDI) aerosol using computational fluid dynamics, physiologically based toxicokinetics and statistical modeling.

OBJECTIVES: This study employed computational fluid dynamics (CFD), physiologically based toxicokinetics (PBTK), and statistical modeling to reconstruct exposure to methylene diphenyl-4,4'-diisocyanate (MDI) aerosol. By utilizing a validated CFD model, human respiratory deposition of MDI aerosol in different workload conditions was investigated, while a PBTK model was calibrated using experimental rat data. Biomonitoring data and Markov Chain Monte Carlo (MCMC) simulation were utilized for exposure assessment. RESULTS: Deposition fraction of MDI in the respiratory tract at the light, moderate, and heavy activity were 0.038, 0.079, and 0.153, respectively. Converged MCMC results as the posterior means and prior values were obtained for several PBTK model parameters. In our study, we calibrated a rat model to investigate the transport, absorption, and elimination of 4,4'-MDI via inhalation exposure. The calibration process successfully captured experimental data in the lungs, liver, blood, and kidneys, allowing for a reasonable representation of MDI distribution within the rat model. Our calibrated model also represents MDI dynamics in the bloodstream, facilitating the assessment of bioavailability. For human exposure, we validated the model for recent and long-term MDI exposure using data from relevant studies. CONCLUSION: Our computational models provide reasonable insights into MDI exposure, contributing to informed risk assessment and the development of effective exposure reduction strategies.

Chemical and biological assessments of environmental mixtures: A review of current trends, advances, and future perspectives.

Considering the chemical complexity and toxicity data gaps of environmental mixtures, most studies evaluate the chemical risk individually. However, humans are usually exposed to a cocktail of chemicals in real life. Mixture health assessment remains to be a research area having significant knowledge gaps. Characterization of chemical composition and bioactivity/toxicity are the two critical aspects of mixture health assessments. This review seeks to introduce the recent progress and tools for the chemical and biological characterization of environmental mixtures. The state-of-the-art techniques include the sampling, extraction, rapid detection methods, and the in vitro, in vivo, and in silico approaches to generate the toxicity data of an environmental mixture. Application of these novel methods, or new approach methodologies (NAMs), has increased the throughput of generating chemical and toxicity data for mixtures and thus refined the mixture health assessment. Combined with computational methods, the chemical and biological information would shed light on identifying the bioactive/toxic components in an environmental mixture.

A Bayesian population physiologically based pharmacokinetic absorption modeling approach to support generic drug development: application to bupropion hydrochloride oral dosage forms.

We propose a Bayesian population modeling and virtual bioequivalence assessment approach to establishing dissolution specifications for oral dosage forms. A generalizable semi-physiologically based pharmacokinetic absorption model with six gut segments and liver, connected to a two-compartment model of systemic disposition for bupropion hydrochloride oral dosage forms was developed. Prior information on model parameters for gut physiology, bupropion physicochemical properties, and drug product properties were obtained from the literature. The release of bupropion hydrochloride from immediate-, sustained- and extended-release oral dosage forms was described by a Weibull function. In vitro dissolution data were used to assign priors to the in vivo release properties of the three bupropion formulations. We applied global sensitivity analysis to identify the influential parameters for plasma bupropion concentrations and calibrated them. To quantify inter- and intra-individual variability, plasma concentration profiles in healthy volunteers that received the three dosage forms, each at two doses, were used. The calibrated model was in good agreement with both in vitro dissolution and in vivo exposure data. Markov Chain Monte Carlo samples from the joint posterior parameter distribution were used to simulate virtual crossover clinical trials for each formulation with distinct drug dissolution profiles. For each trial, an allowable range of dissolution parameters ("safe space") in which bioequivalence can be anticipated was established. These findings can be used to assure consistent product performance throughout the drug product life-cycle and to support manufacturing changes. Our framework provides a comprehensive approach to support decision-making in drug product development.

Risk Characterization and Probabilistic Concentration-Response Modeling of Complex Environmental Mixtures Using New Approach Methodologies (NAMs) Data from Organotypic in Vitro Human Stem Cell Assays.

BACKGROUND: Risk assessment of chemical mixtures or complex substances remains a major methodological challenge due to lack of available hazard or exposure data. Therefore, risk assessors usually infer hazard or risk from data on the subset of constituents with available toxicity values. OBJECTIVES: We evaluated the validity of the widely used traditional mixtures risk assessment paradigms, Independent Action (IA) and Concentration Addition (CA), with new approach methodologies (NAMs) data from human cell-based in vitro assays. METHODS: A diverse set of 42 chemicals was tested both individually and as mixtures for functional and cytotoxic effects in vitro. A panel of induced pluripotent stem cell (iPSCs)-derived models (hepatocytes, cardiomyocytes, endothelial, and neurons) and one primary cell type (HUVEC) were used. Bayesian concentration-response modeling of individual chemicals or their mixtures was performed for a total of 47 phenotypes to derive point-of-departure (POD) values. Probabilistic IA or CA was conducted to estimate the mixture effects based on the bioactivity profiles from the individual chemicals and compared with mixture bioactivity. RESULTS: All mixtures showed significant bioactivity, even though some were constructed using individual chemical concentrations considered "low" or "safe." Even though CA is much more accurate as a predictor of mixture effects in comparison with IA, with CA-based POD typically within an order of magnitude of the actual mixture, in some cases, the bioactivity of the mixtures appeared to be much greater than that of their components under either additivity assumption. DISCUSSION: These results suggest that CA is a preferred first approximation for predicting mixture toxicity when data for all constituents are available. However, because the accuracy of additivity assumptions varies greatly across phenotypes, we posit that mixtures and complex substances need to be directly tested for their hazard potential. NAMs provide a practical solution that rapidly yields highly informative data for mixtures risk assessment. https://doi.org/10.1289/EHP7600.

Well-tempered MCMC simulations for population pharmacokinetic models.

A full Bayesian statistical treatment of complex pharmacokinetic or pharmacodynamic models, in particular in a population context, gives access to powerful inference, including on model structure. Markov Chain Monte Carlo (MCMC) samplers are typically used to estimate the joint posterior parameter distribution of interest. Among MCMC samplers, the simulated tempering algorithm (TMCMC) has a number of advantages: it can sample from sharp multi-modal posteriors; it provides insight into identifiability issues useful for model simplification; it can be used to compute accurate Bayes factors for model choice; the simulated Markov chains mix quickly and have assured convergence in certain conditions. The main challenge when implementing this approach is to find an adequate scale of auxiliary inverse temperatures (perks) and associated scaling constants. We solved that problem by adaptive stochastic optimization and describe our implementation of TMCMC sampling in the GNU MCSim software. Once a grid of perks is obtained, it is easy to perform posterior-tempered MCMC sampling or likelihood-tempered MCMC (thermodynamic integration, which bridges the joint prior and the posterior parameter distributions, with assured convergence of a single sampling chain). We compare TMCMC to other samplers and demonstrate its efficient sampling of multi-modal posteriors and calculation of Bayes factors in two stylized case-studies and two realistic population pharmacokinetic inference problems, one of them involving a large PBPK model.

pksensi: An R package to apply global sensitivity analysis in physiologically based kinetic modeling.

Sensitivity analysis (SA) is an essential tool for modelers to understand the influence of model parameters on model outputs. It is also increasingly used in developing and assessing physiologically based kinetic (PBK) models. For instance, several studies have applied global SA to reduce the computational burden in the Bayesian Markov chain Monte Carlo-based calibration process PBK models. Although several SA algorithms and software packages are available, no comprehensive software package exists that allows users to seamlessly solve differential equations in a PBK model, conduct and visualize SA results, and discriminate between the non-influential model parameters that can be fixed and those that need calibration. Therefore, we developed an R package, named pksensi, to make global SA more accessible in PBK modeling. This package can investigate both uncertainty and sensitivity in PBK models, including those with multivariate model outputs. It also includes functions to check the convergence of the global SA results. Overall, pksensi improves the user experience of performing global SA and can create robust and reproducible results for decision making in PBK model calibration.

Using Collaborative Cross Mouse Population to Fill Data Gaps in Risk Assessment: A Case Study of Population-Based Analysis of Toxicokinetics and Kidney Toxicodynamics of Tetrachloroethylene.

BACKGROUND: Interindividual variability in susceptibility remains poorly characterized for environmental chemicals such as tetrachloroethylene (PERC). Development of population-based experimental models provide a potential approach to fill this critical need in human health risk assessment. OBJECTIVES: In this study, we aimed to better characterize the contribution of glutathione (GSH) conjugation to kidney toxicity of PERC and the degree of associated interindividual toxicokinetic (TK) and toxicodynamic (TD) variability by using the Collaborative Cross (CC) mouse population. METHODS: Male mice from 45 strains were intragastrically dosed with PERC ([Formula: see text]) or vehicle (5% Alkamuls EL-620 in saline), and time-course samples were collected for up to 24 h. Population variability in TK of S-(1,2,2-trichlorovinyl)GSH (TCVG), S-(1,2,2-trichlorovinyl)-L-cysteine (TCVC), and N-acetyl-S-(1,2,2-trichlorovinyl)-L-cysteine (NAcTCVC) was quantified in serum, liver, and kidney, and analyzed using a toxicokinetic model. Effects of PERC on kidney weight, fatty acid metabolism-associated genes [ Acot1 (Acyl-CoA thioesterase 1), Fabp1 (fatty acid-binding protein 1), and Ehhadh (enoyl-coenzyme A, hydratase/3-hydroxyacyl coenzyme A dehydrogenase)], and a marker of proximal tubular injury [KIM-1 (kidney injury molecule-1)/Hepatitis A virus cellular receptor 1 ( Havcr1)] were evaluated. Finally, quantitative data on interstrain variability in both formation of GSH conjugation metabolites of PERC and its kidney effects was used to calculate adjustment factors for the interindividual variability in both TK and TD. RESULTS: Mice treated with PERC had significantly lower kidney weight, higher kidney-to-body weight (BW) ratio, and higher expression of fatty acid metabolism-associated genes ( Acot1, Fabp1, and Ehhadh) and a marker of proximal tubular injury (KIM-1/ Havcr1). Liver levels of TCVG were significantly correlated with KIM-1/ Havcr1 in kidney, consistent with kidney injury being associated with GSH conjugation. We found that the default uncertainty factor for human variability may be marginally adequate to protect 95%, but not more, of the population for kidney toxicity mediated by PERC. DISCUSSION: Overall, this study demonstrates the utility of the CC mouse population in characterizing metabolism-toxicity interactions and quantifying interindividual variability. Further refinement of the characterization of interindividual variability can be accomplished by incorporating these data into in silico population models both for TK (such as a physiologically based pharmacokinetic model), as well as for toxicodynamic responses. https://doi.org/10.1289/EHP5105.

Assessing the seasonality of occupancy number-associated CO2 level in a Taiwan hospital.

This study enabled the assessment of indoor CO2 levels and evaluated the relationship between occupancy numbers with CO2 levels in a Taiwan hospital. The measurements were conducted over four seasons for five working days (Monday to Friday), with sampling conducted simultaneously from 09:00 am to 5:00 pm and across six locations (for spatial variability): hall (H), registration and cashier (RC), waiting area (WA), occupational therapy room (OT), physical therapy room (PT), and outdoors (O). Based on the analysis, three of the five indoor sampling sites showed significant differences in seasonal CO2 concentrations (p < 0.0001). Based on our result, the physical therapy room had the highest level of CO2 concentration that exceeded the IAQ standard in Taiwan Environmental Protection Agency (EPA) in all seasons, in that the number of occupants contributing to nearly 40% of the variation in CO2 measured. Our results also showed that the indoor/outdoor (I/O) ratios of CO2 concentration for all locations and seasons exceeded 1 in ~ 100% of those locations. The median I/O ratio at sites WA and OT was 2.37 and 2.08 during four seasons, respectively. The highest median I/O ratio was found at site PT, with a calculated range of 2.69 in spring to 3.90 in fall. The highest correlation of occupancy number and CO2 concentration also occurred in PT which correlation coefficients were estimated at 0.47, 0.65, 0.63, and 0.40 in spring, summer, fall, and winter. The findings of the present study can be used to understand occupancy number and its effect on CO2 levels in a hospital environment, as well as the effect of time of day (Monday to Friday) on the number of patients admitted.

Thorough QT/QTc in a Dish: An In Vitro Human Model That Accurately Predicts Clinical Concentration-QTc Relationships.

"Thorough QT/corrected QT (QTc)" (TQT) studies are cornerstones of clinical cardiovascular safety assessment. However, TQT studies are resource intensive, and preclinical models predictive of the threshold of regulatory concern are lacking. We hypothesized that an in vitro model using induced pluripotent stem cell (iPSC)-derived cardiomyocytes from a diverse sample of human subjects can serve as a "TQT study in a dish." For 10 positive and 3 negative control drugs, in vitro concentration-QTc, computed using a population Bayesian model, accurately predicted known in vivo concentration-QTc. Moreover, predictions of the percent confidence that the regulatory threshold of 10 ms QTc prolongation would be breached were also consistent with in vivo evidence. This "TQT study in a dish," consisting of a population-based iPSC-derived cardiomyocyte model and Bayesian concentration-QTc modeling, has several advantages over existing in vitro platforms, including higher throughput, lower cost, and the ability to accurately predict the in vivo concentration range below the threshold of regulatory concern.

Assessing health burden risk and control effect on dengue fever infection in the southern region of Taiwan.

BACKGROUND: The high prevalence of dengue in Taiwan and the consecutive large dengue outbreaks in the period 2014-2015 suggest that current control interventions are suboptimal. Understanding the effect of control effort is crucial to inform future control strategies. OBJECTIVES: We developed a framework to measure season-based health burden risk from 2001 to 2014. We reconstructed various intervention coverage to assess the attributable effect of dengue infection control efforts. MATERIALS AND METHODS: A dengue-mosquito-human transmission dynamic was used to quantify the vector-host interactions and to estimate the disease epidemics. We used disability-adjusted life years (DALYs) to assess health burden risk. A temperature-basic reproduction number (R0)-DALYs relationship was constructed to examine the potential impacts of temperature on health burden. Finally, a health burden risk model linked a control measure model to evaluate the effect of dengue control interventions. RESULTS: We showed that R0 and DALYs peaked at 25°C with estimates of 2.37 and 1387, respectively. Results indicated that most dengue cases occurred in fall with estimated DALYs of 323 (267-379, 95% CI) at 50% risk probability. We found that repellent spray had by far the largest control effect with an effectiveness of ~71% in all seasons. Pesticide spray and container clean-up have both made important contributions to reducing prevalence/incidence. Repellent, pesticide spray, container clean-up together with Wolbachia infection suppress dengue outbreak by ~90%. CONCLUSION: Our presented modeling framework provides a useful tool to measure dengue health burden risk and to quantify the effect of dengue control on dengue infection prevalence and disease incidence in the southern region of Taiwan.

A human population-based organotypic in vitro model for cardiotoxicity screening.

Assessing inter-individual variability in responses to xenobiotics remains a substantial challenge, both in drug development with respect to pharmaceuticals and in public health with respect to environmental chemicals. Although approaches exist to characterize pharmacokinetic variability, there are no methods to routinely address pharmacodynamic variability. In this study, we aimed to demonstrate the feasibility of characterizing inter-individual variability in a human in vitro model. Specifically, we hypothesized that genetic variability across a population of iPSC-derived cardiomyocytes translates into reproducible variability in both baseline phenotypes and drug responses. We measured baseline and drug-related effects in iPSC-derived cardiomyocytes from 27 healthy donors on kinetic Ca2+ flux and high-content live cell imaging. Cells were treated in concentration-response with cardiotoxic drugs: isoproterenol (ß-adrenergic receptor agonist/positive inotrope), propranolol (ß-adrenergic receptor antagonist/negative inotrope), and cisapride (hERG channel inhibitor/QT prolongation). Cells from four of the 27 donors were further evaluated in terms of baseline and treatment-related gene expression. Reproducibility of phenotypic responses was evaluated across batches and time. iPSC-derived cardiomyocytes exhibited reproducible donor-specific differences in baseline function and drug-induced effects. We demonstrate the feasibility of using a panel of population-based organotypic cells from healthy donors as an animal replacement experimental model. This model can be used to rapidly screen drugs and chemicals for inter-individual variability in cardiotoxicity. This approach demonstrates the feasibility of quantifying inter-individual variability in xenobiotic responses, and can be expanded to other cell types for which in vitro populations can be derived from iPSCs.

Comparative analysis of metabolism of trichloroethylene and tetrachloroethylene among mouse tissues and strains.

Trichloroethylene (TCE) and tetrachloroethylene (PCE) are structurally similar chemicals that are metabolized through oxidation and glutathione conjugation pathways. Both chemicals have been shown to elicit liver and kidney toxicity in rodents and humans; however, TCE has been studied much more extensively in terms of both metabolism and toxicity. Despite their qualitative similarities, quantitative comparison of tissue- and strain-specific metabolism of TCE and PCE has not been performed. To fill this gap, we conducted a comparative toxicokinetic study where equimolar single oral doses of TCE (800 mg/kg) or PCE (1000 mg/kg) were administered to male mice of C57BL/6J, B6C3F1/J, and NZW/LacJ strains. Samples of liver, kidney, serum, brain, and lung were obtained for up to 36 h after dosing. For each tissue, concentrations of parent compounds, as well as their oxidative and glutathione conjugation metabolites were measured and concentration-time profiles constructed. A multi-compartment toxicokinetic model was developed to quantitatively compare TCE and PCE metabolism. As expected, the flux through oxidation metabolism pathway predominated over that through conjugation across all mouse strains examined, it is 1,200-3,800 fold higher for TCE and 26-34 fold higher for PCE. However, the flux through glutathione conjugation, albeit a minor metabolic pathway, was 21-fold higher for PCE as compared to TCE. The degree of inter-strain variability was greatest for oxidative metabolites in TCE-treated and for glutathione conjugation metabolites in PCE-treated mice. This study provides critical data for quantitative comparisons of TCE and PCE metabolism, and may explain the differences in organ-specific toxicity between these structurally similar chemicals.

Applying a Global Sensitivity Analysis Workflow to Improve the Computational Efficiencies in Physiologically-Based Pharmacokinetic Modeling.

Traditionally, the solution to reduce parameter dimensionality in a physiologically-based pharmacokinetic (PBPK) model is through expert judgment. However, this approach may lead to bias in parameter estimates and model predictions if important parameters are fixed at uncertain or inappropriate values. The purpose of this study was to explore the application of global sensitivity analysis (GSA) to ascertain which parameters in the PBPK model are non-influential, and therefore can be assigned fixed values in Bayesian parameter estimation with minimal bias. We compared the elementary effect-based Morris method and three variance-based Sobol indices in their ability to distinguish "influential" parameters to be estimated and "non-influential" parameters to be fixed. We illustrated this approach using a published human PBPK model for acetaminophen (APAP) and its two primary metabolites APAP-glucuronide and APAP-sulfate. We first applied GSA to the original published model, comparing Bayesian model calibration results using all the 21 originally calibrated model parameters (OMP, determined by "expert judgment"-based approach) vs. the subset of original influential parameters (OIP, determined by GSA from the OMP). We then applied GSA to all the PBPK parameters, including those fixed in the published model, comparing the model calibration results using this full set of 58 model parameters (FMP) vs. the full set influential parameters (FIP, determined by GSA from FMP). We also examined the impact of different cut-off points to distinguish the influential and non-influential parameters. We found that Sobol indices calculated by eFAST provided the best combination of reliability (consistency with other variance-based methods) and efficiency (lowest computational cost to achieve convergence) in identifying influential parameters. We identified several originally calibrated parameters that were not influential, and could be fixed to improve computational efficiency without discernable changes in prediction accuracy or precision. We further found six previously fixed parameters that were actually influential to the model predictions. Adding these additional influential parameters improved the model performance beyond that of the original publication while maintaining similar computational efficiency. We conclude that GSA provides an objective, transparent, and reproducible approach to improve the performance and computational efficiency of PBPK models.

PBPK/PD assessment for Parkinson's disease risk posed by airborne pesticide paraquat exposure.

Exposure to several specific pesticides has led to an increase of Parkinson's disease (PD) risk. However, it is difficult to quantify the PD population risk related to certain pesticides in regions where environmental exposure data are scarce. Furthermore, the time trend of the prevalence and incidence of PD embedded in the background relationship between PD risk and pesticide exposures has not been well characterized. It has been convincingly identified that a key pesticide associated significantly with an increased risk trend of PD is paraquat (PQ). Here, we present a novel, probabilistic population-based exposure-response approach to quantify the contribution from PQ exposure to prevalence risk of PD. We found that the largest PQ exposure contributions occurred in its positive trend during 2004-2011, with the PQ contributing nearly 21 and 24%, respectively, to the PD prevalence rates among the age groups of 70-79 and ≥ 80 years in Taiwan. We also employed the present population risk model to predict the PQ-induced PD prevalence based on the projected rates of increase in PQ exposure associated with age-specific population. The predicted outcome can be used as an early warning signal for public health authorities. We suggest that a mechanistic understanding of the contribution of a specific pesticide exposure to PD risk trends is crucial to enhance our insights into the perspective on the impacts of environmental exposure on the neurodegenerative diseases.

Assessing the oseltamivir-induced resistance risk and implications for influenza infection control strategies.

BACKGROUND: Oseltamivir-resistant mutants with higher drug resistance rates and low trans-mission fitness costs have not accounted for influenza (sub)type viruses. Predicting the impacts of neuraminidase inhibitor therapy on infection rates and transmission of drug-resistant viral strains requires further investigation. OBJECTIVES: The purpose of this study was to assess the potential risk of oseltamivir-induced resistance for influenza A (H1N1) and A (H3N2) viruses. MATERIALS AND METHODS: An immune-response-based virus dynamic model was used to best fit the oseltamivir-resistant A (H1N1) and A (H3N2) infection data. A probabilistic risk assessment model was developed by incorporating branching process-derived probability distribution of resistance to estimate oseltamivir-induced resistance risk. RESULTS: Mutation rate and sensitive strain number were key determinants in assessing resistance risk. By increasing immune response, antiviral efficacy, and fitness cost, the spread of resistant strains for A (H1N1) and A (H3N2) were greatly decreased. Probability of resistance depends most strongly on the sensitive strain number described by a Poisson model. Risk of oseltamivir-induced resistance increased with increasing the mutation rate for A (H1N1) only. The ≥50% of resistance risk induced by A (H1N1) and A (H3N2) sensitive infected cells were 0.4 (95% CI: 0.28-0.43) and 0.95 (95% CI 0.93-0.99) at a mutation rate of 10-6, respectively. Antiviral drugs must be administrated within 1-1.5 days for A (H1N1) and 2-2.5 days for A (H3N2) virus infections to limit viral production. CONCLUSION: Probabilistic risk assessment of antiviral drug-induced resistance is crucial in the decision-making process for preventing influenza virus infections.

Mathematical modeling of postcoinfection with influenza A virus and Streptococcus pneumoniae, with implications for pneumonia and COPD-risk assessment.

BACKGROUND: The interaction between influenza and pneumococcus is important for understanding how coinfection may exacerbate pneumonia. Secondary pneumococcal pneumonia associated with influenza infection is more likely to increase respiratory morbidity and mortality. This study aimed to assess exacerbated inflammatory effects posed by secondary pneumococcal pneumonia, given prior influenza infection. MATERIALS AND METHODS: A well-derived mathematical within-host dynamic model of coinfection with influenza A virus and Streptococcus pneumoniae (SP) integrated with dose-response relationships composed of previously published mouse experimental data and clinical studies was implemented to study potentially exacerbated inflammatory responses in pneumonia based on a probabilistic approach. RESULTS: We found that TNFα is likely to be the most sensitive biomarker reflecting inflammatory response during coinfection among three explored cytokines. We showed that the worst inflammatory effects would occur at day 7 SP coinfection, with risk probability of 50% (likely) to develop severe inflammatory responses. Our model also showed that the day of secondary SP infection had much more impact on the severity of inflammatory responses in pneumonia compared to the effects caused by initial virus titers and bacteria loads. CONCLUSION: People and health care workers should be wary of secondary SP infection on day 7 post-influenza infection for prompt and proper control-measure implementation. Our quantitative risk-assessment framework can provide new insights into improvements in respiratory health especially, predominantly due to chronic obstructive pulmonary disease (COPD).

Anemia risk in relation to lead exposure in lead-related manufacturing.

BACKGROUND: Lead-exposed workers may suffer adverse health effects under the currently regulated blood lead (BPb) levels. However, a probabilistic assessment about lead exposure-associated anemia risk is lacking. The goal of this study was to examine the association between lead exposure and anemia risk among factory workers in Taiwan. METHODS: We first collated BPb and indicators of hematopoietic function data via health examination records that included 533 male and 218 female lead-exposed workers between 2012 and 2014. We used benchmark dose (BMD) modeling to estimate the critical effect doses for detection of abnormal indicators. A risk-based probabilistic model was used to characterize the potential hazard of lead poisoning for job-specific workers by hazard index (HI). We applied Bayesian decision analysis to determine whether BMD could be implicated as a suitable BPb standard. RESULTS: Our results indicated that HI for total lead-exposed workers was 0.78 (95% confidence interval: 0.50-1.26) with risk occurrence probability of 11.1%. The abnormal risk of anemia indicators for male and female workers could be reduced, respectively, by 67-77% and 86-95% by adopting the suggested BPb standards of 25 and 15 µg/dL. CONCLUSIONS: We conclude that cumulative exposure to lead in the workplace was significantly associated with anemia risk. This study suggests that current BPb standard needs to be better understood for the application of lead-exposed population protection in different scenarios to provide a novel standard for health management. Low-level lead exposure risk is an occupational and public health problem that should be paid more attention.

Mixture risk assessment due to ingestion of arsenic, copper, and zinc from milkfish farmed in contaminated coastal areas.

Human health risks associated with the consumption of metal-contaminated fish over extended periods have become a concern particularly in Taiwan, where fish is consumed on a large scale. This study applied the interaction-based hazard index (HI) to assess the mixture health risks for fishers and non-fishers who consume the arsenic (As), copper (Cu), and zinc (Zn) contaminated milkfish from As-contaminated coastal areas in Taiwan, taking into account joint toxic actions and potential toxic interactions. We showed that the interactions of As-Zn and Cu-Zn were antagonistic, whereas As-Cu interaction was additive. We found that HI estimates without interactions considered were 1.3-1.6 times higher than interactive HIs. Probability distributions of HI estimates for non-fishers were less than 1, whereas all 97.5%-tile HI estimates for fishers were >1. Analytical results revealed that the level of inorganic As in milkfish was the main contributor to HIs, indicating a health risk posed to consumers of fish farmed in As-contaminated areas. However, we found that Zn supplementation could significantly decrease As-induced risk of hematological effect by activating a Zn-dependent enzyme. In order to improve the accuracy of health risk due to exposure to multiple metals, further toxicological data, regular environmental monitoring, dietary survey, and refinement approaches for interactive risk assessment are warranted.

Site-specific water quality criteria for lethal/sublethal protection of freshwater fish exposed to zinc in southern Taiwan.

There were considerable concerns about the zinc (Zn) pollution caused by electroplating, chemical, and computer-related high-tech industrial discharges in Kaohsiung Rivers situated at south Taiwan. There is, however, a lack of site-specific water chemistry based toxicity assessment and little is known about the sublethal toxicity on freshwater fish. This study proposes an integrated framework to link experimental and mechanistic model-based data analysis of lethal and sublethal Zn toxicities for grass carp (Ctenopharyn odon idellus) populations for providing the site-specific Zn water quality threshold in Kaohsiung Rivers. A biotic ligand model (BLM) that relates toxicity impairment of physiological active sites impacted by Zn species was used to elucidate the site-specific water chemistry affecting the bioavailability and biological response of grass carp exposed to Zn. Results indicated that 96-h LC50 for mortality and 28-d EC50 for growth inhibition were 474.7 ± 1.3 (mean ± SE) and 149 ± 23.5 µg L(-1), respectively. Here the BLM-based predicted steady-state LC50s for mortality were 2110.7, 818.2, 1303.6, 563.3, and 497.1 µg L(-1), whereas measured steady-state EC50s for growth inhibition were 726.8, 326.2, 373.4, 193.9, and 170.5 µg L(-1) for the Agongdian, Houling, Love, Fengshan, and Gaoping Rivers, respectively. A positive correlation between Mg(2+) and EC50 values were found in both acute (r = 0.94, p < 0.01) and chronic (r = 0.97, p < 0.01) Zn exposures. This study suggests that the use of site-specific water chemistry data and ecophysiological traits would enhance the predictive capacities to assess the potential effect of metal toxicity posed to aquatic organisms.