Interspecies scaling of toxicity reference values in human health versus ecological risk assessments: A critical review

Risk assessments that focus on anthropogenic chemicals in environmental media-whether considering human health or ecological effects-often rely on toxicity data from experimentally studied species to estimate safe exposures for species that lack similar data. Current default extrapolation approaches used in both human health risk assessments and ecological risk assessments (ERAs) account for differences in body weight between the test organisms and the species of interest, but the two default approaches differ in important ways. Human health risk assessments currently employ a default based on body weight raised to the three-quarters power. Ecological risk assessments for wildlife (i.e., mammals and birds) are typically based directly on body weight, as measured in the test organism and receptor species. This review describes differences in the experimental data underlying these default practices and discusses the many factors that affect interspecies variability in chemical exposures. The interplay of these different factors can lead to substantial departures from default expectations. Alternative methodologies for conducting more accurate interspecies extrapolations in ERAs for wildlife are discussed, including tissue-based toxicity reference values, physiologically based toxicokinetic and/or toxicodynamic modeling, chemical read-across, and a system of categorical defaults based on route of exposure and toxic mode of action. Integr Environ Assess Manag 2024;20:749-764. © 2023 SETAC.

Uncertainty analysis and updated user guidance for interspecies correlation estimation models and low toxicity compounds

Interspecies correlation estimation (ICE) models are log-linear relationships of acute sensitivity between two species that estimate the sensitivity of an untested species from the known sensitivity of a surrogate. As ICE model use increases globally, additional user guidance is required to ensure consistent use across chemicals and applications. The present study expands ICE uncertainty analyses and user guidance with a focus on low toxicity compounds whose acute values (i.e., reported as mg/L) can be greater than those used to develop a model. In these cases, surrogate values may be outside the ICE model domain and require additional extrapolations to predict acute toxicity. We use the extensive, standardized acute toxicity database underlying ICE models to broadly summarize inter-test variability of acute toxicity data as a measure by which model prediction accuracy can be evaluated. Using the data and models found on the USEPA Web-ICE (www3.epa.gov/webice), we created a set of "truncated" models from data corresponding to the lower 75th percentile of surrogate toxicity. We predicted toxicity for chemicals in the upper 25th percentile as both μg/L beyond the model domain and converted to mg/L (i.e., "scaled" value) and compared these predictions with those from cross-validation of whole ICE models and to the measured value. For ICE models with slopes in the range 0.66-1.33, prediction accuracy of scaled values did not differ from the accuracy of the models when data were entered as μg/L within or beyond the model domain. An uncertainty analysis of ICE confidence intervals was conducted and an interval range of two orders of magnitude was determined to minimize type I and II errors when accepting or rejecting ICE predictions. We updated the ICE user guidance based on these analyses to advance the state of the science for ICE model application and interpretation. Integr Environ Assess Manag 2024;00:1-12. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.

A physiologically-based pharmacokinetic model for predicting doxorubicin disposition in multiple tissue levels and quantitative toxicity assessment

Doxorubicin is a widely-used chemotherapeutic drug, however its high toxicity poses a significant challenge for its clinical use. To address this issue, a physiologically-based pharmacokinetic (PBPK) model was implemented to quantitatively assess doxorubicin toxicity at cellular scale. Due to its unique pharmacokinetic behavior (e.g. high volume of distribution and affinity to extra-plasma tissue compartments), we proposed a modified PBPK model structure and developed the model with multispecies extrapolation to compensate for the limitation of obtaining clinical tissue data. Our model predicted the disposition of doxorubicin in multiple tissues including clinical tissue data with an overall absolute average fold error (AAFE) of 2.12. The model's performance was further validated with 8 clinical datasets in combined with intracellular doxorubicin concentration with an average AAFE of 1.98. To assess the potential cellular toxicity, toxicity levels and area under curve (AUC) were defined for different dosing regimens in toxic and non-toxic scenarios. The cellular concentrations of doxorubicin in multiple organ sites associated with commonly observed adverse effects (AEs) were simulated and calculated the AUC for quantitative assessments. Our findings supported the clinical dosing regimen of 75 mg/m2 with a 21-day interval and suggest that slow infusion and separated single high doses may lower the risk of developing AEs from a cellular level, providing valuable insights for the risk assessment of doxorubicin chemotherapy. In conclusion, our work highlights the potential of PBPK modelling to provide quantitative assessments of cellular toxicity and supports the use of clinical dosing regimens to mitigate the risk of adverse effects.

Modelling the biodistribution of inhaled gold nanoparticles in rats with interspecies extrapolation to humans

The increasing intentional and non-intentional exposure to nanoparticles (NPs) has raised the interest concerning their fate and biodistribution in the body of animals and humans after inhalation. In this context, Physiologically Based (pharmaco)Kinetic (PBK) modelling has emerged as an in silico approach that simulates the biodistribution kinetics of NPs in the body using mathematical equations. Due to restrictions in data availability, such models are first developed for rats or mice. In this work, we present the interspecies extrapolation of a PBK model initially developed for rats, in order to estimate the biodistribution of inhaled gold NPs (AuNPs) in humans. The extrapolation framework is validated by comparing the model results with experimental data from a clinical study performed on humans for inhaled AuNPs of two different sizes, namely 34 nm and 4 nm. The novelty of this work lies in the extrapolation of a PBK model for inhaled AuNPs to humans and comparison with clinical data. The extrapolated model is in good agreement with the experimental data, and provides insights for the mechanisms of inhaled AuNP translocation to the blood circulation, after inhalation. Finally, the biodistribution of the two sizes of AuNPs in the human body after 28 days post-exposure is estimated by the model and discussed.

Translating dosimetry of Dibenzo[def,p]chrysene (DBC) and metabolites across dose and species using physiologically based pharmacokinetic (PBPK) modeling

Dibenzo[def,p]chrysene (DBC) is an environmental polycyclic aromatic hydrocarbon (PAH) that causes tumors in mice and has been classified as a probable human carcinogen by the International Agency for Research on Cancer. Animal toxicity studies often utilize higher doses than are found in relevant human exposures. Additionally, like many PAHs, DBC requires metabolic bioactivation to form the ultimate toxicant, and species differences in DBC and DBC metabolite metabolism have been observed. To understand the implications of dose and species differences, a physiologically based pharmacokinetic model (PBPK) for DBC and major metabolites was developed in mice and humans. Metabolism parameters used in the model were obtained from experimental in vitro metabolism assays using mice and human hepatic microsomes. PBPK model simulations were evaluated against mice dosed with 15 mg/kg DBC by oral gavage and human volunteers orally microdosed with 29 ng of DBC. DBC and its primary metabolite DBC-11,12-diol were measured in blood of mice and humans, while in urine, the majority of DBC metabolites were obeserved as conjugated DBC-11,12-diol, conjugated DBC tetrols, and unconjugated DBC tetrols. The PBPK model was able to predict the time course concentrations of DBC, DBC-11,12-diol, and other DBC metabolites in blood and urine of human volunteers and mice with reasonable accuracy. Agreement between model simulations and measured pharmacokinetic data in mice and human studies demonstrate the success and versatility of our model for interspecies extrapolation and applicability for different doses. Furthermore, our simulations show that internal dose metrics used for risk assessment do not necessarily scale allometrically, and that PBPK modeling provides a reliable approach to appropriately account for interspecies differences in metabolism and physiology.

Detailed comparison of anatomy and airflow dynamics in human and cynomolgus monkey nasal cavity

Nonhuman primates are occasionally used as laboratory models for sophisticated medical research as they bear the closest resemblance to humans in morphometry and physiological functions. A range of nonhuman primate species have been employed in the inhalation toxicity, nasal drug delivery and respiratory viral infection studies, and they provided valuable insight to disease pathogenesis while other laboratory animals such as rodents cannot recapitulate due to the lesser degree of similarity in metabolism, anatomy and cellular response to that of humans. It is anticipated that nonhuman primate models of respiratory diseases will continue to be instrumental for translating biomedical research for improvement of human health, and the confidence in laboratory data extrapolation between species will play a pivotal role. From the morphometry and flow dynamics point of view, this study performed a detailed comparative analysis between human and a cynomolgus monkey nasal airway, with intention to provide high-fidelity qualitative and quantitative linkage between the two species for more effective laboratory data extrapolation. The study revealed that cynomolgus monkey could be a good human surrogate in nasal inhalation studies; however, care should be given for interspecies data extrapolation as subtle differences in anatomy and airflow dynamics were present between the two species.

Uniqueness of inspiratory airflow patterns in a realistic rat nasal cavity

In this study, we present a detailed flow analysis using an anatomically accurate rat nasal cavity model, in which the anatomy and physiology of the nasal airway was thoroughly examined. Special efforts were given to the swirling flow structures in the nasal vestibule (anterior section of the nose, lined by squamous epithelium), fractional flow patterns in the olfactory (posterior superior section of the rat nose, lined by olfactory epithelium), and a designated method to precisely quantify flow apportionment in the olfactory region was developed. Results revealed distinct inspiratory flow patterns in the anterior vestibule region, where the accelerated airflow undergoes two sharp turns as traveling through the tortuous airway, making a route in a shape of 8. Besides this, exceptionally large flow apportionment was observed at the interface of the olfactory recess, which can be as much as 15 times greater than that in the human nose. The thorough understanding of the airflow dynamics in the rat nasal cavity is necessary to avoid potential misinterpretation of rat-derived inhalation toxicity results. Research findings are expected to play a fundamental role in developing unbiased rat to human interspecies data extrapolation schemes.

The kernel-weighted local polynomial regression (KwLPR) approach: an efficient, novel tool for development of QSAR/QSAAR toxicity extrapolation models

The ability of accurate predictions of biological response (biological activity/property/toxicity) of a given chemical makes the quantitative structure‐activity/property/toxicity relationship (QSAR/QSPR/QSTR) models unique among the in silico tools. In addition, experimental data of selected species can also be used as an independent variable along with other structural as well as physicochemical variables to predict the response for different species formulating quantitative activity–activity relationship (QAAR)/quantitative structure–activity–activity relationship (QSAAR) approach. Irrespective of the models' type, the developed model's quality, and reliability need to be checked through multiple classical stringent validation metrics. Among the validation metrics, error-based metrics are more significant as the basic idea of a good predictive model is to improve the predictions' quality by lowering the predicted residuals for new query compounds. Following the concept, we have checked the predictive quality of the QSAR and QSAAR models employing kernel-weighted local polynomial regression (KwLPR) approach over the traditional linear and non-linear regression-based approaches tools such as multiple linear regression (MLR) and k nearest neighbors (kNN). Five datasets which were previously modeled using linear and non-linear regression method were considered to implement the KwPLR approach, followed by comparison of their validation metrics outcomes. For all five cases, the KwLPR based models reported better results over the traditional approaches. The present study's focus is not to develop a better or improved QSAR/QSAAR model over the previous ones, but to demonstrate the advantage, prediction power, and reliability of the KwLPR algorithm and establishing it as a novel, powerful cheminformatic tool. To facilitate the use of the KwLPR algorithm for QSAR/QSPR/QSTR/QSAAR modeling, the authors provide an in-house developed KwLPR.RMD script under the open-source R programming language.

Probabilistic human health risk assessment of perfluorooctane sulfonate (PFOS) by integrating in vitro, in vivo toxicity, and human epidemiological studies using a Bayesian-based dose-response assessment coupled with physiologically based pharmacokinetic (PBPK) modeling approach

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).

Bayesian evaluation of a physiologically based pharmacokinetic (PBPK) model for perfluorooctane sulfonate (PFOS) to characterize the interspecies uncertainty between mice, rats, monkeys, and humans: Development and performance verification

A challenge in the risk assessment of perfluorooctane sulfonate (PFOS) is the large interspecies differences in its toxicokinetics that results in substantial uncertainty in the dosimetry and toxicity extrapolation from animals to humans. To address this challenge, the objective of this study was to develop an open-source physiologically based pharmacokinetic (PBPK) model accounting for species-specific toxicokinetic parameters of PFOS. Considering available knowledge about the toxicokinetic properties of PFOS, a PBPK model for PFOS in mice, rats, monkeys, and humans after intravenous and oral administrations was created. Available species-specific toxicokinetic data were used for model calibration and optimization, and independent datasets were used for model evaluation. Bayesian statistical analysis using Markov chain Monte Carlo (MCMC) simulation was performed to optimize the model and to characterize the uncertainty and interspecies variability of chemical-specific parameters. The model predictions well correlated with the majority of datasets for all four species, and the model was validated with independent data in rats, monkeys, and humans. The model was applied to predict human equivalent doses (HEDs) based on reported points of departure in selected critical toxicity studies in rats and monkeys following U.S. EPA's guidelines. The lower bounds of the model-derived HEDs were overall lower than the HEDs estimated by U.S. EPA (e.g., 0.2 vs. 1.3 μg/kg/day based on the rat plasma data). This integrated and comparative analysis provides an important step towards improving interspecies extrapolation and quantitative risk assessment of PFOS, and this open-source model provides a foundation for developing models for other perfluoroalkyl substances.

Comparison of the acute inhibitory effects of Tetrodotoxin (TTX) in rat and human neuronal networks for risk assessment purposes

Tetrodotoxin (TTX) is an extremely toxic marine neurotoxin. TTX inhibits voltage-gated sodium channels, resulting in a potentially lethal inhibition of neurotransmission. Despite numerous intoxications in Asia and Europe, limited (human) toxicological data are available for TTX. Additionally, the degree of interspecies differences for TTX is not well established, hampering the use of available (animal) data for human risk assessment and establishing regulatory limits for TTX concentrations in (shell)fish. We therefore used micro-electrode array (MEA) recordings as an integrated measure of neurotransmission to demonstrate that TTX inhibits neuronal electrical activity in both primary rat cortical cultures and human-induced pluripotent stem cell (hIPSC)-derived iCell^® neurons in co-culture with hIPSC-derived iCell^® astrocytes, with IC₅₀ values of 7 and 10nM, respectively. From these data combined with LD₅₀ values and IC₅₀ concentrations of voltage-gated sodium channels derived from literature it can be concluded that interspecies differences are limited for TTX. Consequently, we used experimental animal data to derive a human acute reference dose of 1.33μg/kg body weight, which corresponds to maximum concentration of TTX in shellfish of 200μg/kg.

Comparison of oral bioavailability of benzo[a]pyrene in soils using rat and swine and the implications for human health risk assessment

BACKGROUND

There are many uncertainties concerning variations in benzo[a]pyrene (B[a]P) soil guidelines protecting human health based on carcinogenic data obtained in animal studies. Although swine is recognised as being much more representative of the human child in terms of body size, gut physiology and genetic profile the rat/mice model is commonly used in practice.

OBJECTIVES

We compare B[a]P bioavailability using a rat model to that estimated in a swine model, to investigate the correlation between these two animal models. This may help reduce uncertainty in applying bioavailability to human health risk assessment.

METHODS

Twelve spiked soil samples and a spiked silica sand (reference material) were dosed to rats in parallel with a swine study. B[a]P bioavailability was estimated by the area under the plasma B[a]P concentration-time curve (AUC) and faecal excretion as well in the rats. Direct comparison between the two animal models was made for: firstly, relative bioavailability (RB) using AUC assay; and secondly, the two assays in the rat model.

RESULTS

Both AUC and faecal excretion assays showed linear dose-response for the reference material. However, absolute bioavailability was significantly higher when using faecal excretion assay (p<0.001). In aged soils faecal excretion estimated based on solvent extraction was not accurate due to the form of non-extractable fraction through ageing. A significant correlation existed between the two models using RB for soil samples (RBrat=0.26RBswine+17.3, R(2)=0.70, p<0.001), despite the regression slope coefficient revealing that the rat model would underestimate RB by about one quarter compared to using swine.

CONCLUSIONS

In the comparison employed in this study, an interspecies difference of four in RB using AUC assay was identified between the rat and swine models regarding pharmacokinetic differences, which supported the body weight scaling method recommended by US EPA. Future research should focus on the carcinogenic competency (pharmacodynamics) used in experiment animals and humans.

A harmonization effort for acceptable daily exposure derivation - Considerations for application of adjustment factors

Acceptable daily exposures (ADEs) are established to determine the quantity of one drug substance that can contaminate another drug product without causing harm to the patient. An important part in setting an ADE for a drug substance, after identification of the unwanted critical effect(s) of the compound (see Bercu et al., 2016, this issue), is the determination of an appropriate overall margin of safety that is need to be maintained below the dose causing a certain critical effect (i.e., the point of departure or PoD). The overall margin of safety used to protect the general patient population from critical effects is derived as the product (i.e., composite adjustment factor) of various individual factors that account for variability and uncertainty in extrapolating from the PoD to an ADE. These factors address the considerations of interindividual variability, interspecies extrapolation, LOAEL-to-NOAEL extrapolation, exposure duration adjustment, effect severity, and database completeness. The factors are considered individually, but are not necessarily independent and their interdependence should be identified, with subsequent adjustment to the composite factor, as appropriate. It is important to identify all sources of variability and uncertainty pertinent to the derivation of the ADE and ensure each is considered in the assessment, at least by one of the adjustment factors. This manuscript highlights the basis for and selection of factors that address variability and uncertainty as used in the guidance documents on setting ADEs or other related health-based limits.