Meta-Analysis of Nanoparticle Distribution in Tumors and Major Organs in Tumor-Bearing Mice.

Low tumor delivery efficiency is a critical barrier in cancer nanomedicine. This study reports an updated version of "Nano-Tumor Database", which increases the number of time-dependent concentration data sets for different nanoparticles (NPs) in tumors from the previous version of 376 data sets with 1732 data points from 200 studies to the current version of 534 data sets with 2345 data points from 297 studies published from 2005 to 2021. Additionally, the current database includes 1972 data sets for five major organs (i.e., liver, spleen, lung, heart, and kidney) with a total of 8461 concentration data points. Tumor delivery and organ distribution are calculated using three pharmacokinetic parameters, including delivery efficiency, maximum concentration, and distribution coefficient. The median tumor delivery efficiency is 0.67% injected dose (ID), which is low but is consistent with previous studies. Employing the best regression model for tumor delivery efficiency, we generate hypothetical scenarios with different combinations of NP factors that may lead to a higher delivery efficiency of >3%ID, which requires further experimentation to confirm. In healthy organs, the highest NP accumulation is in the liver (10.69%ID/g), followed by the spleen 6.93%ID/g and the kidney 3.22%ID/g. Our perspective on how to facilitate NP design and clinical translation is presented. This study reports a substantially expanded "Nano-Tumor Database" and several statistical models that may help nanomedicine design in the future.

Development and application of an interactive generic physiologically based pharmacokinetic (igPBPK) model for adult beef cattle and lactating dairy cows to estimate tissue distribution and edible tissue and milk withdrawal intervals for per- and polyfluoroalkyl substances (PFAS).

Humans can be exposed to per- and polyfluoroalkyl substances (PFAS) through dietary intake from milk and edible tissues from food animals. This study developed a physiologically based pharmacokinetic (PBPK) model to predict tissue and milk residues and estimate withdrawal intervals (WDIs) for multiple PFAS including PFOA, PFOS and PFHxS in beef cattle and lactating dairy cows. Results showed that model predictions were mostly within a two-fold factor of experimental data for plasma, tissues, and milk with an estimated coefficient of determination (R2) of >0.95. The predicted muscle WDIs for beef cattle were <1 day for PFOA, 449 days for PFOS, and 69 days for PFHxS, while the predicted milk WDIs in dairy cows were <1 day for PFOA, 1345 days for PFOS, and zero day for PFHxS following a high environmental exposure scenario (e.g., 49.3, 193, and 161 ng/kg/day for PFOA, PFOS, and PFHxS, respectively, for beef cattle for 2 years). The model was converted to a web-based interactive generic PBPK (igPBPK) platform to provide a user-friendly dashboard for predictions of tissue and milk WDIs for PFAS in cattle. This model serves as a foundation for extrapolation to other PFAS compounds to improve safety assessment of cattle-derived food products.

An artificial intelligence-assisted physiologically-based pharmacokinetic model to predict nanoparticle delivery to tumors in mice.

The critical barrier for clinical translation of cancer nanomedicine stems from the inefficient delivery of nanoparticles (NPs) to target solid tumors. Rapid growth of computational power, new machine learning and artificial intelligence (AI) approaches provide new tools to address this challenge. In this study, we established an AI-assisted physiologically based pharmacokinetic (PBPK) model by integrating an AI-based quantitative structure-activity relationship (QSAR) model with a PBPK model to simulate tumor-targeted delivery efficiency (DE) and biodistribution of various NPs. The AI-based QSAR model was developed using machine learning and deep neural network algorithms that were trained with datasets from a published "Nano-Tumor Database" to predict critical input parameters of the PBPK model. The PBPK model with optimized NP cellular uptake kinetic parameters was used to predict the maximum delivery efficiency (DEmax) and DE at 24 (DE24) and 168 h (DE168) of different NPs in the tumor after intravenous injection and achieved a determination coefficient of R2 = 0.83 [root mean squared error (RMSE) = 3.01] for DE24, R2 = 0.56 (RMSE = 2.27) for DE168, and R2 = 0.82 (RMSE = 3.51) for DEmax. The AI-PBPK model predictions correlated well with available experimentally-measured pharmacokinetic profiles of different NPs in tumors after intravenous injection (R2 ≥ 0.70 for 133 out of 288 datasets). This AI-based PBPK model provides an efficient screening tool to rapidly predict delivery efficiency of a NP based on its physicochemical properties without relying on an animal training dataset.

Integration of In Vitro and In Vivo Models to Predict Cellular and Tissue Dosimetry of Nanomaterials Using Physiologically Based Pharmacokinetic Modeling.

Nanomaterials (NMs) have been increasingly used in a number of areas, including consumer products and nanomedicine. Target tissue dosimetry is important in the evaluation of safety, efficacy, and potential toxicity of NMs. Current evaluation of NM efficacy and safety involves the time-consuming collection of pharmacokinetic and toxicity data in animals and is usually completed one material at a time. This traditional approach no longer meets the demand of the explosive growth of NM-based products. There is an emerging need to develop methods that can help design safe and effective NMs in an efficient manner. In this review article, we critically evaluate existing studies on in vivo pharmacokinetic properties, in vitro cellular uptake and release and kinetic modeling, and whole-body physiologically based pharmacokinetic (PBPK) modeling studies of different NMs. Methods on how to simulate in vitro cellular uptake and release kinetics and how to extrapolate cellular and tissue dosimetry of NMs from in vitro to in vivo via PBPK modeling are discussed. We also share our perspectives on the current challenges and future directions of in vivo pharmacokinetic studies, in vitro cellular uptake and kinetic modeling, and whole-body PBPK modeling studies for NMs. Finally, we propose a nanomaterial in vitro to in vivo extrapolation via physiologically based pharmacokinetic modeling (Nano-IVIVE-PBPK) framework for high-throughput screening of target cellular and tissue dosimetry as well as potential toxicity of different NMs in order to meet the demand of efficient evaluation of the safety, efficacy, and potential toxicity of a rapidly increasing number of NM-based products.

Development of a multi-route physiologically based pharmacokinetic (PBPK) model for nanomaterials: a comparison between a traditional versus a new route-specific approach using gold nanoparticles in rats.

BACKGROUND: Physiologically based pharmacokinetic (PBPK) modeling is an important tool in predicting target organ dosimetry and risk assessment of nanoparticles (NPs). The methodology of building a multi-route PBPK model for NPs has not been established, nor systematically evaluated. In this study, we hypothesized that the traditional route-to-route extrapolation approach of PBPK modeling that is typically used for small molecules may not be appropriate for NPs. To test this hypothesis, the objective of this study was to develop a multi-route PBPK model for different sizes (1.4-200 nm) of gold nanoparticles (AuNPs) in adult rats following different routes of administration (i.e., intravenous (IV), oral gavage, intratracheal instillation, and endotracheal inhalation) using two approaches: a traditional route-to-route extrapolation approach for small molecules and a new approach that is based on route-specific data that we propose to be applied generally to NPs. RESULTS: We found that the PBPK model using this new approach had superior performance than the traditional approach. The final PBPK model was optimized rigorously using a Bayesian hierarchical approach with Markov chain Monte Carlo simulations, and then converted to a web-based interface using R Shiny. In addition, quantitative structure-activity relationships (QSAR) based multivariate linear regressions were established to predict the route-specific key biodistribution parameters (e.g., maximum uptake rate) based on the physicochemical properties of AuNPs (e.g., size, surface area, dose, Zeta potential, and NP numbers). These results showed the size and surface area of AuNPs were the main determinants for endocytic/phagocytic uptake rates regardless of the route of administration, while Zeta potential was an important parameter for the estimation of the exocytic release rates following IV administration. CONCLUSIONS: This study suggests that traditional route-to-route extrapolation approaches for PBPK modeling of small molecules are not applicable to NPs. Therefore, multi-route PBPK models for NPs should be developed using route-specific data. This novel PBPK-based web interface serves as a foundation for extrapolating to other NPs and to humans to facilitate biodistribution estimation, safety, and risk assessment of NPs.

Predicting Nanoparticle Delivery to Tumors Using Machine Learning and Artificial Intelligence Approaches.

Background: Low delivery efficiency of nanoparticles (NPs) to the tumor is a critical barrier in the field of cancer nanomedicine. Strategies on how to improve NP tumor delivery efficiency remain to be determined. Methods: This study analyzed the roles of NP physicochemical properties, tumor models, and cancer types in NP tumor delivery efficiency using multiple machine learning and artificial intelligence methods, using data from a recently published Nano-Tumor Database that contains 376 datasets generated from a physiologically based pharmacokinetic (PBPK) model. Results: The deep neural network model adequately predicted the delivery efficiency of different NPs to different tumors and it outperformed all other machine learning methods; including random forest, support vector machine, linear regression, and bagged model methods. The adjusted determination coefficients (R2) in the full training dataset were 0.92, 0.77, 0.77 and 0.76 for the maximum delivery efficiency (DEmax), delivery efficiency at 24 h (DE24), at 168 h (DE168), and at the last sampling time (DETlast). The corresponding R2 values in the test dataset were 0.70, 0.46, 0.33 and 0.63, respectively. Also, this study showed that cancer type was an important determinant for the deep neural network model in predicting the tumor delivery efficiency across all endpoints (19-29%). Among all physicochemical properties, the Zeta potential and core material played a greater role than other properties, such as the type, shape, and targeting strategy. Conclusion: This study provides a quantitative model to improve the design of cancer nanomedicine with greater tumor delivery efficiency. These results help to improve our understanding of the causes of low NP tumor delivery efficiency. This study demonstrates the feasibility of integrating artificial intelligence with PBPK modeling approaches to study cancer nanomedicine.

Meta-Analysis of Nanoparticle Delivery to Tumors Using a Physiologically Based Pharmacokinetic Modeling and Simulation Approach.

Numerous studies have engineered nanoparticles with different physicochemical properties to enhance the delivery efficiency to solid tumors, yet the mean and median delivery efficiencies are only 1.48% and 0.70% of the injected dose (%ID), respectively, according to a study using a nonphysiologically based modeling approach based on published data from 2005 to 2015. In this study, we used physiologically based pharmacokinetic (PBPK) models to analyze 376 data sets covering a wide range of nanomedicines published from 2005 to 2018 and found mean and median delivery efficiencies at the last sampling time point of 2.23% and 0.76%ID, respectively. Also, the mean and median delivery efficiencies were 2.24% and 0.76%ID at 24 h and were decreased to 1.23% and 0.35%ID at 168 h, respectively, after intravenous administration. While these delivery efficiencies appear to be higher than previous findings, they are still quite low and represent a critical barrier in the clinical translation of nanomedicines. We explored the potential causes of this poor delivery efficiency using the more mechanistic PBPK perspective applied to a subset of gold nanoparticles and found that low delivery efficiency was associated with low distribution and permeability coefficients at the tumor site (P < 0.01). We also demonstrate how PBPK modeling and simulation can be used as an effective tool to investigate tumor delivery efficiency of nanomedicines.

Translating Nanomedicine to Comparative Oncology-the Case for Combining Zinc Oxide Nanomaterials with Nucleic Acid Therapeutic and Protein Delivery for Treating Metastatic Cancer.

The unique anticancer, biochemical, and immunologic properties of nanomaterials are becoming a new tool in biomedical research. Their translation into the clinic promises a new wave of targeted therapies. One nanomaterial of particular interest are zinc oxide (ZnO) nanoparticles (NPs), which has distinct mechanisms of anticancer activity including unique surface, induction of reactive oxygen species, lipid oxidation, pH, and also ionic gradients within cancer cells and the tumor microenvironment. It is recognized that ZnO NPs can serve as a direct enzyme inhibitor. Significantly, ZnO NPs inhibit extracellular signal-regulated kinase (ERK) and protein kinase B (AKT) associated with melanoma progression, drug resistance, and metastasis. Indeed, direct intratumoral injection of ZnO NPs or a complex of ZnO with RNA significantly suppresses ERK and AKT phosphorylation. These data suggest ZnO NPs and their complexes or conjugates with nucleic acid therapeutic or anticancer protein may represent a potential new strategy for the treatment of metastatic melanoma, and potentially other cancers. This review focuses on the anticancer mechanisms of ZnO NPs and what is currently known about its biochemical effects on melanoma, biologic activity, and pharmacokinetics in rodents and its potential for translation into large animal, spontaneously developing models of melanoma and other cancers, which represent models of comparative oncology.

Integration of Food Animal Residue Avoidance Databank (FARAD) empirical methods for drug withdrawal interval determination with a mechanistic population-based interactive physiologically based pharmacokinetic (iPBPK) modeling platform: example for flunixin meglumine administration.

Violative chemical residues in animal-derived food products affect food safety globally and have impact on the trade of international agricultural products. The Food Animal Residue Avoidance Databank program has been developing scientific tools to provide appropriate withdrawal interval (WDI) estimations after extralabel drug use in food animals for the past three decades. One of the tools is physiologically based pharmacokinetic (PBPK) modeling, which is a mechanistic-based approach that can be used to predict tissue residues and WDIs. However, PBPK models are complicated and difficult to use by non-modelers. Therefore, a user-friendly PBPK modeling framework is needed to move this field forward. Flunixin was one of the top five violative drug residues identified in the United States from 2010 to 2016. The objective of this study was to establish a web-based user-friendly framework for the development of new PBPK models for drugs administered to food animals. Specifically, a new PBPK model for both cattle and swine after administration of flunixin meglumine was developed. Population analysis using Monte Carlo simulations was incorporated into the model to predict WDIs following extralabel administration of flunixin meglumine. The population PBPK model was converted to a web-based interactive PBPK (iPBPK) framework to facilitate its application. This iPBPK framework serves as a proof-of-concept for further improvements in the future and it can be applied to develop new models for other drugs in other food animal species, thereby facilitating the application of PBPK modeling in WDI estimation and food safety assessment.

Assessing Global Human Exposure to T-2 Toxin via Poultry Meat Consumption Using a Lifetime Physiologically Based Pharmacokinetic Model.

Residue depletion of T-2 toxin in chickens after oral gavage at 2.0 mg/kg twice daily for 2 days was determined in this study. A flow-limited physiologically based pharmacokinetic (PBPK) model was developed for lifetime exposure assessment in chickens. The model was calibrated with data from the residue depletion study and then validated with independent data. A local sensitivity analysis was performed, and 16 sensitive parameters were subjected to Monte Carlo analysis. The population PBPK model was applied to estimate daily intake values of T-2 toxin in different countries based on reported consumption factors and the guidance value of 0.25 mg/kg in feed for chickens by the European Food Safety Authority (EFSA). The predicted daily intakes in different countries were all lower than the EFSA's total daily intake, suggesting that the EFSA's guidance value has minimal risk. This model provides a foundation for scaling to other mycotoxins and other food animal species.

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.

Probabilistic risk assessment of gold nanoparticles after intravenous administration by integrating in vitro and in vivo toxicity with physiologically based pharmacokinetic modeling.

This study aimed to conduct an integrated and probabilistic risk assessment of gold nanoparticles (AuNPs) based on recently published in vitro and in vivo toxicity studies coupled to a physiologically based pharmacokinetic (PBPK) model. Dose-response relationships were characterized based on cell viability assays in various human cell types. A previously well-validated human PBPK model for AuNPs was applied to quantify internal concentrations in liver, kidney, skin, and venous plasma. By applying a Bayesian-based probabilistic risk assessment approach incorporating Monte Carlo simulation, probable human cell death fractions were characterized. Additionally, we implemented in vitro to in vivo and animal-to-human extrapolation approaches to independently estimate external exposure levels of AuNPs that cause minimal toxicity. Our results suggest that under the highest dosing level employed in existing animal studies (worst-case scenario), AuNPs coated with branched polyethylenimine (BPEI) would likely induce ∼90-100% cellular death, implying high cytotoxicity compared to <10% cell death induced by low-to-medium animal dosing levels, which are commonly used in animal studies. The estimated human equivalent doses associated with 5% cell death in liver and kidney were around 1 and 3 mg/kg, respectively. Based on points of departure reported in animal studies, the human equivalent dose estimates associated with gene expression changes and tissue cell apoptosis in liver were 0.005 and 0.5 mg/kg, respectively. Our analyzes provide insights into safety evaluation, risk prediction, and point of departure estimation of AuNP exposure for humans and illustrate an approach that could be applied to other NPs when sufficient data are available.

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.

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).

Evaluation on subcellular partitioning and biodynamics of pulse copper toxicity in tilapia reveals impacts of a major environmental disturbance.

Fluctuation exposure of trace metal copper (Cu) is ubiquitous in aquatic environments. The purpose of this study was to investigate the impacts of chronically pulsed exposure on biodynamics and subcellular partitioning of Cu in freshwater tilapia (Oreochromis mossambicus). Long-term 28-day pulsed Cu exposure experiments were performed to explore subcellular partitioning and toxicokinetics/toxicodynamics of Cu in tilapia. Subcellular partitioning linking with a metal influx scheme was used to estimate detoxification and elimination rates. A biotic ligand model-based damage assessment model was used to take into account environmental effects and biological mechanisms of Cu toxicity. We demonstrated that the probability causing 50% of susceptibility risk in response to pulse Cu exposure in generic Taiwan aquaculture ponds was ~33% of Cu in adverse physiologically associated, metabolically active pool, implicating no significant susceptibility risk for tilapia. We suggest that our integrated ecotoxicological models linking chronic exposure measurements with subcellular partitioning can facilitate a risk assessment framework that provides a predictive tool for preventive susceptibility reduction strategies for freshwater fish exposed to pulse metal stressors.

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.

Probabilistic integrated risk assessment of human exposure risk to environmental bisphenol A pollution sources.

Environmental bisphenol A (BPA) exposure has been linked to a variety of adverse health effects such as developmental and reproductive issues. However, establishing a clear association between BPA and the likelihood of human health is complex yet fundamentally uncertain. The purpose of this study was to assess the potential exposure risks from environmental BPA among Chinese population based on five human health outcomes, namely immune response, uterotrophic assay, cardiovascular disease (CVD), diabetes, and behavior change. We addressed these health concerns by using a stochastic integrated risk assessment approach. The BPA dose-dependent likelihood of effects was reconstructed by a series of Hill models based on animal models or epidemiological data. We developed a physiologically based pharmacokinetic (PBPK) model that allows estimation of urinary BPA concentration from external exposures. Here we showed that the daily average exposure concentrations of BPA and urinary BPA estimates were consistent with the published data. We found that BPA exposures were less likely to pose significant risks for infants (0-1 year) and adults (male and female >20 years) with <10(-6)-fold increase in uterus weight and immune response outcomes, respectively. Moreover, our results indicated that there was 50 % risk probability that the response outcomes of CVD, diabetes, and behavior change with or without skin absorption would increase 10(-4)-10(-2)-fold. We conclude that our approach provides a powerful tool for tracking and managing human long-term BPA susceptibility in relation to multiple exposure pathways, and for informing the public of the negligible magnitude of environmental BPA pollution impacts on human health.

In situ remediation-released zero-valent iron nanoparticles impair soil ecosystems health: A C. elegans biomarker-based risk assessment.

There is considerable concern over the potential ecotoxicity to soil ecosystems posed by zero-valent iron nanoparticles (Fe(0) NPs) released from in situ environmental remediation. However, a lack of quantitative risk assessment has hampered the development of appropriate testing methods used in environmental applications. Here we present a novel, empirical approach to assess Fe(0) NPs-associated soil ecosystems health risk using the nematode Caenorhabditis elegans as a model organism. A Hill-based dose-response model describing the concentration-fertility inhibition relationships was constructed. A Weibull model was used to estimate thresholds as a guideline to protect C. elegans from infertility when exposed to waterborne or foodborne Fe(0) NPs. Finally, the risk metrics, exceedance risk (ER) and risk quotient (RQ) of Fe(0) NPs in various depths and distances from remediation sites can then be predicted. We showed that under 50% risk probability (ER=0.5), upper soil layer had the highest infertility risk (95% confidence interval: 13.18-57.40%). The margins of safety and acceptable criteria for soil ecosystems health for using Fe(0) NPs in field scale applications were also recommended. Results showed that RQs are larger than 1 in all soil layers when setting a stricter threshold of ∼1.02mgL(-1) of Fe(0) NPs. This C. elegans biomarker-based risk model affords new insights into the links between widespread use of Fe(0) NPs and environmental risk assessment and offers potential environmental implications of metal-based NPs for in situ remediation.