Bayesian methods for uncertainty factor application for derivation of reference values

Key challenges and developments in wildlife ecological risk assessment: Problem formulation

Problem formulation (PF) is a critical initial step in planning risk assessments for chemical exposures to wildlife, used either explicitly or implicitly in various jurisdictions to include registration of new pesticides, evaluation of new and existing chemicals released to the environment, and characterization of impact when chemical releases have occurred. Despite improvements in our understanding of the environment, ecology, and biological sciences, few risk assessments have used this information to enhance their value and predictive capabilities. In addition to advances in organism-level mechanisms and methods, there have been substantive developments that focus on population- and systems-level processes. Although most of the advances have been recognized as being state-of-the-science for two decades or more, there is scant evidence that they have been incorporated into wildlife risk assessment or risk assessment in general. In this article, we identify opportunities to consider elevating the relevance of wildlife risk assessments by focusing on elements of the PF stage of risk assessment, especially in the construction of conceptual models and selection of assessment endpoints that target population- and system-level endpoints. Doing so will remain consistent with four established steps of existing guidance: (1) establish clear protection goals early in the process; (2) consider how data collection using new methods will affect decisions, given all possibilities, and develop a decision plan a priori; (3) engage all relevant stakeholders in creating a robust, holistic conceptual model that incorporates plausible stressors that could affect the targets defined in the protection goals; and (4) embrace the need for iteration throughout the PF steps (recognizing that multiple passes may be required before agreeing on a feasible plan for the rest of the risk assessment). Integr Environ Assess Manag 2024;20:658-673. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

A tiered approach of hazard-prioritization and risk-ranking for chemical hazards in food commodities: Application for selected mycotoxins

Chemical hazards in foods, especially naturally occurring food contaminants like mycotoxins, are of serious public health concern. It is important to develop a practical framework to assess and rank health risks of chemical contaminants which can be further utilized by regulatory agencies to prioritize resources for risk assessment and management. In this study, a tiered hazard-prioritization and risk-ranking approach, which included two steps: exposure-based screening and margin of exposure (MOE)-based probabilistic risk ranking; was proposed to efficiently identify and rank chemicals of health concerns. Given the exposure-based hazard prioritization, chemicals with negligible or low health risks were first excluded. The remaining chemicals, imposing a higher health risk, were then ranked to facilitate risk-based decision making. The proposed approach was applied to identify and rank the mycotoxins with substantial health concerns in food commodities randomly sampled in China. A total of 19 mycotoxins were analyzed in 783 food commodities, including infant cookie, noodle, rice flour samples, wheat flour, millet, and rice. Results showed that the mycotoxins in infant foods with the highest health risk were Tenuazonic acid, Deoxynivalenol, and Enniatin B1, but as indicated by the probabilistic MOE estimation, the risks were still in the acceptable range and generally lower than the risks imposed by trace elements (e.g., Arsenic and Cadmium). The health risks of the other 16 mycotoxins were negligible mainly due to their low exposure levels. This study demonstrated that the proposed tiered approach was an efficient and effective tool to quantify and prioritize health risks in support of human health risk management.

A mode of action-based probabilistic framework of dose-response assessment for nonmutagenic liver carcinogens: a case study of PCB-126

A main function of dose-response assessment is to estimate a "safe" dose in the target population to support chemical risk assessment. Typically, a "safe" dose is developed differently for cancer and noncancer effects based on a 2-step procedure, ie, point of departure (POD) derivation and low-dose extrapolation. However, the current dose-response assessment framework is criticized for its dichotomized strategy without integrating the mode of action (MOA) information. The objective of this study was, based on our previous work, to develop a MOA-based probabilistic dose-response framework that quantitatively synthesizes a biological pathway in a dose-response modeling process to estimate the risk of chemicals that have carcinogenic potential. 3,3',4,4',5-Pentachlorobiphenyl (PCB-126) was exemplified to demonstrate our proposed approach. There were 4 major steps in the new modeling framework, including (1) key quantifiable events (KQEs) identification and extraction, (2) essential dose calculation, (3) MOA-based POD derivation, and (4) MOA-based probabilistic reference dose (RfD) estimation. Compared with reported PODs and traditional RfDs, the MOA-based estimates derived from our approach were comparable and plausible. One key feature of our approach was the use of overall MOA information to build the dose-response relationship on the entire dose continuum including the low-dose region. On the other hand, by adjusting uncertainty and variability in a probabilistic manner, the MOA-based probabilistic RfDs can provide useful insights of health protection for the specific proportion of population. Moreover, the proposed framework had important potential to be generalized to assess different types of chemicals other than nonmutagenic carcinogens, highlighting its utility to improve current chemical risk assessment.

Comparative influences of dermal and inhalational routes of exposure on hazards of cleaning product ingredients among mammalian model organisms

Health risks resulting from dermal or inhalational exposures are frequently assessed based on rodent oral toxicity information due to a lack of species- or route-specific toxicity data. Default uncertainty factors (UFs; e.g., 10-fold) are also applied during risk assessments to account for variability such as inter-species, intra-species, exposure duration, dose-response, and route-to-route extrapolations. However, whether rodent oral data and a default UF approach can provide adequate protection for other mammalian species under dermal or inhalational exposure scenarios remains understudied, particularly for cleaning product ingredients. Therefore, we collated and examined publicly available median lethal dose (LD₅₀), no-observed-adverse-effect level (NOAEL) and lowest-observed-adverse-effect level (LOAEL) values from different types of standard mammalian toxicity studies for rats (dermal and inhalational), mice, rabbits, guinea pigs, and dogs (oral, dermal and inhalational) using the Cleaning Product Ingredient Safety Initiative (CPISI) database. Probabilistic hazard assessments using chemical toxicity distributions (CTDs) were subsequently conducted, and threshold concentrations (TCs) and 95% confidence intervals (95% CIs) were derived to identify thresholds of toxicological concern (TTCs). Relative sensitivities among or between mammalian species, exposure routes, and chemical classes were also compared based on calculated TC₅s and 95% CIs to support future toxicology studies and hazard and risk assessments. We then identified uncertainty factors (UFs) using both CTD comparisons and individual UF probability distributional approaches. Based on available rodent inhalational data, chemical category-specific UFs were derived for ethers. Additionally, we also determined whether default UFs of 10 or 100 would be protective for various distributions of cleaning product ingredients. Our novel observations among these routes of exposure and common mammalian model organisms appear particularly useful for read across and screening-level health hazard and risk assessments when limited data exists for specific chemicals.

An overview of current practices for regulatory risk assessment with lessons learnt from cosmetics in the European Union

Risk assessments of various types of chemical compounds are carried out in the European Union (EU) foremost to comply with legislation and to support regulatory decision-making with respect to their safety. Historically, risk assessment has relied heavily on animal experiments. However, the EU is committed to reduce animal experimentation and has implemented several legislative changes, which have triggered a paradigm shift towards human-relevant animal-free testing in the field of toxicology, in particular for risk assessment. For some specific endpoints, such as skin corrosion and irritation, validated alternatives are available whilst for other endpoints, including repeated dose systemic toxicity, the use of animal data is still central to meet the information requirements stipulated in the different legislations. The present review aims to provide an overview of established and more recently introduced methods for hazard assessment and risk characterisation for human health, in particular in the context of the EU Cosmetics Regulation (EC No 1223/2009) as well as the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) Regulation (EC 1907/2006).

Human Health Risk Assessment of a pilot-plant for catalytic pyrolysis of mixed waste plastics for fuel production

Dioxins, PCBs and VOCs are the main hazardous chemicals emitted by gaseous streams from catalytic pyrolysis of waste plastics. In this work we propose a methodology to assess toxic and cancer risk under uncertainty, due to inhalation and ingestion of these chemicals by considering complex scenarios, as repeated start-ups and short continuous operation that may occur in a pilot-plant. Different simulation tools are combined to evaluate the expected concentration of pollutants in the environmental compartments and food. Hazard Index and Cancer Risk remain under the threshold for both dioxins (HI < 0.012, CR < 5.03 10^-7) and PCBdl (HI < 1.3 10^-7, CR < 2.49 10^-12) in all the simulated scenarios, also for the worst case of children ingesting vegetables and meat and uncertainty factors up to 1000. Different results are obtained for VOCs since repeated leakages during the pilot-plant operation are possible. All the risk indexes for benzene are under the threshold (HI < 0.175, CR < 1.41 10^-7); acute toxic risk due to inhalation and cancer risk due to ingestion of grain/vegetables are over the threshold if all the uncertainties are considered. Lesson learned: HHRA is important also during scale-up; pilot-plants for pyrolysis of waste plastics must always be equipped with all the abatement systems designed for the final plant.

Suggestions for Improving the Characterization of Risk from Exposures to Per and Polyfluorinated Alkyl Substances

Many state and federal environmental and health agencies have developed risk-based criteria for assessing the risk of adverse health effects of per- and polyfluorinated alkyl substances (PFAS) exposure to humans and the environment. However, the criteria that have been developed vary; drinking water criteria developed for perfluorooctanoic acid, for example, can vary by up to 750 fold. This is due to differences and variability in the data and information used, study/endpoint selection, assumptions and magnitude of uncertainty factors used in the absence and extrapolation of critical effect data, differences in underlying approaches to addressing exposure within criteria development, and/or policy decisions on levels of acceptable risk. We have critically evaluated the methods used to develop these criteria while focusing on derivation and application of drinking water criteria and discuss a range of improvements to risk-characterization practice recently presented at a Focused Topic Meeting on PFAS conducted by the Society of Environmental Toxicology and Chemistry in Durham, North Carolina, USA, 12 to 15 August 2019. We propose methods that consider maximizing the use of disparate data streams, seeking patterns, and proposing biologically based approaches to evidence integration toward informed criteria development. Environ Toxicol Chem 2021;40:883-898. © 2020 SETAC.

Using Evidence Integration Techniques in the Development of Health-Based Occupational Exposure Levels

Development of toxicology-based criteria such as occupational exposure levels (OELs) are rarely straightforward. This process requires a rigorous review of the literature, searching for patterns in toxicity, biological plausibility, coherence, and dose-response relationships. Despite the direct applicability, human data are rarely used primarily because of imprecise exposure estimates, unknown influence of assumptions, and confounding factors. As a result, high reliance is often placed on laboratory animal data. Often, data from a single study is typically used to represent an entire database to extrapolate an OEL, even for data-rich compounds. Here we present a holistic framework for evaluating epidemiological, controlled in vivo, mechanistic/in vitro, and computational evidence that can be useful in deriving OELs. It begins with describing a documented review process of the literature, followed by sorting of data into either controlled laboratory in vivo, in silico/read-across, mechanistic/in vitro, or epidemiological/field data categories. Studies are then evaluated and qualified based on rigor, risk of bias, and applicability for point of departure development. Other data (eg, in vitro, in silico estimates, read-across data and mechanistic information, and data that failed to meet the former criteria) are used alongside qualified epidemiological exposure estimates to help inform points of departure or human-equivalent concentrations that are based on toxic end points. Bayesian benchmark dose methods are used to estimate points of departure and for estimating uncertainty factors (UFs) to develop preliminary OELs. These are then compared with epidemiological data to support the OEL and the use and magnitude of UFs, when appropriate.

Application of a unified probabilistic framework to the dose-response assessment of acrolein

BACKGROUND

In quantitative chemical risk assessment, a reference value is an estimate of an exposure to a chemical that is "likely to be without appreciable risk." Because current "deterministic" approaches do not quantitatively characterize the likelihood or severity of harm, the National Academies has recommended using reference values derived from a risk-specific dose that are treated as random variables, with probability distributions characterizing uncertainty and variability.

OBJECTIVES

In order to build familiarity and address issues needed for routine and standardized derivation of probabilistic risk-specific dose distributions, a case example applying the unified probabilistic framework presented in Chiu and Slob (2015) is developed for acrolein. This case study is based on an updated systematic evidence map of literature (Keshava et al., 2020) identifying nasal lesions reported in Dorman et al. (2008) as the most appropriate endpoint and study for reference value derivation.

METHODS

The probability distribution was calculated for the risk-specific dose, which in this implementation of the approach was calculated for the dose at which 1% of the human population is estimated to experience minimal lesions, and a probabilistic reference value was computed as the 5th percentile of this distribution. A deterministic reference value was also derived for comparison, and a sensitivity analysis of the probabilistic reference value was conducted investigating alternative assumptions for the point of departure type and exposure duration.

RESULTS

The probabilistic reference value of 6 × 10-4 mg/m3 was slightly lower than the deterministic reference value of 8 × 10-4 mg/m3, and the risk-specific dose distribution had an uncertainty spanning a factor of 137 (95th-5th percentile ratio). Sensitivity analysis yielded slightly higher probabilistic reference values ranging between 9 × 10-4 mg/m3 and 2 × 10-3 mg/m3.

CONCLUSIONS

Using a probabilistic approach for deriving a reference value allows quantitative characterization of the severity, incidence, and uncertainty of effects at a given dose. The results can be used to inform risk management decisions and improve risk communication.

Use of mechanistic information to derive chemical-specific adjustment factors - Refinement of risk assessment

When extrapolating data from animal toxicological studies a default factor (dUF) of 100 is applied to derive a heath based guidance value. The UF takes into account the interspecies differences (ID) and the intraspecies variability (IV). When re-evaluating the safety of phosphates used as food additives nephrocalcinosis was identified as the critical endpoint. The underlying mechanism for nephrocalcinosis was attributed to the precipitation of calcium phosphate in the kidney, depending on its solubility, irrespective of the species and the population. Based on the mechanism, the volume of primary urine, for which the glomerular filtration rate (GFR) was used as a proxy, was considered to be the only parameter relevant for ID and IV. Median value of GFR in rats was 4.0 ml/min/kg bw. In humans it was 1.6 ml/min/kg bw in healthy adults and 0.9 in elderly. These values were calculated from the distribution of the GFR data from 8 studies in rats (n = 191), 16 studies in adults (n = 1540) and 5 studies in elderly (n = 2608). Multiplying the distribution of the ratio rat/healthy humans (ID) with the distribution of the ratio healthy humans/elderly human (IV) resulted in a phosphate specific factor of 4.5 (3.3-6.7) (median; 25th - 75th percentile).

Application of qualitative and quantitative uncertainty assessment tools in developing ranges of plausible toxicity values for 2,3,7,8-tetrachlorodibenzo-p-dioxin

Increasing interest in characterizing risk assessment uncertainty is highlighted by recent recommendations from the National Academy of Sciences. In this paper we demonstrate the utility of applying qualitative and quantitative methods for assessing uncertainty to enhance risk-based decision-making for 2,3,7,8-tetrachlorodibenzo-p-dioxin. The approach involved deconstructing the reference dose (RfD) via evaluation of the different assumptions, options, models and methods associated with derivation of the value, culminating in the development of a plausible range of potential values based on such areas of uncertainty. The results demonstrate that overall RfD uncertainty was high based on limitations in the process for selection (e.g., compliance with inclusion criteria related to internal validity of the co-critical studies, consistency with other studies), external validity (e.g., generalizing findings of acute, high-dose exposure scenarios to the general population), and selection and classification of the point of departure using data from the individual studies (e.g., lack of statistical and clinical significance). Building on sensitivity analyses conducted by the US Environmental Protection Agency in 2012, the resulting estimates of RfD values that account for the uncertainties ranged from ~1.5 to 179 pg/kg/day. It is anticipated that the range of RfDs presented herein, along with the characterization of uncertainties, will improve risk assessments of dioxins and provide important information to risk managers, because reliance on a single toxicity value limits the information needed for making decisions and gives a false sense of precision and accuracy.

Beyond the RfD: Broad Application of a Probabilistic Approach to Improve Chemical Dose-Response Assessments for Noncancer Effects

BACKGROUND

The National Academies recommended risk assessments redefine the traditional noncancer Reference Dose (RfD) as a probabilistically derived risk-specific dose, a framework for which was recently developed by the World Health Organization (WHO).

OBJECTIVES

Our aim was to assess the feasibility and implications of replacing traditional RfDs with probabilistic estimates of the human dose associated with an effect magnitude M and population incidence I (HD_MI).

METHODS

We created a comprehensive, curated database of RfDs derived from animal data and developed a standardized, automated, web-accessible probabilistic dose-response workflow implementing the WHO framework.

RESULTS

We identified 1,464 RfDs and associated endpoints, representing 608 chemicals across many types of effects. Applying our standardized workflow resulted in 1,522 HD_MI values. Traditional RfDs are generally within an order of magnitude of the HD_MI lower confidence bound for I=1% and M values commonly used for benchmark doses. The greatest contributor to uncertainty was lack of benchmark dose estimates, followed by uncertainty in the extent of human variability. Exposure at the traditional RfD frequently implies an upper 95% confidence bound of several percent of the population affected. Whether such incidences are considered acceptable is likely to vary by chemical and risk context, especially given the wide range of severity of the associated effects, from clinical chemistry to mortality.

CONCLUSIONS

Overall, replacing RfDs with HD_MI estimates can provide a more consistent, scientifically rigorous, and transparent basis for risk management decisions, as well as support additional decision contexts such as economic benefit-cost analysis, risk-risk tradeoffs, life-cycle impact analysis, and emergency response. https://doi.org/10.1289/EHP3368.

Identification of novel uncertainty factors and thresholds of toxicological concern for health hazard and risk assessment: Application to cleaning product ingredients

Uncertainty factors (UFs) are commonly used during hazard and risk assessments to address uncertainties, including extrapolations among mammals and experimental durations. In risk assessment, default values are routinely used for interspecies extrapolation and interindividual variability. Whether default UFs are sufficient for various chemical uses or specific chemical classes remains understudied, particularly for ingredients in cleaning products. Therefore, we examined publicly available acute median lethal dose (LD50), and reproductive and developmental no-observed-adverse-effect level (NOAEL) and lowest-observed-adverse-effect level (LOAEL) values for the rat model (oral). We employed probabilistic chemical toxicity distributions to identify likelihoods of encountering acute, subacute, subchronic and chronic toxicity thresholds for specific chemical categories and ingredients in cleaning products. We subsequently identified thresholds of toxicological concern (TTC) and then various UFs for: 1) acute (LD50s)-to-chronic (reproductive/developmental NOAELs) ratios (ACRs), 2) exposure duration extrapolations (e.g., subchronic-to-chronic; reproductive/developmental), and 3) LOAEL-to-NOAEL ratios considering subacute/acute developmental responses. These ratios (95% CIs) were calculated from pairwise threshold levels using Monte Carlo simulations to identify UFs for all ingredients in cleaning products. Based on data availability, chemical category-specific UFs were also identified for aliphatic acids and salts, aliphatic alcohols, inorganic acids and salts, and alkyl sulfates. In a number of cases, derived UFs were smaller than default values (e.g., 10) employed by regulatory agencies; however, larger UFs were occasionally identified. Such UFs could be used by assessors instead of relying on default values. These approaches for identifying mammalian TTCs and diverse UFs represent robust alternatives to application of default values for ingredients in cleaning products and other chemical classes. Findings can also support chemical substitutions during alternatives assessment, and data dossier development (e.g., read across), identification of TTCs, and screening-level hazard and risk assessment when toxicity data is unavailable for specific chemicals.

Evaluation and calibration of high-throughput predictions of chemical distribution to tissues

Toxicokinetics (TK) provides critical information for integrating chemical toxicity and exposure assessments in order to determine potential chemical risk (i.e., the margin between toxic doses and plausible exposures). For thousands of chemicals that are present in our environment, in vivo TK data are lacking. The publicly available R package "httk" (version 1.8, named for "high throughput TK") draws from a database of in vitro data and physico-chemical properties in order to run physiologically-based TK (PBTK) models for 553 compounds. The PBTK model parameters include tissue:plasma partition coefficients (K_p) which the httk software predicts using the model of Schmitt (Toxicol In Vitro 22 (2):457-467, 2008). In this paper we evaluated and modified httk predictions, and quantified confidence using in vivo literature data. We used 964 rat K_p measured by in vivo experiments for 143 compounds. Initially, predicted K_p were significantly larger than measured K_p for many lipophilic compounds (log₁₀ octanol:water partition coefficient > 3). Hence the approach for predicting K_p was revised to account for possible deficiencies in the in vitro protein binding assay, and the method for predicting membrane affinity was revised. These changes yielded improvements ranging from a factor of 10 to nearly a factor of 10,000 for 83 K_p across 23 compounds with only 3 K_p worsening by more than a factor of 10. The vast majority (92%) of K_p were predicted within a factor of 10 of the measured value (overall root mean squared error of 0.59 on log₁₀-transformed scale). After applying the adjustments, regressions were performed to calibrate and evaluate the predictions for 12 tissues. Predictions for some tissues (e.g., spleen, bone, gut, lung) were observed to be better than predictions for other tissues (e.g., skin, brain, fat), indicating that confidence in the application of in silico tools to predict chemical partitioning varies depending upon the tissues involved. Our calibrated model was then evaluated using a second data set of human in vivo measurements of volume of distribution (V_ss) for 498 compounds reviewed by Obach et al. (Drug Metab Dispos 36(7):1385-1405, 2008). We found that calibration of the model improved performance: a regression of the measured values as a function of the predictions has a slope of 1.03, intercept of - 0.04, and R² of 0.43. Through careful evaluation of predictive methods for chemical partitioning into tissues, we have improved and calibrated these methods and quantified confidence for TK predictions in humans and rats.

Evaluation and calibration of high-throughput predictions of chemical distribution to tissues

Toxicokinetics (TK) provides critical information for integrating chemical toxicity and exposure assessments in order to determine potential chemical risk (i.e., the margin between toxic doses and plausible exposures). For thousands of chemicals that are present in our environment, in vivo TK data are lacking. The publicly available R package "httk" (version 1.8, named for "high throughput TK") draws from a database of in vitro data and physico-chemical properties in order to run physiologically-based TK (PBTK) models for 553 compounds. The PBTK model parameters include tissue:plasma partition coefficients (Kp) which the httk software predicts using the model of Schmitt (Toxicol In Vitro 22 (2):457-467, 2008). In this paper we evaluated and modified httk predictions, and quantified confidence using in vivo literature data. We used 964 rat Kp measured by in vivo experiments for 143 compounds. Initially, predicted Kp were significantly larger than measured Kp for many lipophilic compounds (log10 octanol:water partition coefficient > 3). Hence the approach for predicting Kp was revised to account for possible deficiencies in the in vitro protein binding assay, and the method for predicting membrane affinity was revised. These changes yielded improvements ranging from a factor of 10 to nearly a factor of 10,000 for 83 Kp across 23 compounds with only 3 Kp worsening by more than a factor of 10. The vast majority (92%) of Kp were predicted within a factor of 10 of the measured value (overall root mean squared error of 0.59 on log10-transformed scale). After applying the adjustments, regressions were performed to calibrate and evaluate the predictions for 12 tissues. Predictions for some tissues (e.g., spleen, bone, gut, lung) were observed to be better than predictions for other tissues (e.g., skin, brain, fat), indicating that confidence in the application of in silico tools to predict chemical partitioning varies depending upon the tissues involved. Our calibrated model was then evaluated using a second data set of human in vivo measurements of volume of distribution (Vss) for 498 compounds reviewed by Obach et al. (Drug Metab Dispos 36(7):1385-1405, 2008). We found that calibration of the model improved performance: a regression of the measured values as a function of the predictions has a slope of 1.03, intercept of - 0.04, and R2 of 0.43. Through careful evaluation of predictive methods for chemical partitioning into tissues, we have improved and calibrated these methods and quantified confidence for TK predictions in humans and rats.