Assessing Toxicokinetic Uncertainty and Variability in Risk Prioritization

Mechanistic Skin Modeling of Plasma Concentrations of Sunscreen Active Ingredients Following Facial Application

Sunscreen products constitute two distinct categories. Recreational sunscreens protect against high-intensity, episodic sun exposure, often applied over the entire body. In contrast, facial sunscreen products are designed for sub-erythemal, low-intensity daily sun exposure. Such different exposures necessitate distinctive product safety assessments. Building on earlier methods for predicting dermal disposition, a mechanistic model was developed to simulate plasma concentrations of seven organic sunscreen active ingredients: avobenzone, ensulizole, homosalate, octinoxate, octisalate, octocrylene, and oxybenzone, following facial application. In vitro permeation testing (IVPT) was performed with two different vehicles using a subset of the UV filters. These IVPT results, in addition to previously published IVPT data and published in vivo Maximal Usage Trial (MUsT) data for the UV filters, were used to train the mechanistic dermal model via a Bayesian Markov chain Monte Carlo (MCMC) method. An external validation of the trained model with real-world in vivo datasets demonstrated that the model's predicted UV filter plasma concentrations align well with experimental measurements and capture the observed inter-individual variability. Predictions of steady-state UV filter plasma concentrations under facial application scenarios at 5% concentration and at the maximal allowable concentrations were then generated by the trained model. Oxybenzone had the greatest predicted plasma concentration following facial application. Homosalate and octisalate predictions had high uncertainty associated with the absence of data. Several application scenarios pertaining to avobenzone, ensulizole, octocrylene and octinoxate were identified in which median plasma concentration levels were at 0.5 ng/ml or below when applied in the recreational or facial product. Model limitations include uncertainty in vehicle/water partitioning, formulation metamorphosis, and UV filter systemic clearance, all of which can be refined with additional data. For UV filters, limiting exposure to facial application reduces human safety concerns based on FDA established thresholds.

Human biomonitoring and toxicokinetics as key building blocks for next generation risk assessment

Human health risk assessment is historically built upon animal testing, often following Organisation for Economic Co-operation and Development (OECD) test guidelines and exposure assessments. Using combinations of human relevant in vitro models, chemical analysis and computational (in silico) approaches bring advantages compared to animal studies. These include a greater focus on the human species and on molecular mechanisms and kinetics, identification of Adverse Outcome Pathways and downstream Key Events as well as the possibility of addressing susceptible populations and additional endpoints. Much of the advancement and progress made in the Next Generation Risk Assessment (NGRA) have been primarily focused on new approach methodologies (NAMs) and physiologically based kinetic (PBK) modelling without incorporating human biomonitoring (HBM). The integration of toxicokinetics (TK) and PBK modelling is an essential component of NGRA. PBK models are essential for describing in quantitative terms the TK processes with a focus on the effective dose at the expected target site. Furthermore, the need for PBK models is amplified by the increasing scientific and regulatory interest in aggregate and cumulative exposure as well as interactions of chemicals in mixtures. Since incorporating HBM data strengthens approaches and reduces uncertainties in risk assessment, here we elaborate on the integrated use of TK, PBK modelling and HBM in chemical risk assessment highlighting opportunities as well as challenges and limitations. Examples are provided where HBM and TK/PBK modelling can be used in both exposure assessment and hazard characterization shifting from external exposure and animal dose/response assays to animal-free, internal exposure-based NGRA.

TKPlate 1.0: An Open-access platform for toxicokinetic and toxicodynamic modelling of chemicals to implement new approach methodologies in chemical risk assessment

This publication is linked to the following EFSA Supporting Publications articles: http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2023.EN-8441/full, http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2023.EN-8440/full, http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2023.EN-8437/full.

Cutting-edge computational chemical exposure research at the U.S. Environmental Protection Agency

Exposure science is evolving from its traditional "after the fact" and "one chemical at a time" approach to forecasting chemical exposures rapidly enough to keep pace with the constantly expanding landscape of chemicals and exposures. In this article, we provide an overview of the approaches, accomplishments, and plans for advancing computational exposure science within the U.S. Environmental Protection Agency's Office of Research and Development (EPA/ORD). First, to characterize the universe of chemicals in commerce and the environment, a carefully curated, web-accessible chemical resource has been created. This DSSTox database unambiguously identifies >1.2 million unique substances reflecting potential environmental and human exposures and includes computationally accessible links to each compound's corresponding data resources. Next, EPA is developing, applying, and evaluating predictive exposure models. These models increasingly rely on data, computational tools like quantitative structure activity relationship (QSAR) models, and machine learning/artificial intelligence to provide timely and efficient prediction of chemical exposure (and associated uncertainty) for thousands of chemicals at a time. Integral to this modeling effort, EPA is developing data resources across the exposure continuum that includes application of high-resolution mass spectrometry (HRMS) non-targeted analysis (NTA) methods providing measurement capability at scale with the number of chemicals in commerce. These research efforts are integrated and well-tailored to support population exposure assessment to prioritize chemicals for exposure as a critical input to risk management. In addition, the exposure forecasts will allow a wide variety of stakeholders to explore sustainable initiatives like green chemistry to achieve economic, social, and environmental prosperity and protection of future generations.

Plasma Protein Binding Evaluations of Per- and Polyfluoroalkyl Substances for Category-Based Toxicokinetic Assessment

New approach methodologies (NAMs) that make use of in vitro screening and in silico approaches to inform chemical evaluations rely on in vitro toxicokinetic (TK) data to translate in vitro bioactive concentrations to exposure metrics reflective of administered dose. With 1364 per- and polyfluoroalkyl substances (PFAS) identified as of interest under Section 8 of the U.S. Toxic Substances Control Act (TSCA) and concern over the lack of knowledge regarding environmental persistence, human health, and ecological effects, the utility of NAMs to understand potential toxicities and toxicokinetics across these data-poor compounds is being evaluated. To address the TK data deficiency, 71 PFAS selected to span a wide range of functional groups and physico-chemical properties were evaluated for in vitro human plasma protein binding (PPB) by ultracentrifugation with liquid chromatography-mass spectrometry analysis. For the 67 PFAS successfully evaluated by ultracentrifugation, fraction unbound in plasma (fup) ranged from less than 0.0001 (pentadecafluorooctanoyl chloride) to 0.7302 (tetrafluorosuccinic acid), with over half of the PFAS showing PPB exceeding 99.5% (fup < 0.005). Category-based evaluations revealed that perfluoroalkanoyl chlorides and perfluorinated carboxylates (PFCAs) with 6-10 carbons were the highest bound, with similar median values for alkyl, ether, and polyether PFCAs. Interestingly, binding was lower for the PFCAs with a carbon chain length of ≥11. Lower binding also was noted for fluorotelomer carboxylic acids when compared to their carbon-equivalent perfluoroalkyl acids. Comparisons of the fup value derived using two PPB methods, ultracentrifugation or rapid equilibrium dialysis (RED), revealed RED failure for a subset of PFAS of high mass and/or predicted octanol-water partition coefficients exceeding 4 due to failure to achieve equilibrium. Bayesian modeling was used to provide uncertainty bounds around fup point estimates for incorporation into TK modeling. This PFAS PPB evaluation and grouping exercise across 67 structures greatly expand our current knowledge and will aid in PFAS NAM development.

Category-Based Toxicokinetic Evaluations of Data-Poor Per- and Polyfluoroalkyl Substances (PFAS) using Gas Chromatography Coupled with Mass Spectrometry

Concern over per- and polyfluoroalkyl substances (PFAS) has increased as more is learned about their environmental presence, persistence, and bioaccumulative potential. The limited monitoring, toxicokinetic (TK), and toxicologic data available are inadequate to inform risk across this diverse domain. Here, 73 PFAS were selected for in vitro TK evaluation to expand knowledge across lesser-studied PFAS alcohols, amides, and acrylates. Targeted methods developed using gas chromatography-tandem mass spectrometry (GC-MS/MS) were used to measure human plasma protein binding and hepatocyte clearance. Forty-three PFAS were successfully evaluated in plasma, with fraction unbound (f_up) values ranging from 0.004 to 1. With a median f_up of 0.09 (i.e., 91% bound), these PFAS are highly bound but exhibit 10-fold lower binding than legacy perfluoroalkyl acids recently evaluated. Thirty PFAS evaluated in the hepatocyte clearance assay showed abiotic loss, with many exceeding 60% loss within 60 min. Metabolic clearance was noted for 11 of the 13 that were successfully evaluated, with rates up to 49.9 μL/(min × million cells). The chemical transformation simulator revealed potential (bio)transformation products to consider. This effort provides critical information to evaluate PFAS for which volatility, metabolism, and other routes of transformation are likely to modulate their environmental fates.

Use of computational toxicology models to predict toxicological points of departure: A case study with triazine herbicides

BACKGROUND

Atrazine simazine and propazine, widely used triazine herbicides on food crops and in residential areas, disrupt the neuroendocrine system raising human health concerns. USEPA developed a PBPK model based on triazine common Mode of Action (MOA)-suppression of luteinizing hormone surge in female rats-to generate human regulatory points of departure (POD: mg/kg/day). We compared triazine Human Administered Equivalent Dose (AED_Human mg/kg/day) predictions from open access computational tools to the PBPK PODs to assess concordance.

METHODS

Computational tools were the following: ToxCast/Tox21 in vitro assays; Toxicogenomic databases to assess concordance with ToxCast/Tox21 targets; integrated chemical environment (ICE) models with ToxCast/Tox21 inputs to predict AED_Human PODs and population-based age-refined high throughput toxicokinetics (HTTK-Pop) to compare to age-related PBPK PODs.

RESULTS

ToxCast/Tox21 assays identified critical targets in the triazine common MOA and gene databases; ICE AED_Human predictions were mainly concordant with the USEPA PBPK PODs quantitatively. Low fold-differences between PBPK POD and ICE AED_Human predictions indicated that the ICE models are health-protective. HTTK-Pop age-refinements were within 10-fold of the USEPA PBPK PODs.

CONCLUSIONS

CompTox tools were used to identify assay targets in the MOA and identify potential molecular initiating targets in the adverse outcome pathway for potential use in risk assessment.

HExpPredict: In Vivo Exposure Prediction of Human Blood Exposome Using a Random Forest Model and Its Application in Chemical Risk Prioritization

BACKGROUND

Due to many substances in the human exposome, there is a dearth of exposure and toxicity information available to assess potential health risks. Quantification of all trace organics in the biological fluids seems impossible and costly, regardless of the high individual exposure variability. We hypothesized that the blood concentration (CB) of organic pollutants could be predicted via their exposure and chemical properties. Developing a prediction model on the annotation of chemicals in human blood can provide new insight into the distribution and extent of exposures to a wide range of chemicals in humans.

OBJECTIVES

Our objective was to develop a machine learning (ML) model to predict blood concentrations (CBs) of chemicals and prioritize chemicals of health concern.

METHODS

We curated the CBs of compounds mostly measured at population levels and developed an ML model for chemical CB predictions by considering chemical daily exposure (DE) and exposure pathway indicators (δij), half-lives (t1/2), and volume of distribution (Vd). Three ML models, including random forest (RF), artificial neural network (ANN) and support vector regression (SVR) were compared. The toxicity potential or prioritization of each chemical was represented as a bioanalytical equivalency (BEQ) and its percentage (BEQ%) estimated based on the predicted CB and ToxCast bioactivity data. We also retrieved the top 25 most active chemicals in each assay to further observe changes in the BEQ% after the exclusion of the drugs and endogenous substances.

RESULTS

We curated the CBs of 216 compounds primarily measured at population levels. RF outperformed the ANN and SVF models with the root mean square error (RMSE) of 1.66 and 2.07μM, the mean absolute error (MAE) values of 1.28 and 1.56μM, the mean absolute percentage error (MAPE) of 0.29 and 0.23, and R2 of 0.80 and 0.72 across test and testing sets. Subsequently, the human CBs of 7,858 ToxCast chemicals were successfully predicted, ranging from 1.29×10-6 to 1.79×10-2 μM. The predicted CBs were then combined with ToxCast in vitro bioassays to prioritize the ToxCast chemicals across 12 in vitro assays with important toxicological end points. It is interesting that we found the most active compounds to be food additives and pesticides rather than widely monitored environmental pollutants.

DISCUSSION

We have shown that the accurate prediction of "internal exposure" from "external exposure" is possible, and this result can be quite useful in the risk prioritization. https://doi.org/10.1289/EHP11305.

A Pragmatic Framework for the Application of New Approach Methodologies in One Health Toxicological Risk Assessment

Globally, industries and regulatory authorities are faced with an urgent need to assess the potential adverse effects of chemicals more efficiently by embracing new approach methodologies (NAMs). NAMs include cell and tissue methods (in vitro), structure-based/toxicokinetic models (in silico), methods that assess toxicant interactions with biological macromolecules (in chemico), and alternative models. Increasing knowledge on chemical toxicokinetics (what the body does with chemicals) and toxicodynamics (what the chemicals do with the body) obtained from in silico and in vitro systems continues to provide opportunities for modernizing chemical risk assessments. However, directly leveraging in vitro and in silico data for derivation of human health-based reference values has not received regulatory acceptance due to uncertainties in extrapolating NAM results to human populations, including metabolism, complex biological pathways, multiple exposures, interindividual susceptibility and vulnerable populations. The objective of this article is to provide a standardized pragmatic framework that applies integrated approaches with a focus on quantitative in vitro to in vivo extrapolation (QIVIVE) to extrapolate in vitro cellular exposures to human equivalent doses from which human reference values can be derived. The proposed framework intends to systematically account for the complexities in extrapolation and data interpretation to support sound human health safety decisions in diverse industrial sectors (food systems, cosmetics, industrial chemicals, pharmaceuticals etc.). Case studies of chemical entities, using new and existing data, are presented to demonstrate the utility of the proposed framework while highlighting potential sources of human population bias and uncertainty, and the importance of Good Method and Reporting Practices.

Prioritization of mixtures of neurotoxic chemicals for biomonitoring using high-throughput toxicokinetics and mixture toxicity modeling

Modern society continues to pollute the environment with larger quantities of chemicals that have also become more structurally and functionally diverse. Risk assessment of chemicals can hardly keep up with the sheer numbers that lead to complex mixtures of increasing chemical diversity including new chemicals, substitution products on top of still abundant legacy compounds. Fortunately, over the last years computational tools have helped us to identify and prioritize chemicals of concern. These include toxicokinetic models to predict exposure to chemicals as well as new approach methodologies such as in-vitro bioassays to address toxicodynamic effects. Combined, they allow for a prediction of mixtures and their respective effects and help overcome the lack of data we face for many chemicals. In this study we propose a high-throughput approach using experimental and predicted exposure, toxicokinetic and toxicodynamic data to simulate mixtures, to which a virtual population is exposed to and predict their mixture effects. The general workflow is adaptable for any type of toxicity, but we demonstrated its applicability with a case study on neurotoxicity. If no experimental data for neurotoxicity were available, we used baseline toxicity predictions as a surrogate. Baseline toxicity is the minimal toxicity any chemical has and might underestimate the true contribution to the mixture effect but many neurotoxicants are not by orders of magnitude more potent than baseline toxicity. Therefore, including baseline-toxic effects in mixture simulations yields a more realistic picture than excluding them in mixture simulations. This workflow did not only correctly identify and prioritize known chemicals of concern like benzothiazoles, organochlorine pesticides and plasticizers but we were also able to identify new potential neurotoxicants that we recommend to include in future biomonitoring studies and if found in humans, to also include in neurotoxicity screening.

Practical application of the interim internal threshold of toxicological concern (iTTC): a case study based on clinical data

We present a case study that provides a practical step-by-step example of how the internal Threshold of Toxicological Concern (iTTC) can be used as a tool to refine a TTC-based assessment for dermal exposures to consumer products. The case study uses a theoretical scenario where there are no systemic toxicity data for the case study chemicals (avobenzone, oxybenzone, octocrylene, homosalate, octisalate, octinoxate, and ecamsule). Human dermal pharmacokinetic data following single and repeat dermal exposure to products containing the case study chemicals were obtained from data published by the US FDA. The clinical studies utilized an application procedure that followed maximal use conditions (product applied as 2 mg/cm² to 75% of the body surface area, 4 times a day). The case study chemicals were first reviewed to determine if they were in the applicability domain of the iTTC, and then, the human plasma concentrations were compared to an iTTC limit of 1 µM. When assessed under maximum usage, the external exposure of all chemicals exceeded the external dose TTC limits. By contrast, the internal exposure to all chemicals, except oxybenzone, was an order of magnitude lower than the 1 µM interim iTTC threshold. This work highlights the importance of understanding internal exposure relative to external dose and how the iTTC can be a valuable tool for assessing low-level internal exposures; additionally, the work demonstrates how to use an iTTC, and highlights considerations and refinement opportunities for the approach.

Simulating toxicokinetic variability to identify susceptible and highly exposed populations

BACKGROUND

Toxicokinetic (TK) data needed for chemical risk assessment are not available for most chemicals. To support a greater number of chemicals, the U.S. Environmental Protection Agency (EPA) created the open-source R package "httk" (High Throughput ToxicoKinetics). The "httk" package provides functions and data tables for simulation and statistical analysis of chemical TK, including a population variability simulator that uses biometrics data from the National Health and Nutrition Examination Survey (NHANES).

OBJECTIVE

Here we modernize the "HTTK-Pop" population variability simulator based on the currently available data and literature. We provide explanations of the algorithms used by "httk" for variability simulation and uncertainty propagation.

METHODS

We updated and revised the population variability simulator in the "httk" package with the most recent NHANES biometrics (up to the 2017-18 NHANES cohort). Model equations describing glomerular filtration rate (GFR) were revised to more accurately represent physiology and population variability. The model output from the updated "httk" package was compared with the current version.

RESULTS

The revised population variability simulator in the "httk" package now provides refined, more relevant, and better justified estimations.

SIGNIFICANCE

Fulfilling the U.S. EPA's mission to provide open-source data and models for evaluations and applications by the broader scientific community, and continuously improving the accuracy of the "httk" package based on the currently available data and literature.

Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment

Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA's ExpoCast ("Exposure Forecasting") project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA's ToxCast ("Toxicity Forecasting") project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.

The chemical landscape of high-throughput new approach methodologies for exposure

The rapid characterization of risk to humans and ecosystems from exogenous chemicals requires information on both hazard and exposure. The U.S. Environmental Protection Agency's ToxCast program and the interagency Tox21 initiative have screened thousands of chemicals in various high-throughput (HT) assay systems for in vitro bioactivity. EPA's ExpoCast program is developing complementary HT methods for characterizing the human and ecological exposures necessary to interpret HT hazard data in a real-world risk context. These new approach methodologies (NAMs) for exposure include computational and analytical tools for characterizing multiple components of the complex pathways chemicals take from their source to human and ecological receptors. Here, we analyze the landscape of exposure NAMs developed in ExpoCast in the context of various chemical lists of scientific and regulatory interest, including the ToxCast and Tox21 libraries and the Toxic Substances Control Act (TSCA) inventory. We examine the landscape of traditional and exposure NAM data covering chemical use, emission, environmental fate, toxicokinetics, and ultimately external and internal exposure. We consider new chemical descriptors, machine learning models that draw inferences from existing data, high-throughput exposure models, statistical frameworks that integrate multiple model predictions, and non-targeted analytical screening methods that generate new HT monitoring information. We demonstrate that exposure NAMs drastically improve the coverage of the chemical landscape compared to traditional approaches and recommend a set of research activities to further expand the development of HT exposure data for application to risk characterization. Continuing to develop exposure NAMs to fill priority data gaps identified here will improve the availability and defensibility of risk-based metrics for use in chemical prioritization and screening. IMPACT: This analysis describes the current state of exposure assessment-based new approach methodologies across varied chemical landscapes and provides recommendations for filling key data gaps.

Evaluation of a rapid, generic human gestational dose model

Chemical risk assessment considers potentially susceptible populations including pregnant women and developing fetuses. Humans encounter thousands of chemicals in their environments, few of which have been fully characterized. Toxicokinetic (TK) information is needed to relate chemical exposure to potentially bioactive tissue concentrations. Observational data describing human gestational exposures are unavailable for most chemicals, but physiologically based TK (PBTK) models estimate such exposures. Development of chemical-specific PBTK models requires considerable time and resources. As an alternative, generic PBTK approaches describe a standardized physiology and characterize chemicals with a set of standard physical and TK descriptors - primarily plasma protein binding and hepatic clearance. Here we report and evaluate a generic PBTK model of a human mother and developing fetus. We used a published set of formulas describing the major anatomical and physiological changes that occur during pregnancy to augment the High-Throughput Toxicokinetics (httk) software package. We simulated the ratio of concentrations in maternal and fetal plasma and compared to literature in vivo measurements. We evaluated the model with literature in vivo time-course measurements of maternal plasma concentrations in pregnant and non-pregnant women. Finally, we prioritized chemicals measured in maternal serum based on predicted fetal brain concentrations. This new model can be used for TK simulations of 859 chemicals with existing human-specific in vitro TK data as well as any new chemicals for which such data become available. This gestational model may allow for in vitro to in vivo extrapolation of point of departure doses relevant to reproductive and developmental toxicity.

Prioritization of chemicals in food for risk assessment by integrating exposure estimates and new approach methodologies: A next generation risk assessment case study

Next generation risk assessment is defined as a knowledge-driven system that allows for cost-efficient assessment of human health risk related to chemical exposure, without animal experimentation. One of the key features of next generation risk assessment is to facilitate prioritization of chemical substances that need a more extensive toxicological evaluation, in order to address the need to assess an increasing number of substances. In this case study focusing on chemicals in food, we explored how exposure data combined with the Threshold of Toxicological Concern (TTC) concept could be used to prioritize chemicals, both for existing substances and new substances entering the market. Using a database of existing chemicals relevant for dietary exposure we calculated exposure estimates, followed by application of the TTC concept to identify substances of higher concern. Subsequently, a selected set of these priority substances was screened for toxicological potential using high-throughput screening (HTS) approaches. Remarkably, this approach resulted in alerts for a selection of substances that are already on the market and represent relevant exposure in consumers. Taken together, the case study provides proof-of-principle for the approach taken to identify substances of concern, and this approach can therefore be considered a supportive element to a next generation risk assessment strategy.

Evaluation of 147 perfluoroalkyl substances for immunotoxic and other (patho)physiological activities through phenotypic screening of human primary cells

A structurally diverse set of 147 per- and polyfluoroalkyl substances (PFAS) was screened in a panel of 12 human primary cell systems by measuring 148 biomarkers relevant to (patho)physiological pathways to inform hypotheses about potential mechanistic effects of data-poor PFAS in human model systems. This analysis focused on immunosuppressive activity, which was previously reported as an in vivo effect of perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS), by comparing PFAS responses to four pharmacological immunosuppressants. The PFOS response profile had little correlation with reference immunosuppressants, suggesting in vivo activity does not occur by similar mechanisms. The PFOA response profile did share features with the profile of dexamethasone, although some distinct features were lacking. Other PFAS, including 2,2,3,3-tetrafluoropropyl acrylate, demonstrated more similarity to the reference immunosuppressants but with additional activities not found in the reference immunosuppressive drugs. Correlation of PFAS profiles with a database of environmental chemical responses and pharmacological probes identified potential mechanisms of bioactivity for some PFAS, including responses similar to ubiquitin ligase inhibitors, deubiquitylating enzyme (DUB) inhibitors, and thioredoxin reductase inhibitors. Approximately 21% of the 147 PFAS with confirmed sample quality were bioactive at nominal testing concentrations in the 1-60 micromolar range in these human primary cell systems. These data provide new hypotheses for mechanisms of action for a subset of PFAS and may further aid in development of a PFAS categorization strategy useful in safety assessment.

Towards best use and regulatory acceptance of generic physiologically based kinetic (PBK) models for in vitro-to-in vivo extrapolation (IVIVE) in chemical risk assessment

With an increasing need to incorporate new approach methodologies (NAMs) in chemical risk assessment and the concomitant need to phase out animal testing, the interpretation of in vitro assay readouts for quantitative hazard characterisation becomes more important. Physiologically based kinetic (PBK) models, which simulate the fate of chemicals in tissues of the body, play an essential role in extrapolating in vitro effect concentrations to in vivo bioequivalent exposures. As PBK-based testing approaches evolve, it will become essential to standardise PBK modelling approaches towards a consensus approach that can be used in quantitative in vitro-to-in vivo extrapolation (QIVIVE) studies for regulatory chemical risk assessment based on in vitro assays. Based on results of an ECETOC expert workshop, steps are recommended that can improve regulatory adoption: (1) define context and implementation, taking into consideration model complexity for building fit-for-purpose PBK models, (2) harmonise physiological input parameters and their distribution and define criteria for quality chemical-specific parameters, especially in the absence of in vivo data, (3) apply Good Modelling Practices (GMP) to achieve transparency and design a stepwise approach for PBK model development for risk assessors, (4) evaluate model predictions using alternatives to in vivo PK data including read-across approaches, (5) use case studies to facilitate discussions between modellers and regulators of chemical risk assessment. Proof-of-concepts of generic PBK modelling approaches are published in the scientific literature at an increasing rate. Working on the previously proposed steps is, therefore, needed to gain confidence in PBK modelling approaches for regulatory use.

SimRFlow: An R-based workflow for automated high-throughput PBPK simulation with the Simcyp® simulator

SimRFlow is a high-throughput physiologically based pharmacokinetic (PBPK) modelling tool which uses Certara’s Simcyp® simulator. The workflow is comprised of three main modules: 1) a Data Collection module for automated curation of physicochemical (from ChEMBL and the Norman Suspect List databases) and experimental data (i.e.: clearance, plasma-protein binding, and blood-to-plasma ratio, from httk-R package databases), 2) a Simulation module which activates the Simcyp® simulator and runs Monte Carlo simulations on virtual subjects using the curated data, and 3) a Data Visualisation module for understanding the simulated compound-specific profiles and predictions. SimRFlow has three administration routes (oral, intravenous, dermal) and allows users to change some simulation parameters including the number of subjects, simulation duration, and dosing. Users are only expected to provide a file of the compounds they wish to simulate, and in return the workflow provides summary statistics, concentration-time profiles of various tissue types, and a database file (containing in-depth results) for each simulated compound. This is presented within a guided and easy-to-use R Shiny interface which provides many plotting options for the visualisation of concentration-time profiles, parameter distributions, trends between the different parameters, as well as comparison of predicted parameters across all batch-simulated compounds. The in-built R functions can be assembled in user-customised scripts which allows for the modification of the workflow for different purposes. SimRFlow proves to be a time-efficient tool for simulating a large number of compounds without any manual curation of physicochemical or experimental data necessary to run Simcyp® simulations.

In Vitro-In Vivo Extrapolation by Physiologically Based Kinetic Modeling: Experience With Three Case Studies and Lessons Learned

Physiologically based kinetic (PBK) modeling has been increasingly used since the beginning of the 21st century to support dose selection to be used in preclinical and clinical safety studies in the pharmaceutical sector. For chemical safety assessment, the use of PBK has also found interest, however, to a smaller extent, although an internationally agreed document was published already in 2010 (IPCS/WHO), but at that time, PBK modeling was based mostly on in vivo data as the example in the IPCS/WHO document indicates. Recently, the OECD has published a guidance document which set standards on how to characterize, validate, and report PBK models for regulatory purposes. In the past few years, we gained experience on using in vitro data for performing quantitative in vitro–in vivo extrapolation (QIVIVE), in which biokinetic data play a crucial role to obtain a realistic estimation of human exposure. In addition, pharmaco-/toxicodynamic aspects have been introduced into the approach. Here, three examples with different drugs/chemicals are described, in which different approaches have been applied. The lessons we learned from the exercise are as follows: 1) in vitro conditions should be considered and compared to the in vivo situation, particularly for protein binding; 2) in vitro inhibition of metabolizing enzymes by the formed metabolites should be taken into consideration; and 3) it is important to extrapolate from the in vitro measured intracellular concentration and not from the nominal concentration to the tissue/organ concentration to come up with an appropriate QIVIVE for the relevant adverse effects.

Are non-animal systemic safety assessments protective? A toolbox and workflow

An important question in toxicological risk assessment is whether non-animal new approach methodologies (NAMs) can be used to make safety decisions that are protective of human health, without being overly conservative. In this work, we propose a core NAM toolbox and workflow for conducting systemic safety assessments for adult consumers. We also present an approach for evaluating how protective and useful the toolbox and workflow are by benchmarking against historical safety decisions. The toolbox includes physiologically based kinetic (PBK) models to estimate systemic C_max levels in humans, and 3 bioactivity platforms, comprising high-throughput transcriptomics, a cell stress panel, and in vitro pharmacological profiling, from which points of departure are estimated. A Bayesian model was developed to quantify the uncertainty in the C_max estimates depending on how the PBK models were parameterized. The feasibility of the evaluation approach was tested using 24 exposure scenarios from 10 chemicals, some of which would be considered high risk from a consumer goods perspective (eg, drugs that are systemically bioactive) and some low risk (eg, existing food or cosmetic ingredients). Using novel protectiveness and utility metrics, it was shown that up to 69% (9/13) of the low risk scenarios could be identified as such using the toolbox, whilst being protective against all (5/5) the high-risk ones. The results demonstrated how robust safety decisions could be made without using animal data. This work will enable a full evaluation to assess how protective and useful the toolbox and workflow are across a broader range of chemical-exposure scenarios.

IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making

During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.

Integration of toxicodynamic and toxicokinetic new approach methods into a weight-of-evidence analysis for pesticide developmental neurotoxicity assessment: A case-study with DL- and L-glufosinate

DL-glufosinate ammonium (DL-GLF) is a registered herbicide for which a guideline Developmental Neurotoxicity (DNT) study has been conducted. Offspring effects included altered brain morphometrics, decreased body weight, and increased motor activity. Guideline DNT studies are not available for its enriched isomers L-GLF acid and L-GLF ammonium; conducting one would be time consuming, resource-intensive, and possibly redundant given the existing DL-GLF DNT. To support deciding whether to request a guideline DNT study for the L-GLF isomers, DL-GLF and the L-GLF isomers were screened using in vitro assays for network formation and neurite outgrowth. DL-GLF and L-GLF isomers were without effects in both assays. DL-GLF and L-GLF (1-100 μM) isomers increased mean firing rate of mature networks to 120-140% of baseline. In vitro toxicokinetic assessments were used to derive administered equivalent doses (AEDs) for the in vitro testing concentrations. The AED for L-GLF was ∼3X higher than the NOAEL from the DL-GLF DNT indicating that the available guideline study would be protective of potential DNT due to L-GLF exposure. Based in part on the results of these in vitro studies, EPA is not requiring L-GLF isomer guideline DNT studies, thereby providing a case study for a useful application of DNT screening assays.

Innovation in regulatory approaches for endocrine disrupting chemicals: The journey to risk assessment modernization in Canada

Globally, regulatory authorities grapple with the challenge of assessing the hazards and risks to human and ecosystem health that may result from exposure to chemicals that disrupt the normal functioning of endocrine systems. Rapidly increasing number of chemicals in commerce, coupled with the reliance on traditional, costly animal experiments for hazard characterization - often with limited sensitivity to many important mechanisms of endocrine disruption -, presents ongoing challenges for chemical regulation. The consequence is a limited number of chemicals for which there is sufficient data to assess if there is endocrine toxicity and hence few chemicals with thorough hazard characterization. To address this challenge, regulatory assessment of endocrine disrupting chemicals (EDCs) is benefiting from a revolution in toxicology that focuses on New Approach Methodologies (NAMs) to more rapidly identify, prioritize, and assess the potential risks from exposure to chemicals using novel, more efficient, and more mechanistically driven methodologies and tools. Incorporated into Integrated Approaches to Testing and Assessment (IATA) and guided by conceptual frameworks such as Adverse Outcome Pathways (AOPs), emerging approaches focus initially on molecular interactions between the test chemical and potentially vulnerable biological systems instead of the need for animal toxicity data. These new toxicity testing methods can be complemented with in silico and computational toxicology approaches, including those that predict chemical kinetics. Coupled with exposure data, these will inform risk-based decision-making approaches. Canada is part of a global network collaborating on building confidence in the use of NAMs for regulatory assessment of EDCs. Herein, we review the current approaches to EDC regulation globally (mainly from the perspective of human health), and provide a perspective on how the advances for regulatory testing and assessment can be applied and discuss the promises and challenges faced in adopting these novel approaches to minimize risks due to EDC exposure in Canada, and our world.

Predictive Performance of Next Generation Physiologically Based Kinetic (PBK) Model Predictions in Rats Based on In Vitro and In Silico Input Data

The goal of the present study was to assess the predictive performance of a minimal generic rat physiologically based kinetic (PBK) model based on in vitro and in silico input data to predict peak plasma concentrations (Cmax) upon single oral dosing. To this purpose, a dataset was generated of 3960 Cmax predictions for 44 compounds, applying different combinations of in vitro and in silico approaches for chemical parameterization, and comparison of the predictions to reported in vivo data. Best performance was obtained when (1) the hepatic clearance was parameterized based on in vitro measured intrinsic clearance values, (2) the method of Rodgers and Rowland for calculating partition coefficients, and (3) in silico calculated fraction unbound plasma and Papp values (the latter especially for very lipophilic compounds). Based on these input data, the median Cmax of 32 compounds could be predicted within 10-fold of the observed Cmax, with 22 out of these 32 compounds being predicted within 5-fold, and 8 compounds within 2-fold. Overestimations of more than 10-fold were observed for 12 compounds, whereas no underestimations of more than 10-fold occurred. Median Cmax predictions were frequently found to be within 10-fold of the observed Cmax when the scaled unbound hepatic intrinsic clearance (Clint,u) was either higher than 20 l/h or lower than 1 l/h. Similar findings were obtained with a test set of 5 in-house BASF compounds. Overall, this study provides relevant insights in the predictive performance of a minimal PBK model based on in vitro and in silico input data.

A new approach method for characterizing inter-species toxicodynamic variability

Inter-species differences in toxicodynamics are often a critical source of uncertainty in safety evaluations and typically dealt with using default adjustment factors. In vitro studies that use cells from different species demonstrated some success for estimating the relationships between life span and/or body weight and sensitivity to cytotoxicity; however, no apparent investigation evaluated the utility of these models for risk assessment. It was hypothesized that an in vitro model using dermal fibroblasts derived from diverse species and individuals might be utilized to inform the extent of inter-species and inter-individual variability in toxicodynamics. To test this hypothesis and characterize both inter-species and inter-individual variability in cytotoxicity, concentration-response cytotoxicity screening of 40 chemicals in primary dermal fibroblasts from 68 individuals of 54 diverse species was conducted. Chemicals examined included drugs, environmental pollutants, and food/flavor/fragrance agents; most of these were previously assessed either in vivo or in vitro for inter-species or inter-individual variation. Species included humans, the typical preclinical species and representatives from other orders of mammals and birds. Data demonstrated that both inter-species and inter-individual components of variability contribute to the observed differences in sensitivity to cell death. Further, it was found that the magnitude of the observed inter-species and inter-individual differences was chemical-dependent. This study contributes to the paradigm shift in risk assessment from reliance on in vivo toxicity testing to higher-throughput in vitro or alternative approaches, extending the strategy to replace use of default adjustment factors with experimental characterization of toxicodynamic inter-individual variability and to also address toxicodynamic inter-species variability.

Sourcing data on chemical properties and hazard data from the US-EPA CompTox Chemicals Dashboard: A practical guide for human risk assessment

For the past six decades, human health risk assessment of chemicals has relied on in vivo data from human epidemiological and experimental animal toxicological studies to inform the derivation of non-cancer toxicity values. The ongoing evolution of this risk assessment paradigm in an environmental landscape of data-poor chemicals has highlighted the need to develop and implement non-testing methods, so-called New Approach Methodologies (NAMs). NAMs include a growing number of in silico and in vitro data streams designed to inform hazard properties of chemicals, including kinetics and dynamics at different levels of biological organization, environmental fate and transport, and exposure. NAMs provide a fit-for-purpose science-basis for human hazard and risk characterization of chemicals ranging from data-gap filling applications to broad evidence-based decision-making. Systematic assembly and delivery of empirical and predicted data for chemicals are paramount to advancing chemical evaluation, and software tools serve an essential role in delivering these data to the scientific community. The CompTox Chemicals Dashboard (from here on referred to as the "Dashboard") is one such tool and is a publicly available web-based application developed by the US Environmental Protection Agency to provide access to chemistry, toxicity and exposure information for ~900,000 chemicals. The Dashboard is increasingly becoming a valuable resource for assessors tasked with the evaluation of potential human health risks associated with chemical exposures. In this context, the significant amount of information present in the Dashboard facilitates: 1) assembly of information on physicochemical properties and environmental fate and transport and exposure parameters and metrics; 2) identification of cancer and non-cancer health effects from extant human and experimental animal studies in the public domain and/or information not available in the public domain (i.e., "grey literature"); 3) systematic literature searching and review for developing cancer and non-cancer hazard evidence bases; and 4) access to mechanistic information that can aid or augment the analysis of traditional toxicology evidence bases, or potentially, serve as the primary basis for informing hazard identification and dose-response when traditional bioassay data are lacking. Finally, in silico predictive tools developed to conduct structure-activity or read-across analyses are also available within the Dashboard. This practical tutorial is intended to address key questions from the human health risk assessment community dealing with chemicals in both food and in the environment. Perspectives for future development or refinement of the Dashboard highlight foreseen activities to further support the research and risk assessment community in cancer and non-cancer chemical evaluations.

Quantitative in Vitro-to-in Vivo Extrapolation for Mixtures: A Case Study of Superfund Priority List Pesticides

In vitro cell-based toxicity testing methods generate large amounts of data informative for risk-based evaluations. To allow extrapolation of the quantitative outputs from cell-based tests to the equivalent exposure levels in humans, reverse toxicokinetic (RTK) modeling is used to conduct in vitro-to-in vivo extrapolation (IVIVE) from in vitro effective concentrations to in vivo oral dose equivalents. IVIVE modeling approaches for individual chemicals are well-established; however, the potential implications of chemical-to-chemical interactions in mixture settings on IVIVE remains largely unexplored. We hypothesized that chemical co-exposures could modulate both protein binding efficiency and hepatocyte clearance of the chemicals in a mixture, which would in turn affect the quantitative IVIVE toxicokinetic parameters. To test this hypothesis, we used 20 pesticides from the Agency for Toxic Substances and Disease Registry (ATSDR) Substance Priority List, both individually and as equimolar mixtures, and investigated the concentration-dependent effects of chemical interactions on in vitro toxicokinetic parameters. Plasma protein binding efficiency was determined by using ultracentrifugation, and hepatocyte clearance was estimated in suspensions of cryopreserved primary human hepatocytes. We found that for single chemicals, the protein binding efficiencies were similar at different test concentrations. In a mixture, however, both protein binding efficiency and hepatocyte clearance were affected. When IVIVE was conducted using mixture-derived toxicokinetic data, more conservative estimates of Activity-to-Exposure Ratios (AERs) were produced as compared to using data from single chemical experiments. Because humans are exposed to mixtures of chemicals, this study is significant as it demonstrates the importance of incorporating mixture-derived parameters into IVIVE for in vitro bioactivity data in order to accurately prioritize risks and facilitate science-based decision-making.

High-throughput PBTK models for in vitro to in vivo extrapolation

INTRODUCTION

Toxicity data are unavailable for many thousands of chemicals in commerce and the environment. Therefore, risk assessors need to rapidly screen these chemicals for potential risk to public health. High-throughput screening (HTS) for in vitro bioactivity, when used with high-throughput toxicokinetic (HTTK) data and models, allows characterization of these thousands of chemicals.

AREAS COVERED

This review covers generic physiologically based toxicokinetic (PBTK) models and high-throughput PBTK modeling for in vitro-in vivo extrapolation (IVIVE) of HTS data. We focus on 'httk', a public, open-source set of computational modeling tools and in vitro toxicokinetic (TK) data.

EXPERT OPINION

HTTK benefits chemical risk assessors with its ability to support rapid chemical screening/prioritization, perform IVIVE, and provide provisional TK modeling for large numbers of chemicals using only limited chemical-specific data. Although generic TK model design can increase prediction uncertainty, these models provide offsetting benefits by increasing model implementation accuracy. Also, public distribution of the models and data enhances reproducibility. For the httk package, the modular and open-source design can enable the tool to be used and continuously improved by a broad user community in support of the critical need for high-throughput chemical prioritization and rapid dose estimation to facilitate rapid hazard assessments.

Evaluation and comparison of in vitro intrinsic clearance rates measured using cryopreserved hepatocytes from humans, rats, and rainbow trout

In vitro and in silico methods that can reduce the need for animal testing are being used with increasing frequency to assess chemical risks to human health and the environment. The rate of hepatic biotransformation is an important species-specific parameter for determining bioaccumulation potential and extrapolating in vitro bioactivity to in vivo effects. One approach to estimating hepatic biotransformation is to employ in vitro systems derived from liver tissue to measure chemical (substrate) depletion over time which can then be translated to a rate of intrinsic clearance (CL_int). In the present study, cryopreserved hepatocytes from humans, rats, and rainbow trout were used to measure CL_int values for 54 industrial and pesticidal chemicals at starting test concentrations of 0.1 and 1 μM. A data evaluation framework that emphasizes the behavior of Heat-Treated Controls (HTC) was developed to identify datasets suitable for rate reporting. Measured or estimated ("greater than" or "less than") CL_int values were determined for 124 of 226 (55 %) species-chemical-substrate concentration datasets with acceptable analytical chemistry. A large percentage of tested chemicals exhibited low HTC recovery values, indicating a substantial abiotic loss of test chemical over time. An evaluation of K_OW values for individual chemicals suggested that in vitro test performance declined with increasing chemical hydrophobicity, although differences in testing devices for mammals and fish also likely played a role. The current findings emphasize the value of negative controls as part of a rigorous approach to data quality assessment for in vitro substrate depletion studies. Changes in current testing protocols can be expected to result in the collection of higher quality data. However, poorly soluble chemicals are likely to remain a challenge for CL_int determination.

Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors

The intrinsic metabolic clearance rate (Cl_int) and the fraction of the chemical unbound in plasma (f_up) serve as important parameters for high-throughput toxicokinetic (TK) models, but experimental data are limited for many chemicals. Open-source quantitative structure-activity relationship (QSAR) models for both parameters were developed to offer reliable in silico predictions for a diverse set of chemicals regulated under the U.S. law, including pharmaceuticals, pesticides, and industrial chemicals. As a case study to demonstrate their utility, model predictions served as inputs to the TK component of a risk-based prioritization approach based on bioactivity/exposure ratios (BERs), in which a BER < 1 indicates that exposures are predicted to exceed a biological activity threshold. When applied to a subset of the Tox21 screening library (6484 chemicals), we found that the proportion of chemicals with BER <1 was similar using either in silico (1133/6484; 17.5%) or in vitro (148/848; 17.5%) parameters. Further, when considering only the chemicals in the Tox21 set with in vitro data, there was a high concordance of chemicals classified with either BER <1 or >1 using either in silico or in vitro parameters (767/848, 90.4%). Thus, the presented QSARs may be suitable for prioritizing the risk posed by many chemicals for which measured in vitro TK data are lacking.

Risk-Based Chemical Ranking and Generating a Prioritized Human Exposome Database

BACKGROUND

Due to the ubiquitous use of chemicals in modern society, humans are increasingly exposed to thousands of chemicals that contribute to a major portion of the human exposome. Should a comprehensive and risk-based human exposome database be created, it would be conducive to the rapid progress of human exposomics research. In addition, once a xenobiotic is biotransformed with distinct half-lives upon exposure, monitoring the parent compounds alone may not reflect the actual human exposure. To address these questions, a comprehensive and risk-prioritized human exposome database is needed.

OBJECTIVES

Our objective was to set up a comprehensive risk-prioritized human exposome database including physicochemical properties as well as risk prediction and develop a graphical user interface (GUI) that has the ability to conduct searches for content associated with chemicals in our database.

METHODS

We built a comprehensive risk-prioritized human exposome database by text mining and database fusion. Subsequently, chemicals were prioritized by integrating exposure level obtained from the Systematic Empirical Evaluation of Models with toxicity data predicted by the Toxicity Estimation Software Tool and the Toxicological Priority Index calculated from the ToxCast database. The biotransformation half-lives (HLBs) of all the chemicals were assessed using the Iterative Fragment Selection approach and biotransformation products were predicted using the previously developed BioTransformer machine-learning method.

RESULTS

We compiled a human exposome database of >20,000 chemicals, prioritized 13,441 chemicals based on probabilistic hazard quotient and 7,770 chemicals based on risk index, and provided a predicted biotransformation metabolite database of >95,000 metabolites. In addition, a user-interactive Java software (Oracle)-based search GUI was generated to enable open access to this new resource.

DISCUSSION

Our database can be used to guide chemical management and enhance scientific understanding to rapidly and effectively prioritize chemicals for comprehensive biomonitoring in epidemiological investigations. https://doi.org/10.1289/EHP7722.

New Approach Methods to Evaluate Health Risks of Air Pollutants: Critical Design Considerations for In Vitro Exposure Testing

Air pollution consists of highly variable and complex mixtures recognized as major contributors to morbidity and mortality worldwide. The vast number of chemicals, coupled with limitations surrounding epidemiological and animal studies, has necessitated the development of new approach methods (NAMs) to evaluate air pollution toxicity. These alternative approaches include in vitro (cell-based) models, wherein toxicity of test atmospheres can be evaluated with increased efficiency compared to in vivo studies. In vitro exposure systems have recently been developed with the goal of evaluating air pollutant-induced toxicity; though the specific design parameters implemented in these NAMs-based studies remain in flux. This review aims to outline important design parameters to consider when using in vitro methods to evaluate air pollutant toxicity, with the goal of providing increased accuracy, reproducibility, and effectiveness when incorporating in vitro data into human health evaluations. This review is unique in that experimental considerations and lessons learned are provided, as gathered from first-hand experience developing and testing in vitro models coupled to exposure systems. Reviewed design aspects include cell models, cell exposure conditions, exposure chambers, and toxicity endpoints. Strategies are also discussed to incorporate in vitro findings into the context of in vivo toxicity and overall risk assessment.