Abstract  Characterizing human variability in susceptibility to chemical toxicity is a critical issue in regulatory decision-making, but is usually addressed by a default 10-fold safety/uncertainty factor. Feasibility of population-based in vitro experimental approaches to more accurately estimate human variability was demonstrated recently using a large (~1000) panel of lymphoblastoid cell lines. However, routine use of such a large population-based model poses cost and logistical challenges. We hypothesize that a Bayesian approach embedded in a tiered workflow provides efficient estimation of variability and enables a tailored and sensible approach to selection of appropriate sample size. We used the previously collected lymphoblastoid cell line in vitro toxicity data to develop a data-derived prior distribution for the uncertainty in the degree of population variability. The resulting prior for the toxicodynamic variability factor (the ratio between the median and 1% most sensitive individuals) has a median (90% CI) of 2.5 (1.4-9.6). We then performed computational experiments using a hierarchical Bayesian population model with lognormal population variability with samples sizes of n = 5 to 100 to determine the change in precision and accuracy with increasing sample size. We propose a tiered Bayesian strategy for fit-for-purpose population variability estimates: (1) a default using the data-derived prior distribution; (2) a pilot experiment using samples sizes of ~20 individuals that reduces prior uncertainty by > 50% with > 80% balanced accuracy for classification; and (3) a high confidence experiment using sample sizes of ~50-100. This approach efficiently uses in vitro data on population variability to inform decision-making.

Abstract  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.

Abstract  Traditional cancer slope factors derived from linear low-dose extrapolation give little consideration to uncertainties in dose-response model choice, interspecies extrapolation, and human variability. As noted previously by the National Academies, probabilistic methods can address these limitations, but have only been demonstrated in a few case studies. Here, we applied probabilistic approaches for Bayesian Model Averaging (BMA), interspecies extrapolation, and human variability distributions to 255 animal cancer bioassay datasets previously used by governmental agencies. We then derived predictions for both population cancer incidence and individual cancer risk. For model uncertainty, we found that lower confidence limits from BMA and from U.S. Environmental Protection Agency (EPA)'s Benchmark Dose Software (BMDS) correlated highly, with 86% differing by <10-fold. Incorporating other uncertainties and human variability, the lower confidence limits of the probabilistic risk-specific dose (RSD) at 10-6 population incidence were typically 3- to 30-fold lower than traditional slope factors. However, in a small (<7%) number of cases of highly non-linear experimental dose-response, the probabilistic RSDs were >10-fold less stringent. Probabilistic RSDs were also protective of individual risks of 10-4 in >99% of the population. We conclude that implementing Bayesian and probabilistic methods provides a more scientifically rigorous basis for cancer dose-response assessment and thereby improves overall cancer risk characterization.

Abstract  The use of nano-scale copper oxide (CuO) and basic copper carbonate (Cu2(OH)2CO3) in both ionic and micronized wood preservatives has raised concerns about the potential of these substances to cause adverse humans health effects. To address these concerns, we performed quantitative (probabilistic) human health risk assessment (HHRA) along the lifecycles of these formulations used in antibacterial and antifungal wood coatings and impregnations by means of the EU FP7 SUN project's Decision Support System (SUNDS, www.sunds.gd). The results from the risk analysis revealed inhalation risks from CuO in exposure scenarios involving workers handling dry powders and performing sanding operations as well as potential ingestion risks for children exposed to nano Cu2(OH)2CO3 in a scenario involving hand-to-mouth transfer of the substance released from impregnated wood. There are, however, substantial uncertainties in these results, so some of the identified risks may stem from the safety margin of extrapolation to fill data gaps and might be resolved by additional testing. Our stochastic approach successfully communicated the contribution of different sources of uncertainty in the risk assessment. The main source of uncertainty was the extrapolation from short to long-term exposure, which was necessary due to the lack of (sub)chronic in vivo studies with CuO and Cu2(OH)2CO3. Considerable uncertainties also stemmed from the use of default inter- and intra-species extrapolation factors.

Abstract  High(er) throughput toxicokinetics (HTTK) encompasses in vitro measures of key determinants of chemical toxicokinetics and reverse dosimetry approaches for in vitro-in vivo extrapolation (IVIVE). With HTTK, the bioactivity identified by any in vitro assay can be converted to human equivalent doses and compared with chemical intake estimates. Biological variability in HTTK has been previously considered, but the relative impact of measurement uncertainty has not. Bayesian methods were developed to provide chemical-specific uncertainty estimates for 2 in vitro toxicokinetic parameters: unbound fraction in plasma (fup) and intrinsic hepatic clearance (Clint). New experimental measurements of fup and Clint are reported for 418 and 467 chemicals, respectively. These data raise the HTTK chemical coverage of the ToxCast Phase I and II libraries to 57%. Although the standard protocol for Clint was followed, a revised protocol for fup measured unbound chemical at 10%, 30%, and 100% of physiologic plasma protein concentrations, allowing estimation of protein binding affinity. This protocol reduced the occurrence of chemicals with fup too low to measure from 44% to 9.1%. Uncertainty in fup was also reduced, with the median coefficient of variation dropping from 0.4 to 0.1. Monte Carlo simulation was used to propagate both measurement uncertainty and biological variability into IVIVE. The uncertainty propagation techniques used here also allow incorporation of other sources of uncertainty such as in silico predictors of HTTK parameters. These methods have the potential to inform risk-based prioritization based on the relationship between in vitro bioactivities and exposures.

Abstract  Quantitative risk assessments for physical, chemical, biological, occupational, or environmental agents rely on scientific studies to support their conclusions. These studies often include relatively few observations, and, as a result, models used to characterize the risk may include large amounts of uncertainty. The motivation, development, and assessment of new methods for risk assessment is facilitated by the availability of a set of experimental studies that span a range of dose-response patterns that are observed in practice. We describe construction of such a historical database focusing on quantal data in chemical risk assessment, and we employ this database to develop priors in Bayesian analyses. The database is assembled from a variety of existing toxicological data sources and contains 733 separate quantal dose-response data sets. As an illustration of the database's use, prior distributions for individual model parameters in Bayesian dose-response analysis are constructed. Results indicate that including prior information based on curated historical data in quantitative risk assessments may help stabilize eventual point estimates, producing dose-response functions that are more stable and precisely estimated. These in turn produce potency estimates that share the same benefit. We are confident that quantitative risk analysts will find many other applications and issues to explore using this database.

Abstract  Though numerous chemical ingredients are used in cleaning products, empirical mammalian toxicology information is often limited for many substances. Such limited data inherently presents challenges to environmental health practitioners performing hazard and risk assessments. Probabilistic hazard assessment using chemical toxicity distributions (CTDs) is an alternative approach for assessments of chemicals when toxicity information is lacking. The CTD concept allows for derivation of thresholds of toxicological concern (TTCs) to predict adverse effect thresholds for mammalian species. Unfortunately, comparative health hazard assessment of cleaning product ingredients in common use categories such as all-purpose cleaners (APC), dish care products (DCP) and laundry care products (LCP) has not been well studied. However, APC, DCP, and LCP are used routinely for household and industrial applications, resulting in residential and industrial occupational exposures. Therefore, we reviewed and then examined hazard information (median lethal dose (LD50), lowest-observed-adverse-effect level (LOAEL), and no-observed-adverse-effect level (NOAEL)) from different types of standard mammalian toxicity studies for oral toxicity in the rat model from the unique Cleaning Product Ingredient Safety Initiative mammalian toxicology database. Probabilistic distributions (CTDs) were subsequently constructed using LD50, NOAEL and LOAEL data from a specific toxicity study type for all available ingredients in these three use categories. Based on data availability, product type-specific and chemical category-specific CTDs were also generated and compared. For each CTD, threshold concentrations (TCs) and their 95% confidence intervals (95% CIs) at 1st, 5th, 10th, 50th, 90th, 95th and 99th percentiles were calculated using the log-normal model. To test whether the common default uncertainty factor (UF) approach (e.g., 3, 10) in mammalian health risk assessment provides sufficient protection, UFs were also derived for LOAEL-to-NOAEL and exposure duration (e.g., subchronic-to-chronic) extrapolations. Relationships between CTDs of acute LD50s and sublethal LOAELs/NOAELs were also examined for acute-to-chronic ratio calculations, which may be useful in extreme circumstances. Results from our critical review and meta-analysis appear particularly useful for hazard and risk practitioners when identifying TTCs for ingredients in product use categories, and other chemical classes. This approach can also support development of regulatory data dossiers through read across, chemical substitutions and screening-level health risk assessments when limited or no empirical toxicity information exists for industrial chemicals.

Abstract  Human health risk assessment currently uses the reference dose or reference concentration (RfD, RfC) approach to describe the level of exposure to chemical hazards without appreciable risk for non-cancer health effects in people. However, this “bright line” approach assumes that there is minimal risk below the RfD/RfC with some undefined level of increased risk at exposures above the RfD/RfC and has limited utility for decision-making. Rather than this dichotomous approach, non-cancer risk assessment can benefit from incorporating probabilistic methods to estimate the amount of risk across a wide range of exposures and define a risk-specific dose. We identify and review existing approaches for conducting probabilistic non-cancer risk assessments. Using perchloroethylene (PCE), a priority chemical for the U.S. Environmental Protection Agency under the Toxic Substances Control Act, we calculate risk-specific doses for the effects on cognitive deficits using probabilistic risk assessment approaches. Our probabilistic risk assessment shows that chronic exposure to 0.004 ppm PCE is associated with approximately 1-in-1,000 risk for a 5% reduced performance on the Wechsler Memory Scale Visual Reproduction subtest with 95% confidence. This exposure level associated with a 1-in-1000 risk for non-cancer neurocognitive deficits is lower than the current RfC for PCE of 0.0059 ppm, which is based on standard point of departure and uncertainty factor approaches for the same neurotoxic effects in occupationally exposed adults. We found that the population-level risk of cognitive deficit (indicating central nervous system dysfunction) is estimated to be greater than the cancer risk level of 1-in-100,000 at a similar chronic exposure level. The extension of toxicological endpoints to more clinically relevant endpoints, along with consideration of magnitude and severity of effect, will help in the selection of acceptable risk targets for non-cancer effects. We find that probabilistic approaches can 1) provide greater context to existing RfDs and RfCs by describing the probability of effect across a range of exposure levels including the RfD/RfC in a diverse population for a given magnitude of effect and confidence level, 2) relate effects of chemical exposures to clinical disease risk so that the resulting risk assessments can better inform decision-makers and benefit-cost analysis, and 3) better reflect the underlying biology and uncertainties of population risks.

Abstract  The International Life Sciences Institute (ILSI) Europe Food Allergy Task Force was founded in response to early public concerns about the growing impact of food allergies almost coincidentally with the publication of the 1995 Food and Agriculture Organization-World Health Organization Technical Consultation on Food Allergies. In line with ILSI principles aimed to foster collaboration between stakeholders to promote consensus on science-based approaches to food safety and nutrition, the task force has played a central role since then in the development of risk assessment for food allergens. This ranged from consideration of the criteria to be applied to identifying allergens of public health concern through methodologies to determine the relationship between dose and the proportion of allergic individuals reacting, as well as the nature of the observed responses. The task force also promoted the application of novel, probabilistic risk assessment methods to better delineate the impact of benchmarks, such as reference doses, and actively participated in major European food allergy projects, such as EUROPREVALL, the European Union (EU)-funded project "The prevalence, cost and basis of food allergy across Europe;" and iFAAM, "Integrated approaches to food allergen and allergy risk management," also an EU-funded project. Over the years, the task force's work has evolved as answers to initial questions raised further issues: Its current work program includes a review of analytical methods and how different ones can best be deployed given their strengths and limitations. Another activity, which has just commenced, aims to develop a framework for stakeholders to achieve consensus on acceptable risk.

Abstract  Toxicology is the discipline that investigates the possible adverse effects of chemical exposure on human, animal and environmental health. Chemical risk assessment is the process that aims to identify potentially hazardous substances and describes the probability of adverse outcomes associated with their exposure. Biological changes and adverse effects do not occur after a threshold level is surpassed, but gradually and following a sequence of linked events. Traditionally, the no-observed-adverse-effect-level (NOAEL) approach has been used to detect the highest dose at which no adverse effect was observed. However, the NOAEL approach has methodological limitations and disadvantages that have resulted in it being increasingly replaced by the scientifically more advanced benchmark dose (BMD) approach. The BMD-modelling approach is a flexible method that takes all uncertainty and variability in the data into account, providing better estimates of doses leading to the potential adverse effects. Nonetheless, there are a number of knowledge gaps that need to be addressed and a lack of consensus persists regarding certain methodological aspects of this modelling strategy. The overall aim of this thesis was to contribute to the BMD field and expand the knowledge base by applying this approach to the areas of risk assessment and pharmaceutical development, addressing some identified challenges and discussing potential improvements. In particular, this thesis covers three topics that are interconnected, namely the choice of the Critical Effect Size (CES) (study I and III to VI), the analysis of multiple endpoints (study II to VI) and the assessment of chemical mixtures (study I, V and VI). These topics were applied to data from studies on chemicals, namely per- and polyfluoroalkyl substances (PFAS) (study I and VI), polychlorinated biphenyls (PCBs) (study IV and V) and a candidate drug in pharmaceutical development (study II and III) and the pesticide norflurazon (study VI). Study I combined human and animal data in order to derive the probabilistic risk for a 10% decrease in total triiodothyronine (T3) hormone levels, depending on residency time. The human data consisted of perfluorooctanesulfonic acid (PFOS) and perfluorohexanesulfonic acid (PFHxS) serum levels from the resident population in Ronneby, a Swedish village that was highly exposed to PFAS through contaminated drinking water. The animal data originated from a 6-month subchronic study in monkeys, exposed to PFOS once a day. This integrated probabilistic risk assessment (IPRA) analysis demonstrated that longer exposure periods were associated with a larger proportion of the population at risk, ranging from 2.1% (90% C.I. 0.4% – 13.1%) to 3.5% (90% C.I. 0.7% – 21.8%) for residents exposed to PFOS and PFHxS for at least 1 or 29 years, respectively. This risk was mostly distributed among women, and exposure duration was thegreatest source of uncertainty (60.8%). It was concluded that IPRA is an advantageous method to calculate the risk for adverse effects, in comparison to the deterministic Margin of Exposure aproach (MoE). Study II analyzed data from three subsequential safety assessment studies performed in rats to investigate the potential toxicity of an anti-oncogenic candidate drug in pharmaceutical development. The partial least squares (PLS) modelling approach was used to detect associations between clinical signs observed during the study, a 5% body weight decrease and pathological findings noted after study termination. Piloerection, eyes half shut and slightly decreased motor activity were the signs that were most strongly associated with the pathological findings, and the models accurately predicted the injuries observed in the thymus, testes, epididymides and bone marrow. The findings indicate that an evaluation of clinical signs as an integrated toxicity evaluation has potential 3R (Replacement, Reduction and Refinement of animal use) gains, especially in terms of Refinement of animal studies. The study suggests that the PLS-modelling approach can be employed to predict pathological changes, monitor animal welfare and support the decision-making process during pre-clinical safety and toxicity assessment studies. Study III analyzed the same data as study II, but applied the BMD-modelling approach instead, with a different objective, namely to describe potential relationships between the dose and the findings made in the 63 examined endpoints. The endpoints modelled included biochemistry and hematology endpoints, body weight changes, organ pathology findings and clinical observations. The resulting BMDs and BMDLs were compared to the study NOAEL (or LOAEL) and were often lower than the estimates of the NOAEL approach. A 5% change was also compared to the findings based on an adversity threshold derived from the observed and endpoint-specific magnitude of change. Additionally, the BMD-modelling was also considered to have a strong focus on the Refinement of animal studies. In summary, it was shown that modelling multiple endpoints is desirable, providing a more complete overview of the potential toxicity of a candidate drug and improving the pharmaceutical development process. Study IV assessed the potential toxicity of PCB-156 (2,3,3′,4,4′,5-hexachlorobiphenyl) following a 90-day study in rats exposed daily through their diet. Dose-dependent toxicological effects were described, including body and organ changes but also in the assessed retinoid system endpoints. Retinoid disruption and effects in the organs of rats were demonstrated employing the BMD dose-response modelling approach, revealing that the apolar liver retinoid concentrations were the most sensitive endpoint. The retinoid system was shown to be sensitive to PCB-156 exposure, and it was suggested that its endpoints should be more often considered for chemical risk assessment purposes. Study V employed the BMD method to calculate relative potency factors (RPFs) for seven PCBs (PCB-28, 77, 105, 118, 128, 153 and 156) and one PCB-mixture. PCB-126 was used as an index chemical, and the eight 90-day regulatory toxicity studies for the individual congeners were performed under the same conditions (the PCB-mixture study was 28 days long). The liver apolar retinoids levels and concentration, and the remaining endpoints examined, estimated greater RPFs than those calculated by the World Health Organization (WHO) in 2006 (Van den Berg et al., 2006), being suggestive of a hazard underestimation. In fact, the potency factors estimated in this study, based on the ethoxyresorufin-O-deethylase (EROD) enzymatic activity (a historically used endpoint to calculate RPFs), were the lowest in comparison to other endpoints for which RPFs were calculated. In summary, RPFs were useful to describe the potential toxicity of structurally similar compounds, expressed in units equivalent to the index chemical, and the retinoid system proved once again to be susceptible to changes following low-dose PCB exposure. Study VI focused on the choice of CES, a matter of debate when applying the BMD method to continuous data. Currently, there is no internationally harmonized approach to choosing the CES, and five strategies were examined: the EFSA default value of 5% or 10%, the US EPA 1 SD approach, an endpoint-specific CES based on historical data, the General Theory of Effect Size (GTES) and expert judgment. All examined strategies featured advantages and limitations, and the different choices of CES led to distinct reference values when applied to five case-studies, analyzing PFAS, PCB-156 and a pesticide (norflurazon) data. Although some of these strategies delivered similar CES values, it was not always the case, and reliance on a single method to choose the CES is not recommendable. It was concluded that expert judgment is irreplaceable and that the decision-making process performed by risk assessors and managers regarding the likely threshold of adversity should be supported by BMD analysis of the data comparing different CES. This could lead to a better overview of the data package and understanding of the doses leading to different magnitudes of effects, which would lead to better motivation of the choices and decisions made. In conclusion, this thesis demonstrates that the BMD method is a flexible modelling approach to assess the potential effects of several classes of substances, such as PFAS, PCBs and candidate drugs. Possible applications in the chemical risk assessment and pharmaceutical development areas were demonstrated. Additionally, it was shown that the BMD approach has a strong 3R potential and extracts a considerable amount of information from the data. The BMD approach is in chemical risk assessment to stay, and much like a Swiss army knife, it is a useful and multi-purpose tool that will support you in the derivation of reference values of superior quality.

Abstract  Probabilistic quantitative microbial risk assessment (QMRA) studies define model inputs as random variables and use Monte-Carlo simulation to generate distributions of potential risk outcomes. If local information on important QMRA model inputs is missing, it is widely accepted to justify assumptions about these model inputs by using external literature information. A question, which remains unexplored, is the extent to which previously published external information should influence local estimates in cases of nonexistent, scarce, and moderate local data. This question can be addressed by employing Bayesian hierarchical modeling (BHM). Thus, we study the effects and potential benefits of BHM on risk and performance target calculations at three wastewater treatment plants (WWTP) in comparison to alternative statistical modeling approaches (separate modeling, no-pooling, complete pooling). The treated wastewater from the WWTPs is used for restricted irrigation, potable reuse, or influences recreational waters, respectively. We quantify the extent to which external data affects local risk estimations in each case depending on the statistical modeling approach applied. Modeling approaches are compared by calculating the pointwise expected log-predictive density for each model. As reference pathogens and example data, we use locally collected Norovirus genogroup II data with varying sample sizes (n = 4, n = 7, n = 27), and complement local information with external information from 44 other WWTPs (n = 307). Results indicate that BHM shows the highest predictive accuracy and improves estimates by reducing parameter uncertainty when data are scarce. In such situations, it may affect risk and performance target calculations by orders of magnitude in comparison to using local data alone. Furthermore, it allows making generalizable inferences about new WWTPs, while providing the necessary flexibility to adjust for different levels of information contained in the local data. Applying this flexible technique more widely may contribute to improving methods and the evidence base for decision-making in future QMRA studies.

Abstract  In ecological risk assessment, acute to chronic ratio (ACR) uncertainty factors are routinely applied to acute mortality benchmarks to estimate chronic toxicity thresholds. To investigate variability of aquatic ACRs, we first compiled and compared 56 and 150 pairs of acute and subchronic/chronic growth/reproductive toxicity data for fishes (Pimephales promelas (53), Danio rerio (2), and Oryzias latipes (1)) and the crustacean Daphnia magna, respectively, for 172 chemicals with different modes of action (MOA). We found that there were only significant relationships between P. promelas acute median lethal concentrations and growth lowest-observed effect concentrations for class 1 (nonpolar narcosis) chemicals, though significant relationships were demonstrated for D. magna to all Verhaar et al. MOA classes (Class 1: nonpolar narcosis, Class 2: polar narcosis, Class 3: reactive chemicals, and Class 4: AChE inhibitors and estrogenics). Probabilistic ecological hazard assessment using chemical toxicity distributions was subsequently employed for each MOA class to estimate acute and chronic thresholds, respectively, to identify MOA and species specific ecological thresholds of toxicological concern. Finally, novel MOA and species specific ACRs using both chemical toxicity distribution comparison and individual ACR probability distribution approaches were identified using representative MOA and chemical categories. Our data-driven approaches and newly identified ACR values represent robust alternatives to application of default ACR values, and can also support future research and risk assessment and management activities for other chemical classes when toxicity information is limited for chemicals with specific MOAs within invertebrates and fish.

Abstract  Paradoxically, risk assessments for the majority of chemicals lack any quantitative characterization as to the likelihood, incidence, or severity of the risks involved. The relatively few cases where "risk" is truly quantified are based on either epidemiologic data or extrapolation of experimental animal cancer bioassay data. The paucity of chemicals and health endpoints for which such data are available severely limits the ability of decisionmakers to account for the impacts of chemical exposures on human health. The development by the World Health Organization International Programme on Chemical Safety (WHO/IPCS) in 2014 of a comprehensive framework for probabilistic dose-response assessment has opened the door to a myriad of potential advances to better support decision making. Building on the pioneering work of Evans, Hattis, and Slob from the 1990s, the WHO/IPCS framework provides both a firm conceptual foundation as well as practical implementation tools to simultaneously assess uncertainty, variability, and severity of effect as a function of exposure. Moreover, such approaches do not depend on the availability of epidemiologic data, nor are they limited to cancer endpoints. Recent work has demonstrated the broad feasibility of such approaches in order to estimate the functional relationship between exposure level and the incidence or severity of health effects. While challenges remain, such as better characterization of the relationship between endpoints observed in experimental animal or in vitro studies and human health effects, the WHO/IPCS framework provides a strong basis for expanding the breadth of risk management decision contexts supported by chemical risk assessment.

Abstract  Salmonella has been shown to survive in tree nuts over long periods of time. This survival capacity and its variability are key elements for risk assessment of Salmonella in tree nuts. The aim of this study was to develop a mathematical model to predict survival of Salmonella in tree nuts at ambient storage temperatures that considers variability and uncertainty separately and can easily be incorporated into a risk assessment model. Data on Salmonella survival on raw almonds, pecans, pistachios and walnuts were collected from the peer reviewed literature. The Weibull model was chosen as the baseline model and various fixed effect and mixed effect models were fit to the data. The best model identified through statistical analysis testing was then used to develop a hierarchical Bayesian model. Salmonella in tree nuts showed slow declines at temperatures ranging from 21 degrees C to 24 degrees C. A high degree of variability in survival was observed across tree nut studies reported in the literature. Statistical analysis results indicated that the best applicable model was a mixed effect model that included a fixed and random variation of delta per tree nut (which is the time it takes for the first log10 reduction) and a fixed variation of rho per tree nut (parameter which defines the shape of the curve). Higher estimated survival rates (delta) were obtained for Salmonella on pistachios, followed in decreasing order by pecans, almonds and walnuts. The posterior distributions obtained from Bayesian inference were used to estimate the variability in the log10 decrease levels in survival for each tree nut, and the uncertainty of these estimates. These modeled uncertainty and variability distributions of the estimates can be used to obtain a complete exposure assessment of Salmonella in tree nuts when including time-temperature parameters for storage and consumption data. The statistical approach presented in this study may be applied to any studies that aim to develop predictive models to be implemented in a probabilistic exposure assessment or a quantitative microbial risk assessment.

Abstract  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.

Abstract  Insects and insect-based food products have entered in the European market, carrying along issues of safety and the need of establishing a new legal framework. The consumption of massively reared insects can pose chemical and microbiological risks, and insect proteins are likely to represent a hazard for a subpopulation of allergic individuals. All insect-based products are considered 'Novel Food' and fall under EU regulation 2015/2283, according to which a specific application to the European Commission, followed by EFSA scientific evaluation, is needed before the product is put on the market. The recent EU Regulation 2017/893, entered into force on 1 July 2017, allowed a shortlist of seven insect species to be included in the formulation of feeds for aquaculture. Previously, the addition of any insect to any feed for farmed animals was not allowed, due to the risk of prion-derived diseases. The introduction of this new Regulation raises the issue to switch from a classical detection method based on microscopy to a more sophisticated and species-specific method. The overall aims of this EU-FORA project were (i) to set up a new next generation sequencing (NGS)-based molecular method for the identification of insect DNA in feeds for aquaculture; and (ii) to carry out a conceptual work on a probabilistic quantitative risk assessment focused on the allergenicity of yellow mealworm (Tenebrio molitor) employed in foods.

Abstract  BACKGROUND: Existing studies have revealed that the benchmark dose (BMD) estimates from short-term in vivo transcriptomics studies can approximate those from long-term guideline toxicity assessments. Existing software applications follow this trend by analyzing omics data through the maximum likelihood estimation and choosing the "best" model for BMD estimates. However, this practice ignores the model uncertainty and may result in over-confident inferences and predictions, leading to an inadequate decision. OBJECTIVE: By generally following the National Toxicology Program Approach to Genomic Dose-Response Modeling, we developed a web-based dose-response modeling and BMD estimation system, Bayesian BMD (BBMD), for genomic data to quantitatively address uncertainty from various sources. The performances of BBMD are compared with BMDExpress. METHODS: The system is primarily based on the previously developed BBMD system and further developed in a genomic perspective. Bayesian model averaging method is applied to BMD estimation and pathways analyses. Generally, the system is unique regarding the flexibility in preparing/storing data and in characterizing uncertainties. RESULTS: This system was tested and validated versus 24 previously published in-vivo microarray dose-response datasets (GSE45892) and 64 molecules data from the Open TG-Gates database. Short term transcriptional BMD values for the median pathway in BBMD are highly correlated with the long-term apical BMD values (R = 0.78-0.91). The BMD estimates obtained by BBMD were compared to those by BMDExpress. The results indicate that BBMD provides more adequate results in terms of less extreme values and no failure in BMD and BMDL calculations. Also, the pathway analysis in BBMD provides a conservative estimate because a broader confidence interval is established. DISCUSSION: Overall, this study demonstrates that dose-response modeling using genomic data can play a substantial role in support of chemical risk assessment. BBMD represents a robust and user-friendly alternative for genomic dose-response data analysis with outstanding functionalities to quantify uncertainty from various sources.

Abstract  BACKGROUND: No systematic investigations have been conducted to assess the lung burden imposed by the chronic inhalation of silver nanoparticles (AgNPs) emitted by spray products. OBJECTIVE: The objective of this study was to formulate a study framework that integrates a probabilistic risk assessment scheme with a mechanistic lung burden model for the estimation of health risks associated with the long-term inhalation of AgNP-containing spray products. MATERIALS AND METHODS: A compartmentalized physiologically based alveolar deposition (PBAD) model was used to estimate AgNP lung burden. Dose-response relationships were established using nanotoxicity data sets obtained from rats (as a model organism). Weibull model-based thresholds of AgNP lung burden based on neutrophil-elevated inflammation bio-markers were estimated from Hill-based exposure-response relationships. Finally, the risks of lung disease posed by various AgNP-containing spray products were assessed. RESULTS: Conservative thresholds for the prevention of pulmonary disease were estimated as follows (mean +/- SE): 34 nm AgNPs (0.32+/-0.22 mg) and 60 nm AgNPs (1.08+/-0.64 mg). Our results indicate that the risk probability was ~0.5 that the hazard quotient (HQ) estimates of deodorant with a count median diameter (CMD) approximately 30 nm exceeded 1. The primary risk posed by AgNPs is transferred from the interstitial region to lymph nodes. Under the condition of 50% risk probability, the 97.5 percentile of HQ for the spray products were as follows: CMD approximately 30 nm (~3.4) and CMD approximately 60 nm (~1.1). CONCLUSION: Our application of the proposed risk assessment scheme to the results obtained in an in vivo animal model proved highly effective in elucidating the relationship between the characteristics of metallic NP-containing spray products and their corresponding toxicity. The integration of the proposed PBAD model with a risk assessment framework enables the rapid assessment of risk posed by spray products containing metallic NPs over various time scales.

Abstract  Citrus Black Spot (CBS), caused by the ascomycete, Phyllosticta citricarpa, is a fruit, foliar, and twig spotting fungal disease affecting the majority of commercial cultivars of citrus. The disease causes cosmetic lesions, may cause fruit drop and P. citricarpa is considered a quarantine pathogen by some countries, impacting domestic and international trade of citrus fruit. Regulatory requirements affecting fruit trade exist even though there is no documented case of disease spread via infected fruit into previously disease-free areas. To clarify the risk of fruit as a potential pathway for the spread of CBS, we developed a quantitative, probabilistic risk assessment model. The model provides an assessment of all steps in the fruit pathway, including production, packinghouse handling, transportation, export-import distribution channels, and consumer endpoints. The model is stochastic and uses Monte Carlo simulation to assess the risk of P. citricarpa moving through all steps in the pathway. We attempted to use all available literature and information to quantitate risk at each point in the potential pathway and by sequentially linking all steps to determine the overall quantitative risk. In addition, we assessed climatological effects on incidence of diseased fruit at production sites and on fungal reproduction and infection, as well as criteria for establishment at endpoints. We examined ten case studies between exporting and importing locations/countries. Model results indicated fruit to be an epidemiologically insignificant means for CBS spread, even between producing countries where CBS occurs and CBS-free importing countries with disease-conducive climates. We created a second model to examine the introduction of infected plant material from countries where CBS occurs. This model demonstrated significant probability of introduction via such infected material. However, pathogen establishment and disease development was still restricted only to areas with conducive climatological conditions. We created a tool to quantitatively explore the viability of various potential pathways via combinations of CBS-present production sites and corresponding pathway endpoints, including environments conducive and non-conducive to CBS. The tool is provided to aid decision makers on phytosanitary risk relative to international trade of citrus fruit.

Abstract  Evidence increasingly suggests molybdenum exposure at environmental levels is still associated with adverse human health, emphasizing the necessity to establish a more protective reference dose (RfD). Herein, we conducted a study measuring 15 urinary metals and 30 clinical health indicators in 2267 participants residing near chemical enterprises across 11 Chinese provinces to investigate their relationships. The kidney and cystatin-C emerged as the most sensitive organ and critical effect indicator of molybdenum exposure, respectively. Odds of cystatin-C-defined chronic kidney disease (CKD) in the highest quantile of molybdenum exposure significantly increased by 133.5% (odds ratio [OR]: 2.34, 95% CI: 1.78, 3.11) and 75.8% (OR: 1.76, 95% CI: 1.24, 2.49) before and after adjusting for urinary 14 metals, respectively. Intriguingly, cystatin-C significantly mediated 15.9-89.5% of molybdenum's impacts on liver and lung function, suggesting nephrotoxicity from molybdenum exposure may trigger hepatotoxicity and pulmonary toxicity. We derived a new RfD for molybdenum exposure (0.87 mug/kg-day) based on cystatin-C-defined estimated glomerular filtration rate by employing Bayesian Benchmark Dose modeling analysis. This RfD is significantly lower than current exposure guidance values (5-30 mug/kg-day). Remarkably, >90% of participants exceeded the new RfD, underscoring the significant health impacts of environmental molybdenum exposure on populations in industrial regions of China.

Abstract  Zinc oxide nanoparticles (n-ZnO) are one of the most ever-increasing utilized nanomaterials in consumer products. Due to their antibacterial properties and superior efficiency in absorbing ultraviolet radiation, they are widely used as additives in food packaging and sunscreens. There is thus a need for scientific understanding of risks to the health of adult populations associated with n-ZnO. However, due to inadequate data in relation to characterizing hazards and exposure, there is a substantial uncertainty in risk assessment. In the present study, probabilistic approaches, including Monte Carlo and bootstrap methods, were integrated to assess the relative uncertainties and risks of n-ZnO to the health of males and females. Two major exposure pathways, oral from food packaging and percutaneous from sunscreen-based comestics, were evaluated by considering the uncertainty and variability involved in the exposure assessment. Given the cumulative uncertainties of all the extrapolation factors, the results showed that the individual margin of exposure (IMoE) of n-ZnO exhibited a minimal risk through oral exposure, with a minimum value of 786 for males and 96.2 for females (5th centile). However, within the entire range of IMoE values by Monte Carlo simulation through dermal exposure, the IMoE values in 11.45% of exposure scenarios for males and 18.87% for females were lower than the upper limit of the acceptable risk (IMoE ≤ 1). Intra-species, inter-species, and subacute-to-chronic extrapolation factors in the hazard assessment process contributed up to 97% of the uncertainty. These findings provided a scientific basis for understanding risks to the health of adult populations that could help allow regulatory acceptance of consumer products containing n-ZnO and highlighted the need for additional studies on hazard and exposure assessments of nanotechnologies.

Abstract  Humans are constantly exposed to multiple environmental chemicals from various sources and via different exposure pathways. Common daily activities, such as eating, drinking …

Abstract  Deoxynivalenol (DON) is a mycotoxin frequently observed in cereals and cereal-based foods, with reported toxicological effects including reduced body weight, immunotoxicity and reproductive defects. The European Food Safety Authority used traditional risk assessment approaches to derive a deterministic Tolerable Daily Intake (TDI) of 1 mug/kg-day, however data from human biomarkers studies indicate widespread and variable exposure worldwide, necessitating more sophisticated and advanced methods to quantify population risk. The World Health Organization/International Programme on Chemical Safety (WHO/IPCS) has previously used DON as a case example in replacing the TDI with a probabilistic toxicity value, using default uncertainty and variability distributions to derive the Human Dose corresponding to an effect size M in the I(th) percentile of the population (HD(M)(I)) for M = 5 % decrease in body weight and I = 1 %. In this study, we extend this case study by incorporating (1) Bayesian modeling approaches, (2) using both in vivo data and in vitro population new approach methods to replace default distributions for interspecies toxicokinetic (TK) differences and intraspecies TK and toxicodynamic (TD) variability, and (3) integrating biomonitoring data and probabilistic dose-response functions to characterize population risk distributions. We first derive an HD(M)(I) of 5.5 [1.4-24] mug/kg-day, also using TK modeling to converted the HD(M)(I) to Biomonitoring Equivalents, BE(M)(I) for comparison with biomonitoring data, with a blood BE(M)(I) of 0.53 [0.17-1.6] mug/L and a urinary excretion BE(M)(I) of 3.9 [1.0-16] mug/kg-day. We then illustrate how this integrative approach can advance quantitative risk characterization using two human biomonitoring datasets, estimating both the fraction of population with an effect size M >/= 5 % as well as the distribution of effect sizes. Overall, we demonstrate that integration of Bayesian modeling, human biomonitoring data, and in vitro population-based TD data within the WHO/IPCS probabilistic framework yields more accurate, precise, and comprehensive risk characterization.

Abstract  BACKGROUND: Environmental exposure to perfluorooctane sulfonate (PFOS) is associated with various adverse outcomes in humans. However, risk assessment for PFOS with the traditional risk estimation method is faced with multiple challenges because there are high variabilities and uncertainties in its toxicokinetics and toxicity between species and among different types of studies.

OBJECTIVES: This study aimed to develop a robust probabilistic risk assessment framework accounting for interspecies and inter-experiment variabilities and uncertainties to derive the human equivalent dose (HED) and reference dose for PFOS.

METHODS: A Bayesian dose-response model was developed to analyze selected 34 critical studies, including human epidemiological, animal in vivo, and ToxCast in vitro toxicity datasets. The dose-response results were incorporated into a multi-species physiologically based pharmacokinetic (PBPK) model to reduce the toxicokinetic/toxicodynamic variabilities. In addition, a population-based probabilistic risk assessment of PFOS was performed for Asian, Australian, European, and North American populations, respectively, based on reported environmental exposure levels.

RESULTS: The 5th percentile of HEDs derived from selected studies was estimated to be 21.5 (95% CI: 10.6-36.3) ng/kg/day. After exposure to environmental levels of PFOS, around 50% of the population in all studied populations would likely have >20% of increase in serum cholesterol, but the effects on other endpoints were estimated to be minimal (<10% changes). There was a small population (~10% of the population) that was highly sensitive to endocrine disruption and cellular response by environmental PFOS exposure.

CONCLUSION: Our results provide insights into a complete risk characterization of PFOS and may help regulatory agencies in the reevaluation of PFOS risk. Our new probabilistic approach can conduct dose-response analysis of different types of toxicity studies simultaneously and this method could be used to improve risk assessment for other perfluoroalkyl substances (PFAS).

Abstract  Ammonium 2,3,3,3-tetrafluoro-2-(heptafluoropropoxy)-propanoate, also known as GenX, is a processing aid used in the manufacture of fluoropolymers. GenX is one of several chemistries developed as an alternative to long-chain poly-fluoroalkyl substances, which tend to have long clearance half-lives and are environmentally persistent. Unlike poly-fluoroalkyl substances, GenX has more rapid clearance, but has been detected in US and international water sources. There are currently no federal drinking water standards for GenX in the USA; therefore, we developed a non-cancer oral reference dose (RfD) for GenX based on available repeated dose studies. The review of the available data indicate that GenX is unlikely to be genotoxic. A combination of traditional frequentist benchmark dose models and Bayesian benchmark dose models were used derive relevant points of departure from mammalian toxicity studies. In addition, deterministic and probabilistic RfD values were developed using available tools and regulatory guidance. The two approaches resulted in a narrow range of RfD values for liver lesions observed in a 2-year bioassay in rats (0.01-0.02 mg/kg/day). The probabilistic approach resulted in the lower, i.e., more conservative RfD. The probabilistic RfD of 0.01 mg/kg/day results in a maximum contaminant level goal of 70 ppb. It is anticipated that these values, along with the hazard identification and dose-response modeling described herein, should be informative for risk assessors and regulators interested in setting health-protective drinking water guideline values for GenX.